multi scale sampler and dataset

ppcls/configs/multi_scale/MobileNetV1_multi_scale.yaml

@@ -0,0 +1,134 @@
+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: ./output/
+  device: gpu
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 120
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: ./inference
+  # training model under @to_static
+  to_static: False
+  use_dali: True
+
+# model architecture
+Arch:
+  name: MobileNetV1
+  class_num: 100
+ 
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: Piecewise
+    learning_rate: 0.1
+    decay_epochs: [30, 60, 90]
+    values: [0.1, 0.01, 0.001, 0.0001]
+  regularizer:
+    name: 'L2'
+    coeff: 0.00003
+
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: MultiScaleDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+
+    sampler:
+      name: MultiScaleSamplerDDP
+      scales: [224, 256]
+      first_bs: 4
+      is_training: True
+
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+  Eval:
+    dataset: 
+      name: ImageNetDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/val_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            resize_short: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 64
+      drop_last: False
+      shuffle: False
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+Infer:
+  infer_imgs: docs/images/whl/demo.jpg
+  batch_size: 10
+  transforms:
+    - DecodeImage:
+        to_rgb: True
+        channel_first: False
+    - ResizeImage:
+        resize_short: 256
+    - CropImage:
+        size: 224
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+        order: ''
+    - ToCHWImage:
+  PostProcess:
+    name: Topk
+    topk: 5
+    class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+  Train:
+    - TopkAcc:
+        topk: [1, 5]
+  Eval:
+    - TopkAcc:
+        topk: [1, 5]

ppcls/data/__init__.py

@@ -28,11 +28,13 @@ from ppcls.data.dataloader.vehicle_dataset import CompCars, VeriWild
 from ppcls.data.dataloader.logo_dataset import LogoDataset
 from ppcls.data.dataloader.icartoon_dataset import ICartoonDataset
 from ppcls.data.dataloader.mix_dataset import MixDataset
+from ppcls.data.dataloader.multi_scale_dataset import MultiScaleDataset
 
 # sampler
 from ppcls.data.dataloader.DistributedRandomIdentitySampler import DistributedRandomIdentitySampler
 from ppcls.data.dataloader.pk_sampler import PKSampler
 from ppcls.data.dataloader.mix_sampler import MixSampler
+from ppcls.data.dataloader.multi_scale_sampler import MultiScaleSamplerDDP
 from ppcls.data import preprocess
 from ppcls.data.preprocess import transform
 

ppcls/data/dataloader/__init__.py

@@ -5,5 +5,7 @@ from ppcls.data.dataloader.vehicle_dataset import CompCars, VeriWild
 from ppcls.data.dataloader.logo_dataset import LogoDataset
 from ppcls.data.dataloader.icartoon_dataset import ICartoonDataset
 from ppcls.data.dataloader.mix_dataset import MixDataset
+from ppcls.data.dataloader.multi_scale_dataset import MultiScaleDataset
 from ppcls.data.dataloader.mix_sampler import MixSampler
+from ppcls.data.dataloader.multi_scale_sampler import MultiScaleSamplerDDP
 from ppcls.data.dataloader.pk_sampler import PKSampler

ppcls/data/dataloader/multi_scale_dataset.py

@@ -0,0 +1,119 @@
+#   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import numpy as np
+import os
+
+from paddle.io import Dataset
+from paddle.vision import transforms
+import cv2
+import warnings
+
+from ppcls.data import preprocess
+from ppcls.data.preprocess import transform
+from ppcls.data.preprocess.ops.operators import DecodeImage
+from ppcls.utils import logger
+
+
+def create_operators(params):
+    """
+    create operators based on the config
+    Args:
+        params(list): a dict list, used to create some operators
+    """
+    assert isinstance(params, list), ('operator config should be a list')
+    ops = []
+    for operator in params:
+        assert isinstance(operator,
+                          dict) and len(operator) == 1, "yaml format error"
+        op_name = list(operator)[0]
+        param = {} if operator[op_name] is None else operator[op_name]
+        op = getattr(preprocess, op_name)(**param)
+        ops.append(op)
+
+    return ops
+
+
+class MultiScaleDataset(Dataset):
+    def __init__(
+            self,
+            image_root,
+            cls_label_path,
+            transform_ops=None, ):
+        self._img_root = image_root
+        self._cls_path = cls_label_path
+        self.transform_ops = transform_ops
+        # if transform_ops:
+        #     self._transform_ops = create_operators(transform_ops)
+
+        self.images = []
+        self.labels = []
+        self._load_anno()
+
+    def _load_anno(self, seed=None):
+        assert os.path.exists(self._cls_path)
+        assert os.path.exists(self._img_root)
+        self.images = []
+        self.labels = []
+
+        with open(self._cls_path) as fd:
+            lines = fd.readlines()
+            if seed is not None:
+                np.random.RandomState(seed).shuffle(lines)
+            for l in lines:
+                l = l.strip().split(" ")
+                self.images.append(os.path.join(self._img_root, l[0]))
+                self.labels.append(np.int64(l[1]))
+                assert os.path.exists(self.images[-1])
+
+
+    def __getitem__(self, properties):
+        # properites is a tuple, contains (width, height, index)
+        img_width = properties[0]
+        img_height = properties[1]
+        index = properties[2]
+        has_crop = False
+        if self.transform_ops:
+            for i in range(len(self.transform_ops)):
+                op = self.transform_ops[i]
+                if 'RandCropImage' in op:
+                    warnings.warn("Multi scale dataset will crop image according to the multi scale resolution")
+                    self.transform_ops[i]['RandCropImage'] = {'size': img_width}
+                    has_crop = True
+        if has_crop == False:
+            raise RuntimeError("Multi scale dateset requests RandCropImage")
+        self._transform_ops = create_operators(self.transform_ops)
+
+        try:
+            with open(self.images[index], 'rb') as f:
+                img = f.read()
+            if self._transform_ops:
+                img = transform(img, self._transform_ops) 
+            img = img.transpose((2, 0, 1))
+            return (img, self.labels[index])
+
+        except Exception as ex:
+            logger.error("Exception occured when parse line: {} with msg: {}".
+                         format(self.images[index], ex))
+            rnd_idx = np.random.randint(self.__len__())
+            return self.__getitem__(rnd_idx)
+
+    def __len__(self):
+        return len(self.images)
+
+    @property
+    def class_num(self):
+        return len(set(self.labels))

ppcls/data/dataloader/multi_scale_sampler.py

@@ -0,0 +1,105 @@
+from paddle.io import Sampler
+import paddle.distributed as dist
+
+import math
+import random
+import numpy as np
+
+from ppcls import data
+
+class MultiScaleSamplerDDP(Sampler):
+    def __init__(self, data_source, scales, first_bs, g):
+        print(scales)
+        # min. and max. spatial dimensions
+        self.data_source = data_source
+        self.n_data_samples = len(self.data_source)
+        
+        if isinstance(scales[0], tuple):
+            width_dims = [i[0] for i in scales]
+            height_dims = [i[1] for i in scales]
+        elif isinstance(scales[0], int):
+            width_dims = scales
+            height_dims = scales
+        base_im_w = width_dims[0]
+        base_im_h = height_dims[0]
+        base_batch_size = first_bs
+        
+        # Get the GPU and node related information
+        num_replicas  =dist.get_world_size()
+        rank = dist.get_rank()
+        # adjust the total samples to avoid batch dropping
+        num_samples_per_replica = int(math.ceil(self.n_data_samples * 1.0 / num_replicas))
+        img_indices = [idx for idx in range(self.n_data_samples)]
+
+        self.shuffle = False
+        if is_training:
+            # compute the spatial dimensions and corresponding batch size
+            # ImageNet models down-sample images by a factor of 32.
+            # Ensure that width and height dimensions are multiples are multiple of 32.
+            width_dims = [int((w // 32) * 32) for w in width_dims]
+            height_dims = [int((h // 32) * 32) for h in height_dims]
+
+            img_batch_pairs = list()
+            base_elements = base_im_w * base_im_h * base_batch_size
+            for (h, w) in zip(height_dims, width_dims):
+                batch_size = int(max(1, (base_elements / (h * w))))
+                img_batch_pairs.append((h, w, batch_size))
+            self.img_batch_pairs = img_batch_pairs
+            self.shuffle = True
+        else:
+            self.img_batch_pairs = [(base_im_h , base_im_w , base_batch_size)]
+        
+        self.img_indices = img_indices
+        self.n_samples_per_replica = num_samples_per_replica
+        self.epoch = 0
+        self.rank = rank
+        self.num_replicas = num_replicas
+        
+        self.batch_list = []
+        self.current = 0
+        indices_rank_i = self.img_indices[self.rank : len(self.img_indices) : self.num_replicas]
+        while self.current < self.n_samples_per_replica:
+            curr_h, curr_w, curr_bsz = random.choice(self.img_batch_pairs)
+
+            end_index = min(self.current + curr_bsz, self.n_samples_per_replica)
+
+            batch_ids = indices_rank_i[self.current:end_index]
+            n_batch_samples = len(batch_ids)
+            if n_batch_samples != curr_bsz:
+                    batch_ids += indices_rank_i[:(curr_bsz - n_batch_samples)]
+            self.current += curr_bsz
+
+            if len(batch_ids) > 0:
+                    batch = [curr_h, curr_w, len(batch_ids)]
+                    self.batch_list.append(batch)
+        self.length = len(self.batch_list)
+
+    def __iter__(self):
+        if self.shuffle:
+            random.seed(self.epoch)
+            random.shuffle(self.img_indices)
+            random.shuffle(self.img_batch_pairs)
+            indices_rank_i = self.img_indices[self.rank : len(self.img_indices) : self.num_replicas]
+        else:
+            indices_rank_i = self.img_indices[self.rank : len(self.img_indices) : self.num_replicas]
+
+        start_index = 0
+        for batch_tuple in self.batch_list:
+            curr_h, curr_w, curr_bsz = batch_tuple
+            end_index = min(start_index + curr_bsz, self.n_samples_per_replica)
+            batch_ids = indices_rank_i[start_index:end_index]
+            n_batch_samples = len(batch_ids)
+            if n_batch_samples != curr_bsz:
+                    batch_ids += indices_rank_i[:(curr_bsz - n_batch_samples)]
+            start_index += curr_bsz
+
+            if len(batch_ids) > 0:
+                    batch = [(curr_h, curr_w, b_id) for b_id in batch_ids]
+                    yield batch
+
+    def set_epoch(self, epoch: int):
+        self.epoch = epoch
+
+    def __len__(self):
+        return self.length
+