From 37f92b7cc393ac2a0a0bce93c85500a47e3e0c83 Mon Sep 17 00:00:00 2001 From: KaiyangZhou Date: Tue, 19 Mar 2019 17:26:08 +0000 Subject: [PATCH] create engine SDK --- README.md | 4 +- args.py => _deprecated/args.py | 0 .../train_imgreid_xent.py | 0 .../train_imgreid_xent_htri.py | 0 .../train_vidreid_xent.py | 0 .../train_vidreid_xent_htri.py | 0 default_parser.py | 196 + .../figures}/deep-person-reid-logo.png | Bin {imgs => docs/figures}/ranked_results.jpg | Bin main.py | 17 + requirements.txt | 7 - setup.py | 42 + torchreid/__init__.py | 23 +- torchreid/data_manager.py | 306 - torchreid/dataset_loader.py | 115 - torchreid/datasets/__init__.py | 57 - torchreid/datasets/bases.py | 192 - torchreid/datasets/cuhk01.py | 141 - torchreid/datasets/cuhk03.py | 258 - torchreid/datasets/dukemtmcreid.py | 99 - torchreid/datasets/dukemtmcvidreid.py | 137 - torchreid/datasets/grid.py | 139 - torchreid/datasets/ilids.py | 162 - torchreid/datasets/ilidsvid.py | 151 - torchreid/datasets/market1501.py | 89 - torchreid/datasets/mars.py | 115 - torchreid/datasets/msmt17.py | 108 - torchreid/datasets/prid.py | 117 - torchreid/datasets/prid2011.py | 87 - torchreid/datasets/prid450s.py | 137 - torchreid/datasets/sensereid.py | 72 - torchreid/datasets/viper.py | 155 - torchreid/engine/__init__.py | 8 + torchreid/engine/engine.py | 198 + torchreid/engine/image/__init__.py | 4 + torchreid/engine/image/softmax.py | 74 + torchreid/engine/image/triplet.py | 85 + torchreid/engine/video/__init__.py | 4 + torchreid/engine/video/softmax.py | 37 + torchreid/engine/video/triplet.py | 24 + torchreid/eval_cylib/Makefile | 7 - torchreid/lr_schedulers.py | 29 - torchreid/metrics/__init__.py | 5 + torchreid/metrics/accuracy.py | 26 + torchreid/metrics/distance.py | 60 + .../{eval_metrics.py => metrics/rank.py} | 10 +- torchreid/metrics/rank_cylib/Makefile | 6 + .../rank_cylib}/__init__.py | 0 torchreid/metrics/rank_cylib/rank_cy.c | 28011 ++++++++++++++++ .../rank_cylib/rank_cy.pyx} | 3 + .../rank_cylib}/setup.py | 7 +- .../rank_cylib}/test_cython.py | 17 +- torchreid/models/__init__.py | 20 +- torchreid/models/densenet.py | 13 +- torchreid/models/hacnn.py | 11 +- torchreid/models/inceptionresnetv2.py | 13 +- torchreid/models/inceptionv4.py | 10 +- torchreid/models/mlfn.py | 13 +- torchreid/models/mobilenetv2.py | 11 +- torchreid/models/mudeep.py | 11 +- torchreid/models/nasnet.py | 10 +- torchreid/models/pcb.py | 13 +- torchreid/models/resnet.py | 13 +- torchreid/models/resnetmid.py | 11 +- torchreid/models/resnext.py | 13 +- torchreid/models/senet.py | 32 +- torchreid/models/shufflenet.py | 13 +- torchreid/models/squeezenet.py | 28 +- torchreid/models/xception.py | 14 +- torchreid/optim/__init__.py | 4 + torchreid/optim/lr_schedulers.py | 38 + torchreid/{ => optim}/optimizers.py | 59 +- torchreid/samplers.py | 87 - torchreid/transforms.py | 159 - torchreid/utils/__init__.py | 7 + torchreid/utils/avgmeter.py | 2 + torchreid/utils/generaltools.py | 16 - torchreid/utils/iotools.py | 36 - torchreid/utils/loggers.py | 4 +- torchreid/utils/reidtools.py | 4 +- torchreid/utils/tools.py | 91 + torchreid/utils/torchtools.py | 43 +- 82 files changed, 29162 insertions(+), 3178 deletions(-) rename args.py => _deprecated/args.py (100%) rename train_imgreid_xent.py => _deprecated/train_imgreid_xent.py (100%) rename train_imgreid_xent_htri.py => _deprecated/train_imgreid_xent_htri.py (100%) rename train_vidreid_xent.py => _deprecated/train_vidreid_xent.py (100%) rename train_vidreid_xent_htri.py => _deprecated/train_vidreid_xent_htri.py (100%) create mode 100644 default_parser.py rename {imgs => docs/figures}/deep-person-reid-logo.png (100%) rename {imgs => docs/figures}/ranked_results.jpg (100%) create mode 100755 main.py delete mode 100644 requirements.txt create mode 100644 setup.py delete mode 100644 torchreid/data_manager.py delete mode 100644 torchreid/dataset_loader.py delete mode 100644 torchreid/datasets/__init__.py delete mode 100644 torchreid/datasets/bases.py delete mode 100644 torchreid/datasets/cuhk01.py delete mode 100644 torchreid/datasets/cuhk03.py delete mode 100644 torchreid/datasets/dukemtmcreid.py delete mode 100644 torchreid/datasets/dukemtmcvidreid.py delete mode 100644 torchreid/datasets/grid.py delete mode 100644 torchreid/datasets/ilids.py delete mode 100644 torchreid/datasets/ilidsvid.py delete mode 100644 torchreid/datasets/market1501.py delete mode 100644 torchreid/datasets/mars.py delete mode 100644 torchreid/datasets/msmt17.py delete mode 100644 torchreid/datasets/prid.py delete mode 100644 torchreid/datasets/prid2011.py delete mode 100644 torchreid/datasets/prid450s.py delete mode 100644 torchreid/datasets/sensereid.py delete mode 100755 torchreid/datasets/viper.py create mode 100644 torchreid/engine/__init__.py create mode 100644 torchreid/engine/engine.py create mode 100644 torchreid/engine/image/__init__.py create mode 100644 torchreid/engine/image/softmax.py create mode 100644 torchreid/engine/image/triplet.py create mode 100644 torchreid/engine/video/__init__.py create mode 100644 torchreid/engine/video/softmax.py create mode 100644 torchreid/engine/video/triplet.py delete mode 100644 torchreid/eval_cylib/Makefile delete mode 100644 torchreid/lr_schedulers.py create mode 100644 torchreid/metrics/__init__.py create mode 100644 torchreid/metrics/accuracy.py create mode 100644 torchreid/metrics/distance.py rename torchreid/{eval_metrics.py => metrics/rank.py} (93%) create mode 100644 torchreid/metrics/rank_cylib/Makefile rename torchreid/{eval_cylib => metrics/rank_cylib}/__init__.py (100%) create mode 100644 torchreid/metrics/rank_cylib/rank_cy.c rename torchreid/{eval_cylib/eval_metrics_cy.pyx => metrics/rank_cylib/rank_cy.pyx} (99%) rename torchreid/{eval_cylib => metrics/rank_cylib}/setup.py (77%) rename torchreid/{eval_cylib => metrics/rank_cylib}/test_cython.py (69%) create mode 100644 torchreid/optim/__init__.py create mode 100644 torchreid/optim/lr_schedulers.py rename torchreid/{ => optim}/optimizers.py (62%) delete mode 100644 torchreid/samplers.py delete mode 100644 torchreid/transforms.py delete mode 100644 torchreid/utils/generaltools.py delete mode 100644 torchreid/utils/iotools.py create mode 100644 torchreid/utils/tools.py diff --git a/README.md b/README.md index 874af8f..762b39a 100755 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@

- logo + logo

This project aims to provide an efficient framework for training and evaluating deep person re-identification models in [Pytorch](http://pytorch.org/). @@ -164,7 +164,7 @@ Use `--eval-freq` to control the evaluation frequency and `--start-eval` to indi To visualize the ranked results, you can use `--visualize-ranks`, which works along with `--evaluate`. The ranked images will be saved in `save_dir/ranked_results/dataset_name` where `save_dir` is the directory you specify with `--save-dir`. This function is implemented in [torchreid/utils/reidtools.py](torchreid/utils/reidtools.py).

- ranked_results + ranked_results

diff --git a/args.py b/_deprecated/args.py similarity index 100% rename from args.py rename to _deprecated/args.py diff --git a/train_imgreid_xent.py b/_deprecated/train_imgreid_xent.py similarity index 100% rename from train_imgreid_xent.py rename to _deprecated/train_imgreid_xent.py diff --git a/train_imgreid_xent_htri.py b/_deprecated/train_imgreid_xent_htri.py similarity index 100% rename from train_imgreid_xent_htri.py rename to _deprecated/train_imgreid_xent_htri.py diff --git a/train_vidreid_xent.py b/_deprecated/train_vidreid_xent.py similarity index 100% rename from train_vidreid_xent.py rename to _deprecated/train_vidreid_xent.py diff --git a/train_vidreid_xent_htri.py b/_deprecated/train_vidreid_xent_htri.py similarity index 100% rename from train_vidreid_xent_htri.py rename to _deprecated/train_vidreid_xent_htri.py diff --git a/default_parser.py b/default_parser.py new file mode 100644 index 0000000..3858dfe --- /dev/null +++ b/default_parser.py @@ -0,0 +1,196 @@ +from __future__ import absolute_import +from __future__ import print_function + +import argparse + + +def init_parser(): + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + + # ************************************************************ + # Method + # ************************************************************ + parser.add_argument('--application', type=str, default='image', choices=['image', 'video'], + help='image-reid or video-reid') + parser.add_argument('--method', type=str, default='softmax', + help='methodology') + + # ************************************************************ + # Datasets + # ************************************************************ + parser.add_argument('--root', type=str, default='data', + help='root path to data directory') + parser.add_argument('-s', '--source-names', type=str, required=True, nargs='+', + help='source datasets (delimited by space)') + parser.add_argument('-t', '--target-names', type=str, required=True, nargs='+', + help='target datasets (delimited by space)') + parser.add_argument('-j', '--workers', type=int, default=4, + help='number of data loading workers (tips: 4 or 8 times number of gpus)') + parser.add_argument('--split-id', type=int, default=0, + help='split index (note: 0-based)') + parser.add_argument('--height', type=int, default=256, + help='height of an image') + parser.add_argument('--width', type=int, default=128, + help='width of an image') + parser.add_argument('--train-sampler', type=str, default='RandomSampler', + help='sampler for trainloader') + parser.add_argument('--combineall', action='store_true', + help='combine all data in a dataset (train+query+gallery) for training') + + # ************************************************************ + # Data augmentation + # ************************************************************ + parser.add_argument('--random-erase', action='store_true', + help='use random erasing for data augmentation') + parser.add_argument('--color-jitter', action='store_true', + help='randomly change the brightness, contrast and saturation') + parser.add_argument('--color-aug', action='store_true', + help='randomly alter the intensities of RGB channels') + + # ************************************************************ + # Video datasets + # ************************************************************ + parser.add_argument('--seq-len', type=int, default=15, + help='number of images to sample in a tracklet') + parser.add_argument('--sample-method', type=str, default='evenly', + help='how to sample images from a tracklet') + parser.add_argument('--pooling-method', type=str, default='avg', choices=['avg', 'max'], + help='how to pool features over a tracklet (for video reid)') + + # ************************************************************ + # Dataset-specific setting + # ************************************************************ + parser.add_argument('--cuhk03-labeled', action='store_true', + help='use labeled images, if false, use detected images') + parser.add_argument('--cuhk03-classic-split', action='store_true', + help='use classic split by Li et al. CVPR\'14') + parser.add_argument('--use-metric-cuhk03', action='store_true', + help='use cuhk03\'s metric for evaluation') + + parser.add_argument('--market1501-500k', action='store_true', + help='add 500k distractors to the gallery set for market1501') + + # ************************************************************ + # Optimization options + # ************************************************************ + parser.add_argument('--optim', type=str, default='adam', + help='optimization algorithm (see optimizers.py)') + parser.add_argument('--lr', type=float, default=0.0003, + help='initial learning rate') + parser.add_argument('--weight-decay', type=float, default=5e-04, + help='weight decay') + # sgd + parser.add_argument('--momentum', type=float, default=0.9, + help='momentum factor for sgd and rmsprop') + parser.add_argument('--sgd-dampening', type=float, default=0, + help='sgd\'s dampening for momentum') + parser.add_argument('--sgd-nesterov', action='store_true', + help='whether to enable sgd\'s Nesterov momentum') + # rmsprop + parser.add_argument('--rmsprop-alpha', type=float, default=0.99, + help='rmsprop\'s smoothing constant') + # adam/amsgrad + parser.add_argument('--adam-beta1', type=float, default=0.9, + help='exponential decay rate for adam\'s first moment') + parser.add_argument('--adam-beta2', type=float, default=0.999, + help='exponential decay rate for adam\'s second moment') + + # ************************************************************ + # Training hyperparameters + # ************************************************************ + parser.add_argument('--max-epoch', type=int, default=60, + help='maximum epochs to run') + parser.add_argument('--start-epoch', type=int, default=0, + help='manual epoch number (useful when restart)') + + parser.add_argument('--train-batch-size', type=int, default=32, + help='training batch size') + parser.add_argument('--test-batch-size', type=int, default=100, + help='test batch size') + + parser.add_argument('--always-fixbase', action='store_true', + help='always fix base network and only train specified layers') + parser.add_argument('--fixbase-epoch', type=int, default=0, + help='how many epochs to fix base network (only train randomly initialized classifier)') + parser.add_argument('--open-layers', type=str, nargs='+', default=['classifier'], + help='open specified layers for training while keeping others frozen') + + parser.add_argument('--staged-lr', action='store_true', + help='set different lr to different layers') + parser.add_argument('--new-layers', type=str, nargs='+', default=['classifier'], + help='newly added layers with default lr') + parser.add_argument('--base-lr-mult', type=float, default=0.1, + help='learning rate multiplier for base layers') + + # ************************************************************ + # Learning rate scheduler options + # ************************************************************ + parser.add_argument('--lr-scheduler', type=str, default='multi_step', + help='learning rate scheduler (see lr_schedulers.py)') + parser.add_argument('--stepsize', type=int, default=[20, 40], nargs='+', + help='stepsize to decay learning rate') + parser.add_argument('--gamma', type=float, default=0.1, + help='learning rate decay') + + # ************************************************************ + # Cross entropy loss-specific setting + # ************************************************************ + parser.add_argument('--label-smooth', action='store_true', + help='use label smoothing regularizer in cross entropy loss') + + # ************************************************************ + # Hard triplet loss-specific setting + # ************************************************************ + parser.add_argument('--margin', type=float, default=0.3, + help='margin for triplet loss') + parser.add_argument('--num-instances', type=int, default=4, + help='number of instances per identity') + parser.add_argument('--weight-t', type=float, default=1, + help='weight to balance hard triplet loss') + parser.add_argument('--weight-x', type=float, default=1, + help='weight to balance cross entropy loss') + + # ************************************************************ + # Architecture + # ************************************************************ + parser.add_argument('-a', '--arch', type=str, default='resnet50') + parser.add_argument('--no-pretrained', action='store_true', + help='do not load pretrained weights') + + # ************************************************************ + # Test settings + # ************************************************************ + parser.add_argument('--load-weights', type=str, default='', + help='load pretrained weights but ignore layers that do not match in size') + parser.add_argument('--evaluate', action='store_true', + help='evaluate only') + parser.add_argument('--eval-freq', type=int, default=-1, + help='evaluation frequency (set to -1 to test only in the end)') + parser.add_argument('--start-eval', type=int, default=0, + help='start to evaluate after a specific epoch') + parser.add_argument('--dist-metric', type=str, default='euclidean', + help='distance metric') + + # ************************************************************ + # Miscs + # ************************************************************ + parser.add_argument('--print-freq', type=int, default=10, + help='print frequency') + parser.add_argument('--seed', type=int, default=1, + help='manual seed') + parser.add_argument('--resume', type=str, default='', metavar='PATH', + help='resume from a checkpoint') + parser.add_argument('--save-dir', type=str, default='log', + help='path to save log and model weights') + parser.add_argument('--use-cpu', action='store_true', + help='use cpu') + parser.add_argument('--gpu-devices', type=str, default='0', + help='gpu device ids for CUDA_VISIBLE_DEVICES') + parser.add_argument('--use-avai-gpus', action='store_true', + help='use available gpus instead of specified devices (useful when using managed clusters)') + parser.add_argument('--visrank', action='store_true', + help='visualize ranked results, only available in evaluation mode') + parser.add_argument('--visrank-topk', type=int, default=20, + help='visualize topk ranks') + + return parser \ No newline at end of file diff --git a/imgs/deep-person-reid-logo.png b/docs/figures/deep-person-reid-logo.png similarity index 100% rename from imgs/deep-person-reid-logo.png rename to docs/figures/deep-person-reid-logo.png diff --git a/imgs/ranked_results.jpg b/docs/figures/ranked_results.jpg similarity index 100% rename from imgs/ranked_results.jpg rename to docs/figures/ranked_results.jpg diff --git a/main.py b/main.py new file mode 100755 index 0000000..374063a --- /dev/null +++ b/main.py @@ -0,0 +1,17 @@ +import torchreid + +dataset = torchreid.data.ImageDatasetManager( + root='data', + source_names='grid', + height=128, + width=64 +) +model = torchreid.models.build_model( + name='squeezenet1_0', + num_classes=dataset.num_train_pids, + loss='softmax' +) +optimizer = torchreid.optim.build_optimizer(model) +scheduler = torchreid.optim.build_lr_scheduler(optimizer, lr_scheduler='multi_step', stepsize=[10, 20]) +engine = torchreid.engine.ImageSoftmaxEngine(dataset, model, optimizer, scheduler=scheduler) +engine.run(max_epoch=3, print_freq=1, fixbase_epoch=3, open_layers='classifier') \ No newline at end of file diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index e4b9ab6..0000000 --- a/requirements.txt +++ /dev/null @@ -1,7 +0,0 @@ -Cython -h5py -numpy -Pillow -scipy>=1.0.0 -torch>=0.4.1 -torchvision>=0.2.1 diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..37879d5 --- /dev/null +++ b/setup.py @@ -0,0 +1,42 @@ +from setuptools import find_packages, setup + + +def readme(): + with open('README.md') as f: + content = f.read() + return content + + +def find_version(): + version_file = 'torchreid/__init__.py' + with open(version_file, 'r') as f: + exec(compile(f.read(), version_file, 'exec')) + return locals()['__version__'] + + +setup( + name='torchreid', + version=find_version(), + description='Pytorch framework for deep-learning person re-identification', + author='Kaiyang Zhou', + author_email='k.zhou.vision@gmail.com', + license='MIT', + long_description=readme(), + url='https://github.com/KaiyangZhou/deep-person-reid', + packages=find_packages(), + install_requires=[ + 'numpy', + 'Cython', + 'h5py', + 'Pillow', + 'six', + 'scipy>=1.0.0', + 'torch>=0.4.1', + 'torchvision>=0.2.1' + ], + keywords=[ + 'Person Re-Identification', + 'Deep Learning', + 'Computer Vision' + ] +) \ No newline at end of file diff --git a/torchreid/__init__.py b/torchreid/__init__.py index e45d650..aad9436 100644 --- a/torchreid/__init__.py +++ b/torchreid/__init__.py @@ -1,11 +1,16 @@ -""" -deep-person-reid -== - -Description: PyTorch implementation of deep person re-identification models. - -Github page: https://github.com/KaiyangZhou/deep-person-reid -""" +from __future__ import absolute_import +from __future__ import print_function +__version__ = '0.7.0' __author__ = 'Kaiyang Zhou' -__homepage__ = 'https://kaiyangzhou.github.io/' \ No newline at end of file +__description__ = 'Pytorch framework for deep-learning person re-identification' + +from torchreid import ( + engine, + models, + losses, + metrics, + data, + optim, + utils +) \ No newline at end of file diff --git a/torchreid/data_manager.py b/torchreid/data_manager.py deleted file mode 100644 index a2935f6..0000000 --- a/torchreid/data_manager.py +++ /dev/null @@ -1,306 +0,0 @@ -from __future__ import absolute_import -from __future__ import print_function - -from torch.utils.data import DataLoader - -from .dataset_loader import ImageDataset, VideoDataset -from .datasets import init_imgreid_dataset, init_vidreid_dataset -from .transforms import build_transforms -from .samplers import build_train_sampler - - -class BaseDataManager(object): - - def __init__( - self, - use_gpu, - source_names, - target_names, - root='data', - split_id=0, - height=256, - width=128, - combineall=False, # combine all data in a dataset for training - train_batch_size=32, - test_batch_size=100, - workers=4, - train_sampler='', - random_erase=False, # use random erasing for data augmentation - color_jitter=False, # randomly change the brightness, contrast and saturation - color_aug=False, # randomly alter the intensities of RGB channels - num_instances=4, # number of instances per identity (for RandomIdentitySampler) - **kwargs - ): - self.use_gpu = use_gpu - self.source_names = source_names - self.target_names = target_names - self.root = root - self.split_id = split_id - self.height = height - self.width = width - self.combineall = combineall - self.train_batch_size = train_batch_size - self.test_batch_size = test_batch_size - self.workers = workers - self.train_sampler = train_sampler - self.random_erase = random_erase - self.color_jitter = color_jitter - self.color_aug = color_aug - self.num_instances = num_instances - - transform_train, transform_test = build_transforms( - self.height, self.width, - random_erase=self.random_erase, - color_jitter=self.color_jitter, - color_aug=self.color_aug - ) - self.transform_train = transform_train - self.transform_test = transform_test - - @property - def num_train_pids(self): - return self._num_train_pids - - @property - def num_train_cams(self): - return self._num_train_cams - - def return_dataloaders(self): - """Return trainloader and testloader dictionary""" - return self.trainloader, self.testloader_dict - - def return_testdataset_by_name(self, name): - """Return query and gallery, each containing a list of (img_path, pid, camid)""" - return self.testdataset_dict[name]['query'], self.testdataset_dict[name]['gallery'] - - -class ImageDataManager(BaseDataManager): - - def __init__( - self, - use_gpu, - source_names, - target_names, - cuhk03_labeled=False, # use cuhk03's labeled or detected images - cuhk03_classic_split=False, # use cuhk03's classic split or 767/700 split - market1501_500k=False, # add 500k distractors to the gallery set for market1501 - **kwargs - ): - super(ImageDataManager, self).__init__(use_gpu, source_names, target_names, **kwargs) - self.cuhk03_labeled = cuhk03_labeled - self.cuhk03_classic_split = cuhk03_classic_split - self.market1501_500k = market1501_500k - - print('=> Initializing train (source) datasets') - train = [] - self._num_train_pids = 0 - self._num_train_cams = 0 - - for name in self.source_names: - dataset = init_imgreid_dataset( - root=self.root, - name=name, - split_id=self.split_id, - combineall=self.combineall, - cuhk03_labeled=self.cuhk03_labeled, - cuhk03_classic_split=self.cuhk03_classic_split, - market1501_500k=self.market1501_500k - ) - - for img_path, pid, camid in dataset.train: - pid += self._num_train_pids - camid += self._num_train_cams - train.append((img_path, pid, camid)) - - self._num_train_pids += dataset.num_train_pids - self._num_train_cams += dataset.num_train_cams - - self.train_sampler = build_train_sampler( - train, - self.train_sampler, - train_batch_size=self.train_batch_size, - num_instances=self.num_instances, - ) - - self.trainloader = DataLoader( - ImageDataset(train, transform=self.transform_train), - sampler=self.train_sampler, - batch_size=self.train_batch_size, - shuffle=False, - num_workers=self.workers, - pin_memory=self.use_gpu, - drop_last=True - ) - - print('=> Initializing test (target) datasets') - self.testloader_dict = {name: {'query': None, 'gallery': None} for name in target_names} - self.testdataset_dict = {name: {'query': None, 'gallery': None} for name in target_names} - - for name in self.target_names: - dataset = init_imgreid_dataset( - root=self.root, - name=name, - split_id=self.split_id, - combineall=self.combineall, - cuhk03_labeled=self.cuhk03_labeled, - cuhk03_classic_split=self.cuhk03_classic_split, - market1501_500k=self.market1501_500k - ) - - self.testloader_dict[name]['query'] = DataLoader( - ImageDataset(dataset.query, transform=self.transform_test), - batch_size=self.test_batch_size, - shuffle=False, - num_workers=self.workers, - pin_memory=self.use_gpu, - drop_last=False - ) - - self.testloader_dict[name]['gallery'] = DataLoader( - ImageDataset(dataset.gallery, transform=self.transform_test), - batch_size=self.test_batch_size, - shuffle=False, - num_workers=self.workers, - pin_memory=self.use_gpu, - drop_last=False - ) - - self.testdataset_dict[name]['query'] = dataset.query - self.testdataset_dict[name]['gallery'] = dataset.gallery - - print('\n') - print(' **************** Summary ****************') - print(' train names : {}'.format(self.source_names)) - print(' # train datasets : {}'.format(len(self.source_names))) - print(' # train ids : {}'.format(self.num_train_pids)) - print(' # train images : {}'.format(len(train))) - print(' # train cameras : {}'.format(self.num_train_cams)) - print(' test names : {}'.format(self.target_names)) - print(' *****************************************') - print('\n') - - -class VideoDataManager(BaseDataManager): - - def __init__( - self, - use_gpu, - source_names, - target_names, - seq_len=15, - sample_method='evenly', - image_training=True, # train the video-reid model with images rather than tracklets - **kwargs - ): - super(VideoDataManager, self).__init__(use_gpu, source_names, target_names, **kwargs) - self.seq_len = seq_len - self.sample_method = sample_method - self.image_training = image_training - - print('=> Initializing train (source) datasets') - train = [] - self._num_train_pids = 0 - self._num_train_cams = 0 - - for name in self.source_names: - dataset = init_vidreid_dataset(root=self.root, name=name, split_id=self.split_id, combineall=self.combineall) - - for img_paths, pid, camid in dataset.train: - pid += self._num_train_pids - camid += self._num_train_cams - if image_training: - # decompose tracklets into images - for img_path in img_paths: - train.append((img_path, pid, camid)) - else: - train.append((img_paths, pid, camid)) - - self._num_train_pids += dataset.num_train_pids - self._num_train_cams += dataset.num_train_cams - - self.train_sampler = build_train_sampler( - train, self.train_sampler, - train_batch_size=self.train_batch_size, - num_instances=self.num_instances, - ) - - if image_training: - # each batch has image data of shape (batch, channel, height, width) - self.trainloader = DataLoader( - ImageDataset(train, transform=self.transform_train), - sampler=self.train_sampler, - batch_size=self.train_batch_size, - shuffle=False, - num_workers=self.workers, - pin_memory=self.use_gpu, - drop_last=True - ) - - else: - # each batch has image data of shape (batch, seq_len, channel, height, width) - self.trainloader = DataLoader( - VideoDataset( - train, - seq_len=self.seq_len, - sample_method=self.sample_method, - transform=self.transform_train - ), - batch_size=self.train_batch_size, - shuffle=True, - num_workers=self.workers, - pin_memory=self.use_gpu, - drop_last=True - ) - raise NotImplementedError('This requires a new trainer') - - print('=> Initializing test (target) datasets') - self.testloader_dict = {name: {'query': None, 'gallery': None} for name in target_names} - self.testdataset_dict = {name: {'query': None, 'gallery': None} for name in target_names} - - for name in self.target_names: - dataset = init_vidreid_dataset(root=self.root, name=name, split_id=self.split_id, combineall=self.combineall,) - - self.testloader_dict[name]['query'] = DataLoader( - VideoDataset( - dataset.query, - seq_len=self.seq_len, - sample_method=self.sample_method, - transform=self.transform_test - ), - batch_size=self.test_batch_size, - shuffle=False, - num_workers=self.workers, - pin_memory=self.use_gpu, - drop_last=False - ) - - self.testloader_dict[name]['gallery'] = DataLoader( - VideoDataset( - dataset.gallery, - seq_len=self.seq_len, - sample_method=self.sample_method, - transform=self.transform_test - ), - batch_size=self.test_batch_size, - shuffle=False, - num_workers=self.workers, - pin_memory=self.use_gpu, - drop_last=False - ) - - self.testdataset_dict[name]['query'] = dataset.query - self.testdataset_dict[name]['gallery'] = dataset.gallery - - print('\n') - print(' **************** Summary ****************') - print(' train names : {}'.format(self.source_names)) - print(' # train datasets : {}'.format(len(self.source_names))) - print(' # train ids : {}'.format(self.num_train_pids)) - if self.image_training: - print(' # train images : {}'.format(len(train))) - else: - print(' # train tracklets: {}'.format(len(train))) - print(' # train cameras : {}'.format(self.num_train_cams)) - print(' test names : {}'.format(self.target_names)) - print(' *****************************************') - print('\n') \ No newline at end of file diff --git a/torchreid/dataset_loader.py b/torchreid/dataset_loader.py deleted file mode 100644 index 633dc16..0000000 --- a/torchreid/dataset_loader.py +++ /dev/null @@ -1,115 +0,0 @@ -from __future__ import absolute_import -from __future__ import print_function -from __future__ import division - -import os -from PIL import Image -import numpy as np -import os.path as osp -import io - -import torch -from torch.utils.data import Dataset - - -def read_image(img_path): - """Keep reading image until succeed. - This can avoid IOError incurred by heavy IO process.""" - got_img = False - if not osp.exists(img_path): - raise IOError('{} does not exist'.format(img_path)) - while not got_img: - try: - img = Image.open(img_path).convert('RGB') - got_img = True - except IOError: - print('IOError incurred when reading "{}". Will redo. Don\'t worry. Just chill.'.format(img_path)) - pass - return img - - -class ImageDataset(Dataset): - """Image Person ReID Dataset""" - def __init__(self, dataset, transform=None): - self.dataset = dataset - self.transform = transform - - def __len__(self): - return len(self.dataset) - - def __getitem__(self, index): - img_path, pid, camid = self.dataset[index] - img = read_image(img_path) - - if self.transform is not None: - img = self.transform(img) - - return img, pid, camid, img_path - - -class VideoDataset(Dataset): - """Video Person ReID Dataset. - Note batch data has shape (batch, seq_len, channel, height, width). - """ - _sample_methods = ['evenly', 'random', 'all'] - - def __init__(self, dataset, seq_len=15, sample_method='evenly', transform=None): - self.dataset = dataset - self.seq_len = seq_len - self.sample_method = sample_method - self.transform = transform - - def __len__(self): - return len(self.dataset) - - def __getitem__(self, index): - img_paths, pid, camid = self.dataset[index] - num = len(img_paths) - - if self.sample_method == 'random': - """ - Randomly sample seq_len items from num items, - if num is smaller than seq_len, then replicate items - """ - indices = np.arange(num) - replace = False if num >= self.seq_len else True - indices = np.random.choice(indices, size=self.seq_len, replace=replace) - # sort indices to keep temporal order (comment it to be order-agnostic) - indices = np.sort(indices) - - elif self.sample_method == 'evenly': - """ - Evenly sample seq_len items from num items. - """ - if num >= self.seq_len: - num -= num % self.seq_len - indices = np.arange(0, num, num/self.seq_len) - else: - # if num is smaller than seq_len, simply replicate the last image - # until the seq_len requirement is satisfied - indices = np.arange(0, num) - num_pads = self.seq_len - num - indices = np.concatenate([indices, np.ones(num_pads).astype(np.int32)*(num-1)]) - assert len(indices) == self.seq_len - - elif self.sample_method == 'all': - """ - Sample all items, seq_len is useless now and batch_size needs - to be set to 1. - """ - indices = np.arange(num) - - else: - raise ValueError('Unknown sample method: {}. Expected one of {}'.format(self.sample_method, self._sample_methods)) - - imgs = [] - for index in indices: - img_path = img_paths[int(index)] - img = read_image(img_path) - if self.transform is not None: - img = self.transform(img) - img = img.unsqueeze(0) - imgs.append(img) - imgs = torch.cat(imgs, dim=0) - - return imgs, pid, camid \ No newline at end of file diff --git a/torchreid/datasets/__init__.py b/torchreid/datasets/__init__.py deleted file mode 100644 index 9d8cc3a..0000000 --- a/torchreid/datasets/__init__.py +++ /dev/null @@ -1,57 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from .market1501 import Market1501 -from .cuhk03 import CUHK03 -from .dukemtmcreid import DukeMTMCreID -from .msmt17 import MSMT17 -from .viper import VIPeR -from .grid import GRID -from .cuhk01 import CUHK01 -from .prid450s import PRID450S -from .ilids import iLIDS -from .sensereid import SenseReID -from .prid import PRID - -from .mars import Mars -from .ilidsvid import iLIDSVID -from .prid2011 import PRID2011 -from .dukemtmcvidreid import DukeMTMCVidReID - - -__imgreid_factory = { - 'market1501': Market1501, - 'cuhk03': CUHK03, - 'dukemtmcreid': DukeMTMCreID, - 'msmt17': MSMT17, - 'viper': VIPeR, - 'grid': GRID, - 'cuhk01': CUHK01, - 'prid450s': PRID450S, - 'ilids': iLIDS, - 'sensereid': SenseReID, - 'prid': PRID -} - - -__vidreid_factory = { - 'mars': Mars, - 'ilidsvid': iLIDSVID, - 'prid2011': PRID2011, - 'dukemtmcvidreid': DukeMTMCVidReID -} - - -def init_imgreid_dataset(name, **kwargs): - avai_datasets = list(__imgreid_factory.keys()) - if name not in avai_datasets: - raise KeyError('Invalid dataset name. Received "{}", but expected to be one of {}'.format(name, avai_datasets)) - return __imgreid_factory[name](**kwargs) - - -def init_vidreid_dataset(name, **kwargs): - avai_datasets = list(__vidreid_factory.keys()) - if name not in avai_datasets: - raise KeyError('Invalid dataset name. Received "{}", but expected to be one of {}'.format(name, avai_datasets)) - return __vidreid_factory[name](**kwargs) \ No newline at end of file diff --git a/torchreid/datasets/bases.py b/torchreid/datasets/bases.py deleted file mode 100644 index cb271e0..0000000 --- a/torchreid/datasets/bases.py +++ /dev/null @@ -1,192 +0,0 @@ -from __future__ import absolute_import -from __future__ import print_function - -import os -import os.path as osp -import numpy as np -import copy - - -class BaseDataset(object): - """Base class of reid dataset""" - - def __init__(self, root): - self.root = osp.expanduser(root) - - def check_before_run(self, required_files): - """Check if required files exist before going deeper""" - for f in required_files: - if not osp.exists(f): - raise RuntimeError('"{}" is not found'.format(f)) - - def extract_data_info(self, data): - """Extract info from data list - - Args: - data (list): contains a list of (img_path, pid, camid) - """ - raise NotImplementedError - - def get_num_pids(self, data): - return self.extract_data_info(data)[0] - - def get_num_cams(self, data): - return self.extract_data_info(data)[2] - - def init_attributes(self, train, query, gallery, combineall=False, **kwargs): - """Initialize class attributes - - Args: - train (list): contains a list of (img_path, pid, camid) - query (list): contains a list of (img_path, pid, camid) - gallery (list): contains a list of (img_path, pid, camid) - combineall (bool): if set to True, combine all data for training, default is False - - Notes: - This method has to be called (at the end) in each dataset class. - """ - self._train = train - self._query = query - self._gallery = gallery - self._num_train_pids = self.get_num_pids(train) - self._num_train_cams = self.get_num_cams(train) - - if combineall: - self._train = self.combine_all(train, query, gallery) - self._num_train_pids = self.get_num_pids(self.train) - - def combine_all(self, train, query, gallery): - """Combine all data for training - - Notes: - 1. In general, we assume that all query identities appear in gallery set. - 2. All pids in train have been relabeled (starts from 0) - 3. pid=0 (background) and pid=-1 (junk) are discarded. - 4. Camera views remain the same across train, query and gallery. - """ - raise NotImplementedError - - @property - def train(self): - # train list containing (img_path, pid, camid) - return self._train - - @property - def query(self): - # query list containing (img_path, pid, camid) - return self._query - - @property - def gallery(self): - # gallery list containing (img_path, pid, camid) - return self._gallery - - @property - def num_train_pids(self): - # number of train identities - return self._num_train_pids - - @property - def num_train_cams(self): - # number of train camera views - return self._num_train_cams - - def print_dataset_statistics(self): - raise NotImplementedError - - -class BaseImageDataset(BaseDataset): - """Base class of image-reid dataset""" - - def extract_data_info(self, data): - pids = set() - cams = set() - for _, pid, camid in data: - pids.add(pid) - cams.add(camid) - num_pids = len(pids) - num_cams = len(cams) - num_imgs = len(data) - return num_pids, num_imgs, num_cams - - def combine_all(self, train, query, gallery): - combined = copy.deepcopy(train) - - # relabel pids in gallery - g_pids = set() - for _, pid, _ in gallery: - if pid==0 or pid==-1: - continue - g_pids.add(pid) - pid2label = {pid: i for i, pid in enumerate(g_pids)} - - def _combine_data(data): - for img_path, pid, camid in data: - if pid==0 or pid==-1: - continue - pid = pid2label[pid] + self.num_train_pids - combined.append((img_path, pid, camid)) - - _combine_data(query) - _combine_data(gallery) - - return combined - - def print_dataset_statistics(self, train, query, gallery): - num_train_pids, num_train_imgs, num_train_cams = self.extract_data_info(train) - num_query_pids, num_query_imgs, num_query_cams = self.extract_data_info(query) - num_gallery_pids, num_gallery_imgs, num_gallery_cams = self.extract_data_info(gallery) - - print('=> Loaded {}'.format(self.__class__.__name__)) - print(' ----------------------------------------') - print(' subset | # ids | # images | # cameras') - print(' ----------------------------------------') - print(' train | {:5d} | {:8d} | {:9d}'.format(num_train_pids, num_train_imgs, num_train_cams)) - print(' query | {:5d} | {:8d} | {:9d}'.format(num_query_pids, num_query_imgs, num_query_cams)) - print(' gallery | {:5d} | {:8d} | {:9d}'.format(num_gallery_pids, num_gallery_imgs, num_gallery_cams)) - print(' ----------------------------------------') - - -class BaseVideoDataset(BaseDataset): - """Base class of video-reid dataset""" - - def extract_data_info(self, data, return_tracklet_stats=False): - pids = set() - cams = set() - tracklet_stats = [] - for img_paths, pid, camid in data: - pids.add(pid) - cams.add(camid) - tracklet_stats += [len(img_paths)] - num_pids = len(pids) - num_cams = len(cams) - num_tracklets = len(data) - if return_tracklet_stats: - return num_pids, num_tracklets, num_cams, tracklet_stats - return num_pids, num_tracklets, num_cams - - def print_dataset_statistics(self, train, query, gallery): - num_train_pids, num_train_tracklets, num_train_cams, train_tracklet_stats = \ - self.extract_data_info(train, return_tracklet_stats=True) - - num_query_pids, num_query_tracklets, num_query_cams, query_tracklet_stats = \ - self.extract_data_info(query, return_tracklet_stats=True) - - num_gallery_pids, num_gallery_tracklets, num_gallery_cams, gallery_tracklet_stats = \ - self.extract_data_info(gallery, return_tracklet_stats=True) - - tracklet_stats = train_tracklet_stats + query_tracklet_stats + gallery_tracklet_stats - min_num = np.min(tracklet_stats) - max_num = np.max(tracklet_stats) - avg_num = np.mean(tracklet_stats) - - print('=> Loaded {}'.format(self.__class__.__name__)) - print(' -------------------------------------------') - print(' subset | # ids | # tracklets | # cameras') - print(' -------------------------------------------') - print(' train | {:5d} | {:11d} | {:9d}'.format(num_train_pids, num_train_tracklets, num_train_cams)) - print(' query | {:5d} | {:11d} | {:9d}'.format(num_query_pids, num_query_tracklets, num_query_cams)) - print(' gallery | {:5d} | {:11d} | {:9d}'.format(num_gallery_pids, num_gallery_tracklets, num_gallery_cams)) - print(' -------------------------------------------') - print(' number of images per tracklet: {} ~ {}, average {:.2f}'.format(min_num, max_num, avg_num)) - print(' -------------------------------------------') \ No newline at end of file diff --git a/torchreid/datasets/cuhk01.py b/torchreid/datasets/cuhk01.py deleted file mode 100644 index 514c65c..0000000 --- a/torchreid/datasets/cuhk01.py +++ /dev/null @@ -1,141 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import glob -import re -import sys -import urllib -import tarfile -import zipfile -import os.path as osp -from scipy.io import loadmat -import numpy as np -import h5py -from scipy.misc import imsave - -from torchreid.utils.iotools import mkdir_if_missing, write_json, read_json -from .bases import BaseImageDataset - - -class CUHK01(BaseImageDataset): - """CUHK01 - - Reference: - Li et al. Human Reidentification with Transferred Metric Learning. ACCV 2012. - - URL: http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html - - Dataset statistics: - # identities: 971 - # images: 3884 - # cameras: 4 - """ - dataset_dir = 'cuhk01' - - def __init__(self, root='data', split_id=0, verbose=True, **kwargs): - super(CUHK01, self).__init__(root) - self.dataset_dir = osp.join(self.root, self.dataset_dir) - self.zip_path = osp.join(self.dataset_dir, 'CUHK01.zip') - self.campus_dir = osp.join(self.dataset_dir, 'campus') - self.split_path = osp.join(self.dataset_dir, 'splits.json') - - self.extract_file() - - required_files = [ - self.dataset_dir, - self.campus_dir - ] - self.check_before_run(required_files) - - self.prepare_split() - splits = read_json(self.split_path) - if split_id >= len(splits): - raise ValueError('split_id exceeds range, received {}, but expected between 0 and {}'.format(split_id, len(splits)-1)) - split = splits[split_id] - - train = split['train'] - query = split['query'] - gallery = split['gallery'] - - train = [tuple(item) for item in train] - query = [tuple(item) for item in query] - gallery = [tuple(item) for item in gallery] - - self.init_attributes(train, query, gallery, **kwargs) - - if verbose: - self.print_dataset_statistics(self.train, self.query, self.gallery) - - def extract_file(self): - if not osp.exists(self.campus_dir): - print('Extracting files') - zip_ref = zipfile.ZipFile(self.zip_path, 'r') - zip_ref.extractall(self.dataset_dir) - zip_ref.close() - - def prepare_split(self): - """ - Image name format: 0001001.png, where first four digits represent identity - and last four digits represent cameras. Camera 1&2 are considered the same - view and camera 3&4 are considered the same view. - """ - if not osp.exists(self.split_path): - print('Creating 10 random splits of train ids and test ids') - img_paths = sorted(glob.glob(osp.join(self.campus_dir, '*.png'))) - img_list = [] - pid_container = set() - for img_path in img_paths: - img_name = osp.basename(img_path) - pid = int(img_name[:4]) - 1 - camid = (int(img_name[4:7]) - 1) // 2 # result is either 0 or 1 - img_list.append((img_path, pid, camid)) - pid_container.add(pid) - - num_pids = len(pid_container) - num_train_pids = num_pids // 2 - - splits = [] - for _ in range(10): - order = np.arange(num_pids) - np.random.shuffle(order) - train_idxs = order[:num_train_pids] - train_idxs = np.sort(train_idxs) - idx2label = {idx: label for label, idx in enumerate(train_idxs)} - - train, test_a, test_b = [], [], [] - for img_path, pid, camid in img_list: - if pid in train_idxs: - train.append((img_path, idx2label[pid], camid)) - else: - if camid == 0: - test_a.append((img_path, pid, camid)) - else: - test_b.append((img_path, pid, camid)) - - # use cameraA as query and cameraB as gallery - split = { - 'train': train, - 'query': test_a, - 'gallery': test_b, - 'num_train_pids': num_train_pids, - 'num_query_pids': num_pids - num_train_pids, - 'num_gallery_pids': num_pids - num_train_pids - } - splits.append(split) - - # use cameraB as query and cameraA as gallery - split = { - 'train': train, - 'query': test_b, - 'gallery': test_a, - 'num_train_pids': num_train_pids, - 'num_query_pids': num_pids - num_train_pids, - 'num_gallery_pids': num_pids - num_train_pids - } - splits.append(split) - - print('Totally {} splits are created'.format(len(splits))) - write_json(splits, self.split_path) - print('Split file saved to {}'.format(self.split_path)) \ No newline at end of file diff --git a/torchreid/datasets/cuhk03.py b/torchreid/datasets/cuhk03.py deleted file mode 100644 index bf8046e..0000000 --- a/torchreid/datasets/cuhk03.py +++ /dev/null @@ -1,258 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import glob -import re -import sys -import urllib -import tarfile -import zipfile -import os.path as osp -from scipy.io import loadmat -import numpy as np -import h5py -from scipy.misc import imsave - -from torchreid.utils.iotools import mkdir_if_missing, write_json, read_json -from .bases import BaseImageDataset - - -class CUHK03(BaseImageDataset): - """CUHK03 - - Reference: - Li et al. DeepReID: Deep Filter Pairing Neural Network for Person Re-identification. CVPR 2014. - - URL: http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html#! - - Dataset statistics: - # identities: 1360 - # images: 13164 - # cameras: 6 - # splits: 20 (classic) - - Args: - split_id (int): split index (default: 0) - cuhk03_labeled (bool): whether to load labeled images; if false, detected images are loaded (default: False) - """ - dataset_dir = 'cuhk03' - - def __init__(self, root='data', split_id=0, cuhk03_labeled=False, cuhk03_classic_split=False, verbose=True, **kwargs): - super(CUHK03, self).__init__(root) - self.dataset_dir = osp.join(self.root, self.dataset_dir) - self.data_dir = osp.join(self.dataset_dir, 'cuhk03_release') - self.raw_mat_path = osp.join(self.data_dir, 'cuhk-03.mat') - - self.imgs_detected_dir = osp.join(self.dataset_dir, 'images_detected') - self.imgs_labeled_dir = osp.join(self.dataset_dir, 'images_labeled') - - self.split_classic_det_json_path = osp.join(self.dataset_dir, 'splits_classic_detected.json') - self.split_classic_lab_json_path = osp.join(self.dataset_dir, 'splits_classic_labeled.json') - - self.split_new_det_json_path = osp.join(self.dataset_dir, 'splits_new_detected.json') - self.split_new_lab_json_path = osp.join(self.dataset_dir, 'splits_new_labeled.json') - - self.split_new_det_mat_path = osp.join(self.dataset_dir, 'cuhk03_new_protocol_config_detected.mat') - self.split_new_lab_mat_path = osp.join(self.dataset_dir, 'cuhk03_new_protocol_config_labeled.mat') - - required_files = [ - self.dataset_dir, - self.data_dir, - self.raw_mat_path, - self.split_new_det_mat_path, - self.split_new_lab_mat_path - ] - self.check_before_run(required_files) - - self.preprocess_split() - - if cuhk03_labeled: - split_path = self.split_classic_lab_json_path if cuhk03_classic_split else self.split_new_lab_json_path - else: - split_path = self.split_classic_det_json_path if cuhk03_classic_split else self.split_new_det_json_path - - splits = read_json(split_path) - assert split_id < len(splits), 'Condition split_id ({}) < len(splits) ({}) is false'.format(split_id, len(splits)) - split = splits[split_id] - - train = split['train'] - query = split['query'] - gallery = split['gallery'] - - self.init_attributes(train, query, gallery, **kwargs) - - if verbose: - self.print_dataset_statistics(self.train, self.query, self.gallery) - - def preprocess_split(self): - """ - This function is a bit complex and ugly, what it does is - 1. extract data from cuhk-03.mat and save as png images - 2. create 20 classic splits (Li et al. CVPR'14) - 3. create new split (Zhong et al. CVPR'17) - """ - if osp.exists(self.imgs_labeled_dir) \ - and osp.exists(self.imgs_detected_dir) \ - and osp.exists(self.split_classic_det_json_path) \ - and osp.exists(self.split_classic_lab_json_path) \ - and osp.exists(self.split_new_det_json_path) \ - and osp.exists(self.split_new_lab_json_path): - return - - mkdir_if_missing(self.imgs_detected_dir) - mkdir_if_missing(self.imgs_labeled_dir) - - print('Extract image data from "{}" and save as png'.format(self.raw_mat_path)) - mat = h5py.File(self.raw_mat_path, 'r') - - def _deref(ref): - return mat[ref][:].T - - def _process_images(img_refs, campid, pid, save_dir): - img_paths = [] # Note: some persons only have images for one view - for imgid, img_ref in enumerate(img_refs): - img = _deref(img_ref) - if img.size==0 or img.ndim<3: - continue # skip empty cell - # images are saved with the following format, index-1 (ensure uniqueness) - # campid: index of camera pair (1-5) - # pid: index of person in 'campid'-th camera pair - # viewid: index of view, {1, 2} - # imgid: index of image, (1-10) - viewid = 1 if imgid<5 else 2 - img_name = '{:01d}_{:03d}_{:01d}_{:02d}.png'.format(campid+1, pid+1, viewid, imgid+1) - img_path = osp.join(save_dir, img_name) - if not osp.isfile(img_path): - imsave(img_path, img) - img_paths.append(img_path) - return img_paths - - def _extract_img(image_type): - print('Processing {} images ...'.format(image_type)) - meta_data = [] - imgs_dir = self.imgs_detected_dir if image_type=='detected' else self.imgs_labeled_dir - for campid, camp_ref in enumerate(mat[image_type][0]): - camp = _deref(camp_ref) - num_pids = camp.shape[0] - for pid in range(num_pids): - img_paths = _process_images(camp[pid,:], campid, pid, imgs_dir) - assert len(img_paths) > 0, 'campid{}-pid{} has no images'.format(campid, pid) - meta_data.append((campid+1, pid+1, img_paths)) - print('- done camera pair {} with {} identities'.format(campid+1, num_pids)) - return meta_data - - meta_detected = _extract_img('detected') - meta_labeled = _extract_img('labeled') - - def _extract_classic_split(meta_data, test_split): - train, test = [], [] - num_train_pids, num_test_pids = 0, 0 - num_train_imgs, num_test_imgs = 0, 0 - for i, (campid, pid, img_paths) in enumerate(meta_data): - - if [campid, pid] in test_split: - for img_path in img_paths: - camid = int(osp.basename(img_path).split('_')[2]) - 1 # make it 0-based - test.append((img_path, num_test_pids, camid)) - num_test_pids += 1 - num_test_imgs += len(img_paths) - else: - for img_path in img_paths: - camid = int(osp.basename(img_path).split('_')[2]) - 1 # make it 0-based - train.append((img_path, num_train_pids, camid)) - num_train_pids += 1 - num_train_imgs += len(img_paths) - return train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs - - print('Creating classic splits (# = 20) ...') - splits_classic_det, splits_classic_lab = [], [] - for split_ref in mat['testsets'][0]: - test_split = _deref(split_ref).tolist() - - # create split for detected images - train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \ - _extract_classic_split(meta_detected, test_split) - splits_classic_det.append({ - 'train': train, 'query': test, 'gallery': test, - 'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs, - 'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs, - 'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs, - }) - - # create split for labeled images - train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \ - _extract_classic_split(meta_labeled, test_split) - splits_classic_lab.append({ - 'train': train, 'query': test, 'gallery': test, - 'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs, - 'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs, - 'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs, - }) - - write_json(splits_classic_det, self.split_classic_det_json_path) - write_json(splits_classic_lab, self.split_classic_lab_json_path) - - def _extract_set(filelist, pids, pid2label, idxs, img_dir, relabel): - tmp_set = [] - unique_pids = set() - for idx in idxs: - img_name = filelist[idx][0] - camid = int(img_name.split('_')[2]) - 1 # make it 0-based - pid = pids[idx] - if relabel: - pid = pid2label[pid] - img_path = osp.join(img_dir, img_name) - tmp_set.append((img_path, int(pid), camid)) - unique_pids.add(pid) - return tmp_set, len(unique_pids), len(idxs) - - def _extract_new_split(split_dict, img_dir): - train_idxs = split_dict['train_idx'].flatten() - 1 # index-0 - pids = split_dict['labels'].flatten() - train_pids = set(pids[train_idxs]) - pid2label = {pid: label for label, pid in enumerate(train_pids)} - query_idxs = split_dict['query_idx'].flatten() - 1 - gallery_idxs = split_dict['gallery_idx'].flatten() - 1 - filelist = split_dict['filelist'].flatten() - train_info = _extract_set(filelist, pids, pid2label, train_idxs, img_dir, relabel=True) - query_info = _extract_set(filelist, pids, pid2label, query_idxs, img_dir, relabel=False) - gallery_info = _extract_set(filelist, pids, pid2label, gallery_idxs, img_dir, relabel=False) - return train_info, query_info, gallery_info - - print('Creating new split for detected images (767/700) ...') - train_info, query_info, gallery_info = _extract_new_split( - loadmat(self.split_new_det_mat_path), - self.imgs_detected_dir, - ) - split = [{ - 'train': train_info[0], - 'query': query_info[0], - 'gallery': gallery_info[0], - 'num_train_pids': train_info[1], - 'num_train_imgs': train_info[2], - 'num_query_pids': query_info[1], - 'num_query_imgs': query_info[2], - 'num_gallery_pids': gallery_info[1], - 'num_gallery_imgs': gallery_info[2], - }] - write_json(split, self.split_new_det_json_path) - - print('Creating new split for labeled images (767/700) ...') - train_info, query_info, gallery_info = _extract_new_split( - loadmat(self.split_new_lab_mat_path), - self.imgs_labeled_dir, - ) - split = [{ - 'train': train_info[0], - 'query': query_info[0], - 'gallery': gallery_info[0], - 'num_train_pids': train_info[1], - 'num_train_imgs': train_info[2], - 'num_query_pids': query_info[1], - 'num_query_imgs': query_info[2], - 'num_gallery_pids': gallery_info[1], - 'num_gallery_imgs': gallery_info[2], - }] - write_json(split, self.split_new_lab_json_path) diff --git a/torchreid/datasets/dukemtmcreid.py b/torchreid/datasets/dukemtmcreid.py deleted file mode 100644 index 1bdfd40..0000000 --- a/torchreid/datasets/dukemtmcreid.py +++ /dev/null @@ -1,99 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import glob -import re -import sys -import urllib -import tarfile -import zipfile -import os.path as osp -from scipy.io import loadmat -import numpy as np -import h5py -from scipy.misc import imsave - -from torchreid.utils.iotools import mkdir_if_missing -from .bases import BaseImageDataset - - -class DukeMTMCreID(BaseImageDataset): - """DukeMTMC-reID - - Reference: - 1. Ristani et al. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. ECCVW 2016. - 2. Zheng et al. Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro. ICCV 2017. - - URL: https://github.com/layumi/DukeMTMC-reID_evaluation - - Dataset statistics: - # identities: 1404 (train + query) - # images:16522 (train) + 2228 (query) + 17661 (gallery) - # cameras: 8 - """ - dataset_dir = 'dukemtmc-reid' - - def __init__(self, root='data', verbose=True, **kwargs): - super(DukeMTMCreID, self).__init__(root) - self.dataset_dir = osp.join(self.root, self.dataset_dir) - self.dataset_url = 'http://vision.cs.duke.edu/DukeMTMC/data/misc/DukeMTMC-reID.zip' - self.train_dir = osp.join(self.dataset_dir, 'DukeMTMC-reID/bounding_box_train') - self.query_dir = osp.join(self.dataset_dir, 'DukeMTMC-reID/query') - self.gallery_dir = osp.join(self.dataset_dir, 'DukeMTMC-reID/bounding_box_test') - - self.download_data() - - required_files = [ - self.dataset_dir, - self.train_dir, - self.query_dir, - self.gallery_dir - ] - self.check_before_run(required_files) - - train = self.process_dir(self.train_dir, relabel=True) - query = self.process_dir(self.query_dir, relabel=False) - gallery = self.process_dir(self.gallery_dir, relabel=False) - - self.init_attributes(train, query, gallery, **kwargs) - - if verbose: - self.print_dataset_statistics(self.train, self.query, self.gallery) - - def download_data(self): - if osp.exists(self.dataset_dir): - return - - print('Creating directory {}'.format(self.dataset_dir)) - mkdir_if_missing(self.dataset_dir) - fpath = osp.join(self.dataset_dir, osp.basename(self.dataset_url)) - - print('Downloading DukeMTMC-reID dataset') - urllib.urlretrieve(self.dataset_url, fpath) - - print('Extracting files') - zip_ref = zipfile.ZipFile(fpath, 'r') - zip_ref.extractall(self.dataset_dir) - zip_ref.close() - - def process_dir(self, dir_path, relabel=False): - img_paths = glob.glob(osp.join(dir_path, '*.jpg')) - pattern = re.compile(r'([-\d]+)_c(\d)') - - pid_container = set() - for img_path in img_paths: - pid, _ = map(int, pattern.search(img_path).groups()) - pid_container.add(pid) - pid2label = {pid:label for label, pid in enumerate(pid_container)} - - dataset = [] - for img_path in img_paths: - pid, camid = map(int, pattern.search(img_path).groups()) - assert 1 <= camid <= 8 - camid -= 1 # index starts from 0 - if relabel: pid = pid2label[pid] - dataset.append((img_path, pid, camid)) - - return dataset \ No newline at end of file diff --git a/torchreid/datasets/dukemtmcvidreid.py b/torchreid/datasets/dukemtmcvidreid.py deleted file mode 100644 index d742397..0000000 --- a/torchreid/datasets/dukemtmcvidreid.py +++ /dev/null @@ -1,137 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import glob -import re -import sys -import urllib -import tarfile -import zipfile -import os.path as osp -from scipy.io import loadmat -import numpy as np -import h5py -from scipy.misc import imsave - -from torchreid.utils.iotools import mkdir_if_missing, write_json, read_json -from .bases import BaseVideoDataset - - -class DukeMTMCVidReID(BaseVideoDataset): - """DukeMTMCVidReID - - Reference: - Wu et al. Exploit the Unknown Gradually: One-Shot Video-Based Person - Re-Identification by Stepwise Learning. CVPR 2018. - - URL: https://github.com/Yu-Wu/DukeMTMC-VideoReID - - Dataset statistics: - # identities: 702 (train) + 702 (test) - # tracklets: 2196 (train) + 2636 (test) - """ - dataset_dir = 'dukemtmc-vidreid' - - def __init__(self, root='data', min_seq_len=0, verbose=True, **kwargs): - super(DukeMTMCVidReID, self).__init__(root) - self.dataset_dir = osp.join(self.root, self.dataset_dir) - self.dataset_url = 'http://vision.cs.duke.edu/DukeMTMC/data/misc/DukeMTMC-VideoReID.zip' - self.train_dir = osp.join(self.dataset_dir, 'DukeMTMC-VideoReID/train') - self.query_dir = osp.join(self.dataset_dir, 'DukeMTMC-VideoReID/query') - self.gallery_dir = osp.join(self.dataset_dir, 'DukeMTMC-VideoReID/gallery') - self.split_train_json_path = osp.join(self.dataset_dir, 'split_train.json') - self.split_query_json_path = osp.join(self.dataset_dir, 'split_query.json') - self.split_gallery_json_path = osp.join(self.dataset_dir, 'split_gallery.json') - self.min_seq_len = min_seq_len - - self.download_data() - - required_files = [ - self.dataset_dir, - self.train_dir, - self.query_dir, - self.gallery_dir - ] - self.check_before_run(required_files) - - train = self.process_dir(self.train_dir, self.split_train_json_path, relabel=True) - query = self.process_dir(self.query_dir, self.split_query_json_path, relabel=False) - gallery = self.process_dir(self.gallery_dir, self.split_gallery_json_path, relabel=False) - - self.init_attributes(train, query, gallery, **kwargs) - - if verbose: - self.print_dataset_statistics(self.train, self.query, self.gallery) - - def download_data(self): - if osp.exists(self.dataset_dir): - return - - print('Creating directory {}'.format(self.dataset_dir)) - mkdir_if_missing(self.dataset_dir) - fpath = osp.join(self.dataset_dir, osp.basename(self.dataset_url)) - - print('Downloading DukeMTMC-VideoReID dataset') - urllib.urlretrieve(self.dataset_url, fpath) - - print('Extracting files') - zip_ref = zipfile.ZipFile(fpath, 'r') - zip_ref.extractall(self.dataset_dir) - zip_ref.close() - - def process_dir(self, dir_path, json_path, relabel): - if osp.exists(json_path): - split = read_json(json_path) - return split['tracklets'] - - print('=> Generating split json file (** this might take a while **)') - pdirs = glob.glob(osp.join(dir_path, '*')) # avoid .DS_Store - print('Processing "{}" with {} person identities'.format(dir_path, len(pdirs))) - - pid_container = set() - for pdir in pdirs: - pid = int(osp.basename(pdir)) - pid_container.add(pid) - pid2label = {pid:label for label, pid in enumerate(pid_container)} - - tracklets = [] - for pdir in pdirs: - pid = int(osp.basename(pdir)) - if relabel: - pid = pid2label[pid] - tdirs = glob.glob(osp.join(pdir, '*')) - for tdir in tdirs: - raw_img_paths = glob.glob(osp.join(tdir, '*.jpg')) - num_imgs = len(raw_img_paths) - - if num_imgs < self.min_seq_len: - continue - - img_paths = [] - for img_idx in range(num_imgs): - # some tracklet starts from 0002 instead of 0001 - img_idx_name = 'F' + str(img_idx+1).zfill(4) - res = glob.glob(osp.join(tdir, '*' + img_idx_name + '*.jpg')) - if len(res) == 0: - print('Warn: index name {} in {} is missing, jump to next'.format(img_idx_name, tdir)) - continue - img_paths.append(res[0]) - img_name = osp.basename(img_paths[0]) - if img_name.find('_') == -1: - # old naming format: 0001C6F0099X30823.jpg - camid = int(img_name[5]) - 1 - else: - # new naming format: 0001_C6_F0099_X30823.jpg - camid = int(img_name[6]) - 1 - img_paths = tuple(img_paths) - tracklets.append((img_paths, pid, camid)) - - print('Saving split to {}'.format(json_path)) - split_dict = { - 'tracklets': tracklets, - } - write_json(split_dict, json_path) - - return tracklets \ No newline at end of file diff --git a/torchreid/datasets/grid.py b/torchreid/datasets/grid.py deleted file mode 100644 index 25aae39..0000000 --- a/torchreid/datasets/grid.py +++ /dev/null @@ -1,139 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import glob -import re -import sys -import urllib -import tarfile -import zipfile -import os.path as osp -from scipy.io import loadmat -import numpy as np -import h5py -from scipy.misc import imsave - -from torchreid.utils.iotools import mkdir_if_missing, write_json, read_json -from .bases import BaseImageDataset - - -class GRID(BaseImageDataset): - """GRID - - Reference: - Loy et al. Multi-camera activity correlation analysis. CVPR 2009. - - URL: http://personal.ie.cuhk.edu.hk/~ccloy/downloads_qmul_underground_reid.html - - Dataset statistics: - # identities: 250 - # images: 1275 - # cameras: 8 - """ - dataset_dir = 'grid' - - def __init__(self, root='data', split_id=0, verbose=True, **kwargs): - super(GRID, self).__init__(root) - self.dataset_dir = osp.join(self.root, self.dataset_dir) - self.dataset_url = 'http://personal.ie.cuhk.edu.hk/~ccloy/files/datasets/underground_reid.zip' - self.probe_path = osp.join(self.dataset_dir, 'underground_reid', 'probe') - self.gallery_path = osp.join(self.dataset_dir, 'underground_reid', 'gallery') - self.split_mat_path = osp.join(self.dataset_dir, 'underground_reid', 'features_and_partitions.mat') - self.split_path = osp.join(self.dataset_dir, 'splits.json') - - self.download_data() - - required_files = [ - self.dataset_dir, - self.probe_path, - self.gallery_path, - self.split_mat_path - ] - self.check_before_run(required_files) - - self.prepare_split() - splits = read_json(self.split_path) - if split_id >= len(splits): - raise ValueError('split_id exceeds range, received {}, but expected between 0 and {}'.format(split_id, len(splits)-1)) - split = splits[split_id] - - train = split['train'] - query = split['query'] - gallery = split['gallery'] - - train = [tuple(item) for item in train] - query = [tuple(item) for item in query] - gallery = [tuple(item) for item in gallery] - - self.init_attributes(train, query, gallery, **kwargs) - - if verbose: - self.print_dataset_statistics(self.train, self.query, self.gallery) - - def download_data(self): - if osp.exists(self.dataset_dir): - return - - print('Creating directory {}'.format(self.dataset_dir)) - mkdir_if_missing(self.dataset_dir) - fpath = osp.join(self.dataset_dir, osp.basename(self.dataset_url)) - - print('Downloading GRID dataset') - urllib.urlretrieve(self.dataset_url, fpath) - - print('Extracting files') - zip_ref = zipfile.ZipFile(fpath, 'r') - zip_ref.extractall(self.dataset_dir) - zip_ref.close() - - def prepare_split(self): - if not osp.exists(self.split_path): - print('Creating 10 random splits') - split_mat = loadmat(self.split_mat_path) - trainIdxAll = split_mat['trainIdxAll'][0] # length = 10 - probe_img_paths = sorted(glob.glob(osp.join(self.probe_path, '*.jpeg'))) - gallery_img_paths = sorted(glob.glob(osp.join(self.gallery_path, '*.jpeg'))) - - splits = [] - for split_idx in range(10): - train_idxs = trainIdxAll[split_idx][0][0][2][0].tolist() - assert len(train_idxs) == 125 - idx2label = {idx: label for label, idx in enumerate(train_idxs)} - - train, query, gallery = [], [], [] - - # processing probe folder - for img_path in probe_img_paths: - img_name = osp.basename(img_path) - img_idx = int(img_name.split('_')[0]) - camid = int(img_name.split('_')[1]) - 1 # index starts from 0 - if img_idx in train_idxs: - train.append((img_path, idx2label[img_idx], camid)) - else: - query.append((img_path, img_idx, camid)) - - # process gallery folder - for img_path in gallery_img_paths: - img_name = osp.basename(img_path) - img_idx = int(img_name.split('_')[0]) - camid = int(img_name.split('_')[1]) - 1 # index starts from 0 - if img_idx in train_idxs: - train.append((img_path, idx2label[img_idx], camid)) - else: - gallery.append((img_path, img_idx, camid)) - - split = { - 'train': train, - 'query': query, - 'gallery': gallery, - 'num_train_pids': 125, - 'num_query_pids': 125, - 'num_gallery_pids': 900 - } - splits.append(split) - - print('Totally {} splits are created'.format(len(splits))) - write_json(splits, self.split_path) - print('Split file saved to {}'.format(self.split_path)) \ No newline at end of file diff --git a/torchreid/datasets/ilids.py b/torchreid/datasets/ilids.py deleted file mode 100644 index 9357e90..0000000 --- a/torchreid/datasets/ilids.py +++ /dev/null @@ -1,162 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import glob -import re -import sys -import urllib -import tarfile -import zipfile -import os.path as osp -from scipy.io import loadmat -import numpy as np -import h5py -from scipy.misc import imsave -from collections import defaultdict -import copy -import random - -from torchreid.utils.iotools import mkdir_if_missing, write_json, read_json -from .bases import BaseImageDataset - - -class iLIDS(BaseImageDataset): - """QMUL-iLIDS - - Reference: - Zheng et al. Associating Groups of People. BMVC 2009. - - Dataset statistics: - # identities: 119 - # images: 476 - # cameras: 8 (not explicitly provided) - """ - dataset_dir = 'ilids' - - def __init__(self, root='data', split_id=0, verbose=True, **kwargs): - super(iLIDS, self).__init__(root) - self.dataset_dir = osp.join(self.root, self.dataset_dir) - self.dataset_url = 'http://www.eecs.qmul.ac.uk/~jason/data/i-LIDS_Pedestrian.tgz' - self.data_dir = osp.join(self.dataset_dir, 'i-LIDS_Pedestrian/Persons') - self.split_path = osp.join(self.dataset_dir, 'splits.json') - - self.download_data() - - required_files = [ - self.dataset_dir, - self.data_dir - ] - self.check_before_run(required_files) - - self.prepare_split() - splits = read_json(self.split_path) - if split_id >= len(splits): - raise ValueError('split_id exceeds range, received {}, but expected between 0 and {}'.format(split_id, len(splits)-1)) - split = splits[split_id] - - train, query, gallery = self.process_split(split) - - self.init_attributes(train, query, gallery, **kwargs) - - if verbose: - self.print_dataset_statistics(self.train, self.query, self.gallery) - - def download_data(self): - if osp.exists(self.dataset_dir): - return - - mkdir_if_missing(self.dataset_dir) - fpath = osp.join(self.dataset_dir, osp.basename(self.dataset_url)) - - print('Downloading QMUL-iLIDS dataset') - urllib.urlretrieve(self.dataset_url, fpath) - - print('Extracting files') - tar = tarfile.open(fpath) - tar.extractall(path=self.dataset_dir) - tar.close() - - def prepare_split(self): - if not osp.exists(self.split_path): - print('Creating splits ...') - - paths = glob.glob(osp.join(self.data_dir, '*.jpg')) - img_names = [osp.basename(path) for path in paths] - num_imgs = len(img_names) - assert num_imgs == 476, 'There should be 476 images, but got {}, please check the data'.format(num_imgs) - - # store image names - # image naming format: - # the first four digits denote the person ID - # the last four digits denote the sequence index - pid_dict = defaultdict(list) - for img_name in img_names: - pid = int(img_name[:4]) - pid_dict[pid].append(img_name) - pids = list(pid_dict.keys()) - num_pids = len(pids) - assert num_pids == 119, 'There should be 119 identities, but got {}, please check the data'.format(num_pids) - - num_train_pids = int(num_pids * 0.5) - num_test_pids = num_pids - num_train_pids # supposed to be 60 - - splits = [] - for _ in range(10): - # randomly choose num_train_pids train IDs and num_test_pids test IDs - pids_copy = copy.deepcopy(pids) - random.shuffle(pids_copy) - train_pids = pids_copy[:num_train_pids] - test_pids = pids_copy[num_train_pids:] - - train = [] - query = [] - gallery = [] - - # for train IDs, all images are used in the train set. - for pid in train_pids: - img_names = pid_dict[pid] - train.extend(img_names) - - # for each test ID, randomly choose two images, one for - # query and the other one for gallery. - for pid in test_pids: - img_names = pid_dict[pid] - samples = random.sample(img_names, 2) - query.append(samples[0]) - gallery.append(samples[1]) - - split = {'train': train, 'query': query, 'gallery': gallery} - splits.append(split) - - print('Totally {} splits are created'.format(len(splits))) - write_json(splits, self.split_path) - print('Split file is saved to {}'.format(self.split_path)) - - def get_pid2label(self, img_names): - pid_container = set() - for img_name in img_names: - pid = int(img_name[:4]) - pid_container.add(pid) - pid2label = {pid: label for label, pid in enumerate(pid_container)} - return pid2label - - def parse_img_names(self, img_names, pid2label=None): - output = [] - for img_name in img_names: - pid = int(img_name[:4]) - if pid2label is not None: - pid = pid2label[pid] - camid = int(img_name[4:7]) - 1 # 0-based - img_path = osp.join(self.data_dir, img_name) - output.append((img_path, pid, camid)) - return output - - def process_split(self, split): - train, query, gallery = [], [], [] - train_pid2label = self.get_pid2label(split['train']) - train = self.parse_img_names(split['train'], train_pid2label) - query = self.parse_img_names(split['query']) - gallery = self.parse_img_names(split['gallery']) - return train, query, gallery \ No newline at end of file diff --git a/torchreid/datasets/ilidsvid.py b/torchreid/datasets/ilidsvid.py deleted file mode 100644 index 3da15c7..0000000 --- a/torchreid/datasets/ilidsvid.py +++ /dev/null @@ -1,151 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import glob -import re -import sys -import urllib -import tarfile -import zipfile -import os.path as osp -from scipy.io import loadmat -import numpy as np -import h5py -from scipy.misc import imsave - -from torchreid.utils.iotools import mkdir_if_missing, write_json, read_json -from .bases import BaseVideoDataset - - -class iLIDSVID(BaseVideoDataset): - """iLIDS-VID - - Reference: - Wang et al. Person Re-Identification by Video Ranking. ECCV 2014. - - URL: http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html - - Dataset statistics: - # identities: 300 - # tracklets: 600 - # cameras: 2 - """ - dataset_dir = 'ilids-vid' - - def __init__(self, root='data', split_id=0, verbose=True, **kwargs): - super(iLIDSVID, self).__init__(root) - self.dataset_dir = osp.join(self.root, self.dataset_dir) - self.dataset_url = 'http://www.eecs.qmul.ac.uk/~xiatian/iLIDS-VID/iLIDS-VID.tar' - self.data_dir = osp.join(self.dataset_dir, 'i-LIDS-VID') - self.split_dir = osp.join(self.dataset_dir, 'train-test people splits') - self.split_mat_path = osp.join(self.split_dir, 'train_test_splits_ilidsvid.mat') - self.split_path = osp.join(self.dataset_dir, 'splits.json') - self.cam_1_path = osp.join(self.dataset_dir, 'i-LIDS-VID/sequences/cam1') - self.cam_2_path = osp.join(self.dataset_dir, 'i-LIDS-VID/sequences/cam2') - - self.download_data() - - required_files = [ - self.dataset_dir, - self.data_dir, - self.split_dir - ] - self.check_before_run(required_files) - - self.prepare_split() - splits = read_json(self.split_path) - if split_id >= len(splits): - raise ValueError('split_id exceeds range, received {}, but expected between 0 and {}'.format(split_id, len(splits)-1)) - split = splits[split_id] - train_dirs, test_dirs = split['train'], split['test'] - - train = self.process_data(train_dirs, cam1=True, cam2=True) - query = self.process_data(test_dirs, cam1=True, cam2=False) - gallery = self.process_data(test_dirs, cam1=False, cam2=True) - - self.init_attributes(train, query, gallery, **kwargs) - - if verbose: - self.print_dataset_statistics(self.train, self.query, self.gallery) - - def download_data(self): - if osp.exists(self.dataset_dir): - return - - mkdir_if_missing(self.dataset_dir) - fpath = osp.join(self.dataset_dir, osp.basename(self.dataset_url)) - - print('Downloading iLIDS-VID dataset') - urllib.urlretrieve(self.dataset_url, fpath) - - print('Extracting files') - tar = tarfile.open(fpath) - tar.extractall(path=self.dataset_dir) - tar.close() - - def prepare_split(self): - if not osp.exists(self.split_path): - print('Creating splits ...') - mat_split_data = loadmat(self.split_mat_path)['ls_set'] - - num_splits = mat_split_data.shape[0] - num_total_ids = mat_split_data.shape[1] - assert num_splits == 10 - assert num_total_ids == 300 - num_ids_each = num_total_ids // 2 - - # pids in mat_split_data are indices, so we need to transform them - # to real pids - person_cam1_dirs = sorted(glob.glob(osp.join(self.cam_1_path, '*'))) - person_cam2_dirs = sorted(glob.glob(osp.join(self.cam_2_path, '*'))) - - person_cam1_dirs = [osp.basename(item) for item in person_cam1_dirs] - person_cam2_dirs = [osp.basename(item) for item in person_cam2_dirs] - - # make sure persons in one camera view can be found in the other camera view - assert set(person_cam1_dirs) == set(person_cam2_dirs) - - splits = [] - for i_split in range(num_splits): - # first 50% for testing and the remaining for training, following Wang et al. ECCV'14. - train_idxs = sorted(list(mat_split_data[i_split, num_ids_each:])) - test_idxs = sorted(list(mat_split_data[i_split, :num_ids_each])) - - train_idxs = [int(i)-1 for i in train_idxs] - test_idxs = [int(i)-1 for i in test_idxs] - - # transform pids to person dir names - train_dirs = [person_cam1_dirs[i] for i in train_idxs] - test_dirs = [person_cam1_dirs[i] for i in test_idxs] - - split = {'train': train_dirs, 'test': test_dirs} - splits.append(split) - - print('Totally {} splits are created, following Wang et al. ECCV\'14'.format(len(splits))) - print('Split file is saved to {}'.format(self.split_path)) - write_json(splits, self.split_path) - - def process_data(self, dirnames, cam1=True, cam2=True): - tracklets = [] - dirname2pid = {dirname:i for i, dirname in enumerate(dirnames)} - - for dirname in dirnames: - if cam1: - person_dir = osp.join(self.cam_1_path, dirname) - img_names = glob.glob(osp.join(person_dir, '*.png')) - assert len(img_names) > 0 - img_names = tuple(img_names) - pid = dirname2pid[dirname] - tracklets.append((img_names, pid, 0)) - - if cam2: - person_dir = osp.join(self.cam_2_path, dirname) - img_names = glob.glob(osp.join(person_dir, '*.png')) - assert len(img_names) > 0 - img_names = tuple(img_names) - pid = dirname2pid[dirname] - tracklets.append((img_names, pid, 1)) - - return tracklets \ No newline at end of file diff --git a/torchreid/datasets/market1501.py b/torchreid/datasets/market1501.py deleted file mode 100644 index 19b1549..0000000 --- a/torchreid/datasets/market1501.py +++ /dev/null @@ -1,89 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import glob -import re -import sys -import urllib -import tarfile -import zipfile -import os.path as osp -from scipy.io import loadmat -import numpy as np -import h5py -from scipy.misc import imsave - -from .bases import BaseImageDataset - - -class Market1501(BaseImageDataset): - """Market1501 - - Reference: - Zheng et al. Scalable Person Re-identification: A Benchmark. ICCV 2015. - - URL: http://www.liangzheng.org/Project/project_reid.html - - Dataset statistics: - # identities: 1501 (+1 for background) - # images: 12936 (train) + 3368 (query) + 15913 (gallery) - """ - dataset_dir = 'market1501' - - def __init__(self, root='data', verbose=True, market1501_500k=False, **kwargs): - super(Market1501, self).__init__(root) - self.dataset_dir = osp.join(self.root, self.dataset_dir) - self.train_dir = osp.join(self.dataset_dir, 'bounding_box_train') - self.query_dir = osp.join(self.dataset_dir, 'query') - self.gallery_dir = osp.join(self.dataset_dir, 'bounding_box_test') - self.extra_gallery_dir = osp.join(self.dataset_dir, 'images') - self.market1501_500k = market1501_500k - - required_files = [ - self.dataset_dir, - self.train_dir, - self.query_dir, - self.gallery_dir - ] - if self.market1501_500k: - required_files.append(self.extra_gallery_dir) - self.check_before_run(required_files) - - train = self.process_dir(self.train_dir, relabel=True) - query = self.process_dir(self.query_dir, relabel=False) - gallery = self.process_dir(self.gallery_dir, relabel=False) - if self.market1501_500k: - gallery += self.process_dir(self.extra_gallery_dir, relabel=False) - - self.init_attributes(train, query, gallery, **kwargs) - - if verbose: - self.print_dataset_statistics(self.train, self.query, self.gallery) - - def process_dir(self, dir_path, relabel=False): - img_paths = glob.glob(osp.join(dir_path, '*.jpg')) - pattern = re.compile(r'([-\d]+)_c(\d)') - - pid_container = set() - for img_path in img_paths: - pid, _ = map(int, pattern.search(img_path).groups()) - if pid == -1: - continue # junk images are just ignored - pid_container.add(pid) - pid2label = {pid:label for label, pid in enumerate(pid_container)} - - dataset = [] - for img_path in img_paths: - pid, camid = map(int, pattern.search(img_path).groups()) - if pid == -1: - continue # junk images are just ignored - assert 0 <= pid <= 1501 # pid == 0 means background - assert 1 <= camid <= 6 - camid -= 1 # index starts from 0 - if relabel: - pid = pid2label[pid] - dataset.append((img_path, pid, camid)) - - return dataset \ No newline at end of file diff --git a/torchreid/datasets/mars.py b/torchreid/datasets/mars.py deleted file mode 100644 index 8992951..0000000 --- a/torchreid/datasets/mars.py +++ /dev/null @@ -1,115 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import glob -import re -import sys -import urllib -import tarfile -import zipfile -import os.path as osp -from scipy.io import loadmat -import numpy as np -import h5py -from scipy.misc import imsave - -from .bases import BaseVideoDataset - - -class Mars(BaseVideoDataset): - """MARS - - Reference: - Zheng et al. MARS: A Video Benchmark for Large-Scale Person Re-identification. ECCV 2016. - - URL: http://www.liangzheng.com.cn/Project/project_mars.html - - Dataset statistics: - # identities: 1261 - # tracklets: 8298 (train) + 1980 (query) + 9330 (gallery) - # cameras: 6 - """ - dataset_dir = 'mars' - - def __init__(self, root='data', min_seq_len=0, verbose=True, **kwargs): - super(Mars, self).__init__(root) - self.dataset_dir = osp.join(self.root, self.dataset_dir) - self.train_name_path = osp.join(self.dataset_dir, 'info/train_name.txt') - self.test_name_path = osp.join(self.dataset_dir, 'info/test_name.txt') - self.track_train_info_path = osp.join(self.dataset_dir, 'info/tracks_train_info.mat') - self.track_test_info_path = osp.join(self.dataset_dir, 'info/tracks_test_info.mat') - self.query_IDX_path = osp.join(self.dataset_dir, 'info/query_IDX.mat') - - required_files = [ - self.dataset_dir, - self.train_name_path, - self.test_name_path, - self.track_train_info_path, - self.track_test_info_path, - self.query_IDX_path - ] - self.check_before_run(required_files) - - train_names = self.get_names(self.train_name_path) - test_names = self.get_names(self.test_name_path) - track_train = loadmat(self.track_train_info_path)['track_train_info'] # numpy.ndarray (8298, 4) - track_test = loadmat(self.track_test_info_path)['track_test_info'] # numpy.ndarray (12180, 4) - query_IDX = loadmat(self.query_IDX_path)['query_IDX'].squeeze() # numpy.ndarray (1980,) - query_IDX -= 1 # index from 0 - track_query = track_test[query_IDX,:] - gallery_IDX = [i for i in range(track_test.shape[0]) if i not in query_IDX] - track_gallery = track_test[gallery_IDX,:] - - train = self.process_data(train_names, track_train, home_dir='bbox_train', relabel=True, min_seq_len=min_seq_len) - query = self.process_data(test_names, track_query, home_dir='bbox_test', relabel=False, min_seq_len=min_seq_len) - gallery = self.process_data(test_names, track_gallery, home_dir='bbox_test', relabel=False, min_seq_len=min_seq_len) - - self.init_attributes(train, query, gallery, **kwargs) - - if verbose: - self.print_dataset_statistics(self.train, self.query, self.gallery) - - def get_names(self, fpath): - names = [] - with open(fpath, 'r') as f: - for line in f: - new_line = line.rstrip() - names.append(new_line) - return names - - def process_data(self, names, meta_data, home_dir=None, relabel=False, min_seq_len=0): - assert home_dir in ['bbox_train', 'bbox_test'] - num_tracklets = meta_data.shape[0] - pid_list = list(set(meta_data[:,2].tolist())) - num_pids = len(pid_list) - - if relabel: pid2label = {pid:label for label, pid in enumerate(pid_list)} - tracklets = [] - - for tracklet_idx in range(num_tracklets): - data = meta_data[tracklet_idx,...] - start_index, end_index, pid, camid = data - if pid == -1: - continue # junk images are just ignored - assert 1 <= camid <= 6 - if relabel: pid = pid2label[pid] - camid -= 1 # index starts from 0 - img_names = names[start_index - 1:end_index] - - # make sure image names correspond to the same person - pnames = [img_name[:4] for img_name in img_names] - assert len(set(pnames)) == 1, 'Error: a single tracklet contains different person images' - - # make sure all images are captured under the same camera - camnames = [img_name[5] for img_name in img_names] - assert len(set(camnames)) == 1, 'Error: images are captured under different cameras!' - - # append image names with directory information - img_paths = [osp.join(self.dataset_dir, home_dir, img_name[:4], img_name) for img_name in img_names] - if len(img_paths) >= min_seq_len: - img_paths = tuple(img_paths) - tracklets.append((img_paths, pid, camid)) - - return tracklets \ No newline at end of file diff --git a/torchreid/datasets/msmt17.py b/torchreid/datasets/msmt17.py deleted file mode 100644 index 7ed8034..0000000 --- a/torchreid/datasets/msmt17.py +++ /dev/null @@ -1,108 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import glob -import re -import sys -import urllib -import tarfile -import zipfile -import os.path as osp -from scipy.io import loadmat -import numpy as np -import h5py -from scipy.misc import imsave - -from .bases import BaseImageDataset - - -# To adapt to different versions -# Log: -# 22.01.2019: v1 and v2 only differ in dir names -TRAIN_DIR_KEY = 'train_dir' -TEST_DIR_KEY = 'test_dir' -VERSION_DICT = { - 'MSMT17_V1': { - TRAIN_DIR_KEY: 'train', - TEST_DIR_KEY: 'test', - }, - 'MSMT17_V2': { - TRAIN_DIR_KEY: 'mask_train_v2', - TEST_DIR_KEY: 'mask_test_v2', - } -} - - -class MSMT17(BaseImageDataset): - """MSMT17 - - Reference: - Wei et al. Person Transfer GAN to Bridge Domain Gap for Person Re-Identification. CVPR 2018. - - URL: http://www.pkuvmc.com/publications/msmt17.html - - Dataset statistics: - # identities: 4101 - # images: 32621 (train) + 11659 (query) + 82161 (gallery) - # cameras: 15 - """ - dataset_dir = 'msmt17' - - def __init__(self, root='data', verbose=True, **kwargs): - super(MSMT17, self).__init__(root) - self.dataset_dir = osp.join(self.root, self.dataset_dir) - has_main_dir = False - for main_dir in VERSION_DICT: - if osp.exists(osp.join(self.dataset_dir, main_dir)): - train_dir = VERSION_DICT[main_dir][TRAIN_DIR_KEY] - test_dir = VERSION_DICT[main_dir][TEST_DIR_KEY] - has_main_dir = True - break - assert has_main_dir, 'Dataset folder not found' - self.train_dir = osp.join(self.dataset_dir, main_dir, train_dir) - self.test_dir = osp.join(self.dataset_dir, main_dir, test_dir) - self.list_train_path = osp.join(self.dataset_dir, main_dir, 'list_train.txt') - self.list_val_path = osp.join(self.dataset_dir, main_dir, 'list_val.txt') - self.list_query_path = osp.join(self.dataset_dir, main_dir, 'list_query.txt') - self.list_gallery_path = osp.join(self.dataset_dir, main_dir, 'list_gallery.txt') - - required_files = [ - self.dataset_dir, - self.train_dir, - self.test_dir - ] - self.check_before_run(required_files) - - train = self.process_dir(self.train_dir, self.list_train_path) - val = self.process_dir(self.train_dir, self.list_val_path) - query = self.process_dir(self.test_dir, self.list_query_path) - gallery = self.process_dir(self.test_dir, self.list_gallery_path) - - # Note: to fairly compare with published methods on the conventional ReID setting, - # do not add val images to the training set. - if 'combineall' in kwargs and kwargs['combineall']: - train += val - - self.init_attributes(train, query, gallery, **kwargs) - - if verbose: - self.print_dataset_statistics(self.train, self.query, self.gallery) - def process_dir(self, dir_path, list_path): - with open(list_path, 'r') as txt: - lines = txt.readlines() - dataset = [] - pid_container = set() - for img_idx, img_info in enumerate(lines): - img_path, pid = img_info.split(' ') - pid = int(pid) # no need to relabel - camid = int(img_path.split('_')[2]) - 1 # index starts from 0 - img_path = osp.join(dir_path, img_path) - dataset.append((img_path, pid, camid)) - pid_container.add(pid) - num_pids = len(pid_container) - for idx, pid in enumerate(pid_container): - if idx != pid: - raise RuntimeError('pid does not start from 0 and increment by 1') - return dataset \ No newline at end of file diff --git a/torchreid/datasets/prid.py b/torchreid/datasets/prid.py deleted file mode 100644 index 1c6d85e..0000000 --- a/torchreid/datasets/prid.py +++ /dev/null @@ -1,117 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import glob -import re -import sys -import urllib -import tarfile -import zipfile -import os.path as osp -from scipy.io import loadmat -import numpy as np -import h5py -from scipy.misc import imsave -from collections import defaultdict -import copy -import random - -from torchreid.utils.iotools import mkdir_if_missing, write_json, read_json -from .bases import BaseImageDataset - - -class PRID(BaseImageDataset): - """PRID (single-shot version of prid-2011) - - Reference: - Hirzer et al. Person Re-Identification by Descriptive and Discriminative Classification. SCIA 2011. - - URL: https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/ - - Dataset statistics: - - Two views - - View A captures 385 identities - - View B captures 749 identities - - 200 identities appear in both views - """ - dataset_dir = 'prid2011' - - def __init__(self, root='data', split_id=0, verbose=True, **kwargs): - super(PRID, self).__init__(root) - self.dataset_dir = osp.join(self.root, self.dataset_dir) - self.cam_a_dir = osp.join(self.dataset_dir, 'prid_2011', 'single_shot', 'cam_a') - self.cam_b_dir = osp.join(self.dataset_dir, 'prid_2011', 'single_shot', 'cam_b') - self.split_path = osp.join(self.dataset_dir, 'splits_single_shot.json') - - required_files = [ - self.dataset_dir, - self.cam_a_dir, - self.cam_b_dir - ] - self.check_before_run(required_files) - - self.prepare_split() - splits = read_json(self.split_path) - if split_id >= len(splits): - raise ValueError('split_id exceeds range, received {}, but expected between 0 and {}'.format(split_id, len(splits)-1)) - split = splits[split_id] - - train, query, gallery = self.process_split(split) - - self.init_attributes(train, query, gallery, **kwargs) - - if verbose: - self.print_dataset_statistics(self.train, self.query, self.gallery) - - def prepare_split(self): - if not osp.exists(self.split_path): - print('Creating splits ...') - - splits = [] - for _ in range(10): - # randomly sample 100 IDs for train and use the rest 100 IDs for test - # (note: there are only 200 IDs appearing in both views) - pids = [i for i in range(1, 201)] - train_pids = random.sample(pids, 100) - train_pids.sort() - test_pids = [i for i in pids if i not in train_pids] - split = {'train': train_pids, 'test': test_pids} - splits.append(split) - - print('Totally {} splits are created'.format(len(splits))) - write_json(splits, self.split_path) - print('Split file is saved to {}'.format(self.split_path)) - - def process_split(self, split): - train, query, gallery = [], [], [] - train_pids = split['train'] - test_pids = split['test'] - - train_pid2label = {pid: label for label, pid in enumerate(train_pids)} - - # train - train = [] - for pid in train_pids: - img_name = 'person_' + str(pid).zfill(4) + '.png' - pid = train_pid2label[pid] - img_a_path = osp.join(self.cam_a_dir, img_name) - train.append((img_a_path, pid, 0)) - img_b_path = osp.join(self.cam_b_dir, img_name) - train.append((img_b_path, pid, 1)) - - # query and gallery - query, gallery = [], [] - for pid in test_pids: - img_name = 'person_' + str(pid).zfill(4) + '.png' - img_a_path = osp.join(self.cam_a_dir, img_name) - query.append((img_a_path, pid, 0)) - img_b_path = osp.join(self.cam_b_dir, img_name) - gallery.append((img_b_path, pid, 1)) - for pid in range(201, 750): - img_name = 'person_' + str(pid).zfill(4) + '.png' - img_b_path = osp.join(self.cam_b_dir, img_name) - gallery.append((img_b_path, pid, 1)) - - return train, query, gallery \ No newline at end of file diff --git a/torchreid/datasets/prid2011.py b/torchreid/datasets/prid2011.py deleted file mode 100644 index aa96aae..0000000 --- a/torchreid/datasets/prid2011.py +++ /dev/null @@ -1,87 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import glob -import re -import sys -import urllib -import tarfile -import zipfile -import os.path as osp -from scipy.io import loadmat -import numpy as np -import h5py -from scipy.misc import imsave - -from torchreid.utils.iotools import mkdir_if_missing, write_json, read_json -from .bases import BaseVideoDataset - - -class PRID2011(BaseVideoDataset): - """PRID2011 - - Reference: - Hirzer et al. Person Re-Identification by Descriptive and Discriminative Classification. SCIA 2011. - - URL: https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/ - - Dataset statistics: - # identities: 200 - # tracklets: 400 - # cameras: 2 - """ - dataset_dir = 'prid2011' - - def __init__(self, root='data', split_id=0, min_seq_len=0, verbose=True, **kwargs): - super(PRID2011, self).__init__(root) - self.dataset_dir = osp.join(self.root, self.dataset_dir) - self.split_path = osp.join(self.dataset_dir, 'splits_prid2011.json') - self.cam_a_dir = osp.join(self.dataset_dir, 'prid_2011', 'multi_shot', 'cam_a') - self.cam_b_dir = osp.join(self.dataset_dir, 'prid_2011', 'multi_shot', 'cam_b') - - required_files = [ - self.dataset_dir, - self.cam_a_dir, - self.cam_b_dir - ] - self.check_before_run(required_files) - - splits = read_json(self.split_path) - if split_id >= len(splits): - raise ValueError('split_id exceeds range, received {}, but expected between 0 and {}'.format(split_id, len(splits)-1)) - split = splits[split_id] - train_dirs, test_dirs = split['train'], split['test'] - - train = self.process_dir(train_dirs, cam1=True, cam2=True) - query = self.process_dir(test_dirs, cam1=True, cam2=False) - gallery = self.process_dir(test_dirs, cam1=False, cam2=True) - - self.init_attributes(train, query, gallery, **kwargs) - - if verbose: - self.print_dataset_statistics(self.train, self.query, self.gallery) - - def process_dir(self, dirnames, cam1=True, cam2=True): - tracklets = [] - dirname2pid = {dirname:i for i, dirname in enumerate(dirnames)} - - for dirname in dirnames: - if cam1: - person_dir = osp.join(self.cam_a_dir, dirname) - img_names = glob.glob(osp.join(person_dir, '*.png')) - assert len(img_names) > 0 - img_names = tuple(img_names) - pid = dirname2pid[dirname] - tracklets.append((img_names, pid, 0)) - - if cam2: - person_dir = osp.join(self.cam_b_dir, dirname) - img_names = glob.glob(osp.join(person_dir, '*.png')) - assert len(img_names) > 0 - img_names = tuple(img_names) - pid = dirname2pid[dirname] - tracklets.append((img_names, pid, 1)) - - return tracklets \ No newline at end of file diff --git a/torchreid/datasets/prid450s.py b/torchreid/datasets/prid450s.py deleted file mode 100644 index 584cadd..0000000 --- a/torchreid/datasets/prid450s.py +++ /dev/null @@ -1,137 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import glob -import re -import sys -import urllib -import tarfile -import zipfile -import os.path as osp -from scipy.io import loadmat -import numpy as np -import h5py -from scipy.misc import imsave - -from torchreid.utils.iotools import mkdir_if_missing, write_json, read_json -from .bases import BaseImageDataset - - -class PRID450S(BaseImageDataset): - """PRID450S - - Reference: - Roth et al. Mahalanobis Distance Learning for Person Re-Identification. PR 2014. - - URL: https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/prid450s/ - - Dataset statistics: - # identities: 450 - # images: 900 - # cameras: 2 - """ - dataset_dir = 'prid450s' - - def __init__(self, root='data', split_id=0, min_seq_len=0, verbose=True, **kwargs): - super(PRID450S, self).__init__(root) - self.dataset_dir = osp.join(self.root, self.dataset_dir) - self.dataset_url = 'https://files.icg.tugraz.at/f/8c709245bb/?raw=1' - self.split_path = osp.join(self.dataset_dir, 'splits.json') - self.cam_a_dir = osp.join(self.dataset_dir, 'cam_a') - self.cam_b_dir = osp.join(self.dataset_dir, 'cam_b') - - self.download_data() - - required_files = [ - self.dataset_dir, - self.cam_a_dir, - self.cam_b_dir - ] - self.check_before_run(required_files) - - self.prepare_split() - splits = read_json(self.split_path) - if split_id >= len(splits): - raise ValueError('split_id exceeds range, received {}, but expected between 0 and {}'.format(split_id, len(splits)-1)) - split = splits[split_id] - - train = split['train'] - query = split['query'] - gallery = split['gallery'] - - train = [tuple(item) for item in train] - query = [tuple(item) for item in query] - gallery = [tuple(item) for item in gallery] - - self.init_attributes(train, query, gallery, **kwargs) - - if verbose: - self.print_dataset_statistics(self.train, self.query, self.gallery) - - def download_data(self): - if osp.exists(self.dataset_dir): - return - - print('Creating directory {}'.format(self.dataset_dir)) - mkdir_if_missing(self.dataset_dir) - fpath = osp.join(self.dataset_dir, 'prid_450s.zip') - - print('Downloading PRID450S dataset') - urllib.urlretrieve(self.dataset_url, fpath) - - print('Extracting files') - zip_ref = zipfile.ZipFile(fpath, 'r') - zip_ref.extractall(self.dataset_dir) - zip_ref.close() - - def prepare_split(self): - if not osp.exists(self.split_path): - cam_a_imgs = sorted(glob.glob(osp.join(self.cam_a_dir, 'img_*.png'))) - cam_b_imgs = sorted(glob.glob(osp.join(self.cam_b_dir, 'img_*.png'))) - assert len(cam_a_imgs) == len(cam_b_imgs) - - num_pids = len(cam_a_imgs) - num_train_pids = num_pids // 2 - - splits = [] - for _ in range(10): - order = np.arange(num_pids) - np.random.shuffle(order) - train_idxs = np.sort(order[:num_train_pids]) - idx2label = {idx: label for label, idx in enumerate(train_idxs)} - - train, test = [], [] - - # processing camera a - for img_path in cam_a_imgs: - img_name = osp.basename(img_path) - img_idx = int(img_name.split('_')[1].split('.')[0]) - if img_idx in train_idxs: - train.append((img_path, idx2label[img_idx], 0)) - else: - test.append((img_path, img_idx, 0)) - - # processing camera b - for img_path in cam_b_imgs: - img_name = osp.basename(img_path) - img_idx = int(img_name.split('_')[1].split('.')[0]) - if img_idx in train_idxs: - train.append((img_path, idx2label[img_idx], 1)) - else: - test.append((img_path, img_idx, 1)) - - split = { - 'train': train, - 'query': test, - 'gallery': test, - 'num_train_pids': num_train_pids, - 'num_query_pids': num_pids - num_train_pids, - 'num_gallery_pids': num_pids - num_train_pids - } - splits.append(split) - - print('Totally {} splits are created'.format(len(splits))) - write_json(splits, self.split_path) - print('Split file saved to {}'.format(self.split_path)) \ No newline at end of file diff --git a/torchreid/datasets/sensereid.py b/torchreid/datasets/sensereid.py deleted file mode 100644 index 9502b24..0000000 --- a/torchreid/datasets/sensereid.py +++ /dev/null @@ -1,72 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import glob -import re -import sys -import urllib -import tarfile -import zipfile -import os.path as osp -from scipy.io import loadmat -import numpy as np -import h5py -from scipy.misc import imsave -import copy - -from .bases import BaseImageDataset - - -class SenseReID(BaseImageDataset): - """SenseReID - - This dataset is used for test purpose only. - - Reference: - Zhao et al. Spindle Net: Person Re-identification with Human Body - Region Guided Feature Decomposition and Fusion. CVPR 2017. - - URL: https://drive.google.com/file/d/0B56OfSrVI8hubVJLTzkwV2VaOWM/view - - Dataset statistics: - - train: 0 ids, 0 images - - query: 522 ids, 1040 images - - gallery: 1717 ids, 3388 images - """ - dataset_dir = 'sensereid' - - def __init__(self, root='data', verbose=True, **kwargs): - super(SenseReID, self).__init__(root) - self.dataset_dir = osp.join(self.root, self.dataset_dir) - self.query_dir = osp.join(self.dataset_dir, 'SenseReID', 'test_probe') - self.gallery_dir = osp.join(self.dataset_dir, 'SenseReID', 'test_gallery') - - required_files = [ - self.dataset_dir, - self.query_dir, - self.gallery_dir - ] - self.check_before_run(required_files) - - query = self.process_dir(self.query_dir) - gallery = self.process_dir(self.gallery_dir) - train = copy.deepcopy(query) # dummy variable - - self.init_attributes(train, query, gallery, **kwargs) - - if verbose: - self.print_dataset_statistics(self.train, self.query, self.gallery) - - def process_dir(self, dir_path): - img_paths = glob.glob(osp.join(dir_path, '*.jpg')) - dataset = [] - - for img_path in img_paths: - img_name = osp.splitext(osp.basename(img_path))[0] - pid, camid = img_name.split('_') - pid, camid = int(pid), int(camid) - dataset.append((img_path, pid, camid)) - - return dataset \ No newline at end of file diff --git a/torchreid/datasets/viper.py b/torchreid/datasets/viper.py deleted file mode 100755 index 70b6c93..0000000 --- a/torchreid/datasets/viper.py +++ /dev/null @@ -1,155 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -import os -import glob -import re -import sys -import urllib -import tarfile -import zipfile -import os.path as osp -from scipy.io import loadmat -import numpy as np -import h5py -from scipy.misc import imsave - -from torchreid.utils.iotools import mkdir_if_missing, write_json, read_json -from .bases import BaseImageDataset - - -class VIPeR(BaseImageDataset): - """VIPeR - - Reference: - Gray et al. Evaluating appearance models for recognition, reacquisition, and tracking. PETS 2007. - - URL: https://vision.soe.ucsc.edu/node/178 - - Dataset statistics: - # identities: 632 - # images: 632 x 2 = 1264 - # cameras: 2 - """ - dataset_dir = 'viper' - - def __init__(self, root='data', split_id=0, verbose=True, **kwargs): - super(VIPeR, self).__init__(root) - self.dataset_dir = osp.join(self.root, self.dataset_dir) - self.dataset_url = 'http://users.soe.ucsc.edu/~manduchi/VIPeR.v1.0.zip' - self.cam_a_dir = osp.join(self.dataset_dir, 'VIPeR', 'cam_a') - self.cam_b_dir = osp.join(self.dataset_dir, 'VIPeR', 'cam_b') - self.split_path = osp.join(self.dataset_dir, 'splits.json') - - self.download_data() - - required_files = [ - self.dataset_dir, - self.cam_a_dir, - self.cam_b_dir - ] - self.check_before_run(required_files) - - self.prepare_split() - splits = read_json(self.split_path) - if split_id >= len(splits): - raise ValueError('split_id exceeds range, received {}, but expected between 0 and {}'.format(split_id, len(splits)-1)) - split = splits[split_id] - - train = split['train'] - query = split['query'] # note: query and gallery share the same images - gallery = split['gallery'] - - train = [tuple(item) for item in train] - query = [tuple(item) for item in query] - gallery = [tuple(item) for item in gallery] - - self.init_attributes(train, query, gallery, **kwargs) - - if verbose: - self.print_dataset_statistics(self.train, self.query, self.gallery) - - def download_data(self): - if osp.exists(self.dataset_dir): - return - - print('Creating directory {}'.format(self.dataset_dir)) - mkdir_if_missing(self.dataset_dir) - fpath = osp.join(self.dataset_dir, osp.basename(self.dataset_url)) - - print('Downloading VIPeR dataset') - urllib.urlretrieve(self.dataset_url, fpath) - - print('Extracting files') - zip_ref = zipfile.ZipFile(fpath, 'r') - zip_ref.extractall(self.dataset_dir) - zip_ref.close() - - def prepare_split(self): - if not osp.exists(self.split_path): - print('Creating 10 random splits of train ids and test ids') - - cam_a_imgs = sorted(glob.glob(osp.join(self.cam_a_dir, '*.bmp'))) - cam_b_imgs = sorted(glob.glob(osp.join(self.cam_b_dir, '*.bmp'))) - assert len(cam_a_imgs) == len(cam_b_imgs) - num_pids = len(cam_a_imgs) - print('Number of identities: {}'.format(num_pids)) - num_train_pids = num_pids // 2 - - """ - In total, there will be 20 splits because each random split creates two - sub-splits, one using cameraA as query and cameraB as gallery - while the other using cameraB as query and cameraA as gallery. - Therefore, results should be averaged over 20 splits (split_id=0~19). - - In practice, a model trained on split_id=0 can be applied to split_id=0&1 - as split_id=0&1 share the same training data (so on and so forth). - """ - splits = [] - for _ in range(10): - order = np.arange(num_pids) - np.random.shuffle(order) - train_idxs = order[:num_train_pids] - test_idxs = order[num_train_pids:] - assert not bool(set(train_idxs) & set(test_idxs)), 'Error: train and test overlap' - - train = [] - for pid, idx in enumerate(train_idxs): - cam_a_img = cam_a_imgs[idx] - cam_b_img = cam_b_imgs[idx] - train.append((cam_a_img, pid, 0)) - train.append((cam_b_img, pid, 1)) - - test_a = [] - test_b = [] - for pid, idx in enumerate(test_idxs): - cam_a_img = cam_a_imgs[idx] - cam_b_img = cam_b_imgs[idx] - test_a.append((cam_a_img, pid, 0)) - test_b.append((cam_b_img, pid, 1)) - - # use cameraA as query and cameraB as gallery - split = { - 'train': train, - 'query': test_a, - 'gallery': test_b, - 'num_train_pids': num_train_pids, - 'num_query_pids': num_pids - num_train_pids, - 'num_gallery_pids': num_pids - num_train_pids - } - splits.append(split) - - # use cameraB as query and cameraA as gallery - split = { - 'train': train, - 'query': test_b, - 'gallery': test_a, - 'num_train_pids': num_train_pids, - 'num_query_pids': num_pids - num_train_pids, - 'num_gallery_pids': num_pids - num_train_pids - } - splits.append(split) - - print('Totally {} splits are created'.format(len(splits))) - write_json(splits, self.split_path) - print('Split file saved to {}'.format(self.split_path)) \ No newline at end of file diff --git a/torchreid/engine/__init__.py b/torchreid/engine/__init__.py new file mode 100644 index 0000000..ac6fa52 --- /dev/null +++ b/torchreid/engine/__init__.py @@ -0,0 +1,8 @@ +from __future__ import absolute_import +from __future__ import print_function + +from .image import ImageSoftmaxEngine +from .image import ImageTripletEngine + +from .video import VideoSoftmaxEngine +from .video import VideoTripletEngine \ No newline at end of file diff --git a/torchreid/engine/engine.py b/torchreid/engine/engine.py new file mode 100644 index 0000000..aff3c0d --- /dev/null +++ b/torchreid/engine/engine.py @@ -0,0 +1,198 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os +import os.path as osp +import time +import datetime +import numpy as np + +import torch +import torch.nn as nn + +import torchreid +from torchreid.utils import AverageMeter, visualize_ranked_results, save_checkpoint +from torchreid.losses import DeepSupervision +from torchreid import metrics + + +class Engine(object): + + def __init__(self, dataset, model, optimizer, scheduler=None, use_cpu=False): + self.dataset = dataset + self.model = model + self.optimizer = optimizer + self.scheduler = scheduler + self.use_gpu = (torch.cuda.is_available() and not use_cpu) + + # check attributes + if not isinstance(self.model, nn.Module): + raise TypeError('model must be an instance of nn.Module') + if not isinstance(self.optimizer, torch.optim.Optimizer): + raise TypeError('optimizer must be an instance of torch.optim.Optimizer') + + def run(self, max_epoch=0, start_epoch=0, fixbase_epoch=0, open_layers=None, + start_eval=0, eval_freq=-1, save_dir='log', test_only=False, print_freq=10, + dist_metric='euclidean', visrank=False, visrank_topk=20, + use_metric_cuhk03=False, ranks=[1, 5, 10, 20]): + trainloader, testloader = self.dataset.return_dataloaders() + + if test_only: + self.test(testloader, dist_metric, visrank, visrank_topk, + save_dir, use_metric_cuhk03, ranks) + return + + time_start = time.time() + print('=> Start training') + + if fixbase_epoch>0 and (open_layers is not None): + print('Pretrain open layers ({}) for {} epochs'.format(open_layers, fixbase_epoch)) + for epoch in range(fixbase_epoch): + self.train(epoch, trainloader, fixbase=True, open_layers=open_layers, + print_freq=print_freq) + print('Done. From now on all layers are open to train for {} epochs'.format(max_epoch)) + + for epoch in range(start_epoch, max_epoch): + self.train(epoch, trainloader, print_freq=print_freq) + + if self.scheduler is not None: + self.scheduler.step() + + if (epoch+1)>start_eval and eval_freq>0 and (epoch+1)%eval_freq==0 and (epoch+1)!=max_epoch: + rank1 = self.test(testloader, dist_metric, visrank, visrank_topk, + save_dir, use_metric_cuhk03, ranks) + self._save_checkpoint(epoch, rank1, save_dir) + + print('=> Final test') + rank1 = self.test( + testloader, dist_metric, visrank, visrank_topk, + save_dir, use_metric_cuhk03, ranks + ) + self._save_checkpoint(epoch, rank1, save_dir) + + elapsed = round(time.time() - time_start) + elapsed = str(datetime.timedelta(seconds=elapsed)) + print('Elapsed {}'.format(elapsed)) + + def train(self): + raise NotImplementedError + + def test(self, testloader, dist_metric='euclidean', visrank=False, visrank_topk=20, + save_dir='', use_metric_cuhk03=False, ranks=[1, 5, 10, 20]): + target_names = list(testloader.keys()) + + for name in target_names: + print('Evaluate {}'.format(name)) + queryloader = testloader[name]['query'] + galleryloader = testloader[name]['gallery'] + rank1 = self._evaluate( + name, queryloader, galleryloader, dist_metric, visrank, + visrank_topk, save_dir, use_metric_cuhk03, ranks + ) + + return rank1 + + @torch.no_grad() + def _evaluate(self, dataset_name, queryloader, galleryloader, dist_metric, + visrank, visrank_topk, save_dir, use_metric_cuhk03, ranks): + batch_time = AverageMeter() + + self.model.eval() + + print('Extracting features from query set ...') + qf, q_pids, q_camids = [], [], [] + for batch_idx, data in enumerate(queryloader): + imgs, pids, camids = self._parse_data_for_eval(data) + if self.use_gpu: + imgs = imgs.cuda() + end = time.time() + features = self._extract_features(imgs) + batch_time.update(time.time() - end) + features = features.data.cpu() + qf.append(features) + q_pids.extend(pids) + q_camids.extend(camids) + qf = torch.cat(qf, 0) + q_pids = np.asarray(q_pids) + q_camids = np.asarray(q_camids) + print('Done, obtained {}-by-{} matrix'.format(qf.size(0), qf.size(1))) + + print('Extracting features from gallery set ...') + gf, g_pids, g_camids = [], [], [] + end = time.time() + for batch_idx, data in enumerate(galleryloader): + imgs, pids, camids = self._parse_data_for_eval(data) + if self.use_gpu: + imgs = imgs.cuda() + end = time.time() + features = self._extract_features(imgs) + batch_time.update(time.time() - end) + features = features.data.cpu() + gf.append(features) + g_pids.extend(pids) + g_camids.extend(camids) + gf = torch.cat(gf, 0) + g_pids = np.asarray(g_pids) + g_camids = np.asarray(g_camids) + print('Done, obtained {}-by-{} matrix'.format(gf.size(0), gf.size(1))) + + print('Speed: {:.4f} sec/batch'.format(batch_time.avg)) + + distmat = metrics.compute_distance_matrix(qf, gf, dist_metric) + + print('Computing CMC and mAP ...') + cmc, mAP = metrics.evaluate_rank( + distmat, + q_pids, + g_pids, + q_camids, + g_camids, + use_metric_cuhk03=use_metric_cuhk03 + ) + + print('** Results') + print('mAP: {:.1%}'.format(mAP)) + print('CMC curve') + for r in ranks: + print('Rank-{:<3}: {:.1%}'.format(r, cmc[r-1])) + + if visrank: + visualize_ranked_results( + distmat, + self.dataset.return_testdataset_by_name(dataset_name), + save_dir=osp.join(save_dir, 'ranked_results', dataset_name), + topk=visrank_topk + ) + + return cmc[0] + + def _compute_loss(self, criterion, outputs, targets): + if isinstance(outputs, (tuple, list)): + loss = DeepSupervision(criterion, outputs, targets) + else: + loss = criterion(outputs, targets) + return loss + + def _extract_features(self, input): + self.model.eval() + return self.model(input) + + def _parse_data_for_train(self, data): + imgs = data[0] + pids = data[1] + return imgs, pids + + def _parse_data_for_eval(self, data): + imgs = data[0] + pids = data[1] + camids = data[2] + return imgs, pids, camids + + def _save_checkpoint(self, epoch, rank1, save_dir, is_best=False): + save_checkpoint({ + 'state_dict': self.model.state_dict(), + 'epoch': epoch + 1, + 'rank1': rank1, + 'optimizer': self.optimizer.state_dict(), + }, save_dir, is_best=is_best) \ No newline at end of file diff --git a/torchreid/engine/image/__init__.py b/torchreid/engine/image/__init__.py new file mode 100644 index 0000000..50af454 --- /dev/null +++ b/torchreid/engine/image/__init__.py @@ -0,0 +1,4 @@ +from __future__ import absolute_import + +from .softmax import ImageSoftmaxEngine +from .triplet import ImageTripletEngine \ No newline at end of file diff --git a/torchreid/engine/image/softmax.py b/torchreid/engine/image/softmax.py new file mode 100644 index 0000000..49f451c --- /dev/null +++ b/torchreid/engine/image/softmax.py @@ -0,0 +1,74 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import time +import datetime + +import torch + +import torchreid +from torchreid.engine import engine +from torchreid.losses import CrossEntropyLoss +from torchreid.utils import AverageMeter, open_specified_layers, open_all_layers +from torchreid import metrics + + +class ImageSoftmaxEngine(engine.Engine): + + def __init__(self, dataset, model, optimizer, scheduler=None, use_cpu=False, + label_smooth=True): + super(ImageSoftmaxEngine, self).__init__(dataset, model, optimizer, scheduler, use_cpu) + + self.criterion = CrossEntropyLoss( + num_classes=self.dataset.num_train_pids, + use_gpu=self.use_gpu, + label_smooth=label_smooth + ) + + def train(self, epoch, trainloader, fixbase=False, open_layers=None, print_freq=10): + losses = AverageMeter() + accs = AverageMeter() + batch_time = AverageMeter() + data_time = AverageMeter() + + self.model.train() + + if fixbase and (open_layers is not None): + open_specified_layers(self.model, open_layers) + else: + open_all_layers(self.model) + + end = time.time() + for batch_idx, data in enumerate(trainloader): + data_time.update(time.time() - end) + + imgs, pids = self._parse_data_for_train(data) + if self.use_gpu: + imgs = imgs.cuda() + pids = pids.cuda() + + self.optimizer.zero_grad() + outputs = self.model(imgs) + loss = self._compute_loss(self.criterion, outputs, pids) + loss.backward() + self.optimizer.step() + + batch_time.update(time.time() - end) + + losses.update(loss.item(), pids.size(0)) + accs.update(metrics.accuracy(outputs, pids)[0].item()) + + if (batch_idx+1) % print_freq==0: + print('Epoch: [{0}][{1}/{2}]\t' + 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' + 'Data {data_time.val:.3f} ({data_time.avg:.3f})\t' + 'Loss {loss.val:.4f} ({loss.avg:.4f})\t' + 'Acc {acc.val:.2f} ({acc.avg:.2f})\t'.format( + epoch + 1, batch_idx + 1, len(trainloader), + batch_time=batch_time, + data_time=data_time, + loss=losses, + acc=accs)) + + end = time.time() \ No newline at end of file diff --git a/torchreid/engine/image/triplet.py b/torchreid/engine/image/triplet.py new file mode 100644 index 0000000..7b55a6c --- /dev/null +++ b/torchreid/engine/image/triplet.py @@ -0,0 +1,85 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import time +import datetime + +import torch + +import torchreid +from torchreid.engine import engine +from torchreid.losses import CrossEntropyLoss, TripletLoss +from torchreid.utils import AverageMeter, open_specified_layers, open_all_layers +from torchreid import metrics + + +class ImageTripletEngine(engine.Engine): + + def __init__(self, dataset, model, optimizer, margin=0.3, + weight_t=1, weight_x=1, scheduler=None, use_cpu=False, + label_smooth=True): + super(ImageTripletEngine, self).__init__(dataset, model, optimizer, scheduler, use_cpu) + + self.weight_t = weight_t + self.weight_x = weight_x + + self.criterion_t = TripletLoss(margin=margin) + self.criterion_x = CrossEntropyLoss( + num_classes=self.dataset.num_train_pids, + use_gpu=self.use_gpu, + label_smooth=label_smooth + ) + + def train(self, epoch, trainloader, fixbase=False, open_layers=None, print_freq=10): + losses_t = AverageMeter() + losses_x = AverageMeter() + accs = AverageMeter() + batch_time = AverageMeter() + data_time = AverageMeter() + + self.model.train() + + if fixbase and (open_layers is not None): + open_specified_layers(self.model, open_layers) + else: + open_all_layers(self.model) + + end = time.time() + for batch_idx, data in enumerate(trainloader): + data_time.update(time.time() - end) + + imgs, pids = self._parse_data_for_train(data) + if self.use_gpu: + imgs = imgs.cuda() + pids = pids.cuda() + + self.optimizer.zero_grad() + outputs, features = self.model(imgs) + loss_t = self._compute_loss(self.criterion_t, features, pids) + loss_x = self._compute_loss(self.criterion_x, outputs, pids) + loss = self.weight_t * loss_t + self.weight_x * loss_x + loss.backward() + self.optimizer.step() + + batch_time.update(time.time() - end) + + losses_t.update(loss_t.item(), pids.size(0)) + losses_x.update(loss_x.item(), pids.size(0)) + accs.update(metrics.accuracy(outputs, pids)[0].item()) + + if (batch_idx+1) % print_freq==0: + print('Epoch: [{0}][{1}/{2}]\t' + 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' + 'Data {data_time.val:.3f} ({data_time.avg:.3f})\t' + 'Triplet {loss_t.val:.4f} ({loss_t.avg:.4f})\t' + 'Softmax {loss_x.val:.4f} ({loss_x.avg:.4f})\t' + 'Acc {acc.val:.2f} ({acc.avg:.2f})\t'.format( + epoch + 1, batch_idx + 1, len(trainloader), + batch_time=batch_time, + data_time=data_time, + loss_t=losses_t, + loss_x=losses_x, + acc=accs)) + + end = time.time() \ No newline at end of file diff --git a/torchreid/engine/video/__init__.py b/torchreid/engine/video/__init__.py new file mode 100644 index 0000000..3f3f4fc --- /dev/null +++ b/torchreid/engine/video/__init__.py @@ -0,0 +1,4 @@ +from __future__ import absolute_import + +from .softmax import VideoSoftmaxEngine +from .triplet import VideoTripletEngine \ No newline at end of file diff --git a/torchreid/engine/video/softmax.py b/torchreid/engine/video/softmax.py new file mode 100644 index 0000000..94d7dea --- /dev/null +++ b/torchreid/engine/video/softmax.py @@ -0,0 +1,37 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import time +import datetime + +import torch + +import torchreid +from torchreid.engine.image import ImageSoftmaxEngine + + +class VideoSoftmaxEngine(ImageSoftmaxEngine): + + def __init__(self, dataset, model, optimizer, scheduler=None, + use_cpu=False, label_smooth=True, pooling_method='avg'): + super(VideoSoftmaxEngine, self).__init__(dataset, model, optimizer, scheduler=scheduler, + use_cpu=use_cpu, label_smooth=label_smooth) + self.pooling_method = pooling_method + + def _extract_features(self, input): + self.model.eval() + # b: batch size + # s: sqeuence length + # c: channel depth + # h: height + # w: width + b, s, c, h, w = input.size() + input = input.view(b*s, c, h, w) + features = self.model(input) + features = features.view(b, s, -1) + if self.pooling_method == 'avg': + features = torch.mean(features, 1) + else: + features = torch.max(features, 1)[0] + return features \ No newline at end of file diff --git a/torchreid/engine/video/triplet.py b/torchreid/engine/video/triplet.py new file mode 100644 index 0000000..522718b --- /dev/null +++ b/torchreid/engine/video/triplet.py @@ -0,0 +1,24 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import time +import datetime + +import torch + +import torchreid +from torchreid.engine.image import ImageTripletEngine +from torchreid.engine.video import VideoSoftmaxEngine + + +class VideoTripletEngine(ImageTripletEngine, VideoSoftmaxEngine): + + def __init__(self, dataset, model, optimizer, margin=0.3, + weight_t=1, weight_x=1, scheduler=None, use_cpu=False, + label_smooth=True, pooling_method='avg'): + super(VideoTripletEngine, self).__init__(dataset, model, optimizer, margin=margin, + weight_t=weight_t, weight_x=weight_x, + scheduler=scheduler, use_cpu=use_cpu, + label_smooth=label_smooth) + self.pooling_method = pooling_method \ No newline at end of file diff --git a/torchreid/eval_cylib/Makefile b/torchreid/eval_cylib/Makefile deleted file mode 100644 index 7bb54d3..0000000 --- a/torchreid/eval_cylib/Makefile +++ /dev/null @@ -1,7 +0,0 @@ -all: - python setup.py build_ext --inplace - rm -rf build - -clean: - rm -rf build - rm -f eval_metrics_cy.c *.so \ No newline at end of file diff --git a/torchreid/lr_schedulers.py b/torchreid/lr_schedulers.py deleted file mode 100644 index 6a556e5..0000000 --- a/torchreid/lr_schedulers.py +++ /dev/null @@ -1,29 +0,0 @@ -from __future__ import absolute_import -from __future__ import print_function - -import torch - - -def init_lr_scheduler( - optimizer, - lr_scheduler='multi_step', # learning rate scheduler - stepsize=[20, 40], # step size to decay learning rate - gamma=0.1, # learning rate decay - ): - - print('Initializing lr_scheduler: {}'.format(lr_scheduler)) - - if lr_scheduler == 'single_step': - scheduler = torch.optim.lr_scheduler.StepLR( - optimizer, step_size=stepsize[0], gamma=gamma - ) - - elif lr_scheduler == 'multi_step': - scheduler = torch.optim.lr_scheduler.MultiStepLR( - optimizer, milestones=stepsize, gamma=gamma - ) - - else: - raise ValueError('Unsupported lr_scheduler: {}'.format(lr_scheduler)) - - return scheduler \ No newline at end of file diff --git a/torchreid/metrics/__init__.py b/torchreid/metrics/__init__.py new file mode 100644 index 0000000..15aa268 --- /dev/null +++ b/torchreid/metrics/__init__.py @@ -0,0 +1,5 @@ +from __future__ import absolute_import + +from .accuracy import accuracy +from .rank import evaluate_rank +from .distance import compute_distance_matrix \ No newline at end of file diff --git a/torchreid/metrics/accuracy.py b/torchreid/metrics/accuracy.py new file mode 100644 index 0000000..54f53b4 --- /dev/null +++ b/torchreid/metrics/accuracy.py @@ -0,0 +1,26 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + + +def accuracy(output, target, topk=(1,)): + """Computes the accuracy over the k top predictions for + the specified values of k + """ + maxk = max(topk) + batch_size = target.size(0) + + if isinstance(output, (tuple, list)): + output = output[0] + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) + acc = correct_k.mul_(100.0 / batch_size) + res.append(acc) + + return res \ No newline at end of file diff --git a/torchreid/metrics/distance.py b/torchreid/metrics/distance.py new file mode 100644 index 0000000..abc63a3 --- /dev/null +++ b/torchreid/metrics/distance.py @@ -0,0 +1,60 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import numpy as np + +import torch +from torch.nn import functional as F + + +def compute_distance_matrix(input1, input2, metric='euclidean'): + # check input + assert isinstance(input1, torch.Tensor) + assert isinstance(input2, torch.Tensor) + assert input1.dim() == 2, 'Expected 2-D tensor, but got {}-D'.format(input1.dim()) + assert input2.dim() == 2, 'Expected 2-D tensor, but got {}-D'.format(input2.dim()) + assert input1.size(1) == input2.size(1) + + if metric == 'euclidean': + distmat = euclidean_squared_distance(input1, input2) + elif metric == 'cosine': + distmat = cosine_distance(input1, input2) + else: + raise ValueError( + 'Unknown distance metric: {}. ' + 'Please choose either "euclidean" or "cosine"'.format(metric) + ) + + return distmat + + +def euclidean_squared_distance(input1, input2): + """ + Args: + input1 (torch.Tensor): 2-D feature matrix + input2 (torch.Tensor): 2-D feature matrix + + Returns: + distmat (numpy.ndarray): distance matrix + """ + m, n = input1.size(0), input2.size(0) + distmat = torch.pow(input1, 2).sum(dim=1, keepdim=True).expand(m, n) + \ + torch.pow(input2, 2).sum(dim=1, keepdim=True).expand(n, m).t() + distmat.addmm_(1, -2, input1, input2.t()) + return distmat.numpy() + + +def cosine_distance(input1, input2): + """ + Args: + input1 (torch.Tensor): 2-D feature matrix + input2 (torch.Tensor): 2-D feature matrix + + Returns: + distmat (numpy.ndarray): distance matrix + """ + input1_normed = F.normalize(input1, p=2, dim=1) + input2_normed = F.normalize(input2, p=2, dim=1) + distmat = 1 - torch.mm(input1_normed, input2_normed.t()) + return distmat.numpy() \ No newline at end of file diff --git a/torchreid/eval_metrics.py b/torchreid/metrics/rank.py similarity index 93% rename from torchreid/eval_metrics.py rename to torchreid/metrics/rank.py index 5cb390e..fb7994e 100644 --- a/torchreid/eval_metrics.py +++ b/torchreid/metrics/rank.py @@ -9,12 +9,14 @@ import sys import warnings try: - from torchreid.eval_cylib.eval_metrics_cy import evaluate_cy + from torchreid.metrics.rank_cylib.rank_cy import evaluate_cy IS_CYTHON_AVAI = True - print('Using Cython evaluation code as the backend') except ImportError: IS_CYTHON_AVAI = False - warnings.warn('Cython evaluation is UNAVAILABLE, which is highly recommended') + warnings.warn( + 'Cython evaluation (very fast, highly recommended) is unavailable, ' + 'now use python evaluation.' + ) def eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank): @@ -155,7 +157,7 @@ def evaluate_py(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metri return eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank) -def evaluate(distmat, q_pids, g_pids, q_camids, g_camids, max_rank=50, use_metric_cuhk03=False, use_cython=True): +def evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank=50, use_metric_cuhk03=False, use_cython=True): if use_cython and IS_CYTHON_AVAI: return evaluate_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03) else: diff --git a/torchreid/metrics/rank_cylib/Makefile b/torchreid/metrics/rank_cylib/Makefile new file mode 100644 index 0000000..d49e655 --- /dev/null +++ b/torchreid/metrics/rank_cylib/Makefile @@ -0,0 +1,6 @@ +all: + $(PYTHON) setup.py build_ext --inplace + rm -rf build +clean: + rm -rf build + rm -f rank_cy.c *.so \ No newline at end of file diff --git a/torchreid/eval_cylib/__init__.py b/torchreid/metrics/rank_cylib/__init__.py similarity index 100% rename from torchreid/eval_cylib/__init__.py rename to torchreid/metrics/rank_cylib/__init__.py diff --git a/torchreid/metrics/rank_cylib/rank_cy.c b/torchreid/metrics/rank_cylib/rank_cy.c new file mode 100644 index 0000000..31c3a83 --- /dev/null +++ b/torchreid/metrics/rank_cylib/rank_cy.c @@ -0,0 +1,28011 @@ +/* Generated by Cython 0.29 */ + +/* BEGIN: Cython Metadata +{ + "distutils": { + "depends": [ + "/usr/local/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h", + "/usr/local/lib/python2.7/site-packages/numpy/core/include/numpy/ufuncobject.h" + ], + "include_dirs": [ + "/usr/local/lib/python2.7/site-packages/numpy/core/include" + ], + "name": "rank_cy", + "sources": [ + "rank_cy.pyx" + ] + }, + "module_name": "rank_cy" +} +END: Cython Metadata */ + +#define PY_SSIZE_T_CLEAN +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) + #error Cython requires Python 2.6+ or Python 3.3+. +#else +#define CYTHON_ABI "0_29" +#define CYTHON_HEX_VERSION 0x001D00F0 +#define CYTHON_FUTURE_DIVISION 0 +#include +#ifndef offsetof + #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#define __PYX_COMMA , +#ifndef HAVE_LONG_LONG + #if PY_VERSION_HEX >= 0x02070000 + #define HAVE_LONG_LONG + #endif +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#ifdef PYPY_VERSION + #define CYTHON_COMPILING_IN_PYPY 1 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 +#elif defined(PYSTON_VERSION) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 1 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 +#else + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 1 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #elif !defined(CYTHON_USE_PYTYPE_LOOKUP) + #define CYTHON_USE_PYTYPE_LOOKUP 1 + #endif + #if PY_MAJOR_VERSION < 3 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #elif !defined(CYTHON_USE_PYLONG_INTERNALS) + #define CYTHON_USE_PYLONG_INTERNALS 1 + #endif + #ifndef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 1 + #endif + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #if PY_VERSION_HEX < 0x030300F0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #elif !defined(CYTHON_USE_UNICODE_WRITER) + #define CYTHON_USE_UNICODE_WRITER 1 + #endif + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #ifndef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 1 + #endif + #ifndef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 1 + #endif + #ifndef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000) + #endif + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1) + #endif + #ifndef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX >= 0x030600B1) + #endif + #ifndef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3) + #endif +#endif +#if !defined(CYTHON_FAST_PYCCALL) +#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) +#endif +#if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #undef SHIFT + #undef BASE + #undef MASK +#endif +#ifndef __has_attribute + #define __has_attribute(x) 0 +#endif +#ifndef __has_cpp_attribute + #define __has_cpp_attribute(x) 0 +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_MAYBE_UNUSED_VAR +# if defined(__cplusplus) + template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } +# else +# define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) +# endif +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) +#ifdef _MSC_VER + #ifndef _MSC_STDINT_H_ + #if _MSC_VER < 1300 + typedef unsigned char uint8_t; + typedef unsigned int uint32_t; + #else + typedef unsigned __int8 uint8_t; + typedef unsigned __int32 uint32_t; + #endif + #endif +#else + #include +#endif +#ifndef CYTHON_FALLTHROUGH + #if defined(__cplusplus) && __cplusplus >= 201103L + #if __has_cpp_attribute(fallthrough) + #define CYTHON_FALLTHROUGH [[fallthrough]] + #elif __has_cpp_attribute(clang::fallthrough) + #define CYTHON_FALLTHROUGH [[clang::fallthrough]] + #elif __has_cpp_attribute(gnu::fallthrough) + #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] + #endif + #endif + #ifndef CYTHON_FALLTHROUGH + #if __has_attribute(fallthrough) + #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) + #else + #define CYTHON_FALLTHROUGH + #endif + #endif + #if defined(__clang__ ) && defined(__apple_build_version__) + #if __apple_build_version__ < 7000000 + #undef CYTHON_FALLTHROUGH + #define CYTHON_FALLTHROUGH + #endif + #endif +#endif + +#ifndef CYTHON_INLINE + #if defined(__clang__) + #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) + #elif defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_INLINE inline + #else + #define CYTHON_INLINE + #endif +#endif + +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) + #define Py_OptimizeFlag 0 +#endif +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyClass_Type +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyType_Type +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#ifndef METH_STACKLESS + #define METH_STACKLESS 0 +#endif +#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) + #ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + #endif + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); + typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #define __Pyx_PyCFunctionFast _PyCFunctionFast + #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords +#endif +#if CYTHON_FAST_PYCCALL +#define __Pyx_PyFastCFunction_Check(func)\ + ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))))) +#else +#define __Pyx_PyFastCFunction_Check(func) 0 +#endif +#if CYTHON_USE_DICT_VERSIONS +#define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ + (version_var) = __PYX_GET_DICT_VERSION(dict);\ + (cache_var) = (value); +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ + (VAR) = __pyx_dict_cached_value;\ + } else {\ + (VAR) = __pyx_dict_cached_value = (LOOKUP);\ + __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ + }\ + } +#else +#define __PYX_GET_DICT_VERSION(dict) (0) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1 + #define PyMem_RawMalloc(n) PyMem_Malloc(n) + #define PyMem_RawRealloc(p, n) PyMem_Realloc(p, n) + #define PyMem_RawFree(p) PyMem_Free(p) +#endif +#if CYTHON_COMPILING_IN_PYSTON + #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif +#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#elif PY_VERSION_HEX >= 0x03060000 + #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() +#elif PY_VERSION_HEX >= 0x03000000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#else + #define __Pyx_PyThreadState_Current _PyThreadState_Current +#endif +#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT) +#include "pythread.h" +#define Py_tss_NEEDS_INIT 0 +typedef int Py_tss_t; +static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { + *key = PyThread_create_key(); + return 0; // PyThread_create_key reports success always +} +static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { + Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); + *key = Py_tss_NEEDS_INIT; + return key; +} +static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) { + PyObject_Free(key); +} +static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) { + return *key != Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) { + PyThread_delete_key(*key); + *key = Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { + return PyThread_set_key_value(*key, value); +} +static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { + return PyThread_get_key_value(*key); +} +#endif // TSS (Thread Specific Storage) API +#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) +#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) +#else +#define __Pyx_PyDict_NewPresized(n) PyDict_New() +#endif +#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS +#define __Pyx_PyDict_GetItemStr(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) +#else +#define __Pyx_PyDict_GetItemStr(dict, name) PyDict_GetItem(dict, name) +#endif +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) + #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) +#else + #define CYTHON_PEP393_ENABLED 0 + #define PyUnicode_1BYTE_KIND 1 + #define PyUnicode_2BYTE_KIND 2 + #define PyUnicode_4BYTE_KIND 4 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) + #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) + #define PyObject_ASCII(o) PyObject_Repr(o) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact + #define PyObject_Unicode PyObject_Str +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#if CYTHON_ASSUME_SAFE_MACROS + #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) +#else + #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : (Py_INCREF(func), func)) +#else + #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) +#endif +#if CYTHON_USE_ASYNC_SLOTS + #if PY_VERSION_HEX >= 0x030500B1 + #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods + #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) + #else + #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) + #endif +#else + #define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef __Pyx_PyAsyncMethodsStruct + typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; + } __Pyx_PyAsyncMethodsStruct; +#endif + +#if defined(WIN32) || defined(MS_WINDOWS) + #define _USE_MATH_DEFINES +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif +#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) +#define __Pyx_truncl trunc +#else +#define __Pyx_truncl truncl +#endif + + +#define __PYX_ERR(f_index, lineno, Ln_error) \ +{ \ + __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \ +} + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__rank_cy +#define __PYX_HAVE_API__rank_cy +/* Early includes */ +#include +#include +#include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" +#include "pythread.h" +#include +#include "pystate.h" +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { + return (size_t) i < (size_t) limit; +} +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) + #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); +#define __Pyx_PySequence_Tuple(obj)\ + (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +#if CYTHON_ASSUME_SAFE_MACROS +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) +#else +#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) +#endif +#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ +static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } + +static PyObject *__pyx_m = NULL; +static PyObject *__pyx_d; +static PyObject *__pyx_b; +static PyObject *__pyx_cython_runtime = NULL; +static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; +static PyObject *__pyx_empty_unicode; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; + +/* Header.proto */ +#if !defined(CYTHON_CCOMPLEX) + #if defined(__cplusplus) + #define CYTHON_CCOMPLEX 1 + #elif defined(_Complex_I) + #define CYTHON_CCOMPLEX 1 + #else + #define CYTHON_CCOMPLEX 0 + #endif +#endif +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #include + #else + #include + #endif +#endif +#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) + #undef _Complex_I + #define _Complex_I 1.0fj +#endif + + +static const char *__pyx_f[] = { + "rank_cy.pyx", + "__init__.pxd", + "stringsource", + "type.pxd", +}; +/* MemviewSliceStruct.proto */ +struct __pyx_memoryview_obj; +typedef struct { + struct __pyx_memoryview_obj *memview; + char *data; + Py_ssize_t shape[8]; + Py_ssize_t strides[8]; + Py_ssize_t suboffsets[8]; +} __Pyx_memviewslice; +#define __Pyx_MemoryView_Len(m) (m.shape[0]) + +/* Atomics.proto */ +#include +#ifndef CYTHON_ATOMICS + #define CYTHON_ATOMICS 1 +#endif +#define __pyx_atomic_int_type int +#if CYTHON_ATOMICS && __GNUC__ >= 4 && (__GNUC_MINOR__ > 1 ||\ + (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) &&\ + !defined(__i386__) + #define __pyx_atomic_incr_aligned(value, lock) __sync_fetch_and_add(value, 1) + #define __pyx_atomic_decr_aligned(value, lock) __sync_fetch_and_sub(value, 1) + #ifdef __PYX_DEBUG_ATOMICS + #warning "Using GNU atomics" + #endif +#elif CYTHON_ATOMICS && defined(_MSC_VER) && 0 + #include + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type LONG + #define __pyx_atomic_incr_aligned(value, lock) InterlockedIncrement(value) + #define __pyx_atomic_decr_aligned(value, lock) InterlockedDecrement(value) + #ifdef __PYX_DEBUG_ATOMICS + #pragma message ("Using MSVC atomics") + #endif +#elif CYTHON_ATOMICS && (defined(__ICC) || defined(__INTEL_COMPILER)) && 0 + #define __pyx_atomic_incr_aligned(value, lock) _InterlockedIncrement(value) + #define __pyx_atomic_decr_aligned(value, lock) _InterlockedDecrement(value) + #ifdef __PYX_DEBUG_ATOMICS + #warning "Using Intel atomics" + #endif +#else + #undef CYTHON_ATOMICS + #define CYTHON_ATOMICS 0 + #ifdef __PYX_DEBUG_ATOMICS + #warning "Not using atomics" + #endif +#endif +typedef volatile __pyx_atomic_int_type __pyx_atomic_int; +#if CYTHON_ATOMICS + #define __pyx_add_acquisition_count(memview)\ + __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) +#else + #define __pyx_add_acquisition_count(memview)\ + __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) +#endif + +/* ForceInitThreads.proto */ +#ifndef __PYX_FORCE_INIT_THREADS + #define __PYX_FORCE_INIT_THREADS 0 +#endif + +/* NoFastGil.proto */ +#define __Pyx_PyGILState_Ensure PyGILState_Ensure +#define __Pyx_PyGILState_Release PyGILState_Release +#define __Pyx_FastGIL_Remember() +#define __Pyx_FastGIL_Forget() +#define __Pyx_FastGilFuncInit() + +/* BufferFormatStructs.proto */ +#define IS_UNSIGNED(type) (((type) -1) > 0) +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; + struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":776 + * # in Cython to enable them only on the right systems. + * + * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + */ +typedef npy_int8 __pyx_t_5numpy_int8_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":777 + * + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t + */ +typedef npy_int16 __pyx_t_5numpy_int16_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":778 + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< + * ctypedef npy_int64 int64_t + * #ctypedef npy_int96 int96_t + */ +typedef npy_int32 __pyx_t_5numpy_int32_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":779 + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< + * #ctypedef npy_int96 int96_t + * #ctypedef npy_int128 int128_t + */ +typedef npy_int64 __pyx_t_5numpy_int64_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":783 + * #ctypedef npy_int128 int128_t + * + * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + */ +typedef npy_uint8 __pyx_t_5numpy_uint8_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":784 + * + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t + */ +typedef npy_uint16 __pyx_t_5numpy_uint16_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":785 + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< + * ctypedef npy_uint64 uint64_t + * #ctypedef npy_uint96 uint96_t + */ +typedef npy_uint32 __pyx_t_5numpy_uint32_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":786 + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< + * #ctypedef npy_uint96 uint96_t + * #ctypedef npy_uint128 uint128_t + */ +typedef npy_uint64 __pyx_t_5numpy_uint64_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":790 + * #ctypedef npy_uint128 uint128_t + * + * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< + * ctypedef npy_float64 float64_t + * #ctypedef npy_float80 float80_t + */ +typedef npy_float32 __pyx_t_5numpy_float32_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":791 + * + * ctypedef npy_float32 float32_t + * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< + * #ctypedef npy_float80 float80_t + * #ctypedef npy_float128 float128_t + */ +typedef npy_float64 __pyx_t_5numpy_float64_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":800 + * # The int types are mapped a bit surprising -- + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t + */ +typedef npy_long __pyx_t_5numpy_int_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":801 + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong longlong_t + * + */ +typedef npy_longlong __pyx_t_5numpy_long_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":802 + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_ulong uint_t + */ +typedef npy_longlong __pyx_t_5numpy_longlong_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":804 + * ctypedef npy_longlong longlong_t + * + * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t + */ +typedef npy_ulong __pyx_t_5numpy_uint_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":805 + * + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulonglong_t + * + */ +typedef npy_ulonglong __pyx_t_5numpy_ulong_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":806 + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_intp intp_t + */ +typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":808 + * ctypedef npy_ulonglong ulonglong_t + * + * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< + * ctypedef npy_uintp uintp_t + * + */ +typedef npy_intp __pyx_t_5numpy_intp_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":809 + * + * ctypedef npy_intp intp_t + * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< + * + * ctypedef npy_double float_t + */ +typedef npy_uintp __pyx_t_5numpy_uintp_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":811 + * ctypedef npy_uintp uintp_t + * + * ctypedef npy_double float_t # <<<<<<<<<<<<<< + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t + */ +typedef npy_double __pyx_t_5numpy_float_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":812 + * + * ctypedef npy_double float_t + * ctypedef npy_double double_t # <<<<<<<<<<<<<< + * ctypedef npy_longdouble longdouble_t + * + */ +typedef npy_double __pyx_t_5numpy_double_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":813 + * ctypedef npy_double float_t + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< + * + * ctypedef npy_cfloat cfloat_t + */ +typedef npy_longdouble __pyx_t_5numpy_longdouble_t; +/* Declarations.proto */ +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + typedef ::std::complex< float > __pyx_t_float_complex; + #else + typedef float _Complex __pyx_t_float_complex; + #endif +#else + typedef struct { float real, imag; } __pyx_t_float_complex; +#endif +static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); + +/* Declarations.proto */ +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + typedef ::std::complex< double > __pyx_t_double_complex; + #else + typedef double _Complex __pyx_t_double_complex; + #endif +#else + typedef struct { double real, imag; } __pyx_t_double_complex; +#endif +static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); + + +/*--- Type declarations ---*/ +struct __pyx_array_obj; +struct __pyx_MemviewEnum_obj; +struct __pyx_memoryview_obj; +struct __pyx_memoryviewslice_obj; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":815 + * ctypedef npy_longdouble longdouble_t + * + * ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<< + * ctypedef npy_cdouble cdouble_t + * ctypedef npy_clongdouble clongdouble_t + */ +typedef npy_cfloat __pyx_t_5numpy_cfloat_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":816 + * + * ctypedef npy_cfloat cfloat_t + * ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<< + * ctypedef npy_clongdouble clongdouble_t + * + */ +typedef npy_cdouble __pyx_t_5numpy_cdouble_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":817 + * ctypedef npy_cfloat cfloat_t + * ctypedef npy_cdouble cdouble_t + * ctypedef npy_clongdouble clongdouble_t # <<<<<<<<<<<<<< + * + * ctypedef npy_cdouble complex_t + */ +typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; + +/* "../../../../../../../../usr/local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":819 + * ctypedef npy_clongdouble clongdouble_t + * + * ctypedef npy_cdouble complex_t # <<<<<<<<<<<<<< + * + * cdef inline object PyArray_MultiIterNew1(a): + */ +typedef npy_cdouble __pyx_t_5numpy_complex_t; +struct __pyx_opt_args_7rank_cy_evaluate_cy; + +/* "rank_cy.pyx":24 + * + * # Main interface + * cpdef evaluate_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03=False): # <<<<<<<<<<<<<< + * distmat = np.asarray(distmat, dtype=np.float32) + * q_pids = np.asarray(q_pids, dtype=np.int64) + */ +struct __pyx_opt_args_7rank_cy_evaluate_cy { + int __pyx_n; + PyObject *use_metric_cuhk03; +}; + +/* "View.MemoryView":104 + * + * @cname("__pyx_array") + * cdef class array: # <<<<<<<<<<<<<< + * + * cdef: + */ +struct __pyx_array_obj { + PyObject_HEAD + struct __pyx_vtabstruct_array *__pyx_vtab; + char *data; + Py_ssize_t len; + char *format; + int ndim; + Py_ssize_t *_shape; + Py_ssize_t *_strides; + Py_ssize_t itemsize; + PyObject *mode; + PyObject *_format; + void (*callback_free_data)(void *); + int free_data; + int dtype_is_object; +}; + + +/* "View.MemoryView":278 + * + * @cname('__pyx_MemviewEnum') + * cdef class Enum(object): # <<<<<<<<<<<<<< + * cdef object name + * def __init__(self, name): + */ +struct __pyx_MemviewEnum_obj { + PyObject_HEAD + PyObject *name; +}; + + +/* "View.MemoryView":329 + * + * @cname('__pyx_memoryview') + * cdef class memoryview(object): # <<<<<<<<<<<<<< + * + * cdef object obj + */ +struct __pyx_memoryview_obj { + PyObject_HEAD + struct __pyx_vtabstruct_memoryview *__pyx_vtab; + PyObject *obj; + PyObject *_size; + PyObject *_array_interface; + PyThread_type_lock lock; + __pyx_atomic_int acquisition_count[2]; + __pyx_atomic_int *acquisition_count_aligned_p; + Py_buffer view; + int flags; + int dtype_is_object; + __Pyx_TypeInfo *typeinfo; +}; + + +/* "View.MemoryView":960 + * + * @cname('__pyx_memoryviewslice') + * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< + * "Internal class for passing memoryview slices to Python" + * + */ +struct __pyx_memoryviewslice_obj { + struct __pyx_memoryview_obj __pyx_base; + __Pyx_memviewslice from_slice; + PyObject *from_object; + PyObject *(*to_object_func)(char *); + int (*to_dtype_func)(char *, PyObject *); +}; + + + +/* "View.MemoryView":104 + * + * @cname("__pyx_array") + * cdef class array: # <<<<<<<<<<<<<< + * + * cdef: + */ + +struct __pyx_vtabstruct_array { + PyObject *(*get_memview)(struct __pyx_array_obj *); +}; +static struct __pyx_vtabstruct_array *__pyx_vtabptr_array; + + +/* "View.MemoryView":329 + * + * @cname('__pyx_memoryview') + * cdef class memoryview(object): # <<<<<<<<<<<<<< + * + * cdef object obj + */ + +struct __pyx_vtabstruct_memoryview { + char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); + PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); + PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *); + PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *); +}; +static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview; + + +/* "View.MemoryView":960 + * + * @cname('__pyx_memoryviewslice') + * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< + * "Internal class for passing memoryview slices to Python" + * + */ + +struct __pyx_vtabstruct__memoryviewslice { + struct __pyx_vtabstruct_memoryview __pyx_base; +}; +static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice; + +/* --- Runtime support code (head) --- */ +/* Refnanny.proto */ +#ifndef CYTHON_REFNANNY + #define CYTHON_REFNANNY 0 +#endif +#if CYTHON_REFNANNY + typedef struct { + void (*INCREF)(void*, PyObject*, int); + void (*DECREF)(void*, PyObject*, int); + void (*GOTREF)(void*, PyObject*, int); + void (*GIVEREF)(void*, PyObject*, int); + void* (*SetupContext)(const char*, int, const char*); 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}} while(0) + #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0) + #define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0) + #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0) +#else + #define __Pyx_RefNannyDeclarations + #define __Pyx_RefNannySetupContext(name, acquire_gil) + #define __Pyx_RefNannyFinishContext() + #define __Pyx_INCREF(r) Py_INCREF(r) + #define __Pyx_DECREF(r) Py_DECREF(r) + #define __Pyx_GOTREF(r) + #define __Pyx_GIVEREF(r) + #define __Pyx_XINCREF(r) Py_XINCREF(r) + #define __Pyx_XDECREF(r) Py_XDECREF(r) + #define __Pyx_XGOTREF(r) + #define __Pyx_XGIVEREF(r) +#endif +#define __Pyx_XDECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_XDECREF(tmp);\ + } while (0) +#define __Pyx_DECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_DECREF(tmp);\ + } while (0) +#define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) +#define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) + +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +/* GetBuiltinName.proto */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +/* GetModuleGlobalName.proto */ +#if CYTHON_USE_DICT_VERSIONS +#define __Pyx_GetModuleGlobalName(var, name) {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ + (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ + __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} +#define __Pyx_GetModuleGlobalNameUncached(var, name) {\ + PY_UINT64_T __pyx_dict_version;\ + PyObject *__pyx_dict_cached_value;\ + (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); +#else +#define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) +#define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); +#endif + +/* PyObjectCall.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +/* MemviewSliceInit.proto */ +#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d +#define __Pyx_MEMVIEW_DIRECT 1 +#define __Pyx_MEMVIEW_PTR 2 +#define __Pyx_MEMVIEW_FULL 4 +#define __Pyx_MEMVIEW_CONTIG 8 +#define __Pyx_MEMVIEW_STRIDED 16 +#define __Pyx_MEMVIEW_FOLLOW 32 +#define __Pyx_IS_C_CONTIG 1 +#define __Pyx_IS_F_CONTIG 2 +static int __Pyx_init_memviewslice( + struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference); +static CYTHON_INLINE int __pyx_add_acquisition_count_locked( + __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); +static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( + __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); +#define __pyx_get_slice_count_pointer(memview) (memview->acquisition_count_aligned_p) +#define __pyx_get_slice_count(memview) (*__pyx_get_slice_count_pointer(memview)) +#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) +#define __PYX_XDEC_MEMVIEW(slice, have_gil) __Pyx_XDEC_MEMVIEW(slice, have_gil, __LINE__) +static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); +static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *, int, int); + +/* RaiseArgTupleInvalid.proto */ +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +/* RaiseDoubleKeywords.proto */ +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +/* ParseKeywords.proto */ +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ + const char* function_name); + +/* PyCFunctionFastCall.proto */ +#if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs); +#else +#define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) +#endif + +/* PyFunctionFastCall.proto */ +#if CYTHON_FAST_PYCALL +#define __Pyx_PyFunction_FastCall(func, args, nargs)\ + __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs); +#else +#define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs) +#endif +#define __Pyx_BUILD_ASSERT_EXPR(cond)\ + (sizeof(char [1 - 2*!(cond)]) - 1) +#ifndef Py_MEMBER_SIZE +#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) +#endif + static size_t __pyx_pyframe_localsplus_offset = 0; + #include "frameobject.h" + #define __Pxy_PyFrame_Initialize_Offsets()\ + ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ + (void)(__pyx_pyframe_localsplus_offset = PyFrame_Type.tp_basicsize - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) + #define __Pyx_PyFrame_GetLocalsplus(frame)\ + (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) +#endif + +/* PyObjectCall2Args.proto */ +static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2); + +/* PyObjectCallMethO.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); +#endif + +/* PyObjectCallOneArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); + +/* GetItemInt.proto */ +#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ + __Pyx_GetItemInt_Generic(o, to_py_func(i)))) +#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, + int is_list, int wraparound, int boundscheck); + +/* ObjectGetItem.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key); +#else +#define __Pyx_PyObject_GetItem(obj, key) PyObject_GetItem(obj, key) +#endif + +/* ListAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + Py_SIZE(list) = len+1; + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_PyList_Append(L,x) PyList_Append(L,x) +#endif + +/* PyObjectGetMethod.proto */ +static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method); + +/* PyObjectCallMethod1.proto */ +static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg); + +/* append.proto */ +static CYTHON_INLINE int __Pyx_PyObject_Append(PyObject* L, PyObject* x); + +/* PyObjectCallNoArg.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func); +#else +#define __Pyx_PyObject_CallNoArg(func) __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL) +#endif + +/* RaiseTooManyValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +/* RaiseNeedMoreValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +/* IterFinish.proto */ +static CYTHON_INLINE int __Pyx_IterFinish(void); + +/* UnpackItemEndCheck.proto */ +static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected); + +/* None.proto */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); + +/* PyThreadStateGet.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; +#define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current; +#define __Pyx_PyErr_Occurred() __pyx_tstate->curexc_type +#else +#define __Pyx_PyThreadState_declare +#define __Pyx_PyThreadState_assign +#define __Pyx_PyErr_Occurred() PyErr_Occurred() +#endif + +/* PyErrFetchRestore.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL) +#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) +#else +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#endif +#else +#define __Pyx_PyErr_Clear() PyErr_Clear() +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) +#endif + +/* RaiseException.proto */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +/* DictGetItem.proto */ +#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY +static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key); +#define __Pyx_PyObject_Dict_GetItem(obj, name)\ + (likely(PyDict_CheckExact(obj)) ?\ + __Pyx_PyDict_GetItem(obj, name) : PyObject_GetItem(obj, name)) +#else +#define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key) +#define __Pyx_PyObject_Dict_GetItem(obj, name) PyObject_GetItem(obj, name) +#endif + +/* RaiseNoneIterError.proto */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +/* ExtTypeTest.proto */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + +/* GetTopmostException.proto */ +#if CYTHON_USE_EXC_INFO_STACK +static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); +#endif + +/* SaveResetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +#else +#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) +#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) +#endif + +/* PyErrExceptionMatches.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); +#else +#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) +#endif + +/* GetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* ArgTypeTest.proto */ +#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ + ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\ + __Pyx__ArgTypeTest(obj, type, name, exact)) +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); + +/* IncludeStringH.proto */ +#include + +/* BytesEquals.proto */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); + +/* UnicodeEquals.proto */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); + +/* StrEquals.proto */ +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals +#else +#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals +#endif + +/* UnaryNegOverflows.proto */ +#define UNARY_NEG_WOULD_OVERFLOW(x)\ + (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) + +static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ +/* GetAttr.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); + +/* decode_c_string_utf16.proto */ +static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) { + int byteorder = 0; + return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); +} +static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) { + int byteorder = -1; + return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); +} +static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) { + int byteorder = 1; + return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); +} + +/* decode_c_string.proto */ +static CYTHON_INLINE PyObject* __Pyx_decode_c_string( + const char* cstring, Py_ssize_t start, Py_ssize_t stop, + const char* encoding, const char* errors, + PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)); + +/* GetAttr3.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); + +/* SwapException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* Import.proto */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +/* FastTypeChecks.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); +#else +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) +#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) +#endif +#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) + +static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +/* ListCompAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len)) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + Py_SIZE(list) = len+1; + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) +#endif + +/* PyIntBinop.proto */ +#if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace); +#else +#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace)\ + (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2)) +#endif + +/* ListExtend.proto */ +static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) { +#if CYTHON_COMPILING_IN_CPYTHON + PyObject* none = _PyList_Extend((PyListObject*)L, v); + if (unlikely(!none)) + return -1; + Py_DECREF(none); + return 0; +#else + return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v); +#endif +} + +/* WriteUnraisableException.proto */ +static void __Pyx_WriteUnraisable(const char *name, int clineno, + int lineno, const char *filename, + int full_traceback, int nogil); + +/* ImportFrom.proto */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); + +/* HasAttr.proto */ +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); + +/* PyObject_GenericGetAttrNoDict.proto */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr +#endif + +/* PyObject_GenericGetAttr.proto */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr +#endif + +/* SetVTable.proto */ +static int __Pyx_SetVtable(PyObject *dict, void *vtable); + +/* SetupReduce.proto */ +static int __Pyx_setup_reduce(PyObject* type_obj); + +/* TypeImport.proto */ +#ifndef __PYX_HAVE_RT_ImportType_proto +#define __PYX_HAVE_RT_ImportType_proto +enum __Pyx_ImportType_CheckSize { + __Pyx_ImportType_CheckSize_Error = 0, + __Pyx_ImportType_CheckSize_Warn = 1, + __Pyx_ImportType_CheckSize_Ignore = 2 +}; +static PyTypeObject *__Pyx_ImportType(PyObject* module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size); +#endif + +/* CLineInTraceback.proto */ +#ifdef CYTHON_CLINE_IN_TRACEBACK +#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) +#else +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); +#endif + +/* CodeObjectCache.proto */ +typedef struct { + PyCodeObject* code_object; + int code_line; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); + +/* AddTraceback.proto */ +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +#if PY_MAJOR_VERSION < 3 + static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); + static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else + #define __Pyx_GetBuffer PyObject_GetBuffer + #define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + + +/* BufferStructDeclare.proto */ +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +/* MemviewSliceIsContig.proto */ +static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim); + +/* OverlappingSlices.proto */ +static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize); + +/* Capsule.proto */ +static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig); + +/* IsLittleEndian.proto */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); + +/* BufferFormatCheck.proto */ +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type); + +/* TypeInfoCompare.proto */ +static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); + +/* MemviewSliceValidateAndInit.proto */ +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_float(PyObject *, int writable_flag); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_long(PyObject *, int writable_flag); + +/* RealImag.proto */ +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #define __Pyx_CREAL(z) ((z).real()) + #define __Pyx_CIMAG(z) ((z).imag()) + #else + #define __Pyx_CREAL(z) (__real__(z)) + #define __Pyx_CIMAG(z) (__imag__(z)) + #endif +#else + #define __Pyx_CREAL(z) ((z).real) + #define __Pyx_CIMAG(z) ((z).imag) +#endif +#if defined(__cplusplus) && CYTHON_CCOMPLEX\ + && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103) + #define __Pyx_SET_CREAL(z,x) ((z).real(x)) + #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) +#else + #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) + #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) +#endif + +/* Arithmetic.proto */ +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq_float(a, b) ((a)==(b)) + #define __Pyx_c_sum_float(a, b) ((a)+(b)) + #define __Pyx_c_diff_float(a, b) ((a)-(b)) + #define __Pyx_c_prod_float(a, b) ((a)*(b)) + #define __Pyx_c_quot_float(a, b) ((a)/(b)) + #define __Pyx_c_neg_float(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero_float(z) ((z)==(float)0) + #define __Pyx_c_conj_float(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs_float(z) (::std::abs(z)) + #define __Pyx_c_pow_float(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero_float(z) ((z)==0) + #define __Pyx_c_conj_float(z) (conjf(z)) + #if 1 + #define __Pyx_c_abs_float(z) (cabsf(z)) + #define __Pyx_c_pow_float(a, b) (cpowf(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex); + #if 1 + static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex); + #endif +#endif + +/* Arithmetic.proto */ +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq_double(a, b) ((a)==(b)) + #define __Pyx_c_sum_double(a, b) ((a)+(b)) + #define __Pyx_c_diff_double(a, b) ((a)-(b)) + #define __Pyx_c_prod_double(a, b) ((a)*(b)) + #define __Pyx_c_quot_double(a, b) ((a)/(b)) + #define __Pyx_c_neg_double(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero_double(z) ((z)==(double)0) + #define __Pyx_c_conj_double(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs_double(z) (::std::abs(z)) + #define __Pyx_c_pow_double(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero_double(z) ((z)==0) + #define __Pyx_c_conj_double(z) (conj(z)) + #if 1 + #define __Pyx_c_abs_double(z) (cabs(z)) + #define __Pyx_c_pow_double(a, b) (cpow(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex); + static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex); + #if 1 + static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex); + #endif +#endif + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +/* MemviewDtypeToObject.proto */ +static CYTHON_INLINE PyObject *__pyx_memview_get_float(const char *itemp); +static CYTHON_INLINE int __pyx_memview_set_float(const char *itemp, PyObject *obj); + +/* MemviewDtypeToObject.proto */ +static CYTHON_INLINE PyObject *__pyx_memview_get_long(const char *itemp); +static CYTHON_INLINE int __pyx_memview_set_long(const char *itemp, PyObject *obj); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value); + +/* MemviewSliceCopyTemplate.proto */ +static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object); + +/* CIntFromPy.proto */ +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_long(PyObject *, int writable_flag); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_float(PyObject *, int writable_flag); + +/* CheckBinaryVersion.proto */ +static int __Pyx_check_binary_version(void); + +/* InitStrings.proto */ +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ +static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ +static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ + +/* Module declarations from 'cython.view' */ + +/* Module declarations from 'cython' */ + +/* Module declarations from 'cpython.buffer' */ + +/* Module declarations from 'libc.string' */ + +/* Module declarations from 'libc.stdio' */ + +/* Module declarations from '__builtin__' */ + +/* Module declarations from 'cpython.type' */ +static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; + +/* Module declarations from 'cpython' */ + +/* Module declarations from 'cpython.object' */ + +/* Module declarations from 'cpython.ref' */ + +/* Module declarations from 'cpython.mem' */ + +/* Module declarations from 'numpy' */ + +/* Module declarations from 'numpy' */ +static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; +static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; +static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; +static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; +static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; +static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ + +/* Module declarations from 'rank_cy' */ +static PyTypeObject *__pyx_array_type = 0; +static PyTypeObject *__pyx_MemviewEnum_type = 0; +static PyTypeObject *__pyx_memoryview_type = 0; +static PyTypeObject *__pyx_memoryviewslice_type = 0; +static PyObject *generic = 0; +static PyObject *strided = 0; +static PyObject *indirect = 0; +static PyObject *contiguous = 0; +static PyObject *indirect_contiguous = 0; +static int __pyx_memoryview_thread_locks_used; +static PyThread_type_lock __pyx_memoryview_thread_locks[8]; +static PyObject *__pyx_f_7rank_cy_evaluate_cy(PyObject *, PyObject *, PyObject *, PyObject *, PyObject *, PyObject *, int __pyx_skip_dispatch, struct __pyx_opt_args_7rank_cy_evaluate_cy *__pyx_optional_args); /*proto*/ +static PyObject *__pyx_f_7rank_cy_eval_cuhk03_cy(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, long, int __pyx_skip_dispatch); /*proto*/ +static PyObject *__pyx_f_7rank_cy_eval_market1501_cy(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, long, int __pyx_skip_dispatch); /*proto*/ +static void __pyx_fuse_3__pyx_f_7rank_cy_function_cumsum(__Pyx_memviewslice, __Pyx_memviewslice, long); /*proto*/ +static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ +static void *__pyx_align_pointer(void *, size_t); /*proto*/ +static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ +static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ +static PyObject *_unellipsify(PyObject *, int); /*proto*/ +static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ +static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ +static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ +static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ +static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ +static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ +static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ +static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ +static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ +static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ +static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/ +static int __pyx_memoryview_err(PyObject *, char *); /*proto*/ +static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ +static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ +static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ +static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ +static __Pyx_TypeInfo __Pyx_TypeInfo_float = { "float", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_long = { "long", NULL, sizeof(long), { 0 }, 0, IS_UNSIGNED(long) ? 'U' : 'I', IS_UNSIGNED(long), 0 }; +#define __Pyx_MODULE_NAME "rank_cy" +extern int __pyx_module_is_main_rank_cy; +int __pyx_module_is_main_rank_cy = 0; + +/* Implementation of 'rank_cy' */ +static PyObject *__pyx_builtin_print; +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_RuntimeError; +static PyObject *__pyx_builtin_ImportError; +static PyObject *__pyx_builtin_MemoryError; +static PyObject *__pyx_builtin_enumerate; +static PyObject *__pyx_builtin_TypeError; +static PyObject *__pyx_builtin_Ellipsis; +static PyObject *__pyx_builtin_id; +static PyObject *__pyx_builtin_IndexError; +static const char __pyx_k_O[] = "O"; +static const char __pyx_k_c[] = "c"; +static const char __pyx_k_id[] = "id"; +static const char __pyx_k_np[] = "np"; +static const char __pyx_k_new[] = "__new__"; +static const char __pyx_k_obj[] = "obj"; +static const char __pyx_k_axis[] = "axis"; +static const char __pyx_k_base[] = "base"; +static const char __pyx_k_dict[] = "__dict__"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_mode[] = "mode"; +static const char __pyx_k_name[] = "name"; +static const char __pyx_k_ndim[] = "ndim"; +static const char __pyx_k_pack[] = "pack"; +static const char __pyx_k_size[] = "size"; +static const char __pyx_k_step[] = "step"; +static const char __pyx_k_stop[] = "stop"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_ASCII[] = "ASCII"; +static const char __pyx_k_class[] = "__class__"; +static const char __pyx_k_dtype[] = "dtype"; +static const char __pyx_k_error[] = "error"; +static const char __pyx_k_flags[] = "flags"; +static const char __pyx_k_int64[] = "int64"; +static const char __pyx_k_items[] = "items"; +static const char __pyx_k_numpy[] = "numpy"; +static const char __pyx_k_print[] = "print"; +static const char __pyx_k_range[] = "range"; +static const char __pyx_k_shape[] = "shape"; +static const char __pyx_k_start[] = "start"; +static const char __pyx_k_zeros[] = "zeros"; +static const char __pyx_k_append[] = "append"; +static const char __pyx_k_astype[] = "astype"; +static const char __pyx_k_choice[] = "choice"; +static const char __pyx_k_encode[] = "encode"; +static const char __pyx_k_format[] = "format"; +static const char __pyx_k_g_pids[] = "g_pids"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_name_2[] = "__name__"; +static const char __pyx_k_pickle[] = "pickle"; +static const char __pyx_k_q_pids[] = "q_pids"; +static const char __pyx_k_random[] = "random"; +static const char __pyx_k_reduce[] = "__reduce__"; +static const char __pyx_k_struct[] = "struct"; +static const char __pyx_k_unpack[] = "unpack"; +static const char __pyx_k_update[] = "update"; +static const char __pyx_k_argsort[] = "argsort"; +static const char __pyx_k_asarray[] = "asarray"; +static const char __pyx_k_distmat[] = "distmat"; +static const char __pyx_k_float32[] = "float32"; +static const char __pyx_k_fortran[] = "fortran"; +static const char __pyx_k_memview[] = "memview"; +static const char __pyx_k_newaxis[] = "newaxis"; +static const char __pyx_k_Ellipsis[] = "Ellipsis"; +static const char __pyx_k_g_camids[] = "g_camids"; +static const char __pyx_k_getstate[] = "__getstate__"; +static const char __pyx_k_itemsize[] = "itemsize"; +static const char __pyx_k_max_rank[] = "max_rank"; +static const char __pyx_k_pyx_type[] = "__pyx_type"; +static const char __pyx_k_q_camids[] = "q_camids"; +static const char __pyx_k_setstate[] = "__setstate__"; +static const char __pyx_k_TypeError[] = "TypeError"; +static const char __pyx_k_enumerate[] = "enumerate"; +static const char __pyx_k_pyx_state[] = "__pyx_state"; +static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; +static const char __pyx_k_IndexError[] = "IndexError"; +static const char __pyx_k_ValueError[] = "ValueError"; +static const char __pyx_k_pyx_result[] = "__pyx_result"; +static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; +static const char __pyx_k_ImportError[] = "ImportError"; +static const char __pyx_k_MemoryError[] = "MemoryError"; +static const char __pyx_k_PickleError[] = "PickleError"; +static const char __pyx_k_collections[] = "collections"; +static const char __pyx_k_defaultdict[] = "defaultdict"; +static const char __pyx_k_RuntimeError[] = "RuntimeError"; +static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; +static const char __pyx_k_stringsource[] = "stringsource"; +static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer"; +static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; +static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; +static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; +static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; +static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; +static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; +static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; +static const char __pyx_k_use_metric_cuhk03[] = "use_metric_cuhk03"; +static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; +static const char __pyx_k_strided_and_direct[] = ""; +static const char __pyx_k_strided_and_indirect[] = ""; +static const char __pyx_k_contiguous_and_direct[] = ""; +static const char __pyx_k_MemoryView_of_r_object[] = ""; +static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; +static const char __pyx_k_contiguous_and_indirect[] = ""; +static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'"; +static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d."; +static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; +static const char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous"; +static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; +static const char __pyx_k_strided_and_direct_or_indirect[] = ""; +static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; +static const char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)"; +static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; +static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; +static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; +static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; +static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; +static const char __pyx_k_Error_all_query_identities_do_no[] = "Error: all query identities do not appear in gallery"; +static const char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd"; +static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))"; +static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; +static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s"; +static const char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported"; +static const char __pyx_k_Note_number_of_gallery_samples_i[] = "Note: number of gallery samples is quite small, got {}"; +static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)"; +static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; +static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)"; +static const char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous"; +static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; +static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import"; +static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; +static const char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short."; +static PyObject *__pyx_n_s_ASCII; +static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri; +static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is; +static PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor; +static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi; +static PyObject *__pyx_kp_s_Cannot_index_with_type_s; +static PyObject *__pyx_n_s_Ellipsis; +static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr; +static PyObject *__pyx_kp_s_Error_all_query_identities_do_no; +static PyObject *__pyx_kp_u_Format_string_allocated_too_shor; +static PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2; +static PyObject *__pyx_n_s_ImportError; +static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0; +static PyObject *__pyx_n_s_IndexError; +static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte; +static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr; +static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d; +static PyObject *__pyx_n_s_MemoryError; +static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x; +static PyObject *__pyx_kp_s_MemoryView_of_r_object; +static PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor; +static PyObject *__pyx_kp_s_Note_number_of_gallery_samples_i; +static PyObject *__pyx_n_b_O; +static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a; +static PyObject *__pyx_n_s_PickleError; +static PyObject *__pyx_n_s_RuntimeError; +static PyObject *__pyx_n_s_TypeError; +static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; +static PyObject *__pyx_n_s_ValueError; +static PyObject *__pyx_n_s_View_MemoryView; +static PyObject *__pyx_n_s_allocate_buffer; +static PyObject *__pyx_n_s_append; +static PyObject *__pyx_n_s_argsort; +static PyObject *__pyx_n_s_asarray; +static PyObject *__pyx_n_s_astype; +static PyObject *__pyx_n_s_axis; +static PyObject *__pyx_n_s_base; +static PyObject *__pyx_n_s_c; +static PyObject *__pyx_n_u_c; +static PyObject *__pyx_n_s_choice; +static PyObject *__pyx_n_s_class; +static PyObject *__pyx_n_s_cline_in_traceback; +static PyObject *__pyx_n_s_collections; +static PyObject *__pyx_kp_s_contiguous_and_direct; +static PyObject *__pyx_kp_s_contiguous_and_indirect; +static PyObject *__pyx_n_s_defaultdict; +static PyObject *__pyx_n_s_dict; +static PyObject *__pyx_n_s_distmat; +static PyObject *__pyx_n_s_dtype; +static PyObject *__pyx_n_s_dtype_is_object; +static PyObject *__pyx_n_s_encode; +static PyObject *__pyx_n_s_enumerate; +static PyObject *__pyx_n_s_error; +static PyObject *__pyx_n_s_flags; +static PyObject *__pyx_n_s_float32; +static PyObject *__pyx_n_s_format; +static PyObject *__pyx_n_s_fortran; +static PyObject *__pyx_n_u_fortran; +static PyObject *__pyx_n_s_g_camids; +static PyObject *__pyx_n_s_g_pids; +static PyObject *__pyx_n_s_getstate; +static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi; +static PyObject *__pyx_n_s_id; +static PyObject *__pyx_n_s_import; +static PyObject *__pyx_n_s_int64; +static PyObject *__pyx_n_s_items; +static PyObject *__pyx_n_s_itemsize; +static PyObject *__pyx_kp_s_itemsize_0_for_cython_array; +static PyObject *__pyx_n_s_main; +static PyObject *__pyx_n_s_max_rank; +static PyObject *__pyx_n_s_memview; +static PyObject *__pyx_n_s_mode; +static PyObject *__pyx_n_s_name; +static PyObject *__pyx_n_s_name_2; +static PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous; +static PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou; +static PyObject *__pyx_n_s_ndim; +static PyObject *__pyx_n_s_new; +static PyObject *__pyx_n_s_newaxis; +static PyObject *__pyx_kp_s_no_default___reduce___due_to_non; +static PyObject *__pyx_n_s_np; +static PyObject *__pyx_n_s_numpy; +static PyObject *__pyx_kp_s_numpy_core_multiarray_failed_to; +static PyObject *__pyx_kp_s_numpy_core_umath_failed_to_impor; +static PyObject *__pyx_n_s_obj; +static PyObject *__pyx_n_s_pack; +static PyObject *__pyx_n_s_pickle; +static PyObject *__pyx_n_s_print; +static PyObject *__pyx_n_s_pyx_PickleError; +static PyObject *__pyx_n_s_pyx_checksum; +static PyObject *__pyx_n_s_pyx_getbuffer; +static PyObject *__pyx_n_s_pyx_result; +static PyObject *__pyx_n_s_pyx_state; +static PyObject *__pyx_n_s_pyx_type; +static PyObject *__pyx_n_s_pyx_unpickle_Enum; +static PyObject *__pyx_n_s_pyx_vtable; +static PyObject *__pyx_n_s_q_camids; +static PyObject *__pyx_n_s_q_pids; +static PyObject *__pyx_n_s_random; +static PyObject *__pyx_n_s_range; +static PyObject *__pyx_n_s_reduce; +static PyObject *__pyx_n_s_reduce_cython; +static PyObject *__pyx_n_s_reduce_ex; +static PyObject *__pyx_n_s_setstate; +static PyObject *__pyx_n_s_setstate_cython; +static PyObject *__pyx_n_s_shape; +static PyObject *__pyx_n_s_size; +static PyObject *__pyx_n_s_start; +static PyObject *__pyx_n_s_step; +static PyObject *__pyx_n_s_stop; +static PyObject *__pyx_kp_s_strided_and_direct; +static PyObject *__pyx_kp_s_strided_and_direct_or_indirect; +static PyObject *__pyx_kp_s_strided_and_indirect; +static PyObject *__pyx_kp_s_stringsource; +static PyObject *__pyx_n_s_struct; +static PyObject *__pyx_n_s_test; +static PyObject *__pyx_kp_s_unable_to_allocate_array_data; +static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str; +static PyObject *__pyx_kp_u_unknown_dtype_code_in_numpy_pxd; +static PyObject *__pyx_n_s_unpack; +static PyObject *__pyx_n_s_update; +static PyObject *__pyx_n_s_use_metric_cuhk03; +static PyObject *__pyx_n_s_zeros; +static PyObject *__pyx_pf_7rank_cy_evaluate_cy(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_distmat, PyObject *__pyx_v_q_pids, PyObject *__pyx_v_g_pids, PyObject *__pyx_v_q_camids, PyObject *__pyx_v_g_camids, PyObject *__pyx_v_max_rank, PyObject *__pyx_v_use_metric_cuhk03); /* proto */ +static PyObject *__pyx_pf_7rank_cy_2eval_cuhk03_cy(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_distmat, __Pyx_memviewslice __pyx_v_q_pids, __Pyx_memviewslice __pyx_v_g_pids, __Pyx_memviewslice __pyx_v_q_camids, __Pyx_memviewslice __pyx_v_g_camids, long __pyx_v_max_rank); /* proto */ +static PyObject *__pyx_pf_7rank_cy_4eval_market1501_cy(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_distmat, __Pyx_memviewslice __pyx_v_q_pids, __Pyx_memviewslice __pyx_v_g_pids, __Pyx_memviewslice __pyx_v_q_camids, __Pyx_memviewslice __pyx_v_g_camids, long __pyx_v_max_rank); /* proto */ +static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ +static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ +static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ +static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); 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"View.MemoryView":836 + * + * + * if have_start: # <<<<<<<<<<<<<< + * if start < 0: + * start += shape + */ + __pyx_t_2 = (__pyx_v_have_start != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":837 + * + * if have_start: + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if start < 0: + */ + __pyx_t_2 = ((__pyx_v_start < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":838 + * if have_start: + * if start < 0: + * start += shape # <<<<<<<<<<<<<< + * if start < 0: + * start = 0 + */ + __pyx_v_start = (__pyx_v_start + __pyx_v_shape); + + /* "View.MemoryView":839 + * if start < 0: + * start += shape + * if start < 0: # <<<<<<<<<<<<<< + * start = 0 + * elif start >= shape: + */ + __pyx_t_2 = ((__pyx_v_start < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":840 + * start += shape + * if start < 0: + * start = 0 # <<<<<<<<<<<<<< + * elif start >= shape: + * if negative_step: + */ + __pyx_v_start = 0; + + /* "View.MemoryView":839 + * if start < 0: + * start += shape + * if start < 0: # <<<<<<<<<<<<<< + * start = 0 + * elif start >= shape: + */ + } + + /* "View.MemoryView":837 + * + * if have_start: + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if start < 0: + */ + goto __pyx_L12; + } + + /* "View.MemoryView":841 + * if start < 0: + * start = 0 + * elif start >= shape: # <<<<<<<<<<<<<< + * if negative_step: + * start = shape - 1 + */ + __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":842 + * start = 0 + * elif start >= shape: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + __pyx_t_2 = (__pyx_v_negative_step != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":843 + * elif start >= shape: + * if negative_step: + * start = shape - 1 # <<<<<<<<<<<<<< + * else: + * start = shape + */ + __pyx_v_start = (__pyx_v_shape - 1); + + /* "View.MemoryView":842 + * start = 0 + * elif start >= shape: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + goto __pyx_L14; + } + + /* "View.MemoryView":845 + * start = shape - 1 + * else: + * start = shape # <<<<<<<<<<<<<< + * else: + * if negative_step: + */ + /*else*/ { + __pyx_v_start = __pyx_v_shape; + } + __pyx_L14:; + + /* "View.MemoryView":841 + * if start < 0: + * start = 0 + * elif start >= shape: # <<<<<<<<<<<<<< + * if negative_step: + * start = shape - 1 + */ + } + __pyx_L12:; + + /* "View.MemoryView":836 + * + * + * if have_start: # <<<<<<<<<<<<<< + * if start < 0: + * start += shape + */ + goto __pyx_L11; + } + + /* "View.MemoryView":847 + * start = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + /*else*/ { + __pyx_t_2 = (__pyx_v_negative_step != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":848 + * else: + * if negative_step: + * start = shape - 1 # <<<<<<<<<<<<<< + * else: + * start = 0 + */ + __pyx_v_start = (__pyx_v_shape - 1); + + /* "View.MemoryView":847 + * start = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + goto __pyx_L15; + } + + /* "View.MemoryView":850 + * start = shape - 1 + * else: + * start = 0 # <<<<<<<<<<<<<< + * + * if have_stop: + */ + /*else*/ { + __pyx_v_start = 0; + } + __pyx_L15:; + } + __pyx_L11:; + + /* "View.MemoryView":852 + * start = 0 + * + * if have_stop: # <<<<<<<<<<<<<< + * if stop < 0: + * stop += shape + */ + __pyx_t_2 = (__pyx_v_have_stop != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":853 + * + * if have_stop: + * if stop < 0: # <<<<<<<<<<<<<< + * stop += shape + * if stop < 0: + */ + __pyx_t_2 = ((__pyx_v_stop < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":854 + * if have_stop: + * if stop < 0: + * stop += shape # <<<<<<<<<<<<<< + * if stop < 0: + * stop = 0 + */ + __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape); + + /* "View.MemoryView":855 + * if stop < 0: + * stop += shape + * if stop < 0: # <<<<<<<<<<<<<< + * stop = 0 + * elif stop > shape: + */ + __pyx_t_2 = ((__pyx_v_stop < 0) != 0); + if (__pyx_t_2) { + + /* 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< 0: # <<<<<<<<<<<<<< + * return -arg + * else: + */ + } + + /* "View.MemoryView":1108 + * return -arg + * else: + * return arg # <<<<<<<<<<<<<< + * + * @cname('__pyx_get_best_slice_order') + */ + /*else*/ { + __pyx_r = __pyx_v_arg; + goto __pyx_L0; + } + + /* "View.MemoryView":1104 + * + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< + * if arg < 0: + * return -arg + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1111 + * + * @cname('__pyx_get_best_slice_order') + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< + * """ + * Figure out the best memory access order for a given slice. + */ + +static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) { + int __pyx_v_i; + Py_ssize_t __pyx_v_c_stride; + Py_ssize_t __pyx_v_f_stride; + char __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_t_4; + + /* "View.MemoryView":1116 + * """ + * cdef int i + * cdef Py_ssize_t c_stride = 0 # <<<<<<<<<<<<<< + * cdef Py_ssize_t f_stride = 0 + * + */ + __pyx_v_c_stride = 0; + + /* "View.MemoryView":1117 + * cdef int i + * cdef Py_ssize_t c_stride = 0 + * cdef Py_ssize_t f_stride = 0 # <<<<<<<<<<<<<< + * + * for i in range(ndim - 1, -1, -1): + */ + __pyx_v_f_stride = 0; + + /* "View.MemoryView":1119 + * cdef Py_ssize_t f_stride = 0 + * + * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] + */ + for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { + __pyx_v_i = __pyx_t_1; + + /* "View.MemoryView":1120 + * + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * c_stride = mslice.strides[i] + * break + */ + __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1121 + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] # 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"View.MemoryView":1126 + * for i in range(ndim): + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] # <<<<<<<<<<<<<< + * break + * + */ + __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1127 + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] + * break # <<<<<<<<<<<<<< + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): + */ + goto __pyx_L7_break; + + /* "View.MemoryView":1125 + * + * for i in range(ndim): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * f_stride = mslice.strides[i] + * break + */ + } + } + __pyx_L7_break:; + + /* "View.MemoryView":1129 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: + */ + __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1130 + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): + * return 'C' # <<<<<<<<<<<<<< + * else: + * return 'F' + */ + __pyx_r = 'C'; + goto __pyx_L0; + + /* "View.MemoryView":1129 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: + */ + } + + /* "View.MemoryView":1132 + * return 'C' + * else: + * return 'F' # <<<<<<<<<<<<<< + * + * @cython.cdivision(True) + */ + /*else*/ { + __pyx_r = 'F'; + goto __pyx_L0; + } + + /* "View.MemoryView":1111 + * + * @cname('__pyx_get_best_slice_order') + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< + * """ + * Figure out the best memory access order for a given slice. + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1135 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, + */ + +static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { + CYTHON_UNUSED Py_ssize_t __pyx_v_i; + CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; + Py_ssize_t __pyx_v_dst_extent; + Py_ssize_t __pyx_v_src_stride; + Py_ssize_t __pyx_v_dst_stride; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + Py_ssize_t __pyx_t_4; + Py_ssize_t __pyx_t_5; + Py_ssize_t __pyx_t_6; + + /* "View.MemoryView":1142 + * + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + */ + __pyx_v_src_extent = (__pyx_v_src_shape[0]); + + /* "View.MemoryView":1143 + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] + */ + __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); + + /* "View.MemoryView":1144 + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + */ + __pyx_v_src_stride = (__pyx_v_src_strides[0]); + + /* "View.MemoryView":1145 + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< + * + * if ndim == 1: + */ + __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); + + /* "View.MemoryView":1147 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":1148 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + + /* "View.MemoryView":1149 + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + */ + __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); + if (__pyx_t_2) { + __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); + } + __pyx_t_3 = (__pyx_t_2 != 0); + __pyx_t_1 = __pyx_t_3; + __pyx_L5_bool_binop_done:; + + /* "View.MemoryView":1148 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + if (__pyx_t_1) { + + /* "View.MemoryView":1150 + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); + + /* "View.MemoryView":1148 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + goto __pyx_L4; + } + + /* "View.MemoryView":1152 + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + */ + /*else*/ { + __pyx_t_4 = __pyx_v_dst_extent; + __pyx_t_5 = __pyx_t_4; + for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { + __pyx_v_i = __pyx_t_6; + + /* "View.MemoryView":1153 + * else: + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< + * src_data += src_stride + * dst_data += dst_stride + */ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); + + /* "View.MemoryView":1154 + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * else: + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1155 + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L4:; + + /* "View.MemoryView":1147 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1157 + * dst_data += dst_stride + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * _copy_strided_to_strided(src_data, src_strides + 1, + * dst_data, dst_strides + 1, + */ + /*else*/ { + __pyx_t_4 = __pyx_v_dst_extent; + __pyx_t_5 = __pyx_t_4; + for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { + __pyx_v_i = __pyx_t_6; + + /* "View.MemoryView":1158 + * else: + * for i in range(dst_extent): + * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< + * dst_data, dst_strides + 1, + * src_shape + 1, dst_shape + 1, + */ + _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); + + /* "View.MemoryView":1162 + * src_shape + 1, dst_shape + 1, + * ndim - 1, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1163 + * ndim - 1, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L3:; + + /* "View.MemoryView":1135 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, + */ + + /* function exit code */ +} + +/* "View.MemoryView":1165 + * dst_data += dst_stride + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) nogil: + */ + +static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { + + /* "View.MemoryView":1168 + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) nogil: + * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<< + * src.shape, dst.shape, ndim, itemsize) + * + */ + _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); + + /* "View.MemoryView":1165 + * dst_data += dst_stride + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) nogil: + */ + + /* function exit code */ +} + +/* "View.MemoryView":1172 + * + * @cname('__pyx_memoryview_slice_get_size') + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef int i + */ + +static 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(__pyx_v_size * (__pyx_v_src->shape[__pyx_v_i])); + } + + /* "View.MemoryView":1180 + * size *= src.shape[i] + * + * return size # <<<<<<<<<<<<<< + * + * @cname('__pyx_fill_contig_strides_array') + */ + __pyx_r = __pyx_v_size; + goto __pyx_L0; + + /* "View.MemoryView":1172 + * + * @cname('__pyx_memoryview_slice_get_size') + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef int i + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1183 + * + * @cname('__pyx_fill_contig_strides_array') + * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< + * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, + * int ndim, char order) nogil: + */ + +static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) { + int __pyx_v_idx; + Py_ssize_t __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_t_4; + + /* "View.MemoryView":1192 + * cdef int idx + * + * if order == 'F': # <<<<<<<<<<<<<< + * for idx in range(ndim): + * strides[idx] = stride + */ + __pyx_t_1 = ((__pyx_v_order == 'F') != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":1193 + * + * if order == 'F': + * for idx in range(ndim): # <<<<<<<<<<<<<< + * strides[idx] = stride + * stride = stride * shape[idx] + */ + __pyx_t_2 = __pyx_v_ndim; + __pyx_t_3 = __pyx_t_2; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_idx = __pyx_t_4; + + /* "View.MemoryView":1194 + * if order == 'F': + * for idx in range(ndim): + * strides[idx] = stride # <<<<<<<<<<<<<< + * stride = stride * shape[idx] + * else: + */ + (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; + + /* "View.MemoryView":1195 + * for idx in range(ndim): + * strides[idx] = stride + * stride = stride * shape[idx] # <<<<<<<<<<<<<< + * else: + * for idx in range(ndim - 1, -1, -1): + */ + __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); + } + + /* "View.MemoryView":1192 + * cdef int idx + * + * if order == 'F': # <<<<<<<<<<<<<< + * for idx in range(ndim): + * strides[idx] = stride + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1197 + * stride = stride * shape[idx] + * else: + * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * strides[idx] = stride + * stride = stride * shape[idx] + */ + /*else*/ { + for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) { + __pyx_v_idx = __pyx_t_2; + + /* "View.MemoryView":1198 + * else: + * for idx in range(ndim - 1, -1, -1): + * strides[idx] = stride # <<<<<<<<<<<<<< + * stride = stride * shape[idx] + * + */ + (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; + + /* "View.MemoryView":1199 + * for idx in range(ndim - 1, -1, -1): + * strides[idx] = stride + * stride = stride * shape[idx] # <<<<<<<<<<<<<< + * + * return stride + */ + __pyx_v_stride = 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'F', ndim): + */ + __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1310 + * + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) # <<<<<<<<<<<<<< + * elif slice_is_contig(src, 'F', ndim): + * direct_copy = slice_is_contig(dst, 'F', ndim) + */ + __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim); + + /* "View.MemoryView":1309 + * + * + * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + */ + goto __pyx_L12; + } + + /* "View.MemoryView":1311 + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + */ + __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1312 + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + * direct_copy = slice_is_contig(dst, 'F', ndim) # <<<<<<<<<<<<<< + * + * if direct_copy: + */ + __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim); + + /* "View.MemoryView":1311 + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + */ + } + __pyx_L12:; + + /* "View.MemoryView":1314 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, False) + */ + __pyx_t_2 = (__pyx_v_direct_copy != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1316 + * if direct_copy: + * + * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, True) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); + + /* "View.MemoryView":1317 + * + * refcount_copying(&dst, dtype_is_object, ndim, False) + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< + * refcount_copying(&dst, dtype_is_object, ndim, True) + * free(tmpdata) + */ + (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim))); + + /* "View.MemoryView":1318 + * refcount_copying(&dst, dtype_is_object, ndim, False) + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< + * free(tmpdata) + * return 0 + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); + + /* "View.MemoryView":1319 + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, True) + * free(tmpdata) # <<<<<<<<<<<<<< + * return 0 + * + */ + free(__pyx_v_tmpdata); + + /* "View.MemoryView":1320 + * refcount_copying(&dst, dtype_is_object, ndim, True) + * free(tmpdata) + * return 0 # <<<<<<<<<<<<<< + * + * if order == 'F' == get_best_order(&dst, ndim): + */ + __pyx_r = 0; + goto __pyx_L0; + + /* "View.MemoryView":1314 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, False) + */ + } + + /* "View.MemoryView":1306 + * src = tmp + * + * if not broadcasting: # <<<<<<<<<<<<<< + * + * + */ + } + + /* "View.MemoryView":1322 + * return 0 + * + * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_2 = (__pyx_v_order == 'F'); + if (__pyx_t_2) { + __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); + } + __pyx_t_8 = (__pyx_t_2 != 0); + if (__pyx_t_8) { + + /* "View.MemoryView":1325 + * + * + * 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PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryview_obj *p; + PyObject *o; + if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + p = ((struct __pyx_memoryview_obj *)o); + p->__pyx_vtab = __pyx_vtabptr_memoryview; + p->obj = Py_None; Py_INCREF(Py_None); + p->_size = Py_None; Py_INCREF(Py_None); + p->_array_interface = Py_None; Py_INCREF(Py_None); + p->view.obj = NULL; + if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad; + return o; + bad: + Py_DECREF(o); o = 0; + return NULL; +} + +static void __pyx_tp_dealloc_memoryview(PyObject *o) { + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + ++Py_REFCNT(o); + __pyx_memoryview___dealloc__(o); + --Py_REFCNT(o); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->obj); + Py_CLEAR(p->_size); + Py_CLEAR(p->_array_interface); + (*Py_TYPE(o)->tp_free)(o); +} + +static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + if (p->obj) { + e = (*v)(p->obj, a); if (e) return e; + } + if (p->_size) { + e = (*v)(p->_size, a); if (e) return e; + } + if (p->_array_interface) { + e = (*v)(p->_array_interface, a); if (e) return e; + } + if (p->view.obj) { + e = (*v)(p->view.obj, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_memoryview(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + tmp = ((PyObject*)p->obj); + p->obj = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_size); + p->_size = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_array_interface); + p->_array_interface = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + Py_CLEAR(p->view.obj); + return 0; +} +static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { + PyObject *r; + PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; + r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); + Py_DECREF(x); + return r; +} + +static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { + if (v) { + return __pyx_memoryview___setitem__(o, i, v); + } + else { + PyErr_Format(PyExc_NotImplementedError, + "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); + return -1; + } +} + +static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o); +} + +static PyMethodDef __pyx_methods_memoryview[] = { + {"is_c_contig", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0}, + {"is_f_contig", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0}, + {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0}, + {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0}, + {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0}, + {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_memoryview[] = { + {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0}, + {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0}, + {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0}, + {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0}, + {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0}, + {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0}, + {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0}, + {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0}, + {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; + +static PySequenceMethods __pyx_tp_as_sequence_memoryview = { + __pyx_memoryview___len__, /*sq_length*/ + 0, /*sq_concat*/ + 0, /*sq_repeat*/ + __pyx_sq_item_memoryview, /*sq_item*/ + 0, /*sq_slice*/ + 0, /*sq_ass_item*/ + 0, /*sq_ass_slice*/ + 0, /*sq_contains*/ + 0, /*sq_inplace_concat*/ + 0, /*sq_inplace_repeat*/ +}; + +static PyMappingMethods __pyx_tp_as_mapping_memoryview = { + __pyx_memoryview___len__, /*mp_length*/ + __pyx_memoryview___getitem__, /*mp_subscript*/ + __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ +}; + +static PyBufferProcs __pyx_tp_as_buffer_memoryview = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_memoryview_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; + +static PyTypeObject __pyx_type___pyx_memoryview = { + PyVarObject_HEAD_INIT(0, 0) + "rank_cy.memoryview", /*tp_name*/ + sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ + 0, /*tp_print*/ + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_memoryview___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/ + &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + __pyx_memoryview___str__, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_memoryview, /*tp_traverse*/ + __pyx_tp_clear_memoryview, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_memoryview, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_memoryview, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_memoryview, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif +}; +static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; + +static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryviewslice_obj *p; + PyObject *o = __pyx_tp_new_memoryview(t, a, k); + if (unlikely(!o)) return 0; + p = ((struct __pyx_memoryviewslice_obj *)o); + p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; + p->from_object = Py_None; Py_INCREF(Py_None); + p->from_slice.memview = NULL; + return o; +} + +static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + ++Py_REFCNT(o); + __pyx_memoryviewslice___dealloc__(o); + --Py_REFCNT(o); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->from_object); + PyObject_GC_Track(o); + __pyx_tp_dealloc_memoryview(o); +} + +static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; + if (p->from_object) { + e = (*v)(p->from_object, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear__memoryviewslice(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + __pyx_tp_clear_memoryview(o); + tmp = ((PyObject*)p->from_object); + p->from_object = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + __PYX_XDEC_MEMVIEW(&p->from_slice, 1); + return 0; +} + +static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o); +} + +static PyMethodDef __pyx_methods__memoryviewslice[] = { + {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0}, + {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = { + {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; + +static PyTypeObject __pyx_type___pyx_memoryviewslice = { + PyVarObject_HEAD_INIT(0, 0) + "rank_cy._memoryviewslice", /*tp_name*/ + sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/ + 0, /*tp_print*/ + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + #if CYTHON_COMPILING_IN_PYPY + __pyx_memoryview___repr__, /*tp_repr*/ + #else + 0, /*tp_repr*/ + #endif + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + #if CYTHON_COMPILING_IN_PYPY + __pyx_memoryview___str__, /*tp_str*/ + #else + 0, /*tp_str*/ + #endif + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + "Internal class for passing memoryview slices to Python", /*tp_doc*/ + __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/ + __pyx_tp_clear__memoryviewslice, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods__memoryviewslice, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets__memoryviewslice, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new__memoryviewslice, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif +}; + +static PyMethodDef __pyx_methods[] = { + {"evaluate_cy", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_7rank_cy_1evaluate_cy, METH_VARARGS|METH_KEYWORDS, 0}, + {"eval_cuhk03_cy", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_7rank_cy_3eval_cuhk03_cy, METH_VARARGS|METH_KEYWORDS, 0}, + {"eval_market1501_cy", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_7rank_cy_5eval_market1501_cy, METH_VARARGS|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; + +#if PY_MAJOR_VERSION >= 3 +#if CYTHON_PEP489_MULTI_PHASE_INIT +static PyObject* __pyx_pymod_create(PyObject *spec, PyModuleDef *def); /*proto*/ +static int __pyx_pymod_exec_rank_cy(PyObject* module); /*proto*/ +static PyModuleDef_Slot __pyx_moduledef_slots[] = { + {Py_mod_create, (void*)__pyx_pymod_create}, + {Py_mod_exec, (void*)__pyx_pymod_exec_rank_cy}, + {0, NULL} +}; +#endif + +static struct PyModuleDef __pyx_moduledef = { + PyModuleDef_HEAD_INIT, + "rank_cy", + 0, /* m_doc */ + #if CYTHON_PEP489_MULTI_PHASE_INIT + 0, /* m_size */ + #else + -1, /* m_size */ + #endif + __pyx_methods /* m_methods */, + #if CYTHON_PEP489_MULTI_PHASE_INIT + __pyx_moduledef_slots, /* m_slots */ + #else + NULL, /* m_reload */ + #endif + NULL, /* m_traverse */ + NULL, /* m_clear */ + NULL /* m_free */ +}; +#endif +#ifndef CYTHON_SMALL_CODE +#if defined(__clang__) + 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+ if (PyDict_SetItem(__pyx_d, __pyx_n_s_pyx_unpickle_Enum, __pyx_t_2) < 0) __PYX_ERR(2, 1, __pyx_L1_error) + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + + /* "(tree fragment)":9 + * __pyx_unpickle_Enum__set_state( __pyx_result, __pyx_state) + * return __pyx_result + * cdef __pyx_unpickle_Enum__set_state(Enum __pyx_result, tuple __pyx_state): # <<<<<<<<<<<<<< + * __pyx_result.name = __pyx_state[0] + * if len(__pyx_state) > 1 and hasattr(__pyx_result, '__dict__'): + */ + + /*--- Wrapped vars code ---*/ + + goto __pyx_L0; + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_1); + __Pyx_XDECREF(__pyx_t_2); + if (__pyx_m) { + if (__pyx_d) { + __Pyx_AddTraceback("init rank_cy", __pyx_clineno, __pyx_lineno, __pyx_filename); + } + Py_CLEAR(__pyx_m); + } else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_ImportError, "init rank_cy"); + } + __pyx_L0:; + __Pyx_RefNannyFinishContext(); + #if CYTHON_PEP489_MULTI_PHASE_INIT + return (__pyx_m != NULL) ? 0 : -1; + #elif PY_MAJOR_VERSION >= 3 + return __pyx_m; + #else + return; + #endif +} + +/* --- Runtime support code --- */ +/* Refnanny */ +#if CYTHON_REFNANNY +static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { + PyObject *m = NULL, *p = NULL; + void *r = NULL; + m = PyImport_ImportModule(modname); + if (!m) goto end; + p = PyObject_GetAttrString(m, "RefNannyAPI"); + if (!p) goto end; + r = PyLong_AsVoidPtr(p); +end: + Py_XDECREF(p); + Py_XDECREF(m); + return (__Pyx_RefNannyAPIStruct *)r; +} +#endif + +/* PyObjectGetAttrStr */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_getattr)) + return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); +#endif + return PyObject_GetAttr(obj, attr_name); +} +#endif + +/* GetBuiltinName */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name) { + PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); 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+ Py_buffer *buf = &memview->view; + __Pyx_RefNannySetupContext("init_memviewslice", 0); + if (!buf) { + PyErr_SetString(PyExc_ValueError, + "buf is NULL."); + goto fail; + } else if (memviewslice->memview || memviewslice->data) { + PyErr_SetString(PyExc_ValueError, + "memviewslice is already initialized!"); + goto fail; + } + if (buf->strides) { + for (i = 0; i < ndim; i++) { + memviewslice->strides[i] = buf->strides[i]; + } + } else { + Py_ssize_t stride = buf->itemsize; + for (i = ndim - 1; i >= 0; i--) { + memviewslice->strides[i] = stride; + stride *= buf->shape[i]; + } + } + for (i = 0; i < ndim; i++) { + memviewslice->shape[i] = buf->shape[i]; + if (buf->suboffsets) { + memviewslice->suboffsets[i] = buf->suboffsets[i]; + } else { + memviewslice->suboffsets[i] = -1; + } + } + memviewslice->memview = memview; + memviewslice->data = (char *)buf->buf; + if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { + Py_INCREF(memview); + } + retval = 0; + goto no_fail; +fail: + memviewslice->memview = 0; + memviewslice->data = 0; + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} +#ifndef Py_NO_RETURN +#define Py_NO_RETURN +#endif +static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { + va_list vargs; + char msg[200]; +#ifdef HAVE_STDARG_PROTOTYPES + va_start(vargs, fmt); +#else + va_start(vargs); +#endif + vsnprintf(msg, 200, fmt, vargs); + va_end(vargs); + Py_FatalError(msg); +} +static CYTHON_INLINE int +__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)++; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE int +__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)--; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE void +__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) +{ + int first_time; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (!memview || (PyObject *) memview == Py_None) + return; + if (__pyx_get_slice_count(memview) < 0) + __pyx_fatalerror("Acquisition count is %d (line %d)", + __pyx_get_slice_count(memview), lineno); + first_time = __pyx_add_acquisition_count(memview) == 0; + if (first_time) { + if (have_gil) { + Py_INCREF((PyObject *) memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_INCREF((PyObject *) memview); + PyGILState_Release(_gilstate); + } + } +} +static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice, + int have_gil, int lineno) { + int last_time; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (!memview ) { + return; + } else if ((PyObject *) memview == Py_None) { + memslice->memview = NULL; + return; + } + if (__pyx_get_slice_count(memview) <= 0) + __pyx_fatalerror("Acquisition count is %d (line %d)", + __pyx_get_slice_count(memview), lineno); + last_time = __pyx_sub_acquisition_count(memview) == 1; + memslice->data = NULL; + if (last_time) { + if (have_gil) { + Py_CLEAR(memslice->memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_CLEAR(memslice->memview); + PyGILState_Release(_gilstate); + } + } else { + memslice->memview = NULL; + } +} + +/* RaiseArgTupleInvalid */ +static void __Pyx_RaiseArgtupleInvalid( + const char* func_name, + int exact, + Py_ssize_t num_min, + Py_ssize_t num_max, + Py_ssize_t num_found) +{ + Py_ssize_t num_expected; + const char *more_or_less; + if (num_found < num_min) { + num_expected = num_min; + more_or_less = "at least"; + } else { + num_expected = num_max; + more_or_less = "at most"; + } + if (exact) { + more_or_less = "exactly"; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", + func_name, more_or_less, num_expected, + (num_expected == 1) ? "" : "s", num_found); +} + +/* RaiseDoubleKeywords */ +static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, + PyObject* kw_name) +{ + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION >= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +/* ParseKeywords */ +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + while (PyDict_Next(kwds, &pos, &key, &value)) { + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; + continue; + } + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = (**name == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION < 3 + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + return -1; +} + +/* PyCFunctionFastCall */ +#if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { + PyCFunctionObject *func = (PyCFunctionObject*)func_obj; + PyCFunction meth = PyCFunction_GET_FUNCTION(func); + PyObject *self = PyCFunction_GET_SELF(func); + int flags = PyCFunction_GET_FLAGS(func); + assert(PyCFunction_Check(func)); + assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))); + assert(nargs >= 0); + assert(nargs == 0 || args != NULL); + /* _PyCFunction_FastCallDict() must not be called with an exception set, + because it may clear it (directly or indirectly) and so the + caller loses its exception */ + assert(!PyErr_Occurred()); + if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) { + return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL); + } else { + return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs); + } +} +#endif + +/* PyFunctionFastCall */ +#if CYTHON_FAST_PYCALL +static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, + PyObject *globals) { + PyFrameObject *f; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject **fastlocals; + Py_ssize_t i; + PyObject *result; + assert(globals != NULL); + /* XXX Perhaps we should create a specialized + PyFrame_New() that doesn't take locals, but does + take builtins without sanity checking them. + */ + assert(tstate != NULL); + f = PyFrame_New(tstate, co, globals, NULL); + if (f == NULL) { + return NULL; + } + fastlocals = __Pyx_PyFrame_GetLocalsplus(f); + for (i = 0; i < na; i++) { + Py_INCREF(*args); + fastlocals[i] = *args++; + } + result = PyEval_EvalFrameEx(f,0); + ++tstate->recursion_depth; + Py_DECREF(f); + --tstate->recursion_depth; + return result; +} +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) { + PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); + PyObject *globals = PyFunction_GET_GLOBALS(func); + PyObject *argdefs = PyFunction_GET_DEFAULTS(func); + PyObject *closure; +#if PY_MAJOR_VERSION >= 3 + PyObject *kwdefs; +#endif + PyObject *kwtuple, **k; + PyObject **d; + Py_ssize_t nd; + Py_ssize_t nk; + PyObject *result; + assert(kwargs == NULL || PyDict_Check(kwargs)); + nk = kwargs ? PyDict_Size(kwargs) : 0; + if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { + return NULL; + } + if ( +#if PY_MAJOR_VERSION >= 3 + co->co_kwonlyargcount == 0 && +#endif + likely(kwargs == NULL || nk == 0) && + co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { + if (argdefs == NULL && co->co_argcount == nargs) { + result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); + goto done; + } + else if (nargs == 0 && argdefs != NULL + && co->co_argcount == Py_SIZE(argdefs)) { + /* function called with no arguments, but all parameters have + a default value: use default values as arguments .*/ + args = &PyTuple_GET_ITEM(argdefs, 0); + result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); + goto done; + } + } + if (kwargs != NULL) { + Py_ssize_t pos, i; + kwtuple = PyTuple_New(2 * nk); + if (kwtuple == NULL) { + result = NULL; + goto done; + } + k = &PyTuple_GET_ITEM(kwtuple, 0); + pos = i = 0; + while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { + Py_INCREF(k[i]); + Py_INCREF(k[i+1]); + i += 2; + } + nk = i / 2; + } + else { + kwtuple = NULL; + k = NULL; + } + closure = PyFunction_GET_CLOSURE(func); +#if PY_MAJOR_VERSION >= 3 + kwdefs = PyFunction_GET_KW_DEFAULTS(func); +#endif + if (argdefs != NULL) { + d = &PyTuple_GET_ITEM(argdefs, 0); + nd = Py_SIZE(argdefs); + } + else { + d = NULL; + nd = 0; + } +#if PY_MAJOR_VERSION >= 3 + result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, + args, nargs, + k, (int)nk, + d, (int)nd, kwdefs, closure); +#else + result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, + args, nargs, + k, (int)nk, + d, (int)nd, closure); +#endif + Py_XDECREF(kwtuple); +done: + Py_LeaveRecursiveCall(); + return result; +} +#endif +#endif + +/* PyObjectCall2Args */ +static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) { + PyObject *args, *result = NULL; + #if CYTHON_FAST_PYCALL + if (PyFunction_Check(function)) { + PyObject *args[2] = {arg1, arg2}; + return __Pyx_PyFunction_FastCall(function, args, 2); + } + #endif + #if CYTHON_FAST_PYCCALL + if (__Pyx_PyFastCFunction_Check(function)) { + PyObject *args[2] = {arg1, arg2}; + return __Pyx_PyCFunction_FastCall(function, args, 2); + } + #endif + args = PyTuple_New(2); + if (unlikely(!args)) goto done; + Py_INCREF(arg1); + PyTuple_SET_ITEM(args, 0, arg1); + Py_INCREF(arg2); + PyTuple_SET_ITEM(args, 1, arg2); + Py_INCREF(function); + result = __Pyx_PyObject_Call(function, args, NULL); + Py_DECREF(args); + Py_DECREF(function); +done: + return result; +} + +/* PyObjectCallMethO */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { + PyObject *self, *result; + PyCFunction cfunc; + cfunc = PyCFunction_GET_FUNCTION(func); + self = PyCFunction_GET_SELF(func); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = cfunc(self, arg); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectCallOneArg */ +#if CYTHON_COMPILING_IN_CPYTHON +static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_New(1); + if (unlikely(!args)) return NULL; + Py_INCREF(arg); + PyTuple_SET_ITEM(args, 0, arg); + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { +#if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCall(func, &arg, 1); + } +#endif + if (likely(PyCFunction_Check(func))) { + if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { + return __Pyx_PyObject_CallMethO(func, arg); +#if CYTHON_FAST_PYCCALL + } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { + return __Pyx_PyCFunction_FastCall(func, &arg, 1); +#endif + } + } + return __Pyx__PyObject_CallOneArg(func, arg); +} +#else +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_Pack(1, arg); + if (unlikely(!args)) return NULL; + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +#endif + +/* GetItemInt */ +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { + PyObject *r; + if (!j) return NULL; + r = PyObject_GetItem(o, j); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyList_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { + PyObject *r = PyList_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyTuple_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); + if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { + PyObject *r = PyList_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } + else if (PyTuple_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); + if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } else { + PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; + if (likely(m && m->sq_item)) { + if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { + Py_ssize_t l = m->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return NULL; + PyErr_Clear(); + } + } + return m->sq_item(o, i); + } + } +#else + if (is_list || PySequence_Check(o)) { + return PySequence_GetItem(o, i); + } +#endif + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +} + +/* ObjectGetItem */ +#if CYTHON_USE_TYPE_SLOTS +static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) { + PyObject *runerr; + Py_ssize_t key_value; + PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence; + if (unlikely(!(m && m->sq_item))) { + PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name); + return NULL; + } + key_value = __Pyx_PyIndex_AsSsize_t(index); + if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { + return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); + } + if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { + PyErr_Clear(); + PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name); + } + return NULL; +} +static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) { + PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping; + if (likely(m && m->mp_subscript)) { + return m->mp_subscript(obj, key); + } + return __Pyx_PyObject_GetIndex(obj, key); +} +#endif + +/* PyObjectGetMethod */ +static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method) { + PyObject *attr; +#if CYTHON_UNPACK_METHODS && CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_PYTYPE_LOOKUP + PyTypeObject *tp = Py_TYPE(obj); + PyObject *descr; + descrgetfunc f = NULL; + PyObject **dictptr, *dict; + int meth_found = 0; + assert (*method == NULL); + if (unlikely(tp->tp_getattro != PyObject_GenericGetAttr)) { + attr = __Pyx_PyObject_GetAttrStr(obj, name); + goto try_unpack; + } + if (unlikely(tp->tp_dict == NULL) && unlikely(PyType_Ready(tp) < 0)) { + return 0; + } + descr = _PyType_Lookup(tp, name); + if (likely(descr != NULL)) { + Py_INCREF(descr); +#if PY_MAJOR_VERSION >= 3 + #ifdef __Pyx_CyFunction_USED + if (likely(PyFunction_Check(descr) || (Py_TYPE(descr) == &PyMethodDescr_Type) || __Pyx_CyFunction_Check(descr))) + #else + if (likely(PyFunction_Check(descr) || (Py_TYPE(descr) == &PyMethodDescr_Type))) + #endif +#else + #ifdef __Pyx_CyFunction_USED + if (likely(PyFunction_Check(descr) || __Pyx_CyFunction_Check(descr))) + #else + if (likely(PyFunction_Check(descr))) + #endif +#endif + { + meth_found = 1; + } else { + f = Py_TYPE(descr)->tp_descr_get; + if (f != NULL && PyDescr_IsData(descr)) { + attr = f(descr, obj, (PyObject *)Py_TYPE(obj)); + Py_DECREF(descr); + goto try_unpack; + } + } + } + dictptr = _PyObject_GetDictPtr(obj); + if (dictptr != NULL && (dict = *dictptr) != NULL) { + Py_INCREF(dict); + attr = __Pyx_PyDict_GetItemStr(dict, name); + if (attr != NULL) { + Py_INCREF(attr); + Py_DECREF(dict); + Py_XDECREF(descr); + goto try_unpack; + } + Py_DECREF(dict); + } + if (meth_found) { + *method = descr; + return 1; + } + if (f != NULL) { + attr = f(descr, obj, (PyObject *)Py_TYPE(obj)); + Py_DECREF(descr); + goto try_unpack; + } + if (descr != NULL) { + *method = descr; + return 0; + } + PyErr_Format(PyExc_AttributeError, +#if PY_MAJOR_VERSION >= 3 + "'%.50s' object has no attribute '%U'", + tp->tp_name, name); +#else + "'%.50s' object has no attribute '%.400s'", + tp->tp_name, PyString_AS_STRING(name)); +#endif + return 0; +#else + attr = __Pyx_PyObject_GetAttrStr(obj, name); + goto try_unpack; +#endif +try_unpack: +#if CYTHON_UNPACK_METHODS + if (likely(attr) && PyMethod_Check(attr) && likely(PyMethod_GET_SELF(attr) == obj)) { + PyObject *function = PyMethod_GET_FUNCTION(attr); + Py_INCREF(function); + Py_DECREF(attr); + *method = function; + return 1; + } +#endif + *method = attr; + return 0; +} + +/* PyObjectCallMethod1 */ +static PyObject* __Pyx__PyObject_CallMethod1(PyObject* method, PyObject* arg) { + PyObject *result = __Pyx_PyObject_CallOneArg(method, arg); + Py_DECREF(method); + return result; +} +static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg) { + PyObject *method = NULL, *result; + int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method); + if (likely(is_method)) { + result = __Pyx_PyObject_Call2Args(method, obj, arg); + Py_DECREF(method); + return result; + } + if (unlikely(!method)) return NULL; + return __Pyx__PyObject_CallMethod1(method, arg); +} + +/* append */ +static CYTHON_INLINE int __Pyx_PyObject_Append(PyObject* L, PyObject* x) { + if (likely(PyList_CheckExact(L))) { + if (unlikely(__Pyx_PyList_Append(L, x) < 0)) return -1; + } else { + PyObject* retval = __Pyx_PyObject_CallMethod1(L, __pyx_n_s_append, x); + if (unlikely(!retval)) + return -1; + Py_DECREF(retval); + } + return 0; +} + +/* PyObjectCallNoArg */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { +#if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCall(func, NULL, 0); + } +#endif +#ifdef __Pyx_CyFunction_USED + if (likely(PyCFunction_Check(func) || __Pyx_CyFunction_Check(func))) +#else + if (likely(PyCFunction_Check(func))) +#endif + { + if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) { + return __Pyx_PyObject_CallMethO(func, NULL); + } + } + return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL); +} +#endif + +/* RaiseTooManyValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +/* RaiseNeedMoreValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +/* IterFinish */ +static CYTHON_INLINE int __Pyx_IterFinish(void) { +#if CYTHON_FAST_THREAD_STATE + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject* exc_type = tstate->curexc_type; + if (unlikely(exc_type)) { + if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) { + PyObject *exc_value, *exc_tb; + exc_value = tstate->curexc_value; + exc_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; + Py_DECREF(exc_type); + Py_XDECREF(exc_value); + Py_XDECREF(exc_tb); + return 0; + } else { + return -1; + } + } + return 0; +#else + if (unlikely(PyErr_Occurred())) { + if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) { + PyErr_Clear(); + return 0; + } else { + return -1; + } + } + return 0; +#endif +} + +/* UnpackItemEndCheck */ +static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) { + if (unlikely(retval)) { + Py_DECREF(retval); + __Pyx_RaiseTooManyValuesError(expected); + return -1; + } else { + return __Pyx_IterFinish(); + } + return 0; +} + +/* None */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { + PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); +} + +/* PyErrFetchRestore */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +} +#endif + +/* RaiseException */ +#if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, + CYTHON_UNUSED PyObject *cause) { + __Pyx_PyThreadState_declare + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_PyThreadState_assign + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } + if (cause) { + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { +#if CYTHON_COMPILING_IN_PYPY + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#else + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +/* DictGetItem */ +#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY +static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { + PyObject *value; + value = PyDict_GetItemWithError(d, key); + if (unlikely(!value)) { + if (!PyErr_Occurred()) { + if (unlikely(PyTuple_Check(key))) { + PyObject* args = PyTuple_Pack(1, key); + if (likely(args)) { + PyErr_SetObject(PyExc_KeyError, args); + Py_DECREF(args); + } + } else { + PyErr_SetObject(PyExc_KeyError, key); + } + } + return NULL; + } + Py_INCREF(value); + return value; +} +#endif + +/* RaiseNoneIterError */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +/* ExtTypeTest */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(__Pyx_TypeCheck(obj, type))) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + +/* GetTopmostException */ +#if CYTHON_USE_EXC_INFO_STACK +static _PyErr_StackItem * +__Pyx_PyErr_GetTopmostException(PyThreadState *tstate) +{ + _PyErr_StackItem *exc_info = tstate->exc_info; + while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) && + exc_info->previous_item != NULL) + { + exc_info = exc_info->previous_item; + } + return exc_info; +} +#endif + +/* SaveResetException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); + *type = exc_info->exc_type; + *value = exc_info->exc_value; + *tb = exc_info->exc_traceback; + #else + *type = tstate->exc_type; + *value = tstate->exc_value; + *tb = tstate->exc_traceback; + #endif + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); +} +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = type; + exc_info->exc_value = value; + exc_info->exc_traceback = tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = type; + tstate->exc_value = value; + tstate->exc_traceback = tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +#endif + +/* PyErrExceptionMatches */ +#if CYTHON_FAST_THREAD_STATE +static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(tuple); +#if PY_MAJOR_VERSION >= 3 + for (i=0; icurexc_type; + if (exc_type == err) return 1; + if (unlikely(!exc_type)) return 0; + if (unlikely(PyTuple_Check(err))) + return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err); + return __Pyx_PyErr_GivenExceptionMatches(exc_type, err); +} +#endif + +/* GetException */ +#if CYTHON_FAST_THREAD_STATE +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) +#endif +{ + PyObject *local_type, *local_value, *local_tb; +#if CYTHON_FAST_THREAD_STATE + PyObject *tmp_type, *tmp_value, *tmp_tb; + local_type = tstate->curexc_type; + local_value = tstate->curexc_value; + local_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#else + PyErr_Fetch(&local_type, &local_value, &local_tb); +#endif + PyErr_NormalizeException(&local_type, &local_value, &local_tb); +#if CYTHON_FAST_THREAD_STATE + if (unlikely(tstate->curexc_type)) +#else + if (unlikely(PyErr_Occurred())) +#endif + goto bad; + #if PY_MAJOR_VERSION >= 3 + if (local_tb) { + if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) + goto bad; + } + #endif + Py_XINCREF(local_tb); + Py_XINCREF(local_type); + Py_XINCREF(local_value); + *type = local_type; + *value = local_value; + *tb = local_tb; +#if CYTHON_FAST_THREAD_STATE + #if CYTHON_USE_EXC_INFO_STACK + { + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = local_type; + exc_info->exc_value = local_value; + exc_info->exc_traceback = local_tb; + } + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = local_type; + tstate->exc_value = local_value; + tstate->exc_traceback = local_tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_SetExcInfo(local_type, local_value, local_tb); +#endif + return 0; +bad: + *type = 0; + *value = 0; + *tb = 0; + Py_XDECREF(local_type); + Py_XDECREF(local_value); + Py_XDECREF(local_tb); + return -1; +} + +/* ArgTypeTest */ +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) +{ + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + else if (exact) { + #if PY_MAJOR_VERSION == 2 + if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(__Pyx_TypeCheck(obj, type))) return 1; + } + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", + name, type->tp_name, Py_TYPE(obj)->tp_name); + return 0; +} + +/* BytesEquals */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY + return PyObject_RichCompareBool(s1, s2, equals); +#else + if (s1 == s2) { + return (equals == Py_EQ); + } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { + const char *ps1, *ps2; + Py_ssize_t length = PyBytes_GET_SIZE(s1); + if (length != PyBytes_GET_SIZE(s2)) + return (equals == Py_NE); + ps1 = PyBytes_AS_STRING(s1); + ps2 = PyBytes_AS_STRING(s2); + if (ps1[0] != ps2[0]) { + return (equals == Py_NE); + } else if (length == 1) { + return (equals == Py_EQ); + } else { + int result; +#if CYTHON_USE_UNICODE_INTERNALS + Py_hash_t hash1, hash2; + hash1 = ((PyBytesObject*)s1)->ob_shash; + hash2 = ((PyBytesObject*)s2)->ob_shash; + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + return (equals == Py_NE); + } +#endif + result = memcmp(ps1, ps2, (size_t)length); + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { + return (equals == Py_NE); + } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { + return (equals == Py_NE); + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +#endif +} + +/* UnicodeEquals */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY + return PyObject_RichCompareBool(s1, s2, equals); +#else +#if PY_MAJOR_VERSION < 3 + PyObject* owned_ref = NULL; +#endif + int s1_is_unicode, s2_is_unicode; + if (s1 == s2) { + goto return_eq; + } + s1_is_unicode = PyUnicode_CheckExact(s1); + s2_is_unicode = PyUnicode_CheckExact(s2); +#if PY_MAJOR_VERSION < 3 + if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { + owned_ref = PyUnicode_FromObject(s2); + if (unlikely(!owned_ref)) + return -1; + s2 = owned_ref; + s2_is_unicode = 1; + } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { + owned_ref = PyUnicode_FromObject(s1); + if (unlikely(!owned_ref)) + return -1; + s1 = owned_ref; + s1_is_unicode = 1; + } else if (((!s2_is_unicode) & (!s1_is_unicode))) { + return __Pyx_PyBytes_Equals(s1, s2, equals); + } +#endif + if (s1_is_unicode & s2_is_unicode) { + Py_ssize_t length; + int kind; + void *data1, *data2; + if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) + return -1; + length = __Pyx_PyUnicode_GET_LENGTH(s1); + if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { + goto return_ne; + } +#if CYTHON_USE_UNICODE_INTERNALS + { + Py_hash_t hash1, hash2; + #if CYTHON_PEP393_ENABLED + hash1 = ((PyASCIIObject*)s1)->hash; + hash2 = ((PyASCIIObject*)s2)->hash; + #else + hash1 = ((PyUnicodeObject*)s1)->hash; + hash2 = ((PyUnicodeObject*)s2)->hash; + #endif + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + goto return_ne; + } + } +#endif + kind = __Pyx_PyUnicode_KIND(s1); + if (kind != __Pyx_PyUnicode_KIND(s2)) { + goto return_ne; + } + data1 = __Pyx_PyUnicode_DATA(s1); + data2 = __Pyx_PyUnicode_DATA(s2); + if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { + goto return_ne; + } else if (length == 1) { + goto return_eq; + } else { + int result = memcmp(data1, data2, (size_t)(length * kind)); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & s2_is_unicode) { + goto return_ne; + } else if ((s2 == Py_None) & s1_is_unicode) { + goto return_ne; + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +return_eq: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ); +return_ne: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_NE); +#endif +} + +/* GetAttr */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { +#if CYTHON_USE_TYPE_SLOTS +#if PY_MAJOR_VERSION >= 3 + if (likely(PyUnicode_Check(n))) +#else + if (likely(PyString_Check(n))) +#endif + return __Pyx_PyObject_GetAttrStr(o, n); +#endif + return PyObject_GetAttr(o, n); +} + +/* decode_c_string */ +static CYTHON_INLINE PyObject* __Pyx_decode_c_string( + const char* cstring, Py_ssize_t start, Py_ssize_t stop, + const char* encoding, const char* errors, + PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { + Py_ssize_t length; + if (unlikely((start < 0) | (stop < 0))) { + size_t slen = strlen(cstring); + if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) { + PyErr_SetString(PyExc_OverflowError, + "c-string too long to convert to Python"); + return NULL; + } + length = (Py_ssize_t) slen; + if (start < 0) { + start += length; + if (start < 0) + start = 0; + } + if (stop < 0) + stop += length; + } + length = stop - start; + if (unlikely(length <= 0)) + return PyUnicode_FromUnicode(NULL, 0); + cstring += start; + if (decode_func) { + return decode_func(cstring, length, errors); + } else { + return PyUnicode_Decode(cstring, length, encoding, errors); + } +} + +/* GetAttr3 */ +static PyObject *__Pyx_GetAttr3Default(PyObject *d) { + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) + return NULL; + __Pyx_PyErr_Clear(); + Py_INCREF(d); + return d; +} +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { + PyObject *r = __Pyx_GetAttr(o, n); + return (likely(r)) ? r : __Pyx_GetAttr3Default(d); +} + +/* SwapException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = *type; + exc_info->exc_value = *value; + exc_info->exc_traceback = *tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = *type; + tstate->exc_value = *value; + tstate->exc_traceback = *tb; + #endif + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); + PyErr_SetExcInfo(*type, *value, *tb); + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#endif + +/* Import */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + #if PY_MAJOR_VERSION < 3 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (!py_import) + goto bad; + #endif + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if (strchr(__Pyx_MODULE_NAME, '.')) { + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, 1); + if (!module) { + if (!PyErr_ExceptionMatches(PyExc_ImportError)) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_MAJOR_VERSION < 3 + PyObject *py_level = PyInt_FromLong(level); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, (PyObject *)NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, level); + #endif + } + } +bad: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(py_import); + #endif + Py_XDECREF(empty_list); + Py_XDECREF(empty_dict); + return module; +} + +/* FastTypeChecks */ +#if CYTHON_COMPILING_IN_CPYTHON +static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { + while (a) { + a = a->tp_base; + if (a == b) + return 1; + } + return b == &PyBaseObject_Type; +} +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { + PyObject *mro; + if (a == b) return 1; + mro = a->tp_mro; + if (likely(mro)) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(mro); + for (i = 0; i < n; i++) { + if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) + return 1; + } + return 0; + } + return __Pyx_InBases(a, b); +} +#if PY_MAJOR_VERSION == 2 +static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { + PyObject *exception, *value, *tb; + int res; + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&exception, &value, &tb); + res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + if (!res) { + res = PyObject_IsSubclass(err, exc_type2); + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + } + __Pyx_ErrRestore(exception, value, tb); + return res; +} +#else +static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { + int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; + if (!res) { + res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); + } + return res; +} +#endif +static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + assert(PyExceptionClass_Check(exc_type)); + n = PyTuple_GET_SIZE(tuple); +#if PY_MAJOR_VERSION >= 3 + for (i=0; i= 0 || (x^b) >= 0)) + return PyInt_FromLong(x); + return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + #endif + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(PyLong_CheckExact(op1))) { + const long b = intval; + long a, x; +#ifdef HAVE_LONG_LONG + const PY_LONG_LONG llb = intval; + PY_LONG_LONG lla, llx; +#endif + const digit* digits = ((PyLongObject*)op1)->ob_digit; + const Py_ssize_t size = Py_SIZE(op1); + if (likely(__Pyx_sst_abs(size) <= 1)) { + a = likely(size) ? digits[0] : 0; + if (size == -1) a = -a; + } else { + switch (size) { + case -2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case 2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case -3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case 3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case -4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case 4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + default: return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + } + x = a + b; + return PyLong_FromLong(x); +#ifdef HAVE_LONG_LONG + long_long: + llx = lla + llb; + return PyLong_FromLongLong(llx); +#endif + + + } + #endif + if (PyFloat_CheckExact(op1)) { + const long b = intval; + double a = PyFloat_AS_DOUBLE(op1); + double result; + PyFPE_START_PROTECT("add", return NULL) + result = ((double)a) + (double)b; + PyFPE_END_PROTECT(result) + return PyFloat_FromDouble(result); + } + return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); +} +#endif + +/* WriteUnraisableException */ +static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno, + CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename, + int full_traceback, CYTHON_UNUSED int nogil) { + PyObject *old_exc, *old_val, *old_tb; + PyObject *ctx; + __Pyx_PyThreadState_declare +#ifdef WITH_THREAD + PyGILState_STATE state; + if (nogil) + state = PyGILState_Ensure(); +#ifdef _MSC_VER + else state = (PyGILState_STATE)-1; +#endif +#endif + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&old_exc, &old_val, &old_tb); + if (full_traceback) { + Py_XINCREF(old_exc); + Py_XINCREF(old_val); + Py_XINCREF(old_tb); + __Pyx_ErrRestore(old_exc, old_val, old_tb); + PyErr_PrintEx(1); + } + #if PY_MAJOR_VERSION < 3 + ctx = PyString_FromString(name); + #else + ctx = PyUnicode_FromString(name); + #endif + __Pyx_ErrRestore(old_exc, old_val, old_tb); + if (!ctx) { + PyErr_WriteUnraisable(Py_None); + } else { + PyErr_WriteUnraisable(ctx); + Py_DECREF(ctx); + } +#ifdef WITH_THREAD + if (nogil) + PyGILState_Release(state); +#endif +} + +/* ImportFrom */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { + PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); + if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { + PyErr_Format(PyExc_ImportError, + #if PY_MAJOR_VERSION < 3 + "cannot import name %.230s", PyString_AS_STRING(name)); + #else + "cannot import name %S", name); + #endif + } + return value; +} + +/* HasAttr */ +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { + PyObject *r; + if (unlikely(!__Pyx_PyBaseString_Check(n))) { + PyErr_SetString(PyExc_TypeError, + "hasattr(): attribute name must be string"); + return -1; + } + r = __Pyx_GetAttr(o, n); + if (unlikely(!r)) { + PyErr_Clear(); + return 0; + } else { + Py_DECREF(r); + return 1; + } +} + +/* PyObject_GenericGetAttrNoDict */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { + PyErr_Format(PyExc_AttributeError, +#if PY_MAJOR_VERSION >= 3 + "'%.50s' object has no attribute '%U'", + tp->tp_name, attr_name); +#else + "'%.50s' object has no attribute '%.400s'", + tp->tp_name, PyString_AS_STRING(attr_name)); +#endif + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { + PyObject *descr; + PyTypeObject *tp = Py_TYPE(obj); + if (unlikely(!PyString_Check(attr_name))) { + return PyObject_GenericGetAttr(obj, attr_name); + } + assert(!tp->tp_dictoffset); + descr = _PyType_Lookup(tp, attr_name); + if (unlikely(!descr)) { + return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); + } + Py_INCREF(descr); + #if PY_MAJOR_VERSION < 3 + if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) + #endif + { + descrgetfunc f = Py_TYPE(descr)->tp_descr_get; + if (unlikely(f)) { + PyObject *res = f(descr, obj, (PyObject *)tp); + Py_DECREF(descr); + return res; + } + } + return descr; +} +#endif + +/* PyObject_GenericGetAttr */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { + if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { + return PyObject_GenericGetAttr(obj, attr_name); + } + return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); +} +#endif + +/* SetVTable */ +static int __Pyx_SetVtable(PyObject *dict, void *vtable) { +#if PY_VERSION_HEX >= 0x02070000 + PyObject *ob = PyCapsule_New(vtable, 0, 0); +#else + PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); +#endif + if (!ob) + goto bad; + if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0) + goto bad; + Py_DECREF(ob); + return 0; +bad: + Py_XDECREF(ob); + return -1; +} + +/* SetupReduce */ +static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { + int ret; + PyObject *name_attr; + name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2); + if (likely(name_attr)) { + ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); + } else { + ret = -1; + } + if (unlikely(ret < 0)) { + PyErr_Clear(); + ret = 0; + } + Py_XDECREF(name_attr); + return ret; +} +static int __Pyx_setup_reduce(PyObject* type_obj) { + int ret = 0; + PyObject *object_reduce = NULL; + PyObject *object_reduce_ex = NULL; + PyObject *reduce = NULL; + PyObject *reduce_ex = NULL; + PyObject *reduce_cython = NULL; + PyObject *setstate = NULL; + PyObject *setstate_cython = NULL; +#if CYTHON_USE_PYTYPE_LOOKUP + if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto GOOD; +#else + if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto GOOD; +#endif +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto BAD; +#else + object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto BAD; +#endif + reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto BAD; + if (reduce_ex == object_reduce_ex) { +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto BAD; +#else + object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto BAD; +#endif + reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto BAD; + if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { + reduce_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_cython); if (unlikely(!reduce_cython)) goto BAD; + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto BAD; + setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate); + if (!setstate) PyErr_Clear(); + if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { + setstate_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate_cython); if (unlikely(!setstate_cython)) goto BAD; + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto BAD; + } + PyType_Modified((PyTypeObject*)type_obj); + } + } + goto GOOD; +BAD: + if (!PyErr_Occurred()) + PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name); + ret = -1; +GOOD: +#if !CYTHON_USE_PYTYPE_LOOKUP + Py_XDECREF(object_reduce); + Py_XDECREF(object_reduce_ex); +#endif + Py_XDECREF(reduce); + Py_XDECREF(reduce_ex); + Py_XDECREF(reduce_cython); + Py_XDECREF(setstate); + Py_XDECREF(setstate_cython); + return ret; +} + +/* TypeImport */ +#ifndef __PYX_HAVE_RT_ImportType +#define __PYX_HAVE_RT_ImportType +static PyTypeObject *__Pyx_ImportType(PyObject *module, const char *module_name, const char *class_name, + size_t size, enum __Pyx_ImportType_CheckSize check_size) +{ + PyObject *result = 0; + char warning[200]; + Py_ssize_t basicsize; +#ifdef Py_LIMITED_API + PyObject *py_basicsize; +#endif + result = PyObject_GetAttrString(module, class_name); + if (!result) + goto bad; + if (!PyType_Check(result)) { + PyErr_Format(PyExc_TypeError, + "%.200s.%.200s is not a type object", + module_name, class_name); + goto bad; + } +#ifndef Py_LIMITED_API + basicsize = ((PyTypeObject *)result)->tp_basicsize; +#else + py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); + if (!py_basicsize) + goto bad; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = 0; + if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) + goto bad; +#endif + if ((size_t)basicsize < size) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s size changed, may indicate binary incompatibility. " + "Expected %zd from C header, got %zd from PyObject", + module_name, class_name, size, basicsize); + goto bad; + } + if (check_size == __Pyx_ImportType_CheckSize_Error && (size_t)basicsize != size) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s size changed, may indicate binary incompatibility. " + "Expected %zd from C header, got %zd from PyObject", + module_name, class_name, size, basicsize); + goto bad; + } + else if (check_size == __Pyx_ImportType_CheckSize_Warn && (size_t)basicsize > size) { + PyOS_snprintf(warning, sizeof(warning), + "%s.%s size changed, may indicate binary incompatibility. " + "Expected %zd from C header, got %zd from PyObject", + module_name, class_name, size, basicsize); + if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; + } + return (PyTypeObject *)result; +bad: + Py_XDECREF(result); + return NULL; +} +#endif + +/* CLineInTraceback */ +#ifndef CYTHON_CLINE_IN_TRACEBACK +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) { + PyObject *use_cline; + PyObject *ptype, *pvalue, *ptraceback; +#if CYTHON_COMPILING_IN_CPYTHON + PyObject **cython_runtime_dict; +#endif + if (unlikely(!__pyx_cython_runtime)) { + return c_line; + } + __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); +#if CYTHON_COMPILING_IN_CPYTHON + cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); + if (likely(cython_runtime_dict)) { + __PYX_PY_DICT_LOOKUP_IF_MODIFIED( + use_cline, *cython_runtime_dict, + __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback)) + } else +#endif + { + PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); + if (use_cline_obj) { + use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; + Py_DECREF(use_cline_obj); + } else { + PyErr_Clear(); + use_cline = NULL; + } + } + if (!use_cline) { + c_line = 0; + PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); + } + else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { + c_line = 0; + } + __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); + return c_line; +} +#endif + +/* CodeObjectCache */ +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} + +/* AddTraceback */ +#include "compile.h" +#include "frameobject.h" +#include "traceback.h" +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(filename); + #else + py_srcfile = PyUnicode_FromString(filename); + #endif + if (!py_srcfile) goto bad; + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + Py_DECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + if (c_line) { + c_line = __Pyx_CLineForTraceback(tstate, c_line); + } + py_code = __pyx_find_code_object(c_line ? -c_line : py_line); + if (!py_code) { + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) goto bad; + __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); + } + py_frame = PyFrame_New( + tstate, /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + __Pyx_PyFrame_SetLineNumber(py_frame, py_line); + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); + PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + if ((0)) {} + else if (__Pyx_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); + view->obj = NULL; + Py_DECREF(obj); +} +#endif + + +/* MemviewSliceIsContig */ +static int +__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) +{ + int i, index, step, start; + Py_ssize_t itemsize = mvs.memview->view.itemsize; + if (order == 'F') { + step = 1; + start = 0; + } else { + step = -1; + start = ndim - 1; + } + for (i = 0; i < ndim; i++) { + index = start + step * i; + if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize) + return 0; + itemsize *= mvs.shape[index]; + } + return 1; +} + +/* OverlappingSlices */ +static void +__pyx_get_array_memory_extents(__Pyx_memviewslice *slice, + void **out_start, void **out_end, + int ndim, size_t itemsize) +{ + char *start, *end; + int i; + start = end = slice->data; + for (i = 0; i < ndim; i++) { + Py_ssize_t stride = slice->strides[i]; + Py_ssize_t extent = slice->shape[i]; + if (extent == 0) { + *out_start = *out_end = start; + return; + } else { + if (stride > 0) + end += stride * (extent - 1); + else + start += stride * (extent - 1); + } + } + *out_start = start; + *out_end = end + itemsize; +} +static int +__pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize) +{ + void *start1, *end1, *start2, *end2; + __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); + __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); + return (start1 < end2) && (start2 < end1); +} + +/* Capsule */ +static CYTHON_INLINE PyObject * +__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) +{ + PyObject *cobj; +#if PY_VERSION_HEX >= 0x02070000 + cobj = PyCapsule_New(p, sig, NULL); +#else + cobj = PyCObject_FromVoidPtr(p, NULL); +#endif + return cobj; +} + +/* IsLittleEndian */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) +{ + union { + uint32_t u32; + uint8_t u8[4]; + } S; + S.u32 = 0x01020304; + return S.u8[0] == 4; +} + +/* BufferFormatCheck */ +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t < '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparseable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static PyObject * +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number; + int ndim = ctx->head->field->type->ndim; +; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) + return PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + if (*ts != ',' && *ts != ')') + return PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + if (*ts == ',') ts++; + i++; + } + if (i != ndim) + return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return NULL; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return Py_None; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case '\r': + case '\n': + ++ts; + break; + case '<': + if (!__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } + CYTHON_FALLTHROUGH; + case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 'p': + if (ctx->enc_type == *ts && got_Z == ctx->is_complex && + ctx->enc_packmode == ctx->new_packmode) { + ctx->enc_count += ctx->new_count; + ctx->new_count = 1; + got_Z = 0; + ++ts; + break; + } + CYTHON_FALLTHROUGH; + case 's': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} + +/* TypeInfoCompare */ + static int +__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) +{ + int i; + if (!a || !b) + return 0; + if (a == b) + return 1; + if (a->size != b->size || a->typegroup != b->typegroup || + a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { + if (a->typegroup == 'H' || b->typegroup == 'H') { + return a->size == b->size; + } else { + return 0; + } + } + if (a->ndim) { + for (i = 0; i < a->ndim; i++) + if (a->arraysize[i] != b->arraysize[i]) + return 0; + } + if (a->typegroup == 'S') { + if (a->flags != b->flags) + return 0; + if (a->fields || b->fields) { + if (!(a->fields && b->fields)) + return 0; + for (i = 0; a->fields[i].type && b->fields[i].type; i++) { + __Pyx_StructField *field_a = a->fields + i; + __Pyx_StructField *field_b = b->fields + i; + if (field_a->offset != field_b->offset || + !__pyx_typeinfo_cmp(field_a->type, field_b->type)) + return 0; + } + return !a->fields[i].type && !b->fields[i].type; + } + } + return 1; +} + +/* MemviewSliceValidateAndInit */ + static int +__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) +{ + if (buf->shape[dim] <= 1) + return 1; + if (buf->strides) { + if (spec & __Pyx_MEMVIEW_CONTIG) { + if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { + if (buf->strides[dim] != sizeof(void *)) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly contiguous " + "in dimension %d.", dim); + goto fail; + } + } else if (buf->strides[dim] != buf->itemsize) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_FOLLOW) { + Py_ssize_t stride = buf->strides[dim]; + if (stride < 0) + stride = -stride; + if (stride < buf->itemsize) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + } else { + if (spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not contiguous in " + "dimension %d", dim); + goto fail; + } else if (spec & (__Pyx_MEMVIEW_PTR)) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not indirect in " + "dimension %d", dim); + goto fail; + } else if (buf->suboffsets) { + PyErr_SetString(PyExc_ValueError, + "Buffer exposes suboffsets but no strides"); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec) +{ + if (spec & __Pyx_MEMVIEW_DIRECT) { + if (buf->suboffsets && buf->suboffsets[dim] >= 0) { + PyErr_Format(PyExc_ValueError, + "Buffer not compatible with direct access " + "in dimension %d.", dim); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_PTR) { + if (!buf->suboffsets || (buf->suboffsets && buf->suboffsets[dim] < 0)) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly accessible " + "in dimension %d.", dim); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) +{ + int i; + if (c_or_f_flag & __Pyx_IS_F_CONTIG) { + Py_ssize_t stride = 1; + for (i = 0; i < ndim; i++) { + if (stride * buf->itemsize != buf->strides[i] && + buf->shape[i] > 1) + { + PyErr_SetString(PyExc_ValueError, + "Buffer not fortran contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { + Py_ssize_t stride = 1; + for (i = ndim - 1; i >- 1; i--) { + if (stride * buf->itemsize != buf->strides[i] && + buf->shape[i] > 1) { + PyErr_SetString(PyExc_ValueError, + "Buffer not C contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } + return 1; +fail: + return 0; +} +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj) +{ + struct __pyx_memoryview_obj *memview, *new_memview; + __Pyx_RefNannyDeclarations + Py_buffer *buf; + int i, spec = 0, retval = -1; + __Pyx_BufFmt_Context ctx; + int from_memoryview = __pyx_memoryview_check(original_obj); + __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); + if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) + original_obj)->typeinfo)) { + memview = (struct __pyx_memoryview_obj *) original_obj; + new_memview = NULL; + } else { + memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + original_obj, buf_flags, 0, dtype); + new_memview = memview; + if (unlikely(!memview)) + goto fail; + } + buf = &memview->view; + if (buf->ndim != ndim) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + ndim, buf->ndim); + goto fail; + } + if (new_memview) { + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; + } + if ((unsigned) buf->itemsize != dtype->size) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " + "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", + buf->itemsize, + (buf->itemsize > 1) ? "s" : "", + dtype->name, + dtype->size, + (dtype->size > 1) ? "s" : ""); + goto fail; + } + for (i = 0; i < ndim; i++) { + spec = axes_specs[i]; + if (!__pyx_check_strides(buf, i, ndim, spec)) + goto fail; + if (!__pyx_check_suboffsets(buf, i, ndim, spec)) + goto fail; + } + if (buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)) + goto fail; + if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, + new_memview != NULL) == -1)) { + goto fail; + } + retval = 0; + goto no_fail; +fail: + Py_XDECREF(new_memview); + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_float(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS_RO | writable_flag, 2, + &__Pyx_TypeInfo_float, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_long(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS_RO | writable_flag, 1, + &__Pyx_TypeInfo_long, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* CIntFromPyVerify */ + #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +/* Declarations */ + #if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return ::std::complex< float >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return x + y*(__pyx_t_float_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + __pyx_t_float_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +/* Arithmetic */ + #if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + #if 1 + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + if (b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); + } else if (fabsf(b.real) >= fabsf(b.imag)) { + if (b.real == 0 && b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); + } else { + float r = b.imag / b.real; + float s = 1.0 / (b.real + b.imag * r); + return __pyx_t_float_complex_from_parts( + (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); + } + } else { + float r = b.real / b.imag; + float s = 1.0 / (b.imag + b.real * r); + return __pyx_t_float_complex_from_parts( + (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); + } + } + #else + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + if (b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); + } else { + float denom = b.real * b.real + b.imag * b.imag; + return __pyx_t_float_complex_from_parts( + (a.real * b.real + a.imag * b.imag) / denom, + (a.imag * b.real - a.real * b.imag) / denom); + } + } + #endif + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrtf(z.real*z.real + z.imag*z.imag); + #else + return hypotf(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + float denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(a, a); + case 3: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(z, a); + case 4: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } else if (b.imag == 0) { + z.real = powf(a.real, b.real); + z.imag = 0; + return z; + } else if (a.real > 0) { + r = a.real; + theta = 0; + } else { + r = -a.real; + theta = atan2f(0, -1); + } + } else { + r = __Pyx_c_abs_float(a); + theta = atan2f(a.imag, a.real); + } + lnr = logf(r); + z_r = expf(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cosf(z_theta); + z.imag = z_r * sinf(z_theta); + return z; + } + #endif +#endif + +/* Declarations */ + #if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return ::std::complex< double >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return x + y*(__pyx_t_double_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + __pyx_t_double_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +/* Arithmetic */ + #if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + #if 1 + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + if (b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); + } else if (fabs(b.real) >= fabs(b.imag)) { + if (b.real == 0 && b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); + } else { + double r = b.imag / b.real; + double s = 1.0 / (b.real + b.imag * r); + return __pyx_t_double_complex_from_parts( + (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); + } + } else { + double r = b.real / b.imag; + double s = 1.0 / (b.imag + b.real * r); + return __pyx_t_double_complex_from_parts( + (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); + } + } + #else + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + if (b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); + } else { + double denom = b.real * b.real + b.imag * b.imag; + return __pyx_t_double_complex_from_parts( + (a.real * b.real + a.imag * b.imag) / denom, + (a.imag * b.real - a.real * b.imag) / denom); + } + } + #endif + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrt(z.real*z.real + z.imag*z.imag); + #else + return hypot(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + double denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(a, a); + case 3: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, a); + case 4: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } else if (b.imag == 0) { + z.real = pow(a.real, b.real); + z.imag = 0; + return z; + } else if (a.real > 0) { + r = a.real; + theta = 0; + } else { + r = -a.real; + theta = atan2(0, -1); + } + } else { + r = __Pyx_c_abs_double(a); + theta = atan2(a.imag, a.real); + } + lnr = log(r); + z_r = exp(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cos(z_theta); + z.imag = z_r * sin(z_theta); + return z; + } + #endif +#endif + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { + const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +/* MemviewDtypeToObject */ + static CYTHON_INLINE PyObject *__pyx_memview_get_float(const char *itemp) { + return (PyObject *) PyFloat_FromDouble(*(float *) itemp); +} +static CYTHON_INLINE int __pyx_memview_set_float(const char *itemp, PyObject *obj) { + float value = __pyx_PyFloat_AsFloat(obj); + if ((value == (float)-1) && PyErr_Occurred()) + return 0; + *(float *) itemp = value; + return 1; +} + +/* MemviewDtypeToObject */ + static CYTHON_INLINE PyObject *__pyx_memview_get_long(const char *itemp) { + return (PyObject *) __Pyx_PyInt_From_long(*(long *) itemp); +} +static CYTHON_INLINE int __pyx_memview_set_long(const char *itemp, PyObject *obj) { + long value = __Pyx_PyInt_As_long(obj); + if ((value == (long)-1) && PyErr_Occurred()) + return 0; + *(long *) itemp = value; + return 1; +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { + const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { + const enum NPY_TYPES neg_one = (enum NPY_TYPES) ((enum NPY_TYPES) 0 - (enum NPY_TYPES) 1), const_zero = (enum NPY_TYPES) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(enum NPY_TYPES) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(enum NPY_TYPES) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), + little, !is_unsigned); + } +} + +/* MemviewSliceCopyTemplate */ + static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object) +{ + __Pyx_RefNannyDeclarations + int i; + __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; + struct __pyx_memoryview_obj *from_memview = from_mvs->memview; + Py_buffer *buf = &from_memview->view; + PyObject *shape_tuple = NULL; + PyObject *temp_int = NULL; + struct __pyx_array_obj *array_obj = NULL; + struct __pyx_memoryview_obj *memview_obj = NULL; + __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); + for (i = 0; i < ndim; i++) { + if (from_mvs->suboffsets[i] >= 0) { + PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " + "indirect dimensions (axis %d)", i); + goto fail; + } + } + shape_tuple = PyTuple_New(ndim); + if (unlikely(!shape_tuple)) { + goto fail; + } + __Pyx_GOTREF(shape_tuple); + for(i = 0; i < ndim; i++) { + temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); + if(unlikely(!temp_int)) { + goto fail; + } else { + PyTuple_SET_ITEM(shape_tuple, i, temp_int); + temp_int = NULL; + } + } + array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); + if (unlikely(!array_obj)) { + goto fail; + } + __Pyx_GOTREF(array_obj); + memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + (PyObject *) array_obj, contig_flag, + dtype_is_object, + from_mvs->memview->typeinfo); + if (unlikely(!memview_obj)) + goto fail; + if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) + goto fail; + if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, + dtype_is_object) < 0)) + goto fail; + goto no_fail; +fail: + __Pyx_XDECREF(new_mvs.memview); + new_mvs.memview = NULL; + new_mvs.data = NULL; +no_fail: + __Pyx_XDECREF(shape_tuple); + __Pyx_XDECREF(temp_int); + __Pyx_XDECREF(array_obj); + __Pyx_RefNannyFinishContext(); + return new_mvs; +} + +/* CIntFromPy */ + static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { + const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(long) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(long) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) + case -2: + if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } +#endif + if (sizeof(long) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + long val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) + case -2: + if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { + const char neg_one = (char) ((char) 0 - (char) 1), const_zero = (char) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(char) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (char) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (char) 0; + case 1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0]) + case 2: + if (8 * sizeof(char) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) { + return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(char) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) { + return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(char) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) { + return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (char) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(char) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (char) 0; + case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(char, digit, +digits[0]) + case -2: + if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(char) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { + return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(char) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(char) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { + return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + } +#endif + if (sizeof(char) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + char val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (char) -1; + } + } else { + char val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (char) -1; + val = __Pyx_PyInt_As_char(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to char"); + return (char) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to char"); + return (char) -1; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_long(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS_RO | writable_flag, 2, + &__Pyx_TypeInfo_long, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_float(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS_RO | writable_flag, 1, + &__Pyx_TypeInfo_float, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* CheckBinaryVersion */ + static int __Pyx_check_binary_version(void) { + char ctversion[4], rtversion[4]; + PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); + if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compiletime version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + return PyErr_WarnEx(NULL, message, 1); + } + return 0; +} + +/* InitStrings */ + static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + if (PyObject_Hash(*t->p) == -1) + return -1; + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); +} +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +#if !CYTHON_PEP393_ENABLED +static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +} +#else +static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (likely(PyUnicode_IS_ASCII(o))) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +} +#endif +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { + return __Pyx_PyUnicode_AsStringAndSize(o, length); + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { + int retval; + if (unlikely(!x)) return -1; + retval = __Pyx_PyObject_IsTrue(x); + Py_DECREF(x); + return retval; +} +static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { +#if PY_MAJOR_VERSION >= 3 + if (PyLong_Check(result)) { + if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, + "__int__ returned non-int (type %.200s). " + "The ability to return an instance of a strict subclass of int " + "is deprecated, and may be removed in a future version of Python.", + Py_TYPE(result)->tp_name)) { + Py_DECREF(result); + return NULL; + } + return result; + } +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type %.200s)", + type_name, type_name, Py_TYPE(result)->tp_name); + Py_DECREF(result); + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { +#if CYTHON_USE_TYPE_SLOTS + PyNumberMethods *m; +#endif + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x) || PyLong_Check(x))) +#else + if (likely(PyLong_Check(x))) +#endif + return __Pyx_NewRef(x); +#if CYTHON_USE_TYPE_SLOTS + m = Py_TYPE(x)->tp_as_number; + #if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = m->nb_int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = m->nb_long(x); + } + #else + if (likely(m && m->nb_int)) { + name = "int"; + res = m->nb_int(x); + } + #endif +#else + if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { + res = PyNumber_Int(x); + } +#endif + if (likely(res)) { +#if PY_MAJOR_VERSION < 3 + if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { +#else + if (unlikely(!PyLong_CheckExact(res))) { +#endif + return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(b); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)b)->ob_digit; + const Py_ssize_t size = Py_SIZE(b); + if (likely(__Pyx_sst_abs(size) <= 1)) { + ival = likely(size) ? digits[0] : 0; + if (size == -1) ival = -ival; + return ival; + } else { + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { + return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +#endif /* Py_PYTHON_H */ diff --git a/torchreid/eval_cylib/eval_metrics_cy.pyx b/torchreid/metrics/rank_cylib/rank_cy.pyx similarity index 99% rename from torchreid/eval_cylib/eval_metrics_cy.pyx rename to torchreid/metrics/rank_cylib/rank_cy.pyx index e62c3fe..c7ad956 100644 --- a/torchreid/eval_cylib/eval_metrics_cy.pyx +++ b/torchreid/metrics/rank_cylib/rank_cy.pyx @@ -15,8 +15,11 @@ https://github.com/cython/cython/wiki/enhancements-compilerdirectives Cython tutorial: https://cython.readthedocs.io/en/latest/src/userguide/numpy_tutorial.html + +Credit to https://github.com/luzai """ + # Main interface cpdef evaluate_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03=False): distmat = np.asarray(distmat, dtype=np.float32) diff --git a/torchreid/eval_cylib/setup.py b/torchreid/metrics/rank_cylib/setup.py similarity index 77% rename from torchreid/eval_cylib/setup.py rename to torchreid/metrics/rank_cylib/setup.py index 1fbdbe2..ca404b1 100644 --- a/torchreid/eval_cylib/setup.py +++ b/torchreid/metrics/rank_cylib/setup.py @@ -12,9 +12,10 @@ def numpy_include(): return numpy_include ext_modules = [ - Extension('eval_metrics_cy', - ['eval_metrics_cy.pyx'], - include_dirs=[numpy_include()], + Extension( + 'rank_cy', + ['rank_cy.pyx'], + include_dirs=[numpy_include()], ) ] diff --git a/torchreid/eval_cylib/test_cython.py b/torchreid/metrics/rank_cylib/test_cython.py similarity index 69% rename from torchreid/eval_cylib/test_cython.py rename to torchreid/metrics/rank_cylib/test_cython.py index 9ab88eb..4032331 100644 --- a/torchreid/eval_cylib/test_cython.py +++ b/torchreid/metrics/rank_cylib/test_cython.py @@ -5,9 +5,8 @@ import os.path as osp import timeit import numpy as np -sys.path.insert(0, osp.dirname(osp.abspath(__file__)) + '/../..') - -from torchreid.eval_metrics import evaluate +sys.path.insert(0, osp.dirname(osp.abspath(__file__)) + '/../../..') +from torchreid import metrics """ Test the speed of cython-based evaluation code. The speed improvements @@ -25,8 +24,8 @@ setup = ''' import sys import os.path as osp import numpy as np -sys.path.insert(0, osp.dirname(osp.abspath(__file__)) + '/../..') -from torchreid.eval_metrics import evaluate +sys.path.insert(0, osp.dirname(osp.abspath(__file__)) + '/../../..') +from torchreid import metrics num_q = 30 num_g = 300 max_rank = 5 @@ -38,15 +37,15 @@ g_camids = np.random.randint(0, 5, size=num_g) ''' print('=> Using market1501\'s metric') -pytime = timeit.timeit('evaluate(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_cython=False)', setup=setup, number=20) -cytime = timeit.timeit('evaluate(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_cython=True)', setup=setup, number=20) +pytime = timeit.timeit('metrics.evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_cython=False)', setup=setup, number=20) +cytime = timeit.timeit('metrics.evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_cython=True)', setup=setup, number=20) print('Python time: {} s'.format(pytime)) print('Cython time: {} s'.format(cytime)) print('Cython is {} times faster than python\n'.format(pytime / cytime)) print('=> Using cuhk03\'s metric') -pytime = timeit.timeit('evaluate(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03=True, use_cython=False)', setup=setup, number=20) -cytime = timeit.timeit('evaluate(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03=True, use_cython=True)', setup=setup, number=20) +pytime = timeit.timeit('metrics.evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03=True, use_cython=False)', setup=setup, number=20) +cytime = timeit.timeit('metrics.evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03=True, use_cython=True)', setup=setup, number=20) print('Python time: {} s'.format(pytime)) print('Cython time: {} s'.format(cytime)) print('Cython is {} times faster than python\n'.format(pytime / cytime)) diff --git a/torchreid/models/__init__.py b/torchreid/models/__init__.py index ae667eb..da19db6 100644 --- a/torchreid/models/__init__.py +++ b/torchreid/models/__init__.py @@ -1,5 +1,7 @@ from __future__ import absolute_import +import torch + from .resnet import * from .resnetmid import * from .resnext import * @@ -54,12 +56,14 @@ __model_factory = { } -def get_names(): - return list(__model_factory.keys()) - - -def init_model(name, *args, **kwargs): - avai_models = get_names() +def build_model(name, num_classes, loss='softmax', pretrained=True, use_gpu=True): + avai_models = list(__model_factory.keys()) if name not in avai_models: - raise KeyError('Unknown model: {}. The available models are: {}'.format(name, avai_models)) - return __model_factory[name](*args, **kwargs) \ No newline at end of file + raise KeyError('Unknown model: {}. Must be one of {}'.format(name, avai_models)) + print('Initializing model: {}'.format(name)) + return __model_factory[name]( + num_classes=num_classes, + loss=loss, + pretrained=pretrained, + use_gpu=use_gpu + ) \ No newline at end of file diff --git a/torchreid/models/densenet.py b/torchreid/models/densenet.py index ad711a2..1b26898 100644 --- a/torchreid/models/densenet.py +++ b/torchreid/models/densenet.py @@ -1,6 +1,8 @@ from __future__ import absolute_import from __future__ import division +__all__ = ['densenet121', 'densenet121_fc512'] + from collections import OrderedDict import math import re @@ -12,9 +14,6 @@ from torch.nn import functional as F import torchvision -__all__ = ['densenet121', 'densenet121_fc512'] - - model_urls = { 'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth', 'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth', @@ -163,9 +162,9 @@ class DenseNet(nn.Module): y = self.classifier(v) - if self.loss == {'xent'}: + if self.loss == 'softmax': return y - elif self.loss == {'xent', 'htri'}: + elif self.loss == 'triplet': return y, v else: raise KeyError('Unsupported loss: {}'.format(self.loss)) @@ -208,7 +207,7 @@ densenet161: num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24) """ -def densenet121(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def densenet121(num_classes, loss='softmax', pretrained=True, **kwargs): model = DenseNet( num_classes=num_classes, loss=loss, @@ -224,7 +223,7 @@ def densenet121(num_classes, loss={'xent'}, pretrained=True, **kwargs): return model -def densenet121_fc512(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def densenet121_fc512(num_classes, loss='softmax', pretrained=True, **kwargs): model = DenseNet( num_classes=num_classes, loss=loss, diff --git a/torchreid/models/hacnn.py b/torchreid/models/hacnn.py index a10614d..d41e76a 100644 --- a/torchreid/models/hacnn.py +++ b/torchreid/models/hacnn.py @@ -1,15 +1,14 @@ from __future__ import absolute_import from __future__ import division +__all__ = ['HACNN'] + import torch from torch import nn from torch.nn import functional as F import torchvision -__all__ = ['HACNN'] - - class ConvBlock(nn.Module): """Basic convolutional block. @@ -199,7 +198,7 @@ class HACNN(nn.Module): learn_region (bool): whether to learn region features (i.e. local branch) """ - def __init__(self, num_classes, loss={'xent'}, nchannels=[128, 256, 384], feat_dim=512, learn_region=True, use_gpu=True, **kwargs): + def __init__(self, num_classes, loss='softmax', nchannels=[128, 256, 384], feat_dim=512, learn_region=True, use_gpu=True, **kwargs): super(HACNN, self).__init__() self.loss = loss self.learn_region = learn_region @@ -362,13 +361,13 @@ class HACNN(nn.Module): if self.learn_region: prelogits_local = self.classifier_local(x_local) - if self.loss == {'xent'}: + if self.loss == 'softmax': if self.learn_region: return (prelogits_global, prelogits_local) else: return prelogits_global - elif self.loss == {'xent', 'htri'}: + elif self.loss == 'triplet': if self.learn_region: return (prelogits_global, prelogits_local), (x_global, x_local) else: diff --git a/torchreid/models/inceptionresnetv2.py b/torchreid/models/inceptionresnetv2.py index c946733..476df54 100644 --- a/torchreid/models/inceptionresnetv2.py +++ b/torchreid/models/inceptionresnetv2.py @@ -1,6 +1,8 @@ from __future__ import absolute_import from __future__ import division +__all__ = ['inceptionresnetv2'] + import torch import torch.nn as nn from torch.nn import functional as F @@ -14,9 +16,6 @@ Code imported from https://github.com/Cadene/pretrained-models.pytorch """ -__all__ = ['inceptionresnetv2'] - - pretrained_settings = { 'inceptionresnetv2': { 'imagenet': { @@ -282,7 +281,7 @@ class InceptionResNetV2(nn.Module): Connections on Learning. AAAI 2017. """ - def __init__(self, num_classes, loss={'xent'}, **kwargs): + def __init__(self, num_classes, loss='softmax', **kwargs): super(InceptionResNetV2, self).__init__() self.loss = loss @@ -385,15 +384,15 @@ class InceptionResNetV2(nn.Module): y = self.classifier(v) - if self.loss == {'xent'}: + if self.loss == 'softmax': return y - elif self.loss == {'xent', 'htri'}: + elif self.loss == 'triplet': return y, v else: raise KeyError('Unsupported loss: {}'.format(self.loss)) -def inceptionresnetv2(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def inceptionresnetv2(num_classes, loss='softmax', pretrained=True, **kwargs): model = InceptionResNetV2( num_classes=num_classes, loss=loss, diff --git a/torchreid/models/inceptionv4.py b/torchreid/models/inceptionv4.py index 15e993c..d465355 100644 --- a/torchreid/models/inceptionv4.py +++ b/torchreid/models/inceptionv4.py @@ -1,5 +1,6 @@ from __future__ import absolute_import from __future__ import division +__all__ = ['inceptionv4'] import torch import torch.nn as nn @@ -9,9 +10,6 @@ import os import sys -__all__ = ['inceptionv4'] - - """ Code imported from https://github.com/Cadene/pretrained-models.pytorch """ @@ -319,9 +317,9 @@ class InceptionV4Base(nn.Module): y = self.classifier(v) - if self.loss == {'xent'}: + if self.loss == 'softmax': return y - elif self.loss == {'xent', 'htri'}: + elif self.loss == 'triplet': return y, v else: raise KeyError('Unsupported loss: {}'.format(self.loss)) @@ -340,7 +338,7 @@ def init_pretrained_weights(model, model_url): print('Initialized model with pretrained weights from {}'.format(model_url)) -def inceptionv4(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def inceptionv4(num_classes, loss='softmax', pretrained=True, **kwargs): model = InceptionV4Base(num_classes, loss, **kwargs) if pretrained: model_url = pretrained_settings['inceptionv4']['imagenet']['url'] diff --git a/torchreid/models/mlfn.py b/torchreid/models/mlfn.py index 748038e..0955aa4 100644 --- a/torchreid/models/mlfn.py +++ b/torchreid/models/mlfn.py @@ -1,6 +1,8 @@ from __future__ import absolute_import from __future__ import division +__all__ = ['mlfn'] + import torch from torch import nn from torch.nn import functional as F @@ -8,9 +10,6 @@ import torchvision import torch.utils.model_zoo as model_zoo -__all__ = ['mlfn'] - - model_urls = { # training epoch = 5, top1 = 51.6 'imagenet': 'http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/imagenet-pretrained/mlfn-9cb5a267.pth.tar', @@ -94,7 +93,7 @@ class MLFN(nn.Module): Chang et al. Multi-Level Factorisation Net for Person Re-Identification. CVPR 2018. """ - def __init__(self, num_classes, loss={'xent'}, groups=32, channels=[64, 256, 512, 1024, 2048], embed_dim=1024, **kwargs): + def __init__(self, num_classes, loss='softmax', groups=32, channels=[64, 256, 512, 1024, 2048], embed_dim=1024, **kwargs): super(MLFN, self).__init__() self.loss = loss self.groups = groups @@ -183,9 +182,9 @@ class MLFN(nn.Module): y = self.classifier(v) - if self.loss == {'xent'}: + if self.loss == 'softmax': return y - elif self.loss == {'xent', 'htri'}: + elif self.loss == 'triplet': return y, v else: raise KeyError('Unsupported loss: {}'.format(self.loss)) @@ -204,7 +203,7 @@ def init_pretrained_weights(model, model_url): print('Initialized model with pretrained weights from {}'.format(model_url)) -def mlfn(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def mlfn(num_classes, loss='softmax', pretrained=True, **kwargs): model = MLFN(num_classes, loss, **kwargs) if pretrained: init_pretrained_weights(model, model_urls['imagenet']) diff --git a/torchreid/models/mobilenetv2.py b/torchreid/models/mobilenetv2.py index 6d5dd4d..a2431a8 100644 --- a/torchreid/models/mobilenetv2.py +++ b/torchreid/models/mobilenetv2.py @@ -1,15 +1,14 @@ from __future__ import absolute_import from __future__ import division +__all__ = ['mobilenetv2_1dot0', 'mobilenetv2_1dot4'] + import torch from torch import nn from torch.nn import functional as F import torch.utils.model_zoo as model_zoo -__all__ = ['mobilenetv2_1dot0', 'mobilenetv2_1dot4'] - - model_urls = { # 1.0: top-1 71.3 'mobilenetv2_1dot0': 'http://eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/imagenet-pretrained/mobilenetv2_1.0-0f5d2d8f.pth', @@ -72,7 +71,7 @@ class MobileNetV2(nn.Module): Sandler et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks. CVPR 2018. """ - def __init__(self, num_classes, width_mult=1, loss={'xent'}, fc_dims=None, dropout_p=None, **kwargs): + def __init__(self, num_classes, width_mult=1, loss='softmax', fc_dims=None, dropout_p=None, **kwargs): super(MobileNetV2, self).__init__() self.loss = loss self.in_channels = int(32 * width_mult) @@ -176,9 +175,9 @@ class MobileNetV2(nn.Module): y = self.classifier(v) - if self.loss == {'xent'}: + if self.loss == 'softmax': return y - elif self.loss == {'xent', 'htri'}: + elif self.loss == 'triplet': return y, v else: raise KeyError("Unsupported loss: {}".format(self.loss)) diff --git a/torchreid/models/mudeep.py b/torchreid/models/mudeep.py index 2aa92f5..a023475 100644 --- a/torchreid/models/mudeep.py +++ b/torchreid/models/mudeep.py @@ -1,15 +1,14 @@ from __future__ import absolute_import from __future__ import division +__all__ = ['MuDeep'] + import torch from torch import nn from torch.nn import functional as F import torchvision -__all__ = ['MuDeep'] - - class ConvBlock(nn.Module): """Basic convolutional block. @@ -159,7 +158,7 @@ class MuDeep(nn.Module): Qian et al. Multi-scale Deep Learning Architectures for Person Re-identification. ICCV 2017. """ - def __init__(self, num_classes, loss={'xent'}, **kwargs): + def __init__(self, num_classes, loss='softmax', **kwargs): super(MuDeep, self).__init__() self.loss = loss @@ -195,9 +194,9 @@ class MuDeep(nn.Module): x = self.fc(x) y = self.classifier(x) - if self.loss == {'xent'}: + if self.loss == 'softmax': return y - elif self.loss == {'xent', 'htri'}: + elif self.loss == 'triplet': return y, x else: raise KeyError('Unsupported loss: {}'.format(self.loss)) \ No newline at end of file diff --git a/torchreid/models/nasnet.py b/torchreid/models/nasnet.py index 6b3bb3a..58a07ae 100644 --- a/torchreid/models/nasnet.py +++ b/torchreid/models/nasnet.py @@ -1,6 +1,8 @@ from __future__ import absolute_import from __future__ import division +__all__ = ['nasnetamobile'] + import torch import torch.nn as nn import torch.nn.functional as F @@ -28,8 +30,6 @@ Thanks to Anastasiia (https://github.com/DagnyT) for the great help, support and Code imported from https://github.com/Cadene/pretrained-models.pytorch """ -__all__ = ['nasnetamobile'] - pretrained_settings = { 'nasnetamobile': { @@ -648,9 +648,9 @@ class NASNetAMobile(nn.Module): y = self.classifier(v) - if self.loss == {'xent'}: + if self.loss == 'softmax': return y - elif self.loss == {'xent', 'htri'}: + elif self.loss == 'triplet': return y, v else: raise KeyError('Unsupported loss: {}'.format(self.loss)) @@ -669,7 +669,7 @@ def init_pretrained_weights(model, model_url): print('Initialized model with pretrained weights from {}'.format(model_url)) -def nasnetamobile(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def nasnetamobile(num_classes, loss='softmax', pretrained=True, **kwargs): model = NASNetAMobile(num_classes, loss, **kwargs) if pretrained: model_url = pretrained_settings['nasnetamobile']['imagenet']['url'] diff --git a/torchreid/models/pcb.py b/torchreid/models/pcb.py index bb20160..751f197 100644 --- a/torchreid/models/pcb.py +++ b/torchreid/models/pcb.py @@ -1,6 +1,8 @@ from __future__ import absolute_import from __future__ import division +__all__ = ['pcb_p6', 'pcb_p4'] + import torch from torch import nn from torch.nn import functional as F @@ -8,9 +10,6 @@ import torchvision import torch.utils.model_zoo as model_zoo -__all__ = ['pcb_p6', 'pcb_p4'] - - model_urls = { 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', @@ -217,9 +216,9 @@ class PCB(nn.Module): y_i = self.classifier[i](v_h_i) y.append(y_i) - if self.loss == {'xent'}: + if self.loss == 'softmax': return y - elif self.loss == {'xent', 'htri'}: + elif self.loss == 'triplet': v_g = F.normalize(v_g, p=2, dim=1) return y, v_g.view(v_g.size(0), -1) else: @@ -239,7 +238,7 @@ def init_pretrained_weights(model, model_url): print('Initialized model with pretrained weights from {}'.format(model_url)) -def pcb_p6(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def pcb_p6(num_classes, loss='softmax', pretrained=True, **kwargs): model = PCB( num_classes=num_classes, loss=loss, @@ -256,7 +255,7 @@ def pcb_p6(num_classes, loss={'xent'}, pretrained=True, **kwargs): return model -def pcb_p4(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def pcb_p4(num_classes, loss='softmax', pretrained=True, **kwargs): model = PCB( num_classes=num_classes, loss=loss, diff --git a/torchreid/models/resnet.py b/torchreid/models/resnet.py index 2323a9d..612097a 100644 --- a/torchreid/models/resnet.py +++ b/torchreid/models/resnet.py @@ -1,6 +1,8 @@ from __future__ import absolute_import from __future__ import division +__all__ = ['resnet50', 'resnet50_fc512'] + import torch from torch import nn from torch.nn import functional as F @@ -8,9 +10,6 @@ import torchvision import torch.utils.model_zoo as model_zoo -__all__ = ['resnet50', 'resnet50_fc512'] - - model_urls = { 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', @@ -215,9 +214,9 @@ class ResNet(nn.Module): y = self.classifier(v) - if self.loss == {'xent'}: + if self.loss == 'softmax': return y - elif self.loss == {'xent', 'htri'}: + elif self.loss == 'triplet': return y, v else: raise KeyError("Unsupported loss: {}".format(self.loss)) @@ -247,7 +246,7 @@ resnet152: block=Bottleneck, layers=[3, 8, 36, 3] """ -def resnet50(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def resnet50(num_classes, loss='softmax', pretrained=True, **kwargs): model = ResNet( num_classes=num_classes, loss=loss, @@ -263,7 +262,7 @@ def resnet50(num_classes, loss={'xent'}, pretrained=True, **kwargs): return model -def resnet50_fc512(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kwargs): model = ResNet( num_classes=num_classes, loss=loss, diff --git a/torchreid/models/resnetmid.py b/torchreid/models/resnetmid.py index 40c02be..e2858e1 100644 --- a/torchreid/models/resnetmid.py +++ b/torchreid/models/resnetmid.py @@ -1,6 +1,8 @@ from __future__ import absolute_import from __future__ import division +__all__ = ['resnet50mid'] + import torch from torch import nn from torch.nn import functional as F @@ -8,9 +10,6 @@ import torchvision import torch.utils.model_zoo as model_zoo -__all__ = ['resnet50mid'] - - model_urls = { 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', @@ -223,9 +222,9 @@ class ResNet(nn.Module): y = self.classifier(v) - if self.loss == {'xent'}: + if self.loss == 'softmax': return y - elif self.loss == {'xent', 'htri'}: + elif self.loss == 'triplet': return y, v else: raise KeyError('Unsupported loss: {}'.format(self.loss)) @@ -255,7 +254,7 @@ resnet152: block=Bottleneck, layers=[3, 8, 36, 3] """ -def resnet50mid(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def resnet50mid(num_classes, loss='softmax', pretrained=True, **kwargs): model = ResNet( num_classes=num_classes, loss=loss, diff --git a/torchreid/models/resnext.py b/torchreid/models/resnext.py index 54ea17f..71566fb 100644 --- a/torchreid/models/resnext.py +++ b/torchreid/models/resnext.py @@ -1,6 +1,8 @@ from __future__ import absolute_import from __future__ import division +__all__ = ['resnext50_32x4d', 'resnext50_32x4d_fc512'] + import math import torch @@ -10,9 +12,6 @@ import torchvision import torch.utils.model_zoo as model_zoo -__all__ = ['resnext50_32x4d', 'resnext50_32x4d_fc512'] - - model_urls = { # top1 = 76.3 'resnext50_32x4d': 'http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/imagenet-pretrained/resnext50_32x4d-453b60f8.pth', @@ -178,9 +177,9 @@ class ResNeXt(nn.Module): y = self.classifier(v) - if self.loss == {'xent'}: + if self.loss == 'softmax': return y - elif self.loss == {'xent', 'htri'}: + elif self.loss == 'triplet': return y, v else: raise KeyError("Unsupported loss: {}".format(self.loss)) @@ -199,7 +198,7 @@ def init_pretrained_weights(model, model_url): print('Initialized model with pretrained weights from {}'.format(model_url)) -def resnext50_32x4d(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs): model = ResNeXt( num_classes=num_classes, loss=loss, @@ -217,7 +216,7 @@ def resnext50_32x4d(num_classes, loss={'xent'}, pretrained=True, **kwargs): return model -def resnext50_32x4d_fc512(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def resnext50_32x4d_fc512(num_classes, loss='softmax', pretrained=True, **kwargs): model = ResNeXt( num_classes=num_classes, loss=loss, diff --git a/torchreid/models/senet.py b/torchreid/models/senet.py index 254e1bf..33723d3 100644 --- a/torchreid/models/senet.py +++ b/torchreid/models/senet.py @@ -1,6 +1,16 @@ from __future__ import absolute_import from __future__ import division +__all__ = [ + 'senet154', + 'se_resnet50', + 'se_resnet101', + 'se_resnet152', + 'se_resnext50_32x4d', + 'se_resnext101_32x4d', + 'se_resnet50_fc512' +] + from collections import OrderedDict import math @@ -16,10 +26,6 @@ Code imported from https://github.com/Cadene/pretrained-models.pytorch """ -__all__ = ['senet154', 'se_resnet50', 'se_resnet101', 'se_resnet152', 'se_resnext50_32x4d', 'se_resnext101_32x4d', - 'se_resnet50_fc512'] - - pretrained_settings = { 'senet154': { 'imagenet': { @@ -405,9 +411,9 @@ class SENet(nn.Module): y = self.classifier(v) - if self.loss == {'xent'}: + if self.loss == 'softmax': return y - elif self.loss == {'xent', 'htri'}: + elif self.loss == 'triplet': return y, v else: raise KeyError("Unsupported loss: {}".format(self.loss)) @@ -426,7 +432,7 @@ def init_pretrained_weights(model, model_url): print('Initialized model with pretrained weights from {}'.format(model_url)) -def senet154(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def senet154(num_classes, loss='softmax', pretrained=True, **kwargs): model = SENet( num_classes=num_classes, loss=loss, @@ -445,7 +451,7 @@ def senet154(num_classes, loss={'xent'}, pretrained=True, **kwargs): return model -def se_resnet50(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def se_resnet50(num_classes, loss='softmax', pretrained=True, **kwargs): model = SENet( num_classes=num_classes, loss=loss, @@ -468,7 +474,7 @@ def se_resnet50(num_classes, loss={'xent'}, pretrained=True, **kwargs): return model -def se_resnet50_fc512(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def se_resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kwargs): model = SENet( num_classes=num_classes, loss=loss, @@ -491,7 +497,7 @@ def se_resnet50_fc512(num_classes, loss={'xent'}, pretrained=True, **kwargs): return model -def se_resnet101(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def se_resnet101(num_classes, loss='softmax', pretrained=True, **kwargs): model = SENet( num_classes=num_classes, loss=loss, @@ -514,7 +520,7 @@ def se_resnet101(num_classes, loss={'xent'}, pretrained=True, **kwargs): return model -def se_resnet152(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def se_resnet152(num_classes, loss='softmax', pretrained=True, **kwargs): model = SENet( num_classes=num_classes, loss=loss, @@ -537,7 +543,7 @@ def se_resnet152(num_classes, loss={'xent'}, pretrained=True, **kwargs): return model -def se_resnext50_32x4d(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def se_resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs): model = SENet( num_classes=num_classes, loss=loss, @@ -560,7 +566,7 @@ def se_resnext50_32x4d(num_classes, loss={'xent'}, pretrained=True, **kwargs): return model -def se_resnext101_32x4d(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def se_resnext101_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs): model = SENet( num_classes=num_classes, loss=loss, diff --git a/torchreid/models/shufflenet.py b/torchreid/models/shufflenet.py index 1835334..0fdce7f 100644 --- a/torchreid/models/shufflenet.py +++ b/torchreid/models/shufflenet.py @@ -1,6 +1,8 @@ from __future__ import absolute_import from __future__ import division +__all__ = ['shufflenet'] + import torch from torch import nn from torch.nn import functional as F @@ -8,9 +10,6 @@ import torchvision import torch.utils.model_zoo as model_zoo -__all__ = ['shufflenet'] - - model_urls = { # training epoch = 90, top1 = 61.8 'imagenet': 'http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/imagenet-pretrained/shufflenet-bee1b265.pth.tar', @@ -85,7 +84,7 @@ class ShuffleNet(nn.Module): Network for Mobile Devices. CVPR 2018. """ - def __init__(self, num_classes, loss={'xent'}, num_groups=3, **kwargs): + def __init__(self, num_classes, loss='softmax', num_groups=3, **kwargs): super(ShuffleNet, self).__init__() self.loss = loss @@ -136,9 +135,9 @@ class ShuffleNet(nn.Module): y = self.classifier(x) - if self.loss == {'xent'}: + if self.loss == 'softmax': return y - elif self.loss == {'xent', 'htri'}: + elif self.loss == 'triplet': return y, x else: raise KeyError('Unsupported loss: {}'.format(self.loss)) @@ -157,7 +156,7 @@ def init_pretrained_weights(model, model_url): print('Initialized model with pretrained weights from {}'.format(model_url)) -def shufflenet(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def shufflenet(num_classes, loss='softmax', pretrained=True, **kwargs): model = ShuffleNet(num_classes, loss, **kwargs) if pretrained: init_pretrained_weights(model, model_urls['imagenet']) diff --git a/torchreid/models/squeezenet.py b/torchreid/models/squeezenet.py index f45dab2..9f3b8b8 100644 --- a/torchreid/models/squeezenet.py +++ b/torchreid/models/squeezenet.py @@ -1,6 +1,12 @@ from __future__ import absolute_import from __future__ import division +__all__ = [ + 'squeezenet1_0', + 'squeezenet1_1', + 'squeezenet1_0_fc512' +] + from collections import OrderedDict import math @@ -13,9 +19,6 @@ import torchvision import torch.utils.model_zoo as model_zoo -__all__ = ['squeezenet1_0', 'squeezenet1_1', 'squeezenet1_0_fc512'] - - model_urls = { 'squeezenet1_0': 'https://download.pytorch.org/models/squeezenet1_0-a815701f.pth', 'squeezenet1_1': 'https://download.pytorch.org/models/squeezenet1_1-f364aa15.pth', @@ -158,9 +161,9 @@ class SqueezeNet(nn.Module): y = self.classifier(v) - if self.loss == {'xent'}: + if self.loss == 'softmax': return y - elif self.loss == {'xent', 'htri'}: + elif self.loss == 'triplet': return y, v else: raise KeyError('Unsupported loss: {}'.format(self.loss)) @@ -179,9 +182,10 @@ def init_pretrained_weights(model, model_url): print('Initialized model with pretrained weights from {}'.format(model_url)) -def squeezenet1_0(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def squeezenet1_0(num_classes, loss='softmax', pretrained=True, **kwargs): model = SqueezeNet( - num_classes, loss, + num_classes, + loss, version=1.0, fc_dims=None, dropout_p=None, @@ -192,9 +196,10 @@ def squeezenet1_0(num_classes, loss={'xent'}, pretrained=True, **kwargs): return model -def squeezenet1_0_fc512(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def squeezenet1_0_fc512(num_classes, loss='softmax', pretrained=True, **kwargs): model = SqueezeNet( - num_classes, loss, + num_classes, + loss, version=1.0, fc_dims=[512], dropout_p=None, @@ -205,9 +210,10 @@ def squeezenet1_0_fc512(num_classes, loss={'xent'}, pretrained=True, **kwargs): return model -def squeezenet1_1(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def squeezenet1_1(num_classes, loss='softmax', pretrained=True, **kwargs): model = SqueezeNet( - num_classes, loss, + num_classes, + loss, version=1.1, fc_dims=None, dropout_p=None, diff --git a/torchreid/models/xception.py b/torchreid/models/xception.py index 2e9952b..da6988e 100644 --- a/torchreid/models/xception.py +++ b/torchreid/models/xception.py @@ -1,6 +1,8 @@ from __future__ import absolute_import from __future__ import division +__all__ = ['xception'] + import math import torch import torch.nn as nn @@ -9,9 +11,6 @@ import torch.utils.model_zoo as model_zoo from torch.nn import init -__all__ = ['xception'] - - pretrained_settings = { 'xception': { 'imagenet': { @@ -231,9 +230,9 @@ class Xception(nn.Module): y = self.classifier(v) - if self.loss == {'xent'}: + if self.loss == 'softmax': return y - elif self.loss == {'xent', 'htri'}: + elif self.loss == 'triplet': return y, v else: raise KeyError('Unsupported loss: {}'.format(self.loss)) @@ -252,9 +251,10 @@ def init_pretrained_weights(model, model_url): print('Initialized model with pretrained weights from {}'.format(model_url)) -def xception(num_classes, loss={'xent'}, pretrained=True, **kwargs): +def xception(num_classes, loss='softmax', pretrained=True, **kwargs): model = Xception( - num_classes, loss, + num_classes, + loss, fc_dims=None, dropout_p=None, **kwargs diff --git a/torchreid/optim/__init__.py b/torchreid/optim/__init__.py new file mode 100644 index 0000000..db9daee --- /dev/null +++ b/torchreid/optim/__init__.py @@ -0,0 +1,4 @@ +from __future__ import absolute_import + +from .optimizers import build_optimizer +from .lr_schedulers import build_lr_scheduler \ No newline at end of file diff --git a/torchreid/optim/lr_schedulers.py b/torchreid/optim/lr_schedulers.py new file mode 100644 index 0000000..094f390 --- /dev/null +++ b/torchreid/optim/lr_schedulers.py @@ -0,0 +1,38 @@ +from __future__ import absolute_import +from __future__ import print_function + +import torch + + +AVAI_SCH = ['single_step', 'multi_step'] + + +def build_lr_scheduler(optimizer, lr_scheduler, stepsize, gamma=0.1): + if lr_scheduler not in AVAI_SCH: + raise ValueError('Unsupported scheduler: {}. Must be one of {}'.format(lr_scheduler, AVAI_SCH)) + + print('Initializing lr_scheduler: {}'.format(lr_scheduler)) + + if lr_scheduler == 'single_step': + if not isinstance(stepsize, int): + raise TypeError( + 'For single_step lr_scheduler, stepsize must ' + 'be an integer, but got {}'.format(type(stepsize)) + ) + + scheduler = torch.optim.lr_scheduler.StepLR( + optimizer, step_size=stepsize, gamma=gamma + ) + + elif lr_scheduler == 'multi_step': + if not isinstance(stepsize, list): + raise TypeError( + 'For multi_step lr_scheduler, stepsize must ' + 'be a list, but got {}'.format(type(stepsize)) + ) + + scheduler = torch.optim.lr_scheduler.MultiStepLR( + optimizer, milestones=stepsize, gamma=gamma + ) + + return scheduler \ No newline at end of file diff --git a/torchreid/optimizers.py b/torchreid/optim/optimizers.py similarity index 62% rename from torchreid/optimizers.py rename to torchreid/optim/optimizers.py index c2cbfb7..c347126 100644 --- a/torchreid/optimizers.py +++ b/torchreid/optim/optimizers.py @@ -5,44 +5,56 @@ import torch import torch.nn as nn -def init_optimizer( - model, - optim='adam', # optimizer choices - lr=0.003, # learning rate - weight_decay=5e-4, # weight decay - momentum=0.9, # momentum factor for sgd and rmsprop - sgd_dampening=0, # sgd's dampening for momentum - sgd_nesterov=False, # whether to enable sgd's Nesterov momentum - rmsprop_alpha=0.99, # rmsprop's smoothing constant - adam_beta1=0.9, # exponential decay rate for adam's first moment - adam_beta2=0.999, # # exponential decay rate for adam's second moment - staged_lr=False, # different lr for different layers - new_layers=None, # new layers use the default lr, while other layers's lr is scaled by base_lr_mult - base_lr_mult=0.1, # learning rate multiplier for base layers +AVAI_OPTIMS = ['adam', 'amsgrad', 'sgd', 'rmsprop'] + + +def build_optimizer( + model, + optim='adam', + lr=0.0003, + weight_decay=5e-04, + momentum=0.9, + sgd_dampening=0, + sgd_nesterov=False, + rmsprop_alpha=0.99, + adam_beta1=0.9, + adam_beta2=0.99, + staged_lr=False, + new_layers=None, + base_lr_mult=0.1 ): + if optim not in AVAI_OPTIMS: + raise ValueError('Unsupported optim: {}. Must be one of {}'.format(optim, AVAI_OPTIMS)) + + if not isinstance(model, nn.Module): + raise TypeError('model given to build_optimizer must be an instance of nn.Module') if staged_lr: - assert new_layers is not None + if isinstance(new_layers, str): + new_layers = [new_layers] + + if isinstance(model, nn.DataParallel): + model = model.module + base_params = [] base_layers = [] new_params = [] - if isinstance(model, nn.DataParallel): - model = model.module + for name, module in model.named_children(): if name in new_layers: new_params += [p for p in module.parameters()] else: base_params += [p for p in module.parameters()] base_layers.append(name) + param_groups = [ {'params': base_params, 'lr': lr * base_lr_mult}, {'params': new_params}, ] + print('Use staged learning rate') - print( - '* Base layers (initial lr = {}): {}'.format(lr * base_lr_mult, base_layers) - ) - print('* New layers (initial lr = {}): {}'.format(lr, new_layers)) + print('Base layers (lr*{}): {}'.format(base_lr_mult, base_layers)) + print('New layers (lr): {}'.format(new_layers)) else: param_groups = model.parameters() @@ -85,7 +97,4 @@ def init_optimizer( alpha=rmsprop_alpha, ) - else: - raise ValueError('Unsupported optimizer: {}'.format(optim)) - - return optimizer + return optimizer \ No newline at end of file diff --git a/torchreid/samplers.py b/torchreid/samplers.py deleted file mode 100644 index be7ef80..0000000 --- a/torchreid/samplers.py +++ /dev/null @@ -1,87 +0,0 @@ -from __future__ import absolute_import -from __future__ import division - -from collections import defaultdict -import numpy as np -import copy -import random - -import torch -from torch.utils.data.sampler import Sampler, RandomSampler - - -class RandomIdentitySampler(Sampler): - """Randomly samples N identities each with K instances. - - Args: - data_source (list): contains a list of (img_path, pid, camid). - batch_size (int): number of examples in a batch. - num_instances (int): number of instances per identity in a batch. - """ - def __init__(self, data_source, batch_size, num_instances): - self.data_source = data_source - self.batch_size = batch_size - self.num_instances = num_instances - self.num_pids_per_batch = self.batch_size // self.num_instances - self.index_dic = defaultdict(list) - for index, (_, pid, _) in enumerate(self.data_source): - self.index_dic[pid].append(index) - self.pids = list(self.index_dic.keys()) - - # estimate number of examples in an epoch - self.length = 0 - for pid in self.pids: - idxs = self.index_dic[pid] - num = len(idxs) - if num < self.num_instances: - num = self.num_instances - self.length += num - num % self.num_instances - - def __iter__(self): - batch_idxs_dict = defaultdict(list) - - for pid in self.pids: - idxs = copy.deepcopy(self.index_dic[pid]) - if len(idxs) < self.num_instances: - idxs = np.random.choice(idxs, size=self.num_instances, replace=True) - random.shuffle(idxs) - batch_idxs = [] - for idx in idxs: - batch_idxs.append(idx) - if len(batch_idxs) == self.num_instances: - batch_idxs_dict[pid].append(batch_idxs) - batch_idxs = [] - - avai_pids = copy.deepcopy(self.pids) - final_idxs = [] - - while len(avai_pids) >= self.num_pids_per_batch: - selected_pids = random.sample(avai_pids, self.num_pids_per_batch) - for pid in selected_pids: - batch_idxs = batch_idxs_dict[pid].pop(0) - final_idxs.extend(batch_idxs) - if len(batch_idxs_dict[pid]) == 0: - avai_pids.remove(pid) - - return iter(final_idxs) - - def __len__(self): - return self.length - - -def build_train_sampler(data_source, train_sampler, train_batch_size, num_instances, **kwargs): - """Builds a training sampler. - - Args: - data_source (list): contains a list of (img_path, pid, camid). - train_sampler (str): sampler name (default: RandomSampler). - train_batch_size (int): training batch size. - num_instances (int): number of instances per identity in a batch (for RandomIdentitySampler). - """ - if train_sampler == 'RandomIdentitySampler': - sampler = RandomIdentitySampler(data_source, train_batch_size, num_instances) - - else: - sampler = RandomSampler(data_source) - - return sampler \ No newline at end of file diff --git a/torchreid/transforms.py b/torchreid/transforms.py deleted file mode 100644 index 097b1b4..0000000 --- a/torchreid/transforms.py +++ /dev/null @@ -1,159 +0,0 @@ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from PIL import Image -import random -import numpy as np -import math - -import torch -from torchvision.transforms import * - - -class Random2DTranslation(object): - """Randomly translates the input image with a probability. - - Specifically, given a predefined shape (height, width), the input is first - resized with the factor of 1.25, leading to (height*1.25, width*1.25), then - a random crop is performed. Such operation is done with a probability. - - Args: - height (int): target image height. - width (int): target image width. - p (float): probability of performing this transformation. Default: 0.5. - """ - - def __init__(self, height, width, p=0.5, interpolation=Image.BILINEAR): - self.height = height - self.width = width - self.p = p - self.interpolation = interpolation - - def __call__(self, img): - if random.uniform(0, 1) > self.p: - return img.resize((self.width, self.height), self.interpolation) - - new_width, new_height = int(round(self.width * 1.125)), int(round(self.height * 1.125)) - resized_img = img.resize((new_width, new_height), self.interpolation) - x_maxrange = new_width - self.width - y_maxrange = new_height - self.height - x1 = int(round(random.uniform(0, x_maxrange))) - y1 = int(round(random.uniform(0, y_maxrange))) - croped_img = resized_img.crop((x1, y1, x1 + self.width, y1 + self.height)) - return croped_img - - -class RandomErasing(object): - '''Randomly erases an image patch. - - Class that performs Random Erasing in Random Erasing Data Augmentation by Zhong et al. - ------------------------------------------------------------------------------------- - probability: The probability that the operation will be performed. - sl: min erasing area - sh: max erasing area - r1: min aspect ratio - mean: erasing value - ------------------------------------------------------------------------------------- - - Imported from https://github.com/zhunzhong07/Random-Erasing - ''' - - def __init__(self, probability=0.5, sl=0.02, sh=0.4, r1=0.3, mean=[0.4914, 0.4822, 0.4465]): - self.probability = probability - self.mean = mean - self.sl = sl - self.sh = sh - self.r1 = r1 - - def __call__(self, img): - if random.uniform(0, 1) > self.probability: - return img - - for attempt in range(100): - area = img.size()[1] * img.size()[2] - - target_area = random.uniform(self.sl, self.sh) * area - aspect_ratio = random.uniform(self.r1, 1/self.r1) - - h = int(round(math.sqrt(target_area * aspect_ratio))) - w = int(round(math.sqrt(target_area / aspect_ratio))) - - if w < img.size()[2] and h < img.size()[1]: - x1 = random.randint(0, img.size()[1] - h) - y1 = random.randint(0, img.size()[2] - w) - if img.size()[0] == 3: - img[0, x1:x1+h, y1:y1+w] = self.mean[0] - img[1, x1:x1+h, y1:y1+w] = self.mean[1] - img[2, x1:x1+h, y1:y1+w] = self.mean[2] - else: - img[0, x1:x1+h, y1:y1+w] = self.mean[0] - return img - - return img - - -class ColorAugmentation(object): - """Randomly alters the intensities of RGB channels. - - Reference: - Krizhevsky et al. ImageNet Classification with Deep ConvolutionalNeural Networks. NIPS 2012. - """ - - def __init__(self, p=0.5): - self.p = p - self.eig_vec = torch.Tensor([ - [0.4009, 0.7192, -0.5675], - [-0.8140, -0.0045, -0.5808], - [0.4203, -0.6948, -0.5836], - ]) - self.eig_val = torch.Tensor([[0.2175, 0.0188, 0.0045]]) - - def _check_input(self, tensor): - assert tensor.dim() == 3 and tensor.size(0) == 3 - - def __call__(self, tensor): - if random.uniform(0, 1) > self.p: - return tensor - alpha = torch.normal(mean=torch.zeros_like(self.eig_val)) * 0.1 - quatity = torch.mm(self.eig_val * alpha, self.eig_vec) - tensor = tensor + quatity.view(3, 1, 1) - return tensor - - -def build_transforms( - height, - width, - random_erase=False, # use random erasing for data augmentation - color_jitter=False, # randomly change the brightness, contrast and saturation - color_aug=False, # randomly alter the intensities of RGB channels - **kwargs - ): - - # use imagenet mean and std as default - imagenet_mean = [0.485, 0.456, 0.406] - imagenet_std = [0.229, 0.224, 0.225] - normalize = Normalize(mean=imagenet_mean, std=imagenet_std) - - # build train transformations - transform_train = [] - transform_train += [Random2DTranslation(height, width)] - transform_train += [RandomHorizontalFlip()] - if color_jitter: - transform_train += [ColorJitter(brightness=0.2, contrast=0.15, saturation=0, hue=0)] - transform_train += [ToTensor()] - if color_aug: - transform_train += [ColorAugmentation()] - transform_train += [normalize] - if random_erase: - transform_train += [RandomErasing()] - transform_train = Compose(transform_train) - - # build test transformations - transform_test = Compose([ - Resize((height, width)), - ToTensor(), - normalize, - ]) - - return transform_train, transform_test \ No newline at end of file diff --git a/torchreid/utils/__init__.py b/torchreid/utils/__init__.py index e69de29..14274b4 100644 --- a/torchreid/utils/__init__.py +++ b/torchreid/utils/__init__.py @@ -0,0 +1,7 @@ +from __future__ import absolute_import + +from .avgmeter import * +from .loggers import * +from .tools import * +from .reidtools import * +from .torchtools import * \ No newline at end of file diff --git a/torchreid/utils/avgmeter.py b/torchreid/utils/avgmeter.py index e3f9b7e..158166d 100644 --- a/torchreid/utils/avgmeter.py +++ b/torchreid/utils/avgmeter.py @@ -1,6 +1,8 @@ from __future__ import absolute_import from __future__ import division +__all__ = ['AverageMeter'] + class AverageMeter(object): """Computes and stores the average and current value""" diff --git a/torchreid/utils/generaltools.py b/torchreid/utils/generaltools.py deleted file mode 100644 index 3ad42f6..0000000 --- a/torchreid/utils/generaltools.py +++ /dev/null @@ -1,16 +0,0 @@ -from __future__ import absolute_import -from __future__ import print_function -from __future__ import division - -import os -import os.path as osp -import random -import numpy as np -import torch - - -def set_random_seed(seed): - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - torch.cuda.manual_seed_all(seed) \ No newline at end of file diff --git a/torchreid/utils/iotools.py b/torchreid/utils/iotools.py deleted file mode 100644 index 261beb9..0000000 --- a/torchreid/utils/iotools.py +++ /dev/null @@ -1,36 +0,0 @@ -from __future__ import absolute_import - -import os -import os.path as osp -import errno -import json -from collections import OrderedDict -import warnings - - -def mkdir_if_missing(directory): - if not osp.exists(directory): - try: - os.makedirs(directory) - except OSError as e: - if e.errno != errno.EEXIST: - raise - - -def check_isfile(path): - isfile = osp.isfile(path) - if not isfile: - warnings.warn('No file found at "{}"'.format(path)) - return isfile - - -def read_json(fpath): - with open(fpath, 'r') as f: - obj = json.load(f) - return obj - - -def write_json(obj, fpath): - mkdir_if_missing(osp.dirname(fpath)) - with open(fpath, 'w') as f: - json.dump(obj, f, indent=4, separators=(',', ': ')) \ No newline at end of file diff --git a/torchreid/utils/loggers.py b/torchreid/utils/loggers.py index fa33f80..fb7374f 100644 --- a/torchreid/utils/loggers.py +++ b/torchreid/utils/loggers.py @@ -1,10 +1,12 @@ from __future__ import absolute_import +__all__ = ['Logger', 'RankLogger'] + import sys import os import os.path as osp -from .iotools import mkdir_if_missing +from .tools import mkdir_if_missing class Logger(object): diff --git a/torchreid/utils/reidtools.py b/torchreid/utils/reidtools.py index 9f1857b..df7cf68 100644 --- a/torchreid/utils/reidtools.py +++ b/torchreid/utils/reidtools.py @@ -1,12 +1,14 @@ from __future__ import absolute_import from __future__ import print_function +__all__ = ['visualize_ranked_results'] + import numpy as np import os import os.path as osp import shutil -from .iotools import mkdir_if_missing +from .tools import mkdir_if_missing def visualize_ranked_results(distmat, dataset, save_dir='log/ranked_results', topk=20): diff --git a/torchreid/utils/tools.py b/torchreid/utils/tools.py new file mode 100644 index 0000000..a7797a7 --- /dev/null +++ b/torchreid/utils/tools.py @@ -0,0 +1,91 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +__all__ = ['mkdir_if_missing', 'check_isfile', 'read_json', 'write_json', + 'set_random_seed', 'download_url', 'read_image'] + +import sys +import os +import os.path as osp +import time +import errno +import json +from collections import OrderedDict +import warnings +import random +import numpy as np +from PIL import Image + +import torch + + +def mkdir_if_missing(directory): + if not osp.exists(directory): + try: + os.makedirs(directory) + except OSError as e: + if e.errno != errno.EEXIST: + raise + + +def check_isfile(path): + isfile = osp.isfile(path) + if not isfile: + warnings.warn('No file found at "{}"'.format(path)) + return isfile + + +def read_json(fpath): + with open(fpath, 'r') as f: + obj = json.load(f) + return obj + + +def write_json(obj, fpath): + mkdir_if_missing(osp.dirname(fpath)) + with open(fpath, 'w') as f: + json.dump(obj, f, indent=4, separators=(',', ': ')) + + +def set_random_seed(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + +def download_url(url, dst): + from six.moves import urllib + print('* url="{}"'.format(url)) + print('* destination="{}"'.format(dst)) + + def _reporthook(count, block_size, total_size): + global start_time + if count == 0: + start_time = time.time() + return + duration = time.time() - start_time + progress_size = int(count * block_size) + speed = int(progress_size / (1024 * duration)) + percent = int(count * block_size * 100 / total_size) + sys.stdout.write('\r...%d%%, %d MB, %d KB/s, %d seconds passed' % + (percent, progress_size / (1024 * 1024), speed, duration)) + sys.stdout.flush() + + urllib.request.urlretrieve(url, dst, _reporthook) + sys.stdout.write('\n') + + +def read_image(path): + got_img = False + if not osp.exists(path): + raise IOError('"{}" does not exist'.format(path)) + while not got_img: + try: + img = Image.open(path).convert('RGB') + got_img = True + except IOError: + print('IOError incurred when reading "{}". Will redo. Don\'t worry. Just chill.'.format(img_path)) + pass + return img \ No newline at end of file diff --git a/torchreid/utils/torchtools.py b/torchreid/utils/torchtools.py index caca857..6c0f475 100644 --- a/torchreid/utils/torchtools.py +++ b/torchreid/utils/torchtools.py @@ -2,6 +2,16 @@ from __future__ import absolute_import from __future__ import print_function from __future__ import division +__all__ = [ + 'save_checkpoint', + 'load_checkpoint', + 'resume_from_checkpoint', + 'open_all_layers', + 'open_specified_layers', + 'count_num_param', + 'load_pretrained_weights' +] + from collections import OrderedDict import shutil import warnings @@ -13,7 +23,7 @@ import pickle import torch import torch.nn as nn -from .iotools import mkdir_if_missing +from .tools import mkdir_if_missing def save_checkpoint(state, save_dir, is_best=False, remove_module_from_keys=False): @@ -104,11 +114,14 @@ def open_specified_layers(model, open_layers): Args: model (nn.Module): neural net model. - open_layers (list): list of layer names. + open_layers (str or list): layers open for training. """ if isinstance(model, nn.DataParallel): model = model.module + if isinstance(open_layers, str): + open_layers = [open_layers] + for layer in open_layers: assert hasattr(model, layer), '"{}" is not an attribute of the model, please provide the correct name'.format(layer) @@ -124,6 +137,11 @@ def open_specified_layers(model, open_layers): def count_num_param(model): + """Counts number of parameters in a model + + Args: + model (nn.Module): neural network + """ num_param = sum(p.numel() for p in model.parameters()) / 1e+06 if isinstance(model, nn.DataParallel): @@ -135,27 +153,6 @@ def count_num_param(model): return num_param -def accuracy(output, target, topk=(1,)): - """Computes the accuracy over the k top predictions for the specified values of k""" - with torch.no_grad(): - maxk = max(topk) - batch_size = target.size(0) - - if isinstance(output, (tuple, list)): - output = output[0] - - _, pred = output.topk(maxk, 1, True, True) - pred = pred.t() - correct = pred.eq(target.view(1, -1).expand_as(pred)) - - res = [] - for k in topk: - correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) - acc = correct_k.mul_(100.0 / batch_size) - res.append(acc.item()) - return res - - def load_pretrained_weights(model, weight_path): """Loads pretrianed weights to model