Open source codes
shaoniangu
parent
1e470fa69a
commit
ca3ffbd506
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|
||||
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|
||||
<caret line="0" column="137" lean-forward="true" selection-start-line="0" selection-start-column="137" selection-end-line="0" selection-end-column="137" />
|
||||
<folding />
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/Experiment-all_tricks-tri_center-market.sh">
|
||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="200">
|
||||
<caret line="10" column="111" lean-forward="true" selection-start-line="10" selection-start-column="111" selection-end-line="10" selection-end-column="111" />
|
||||
<folding />
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/Experiment-all_tricks-without_center-duke.sh">
|
||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="0">
|
||||
<caret line="0" column="115" lean-forward="false" selection-start-line="0" selection-start-column="115" selection-end-line="0" selection-end-column="115" />
|
||||
<folding />
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/Experiment-all_tricks-without_center-market.sh">
|
||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="0">
|
||||
<caret line="0" column="115" lean-forward="false" selection-start-line="0" selection-start-column="115" selection-end-line="0" selection-end-column="115" />
|
||||
<folding />
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/Test-all_tricks-without_center-feat_after_bn-cos-duke.sh">
|
||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="0">
|
||||
<caret line="0" column="44" lean-forward="false" selection-start-line="0" selection-start-column="44" selection-end-line="0" selection-end-column="44" />
|
||||
<folding />
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/Test-all_tricks-without_center-feat_after_bn-cos-market.sh">
|
||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="0">
|
||||
<caret line="0" column="13" lean-forward="true" selection-start-line="0" selection-start-column="13" selection-end-line="0" selection-end-column="62" />
|
||||
<folding />
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/Test-reranking-all_tricks-tri_center-feat_after_bn-cos-market.sh">
|
||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="220">
|
||||
<caret line="11" column="134" lean-forward="false" selection-start-line="11" selection-start-column="134" selection-end-line="11" selection-end-column="134" />
|
||||
<folding />
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/Test-reranking-all_tricks-tri_center-feat_after_bn-cos-duke.sh">
|
||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="220">
|
||||
<caret line="11" column="211" lean-forward="false" selection-start-line="11" selection-start-column="211" selection-end-line="11" selection-end-column="211" />
|
||||
<folding />
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/engine/trainer.py">
|
||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="-1060">
|
||||
<caret line="144" column="129" lean-forward="false" selection-start-line="144" selection-start-column="129" selection-end-line="144" selection-end-column="129" />
|
||||
<folding />
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
</component>
|
||||
</project>
|
||||
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|
|
@ -1,7 +0,0 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
@author: sherlock
|
||||
@contact: sherlockliao01@gmail.com
|
||||
"""
|
||||
|
||||
from .build import make_data_loader
|
||||
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|
|
@ -1,45 +0,0 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
@author: liaoxingyu
|
||||
@contact: sherlockliao01@gmail.com
|
||||
"""
|
||||
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from .collate_batch import train_collate_fn, val_collate_fn
|
||||
from .datasets import init_dataset, ImageDataset
|
||||
from .samplers import RandomIdentitySampler, RandomIdentitySampler_alignedreid # New add by gu
|
||||
from .transforms import build_transforms
|
||||
|
||||
|
||||
def make_data_loader(cfg):
|
||||
train_transforms = build_transforms(cfg, is_train=True)
|
||||
val_transforms = build_transforms(cfg, is_train=False)
|
||||
num_workers = cfg.DATALOADER.NUM_WORKERS
|
||||
if len(cfg.DATASETS.NAMES) == 1:
|
||||
dataset = init_dataset(cfg.DATASETS.NAMES)
|
||||
else:
|
||||
# TODO: add multi dataset to train
|
||||
dataset = init_dataset(cfg.DATASETS.NAMES)
|
||||
|
||||
num_classes = dataset.num_train_pids
|
||||
train_set = ImageDataset(dataset.train, train_transforms)
|
||||
if cfg.DATALOADER.SAMPLER == 'softmax':
|
||||
train_loader = DataLoader(
|
||||
train_set, batch_size=cfg.SOLVER.IMS_PER_BATCH, shuffle=True, num_workers=num_workers,
|
||||
collate_fn=train_collate_fn
|
||||
)
|
||||
else:
|
||||
train_loader = DataLoader(
|
||||
train_set, batch_size=cfg.SOLVER.IMS_PER_BATCH,
|
||||
sampler=RandomIdentitySampler(dataset.train, cfg.SOLVER.IMS_PER_BATCH, cfg.DATALOADER.NUM_INSTANCE),
|
||||
# sampler=RandomIdentitySampler_alignedreid(dataset.train, cfg.DATALOADER.NUM_INSTANCE), # new add by gu
|
||||
num_workers=num_workers, collate_fn=train_collate_fn
|
||||
)
|
||||
|
||||
val_set = ImageDataset(dataset.query + dataset.gallery, val_transforms)
|
||||
val_loader = DataLoader(
|
||||
val_set, batch_size=cfg.TEST.IMS_PER_BATCH, shuffle=False, num_workers=num_workers,
|
||||
collate_fn=val_collate_fn
|
||||
)
|
||||
return train_loader, val_loader, len(dataset.query), num_classes
|
||||
|
|
@ -1,18 +0,0 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
@author: liaoxingyu
|
||||
@contact: sherlockliao01@gmail.com
|
||||
"""
|
||||
|
||||
import torch
|
||||
|
||||
|
||||
def train_collate_fn(batch):
|
||||
imgs, pids, _, _, = zip(*batch)
|
||||
pids = torch.tensor(pids, dtype=torch.int64)
|
||||
return torch.stack(imgs, dim=0), pids
|
||||
|
||||
|
||||
def val_collate_fn(batch):
|
||||
imgs, pids, camids, _ = zip(*batch)
|
||||
return torch.stack(imgs, dim=0), pids, camids
|
||||
|
|
@ -1,27 +0,0 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
@author: liaoxingyu
|
||||
@contact: sherlockliao01@gmail.com
|
||||
"""
|
||||
from .cuhk03 import CUHK03
|
||||
from .dukemtmcreid import DukeMTMCreID
|
||||
from .market1501 import Market1501
|
||||
from .msmt17 import MSMT17
|
||||
from .dataset_loader import ImageDataset
|
||||
|
||||
__factory = {
|
||||
'market1501': Market1501,
|
||||
'cuhk03': CUHK03,
|
||||
'dukemtmc': DukeMTMCreID,
|
||||
'msmt17': MSMT17,
|
||||
}
|
||||
|
||||
|
||||
def get_names():
|
||||
return __factory.keys()
|
||||
|
||||
|
||||
def init_dataset(name, *args, **kwargs):
|
||||
if name not in __factory.keys():
|
||||
raise KeyError("Unknown datasets: {}".format(name))
|
||||
return __factory[name](*args, **kwargs)
|
||||
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|
|
@ -1,95 +0,0 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
@author: sherlock
|
||||
@contact: sherlockliao01@gmail.com
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
class BaseDataset(object):
|
||||
"""
|
||||
Base class of reid dataset
|
||||
"""
|
||||
|
||||
def get_imagedata_info(self, data):
|
||||
pids, cams = [], []
|
||||
for _, pid, camid in data:
|
||||
pids += [pid]
|
||||
cams += [camid]
|
||||
pids = set(pids)
|
||||
cams = set(cams)
|
||||
num_pids = len(pids)
|
||||
num_cams = len(cams)
|
||||
num_imgs = len(data)
|
||||
return num_pids, num_imgs, num_cams
|
||||
|
||||
def get_videodata_info(self, data, return_tracklet_stats=False):
|
||||
pids, cams, tracklet_stats = [], [], []
|
||||
for img_paths, pid, camid in data:
|
||||
pids += [pid]
|
||||
cams += [camid]
|
||||
tracklet_stats += [len(img_paths)]
|
||||
pids = set(pids)
|
||||
cams = set(cams)
|
||||
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):
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class BaseImageDataset(BaseDataset):
|
||||
"""
|
||||
Base class of image reid dataset
|
||||
"""
|
||||
|
||||
def print_dataset_statistics(self, train, query, gallery):
|
||||
num_train_pids, num_train_imgs, num_train_cams = self.get_imagedata_info(train)
|
||||
num_query_pids, num_query_imgs, num_query_cams = self.get_imagedata_info(query)
|
||||
num_gallery_pids, num_gallery_imgs, num_gallery_cams = self.get_imagedata_info(gallery)
|
||||
|
||||
print("Dataset statistics:")
|
||||
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 print_dataset_statistics(self, train, query, gallery):
|
||||
num_train_pids, num_train_tracklets, num_train_cams, train_tracklet_stats = \
|
||||
self.get_videodata_info(train, return_tracklet_stats=True)
|
||||
|
||||
num_query_pids, num_query_tracklets, num_query_cams, query_tracklet_stats = \
|
||||
self.get_videodata_info(query, return_tracklet_stats=True)
|
||||
|
||||
num_gallery_pids, num_gallery_tracklets, num_gallery_cams, gallery_tracklet_stats = \
|
||||
self.get_videodata_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("Dataset statistics:")
|
||||
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(" -------------------------------------------")
|
||||
|
|
@ -1,259 +0,0 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
@author: liaoxingyu
|
||||
@contact: liaoxingyu2@jd.com
|
||||
"""
|
||||
|
||||
import h5py
|
||||
import os.path as osp
|
||||
from scipy.io import loadmat
|
||||
from scipy.misc import imsave
|
||||
|
||||
from 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='/home/haoluo/data', split_id=0, cuhk03_labeled=False,
|
||||
cuhk03_classic_split=False, verbose=True,
|
||||
**kwargs):
|
||||
super(CUHK03, self).__init__()
|
||||
self.dataset_dir = osp.join(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')
|
||||
|
||||
self._check_before_run()
|
||||
self._preprocess()
|
||||
|
||||
if cuhk03_labeled:
|
||||
image_type = 'labeled'
|
||||
split_path = self.split_classic_lab_json_path if cuhk03_classic_split else self.split_new_lab_json_path
|
||||
else:
|
||||
image_type = 'detected'
|
||||
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]
|
||||
print("Split index = {}".format(split_id))
|
||||
|
||||
train = split['train']
|
||||
query = split['query']
|
||||
gallery = split['gallery']
|
||||
|
||||
if verbose:
|
||||
print("=> CUHK03 ({}) loaded".format(image_type))
|
||||
self.print_dataset_statistics(train, query, gallery)
|
||||
|
||||
self.train = train
|
||||
self.query = query
|
||||
self.gallery = gallery
|
||||
|
||||
self.num_train_pids, self.num_train_imgs, self.num_train_cams = self.get_imagedata_info(self.train)
|
||||
self.num_query_pids, self.num_query_imgs, self.num_query_cams = self.get_imagedata_info(self.query)
|
||||
self.num_gallery_pids, self.num_gallery_imgs, self.num_gallery_cams = self.get_imagedata_info(self.gallery)
|
||||
|
||||
def _check_before_run(self):
|
||||
"""Check if all files are available before going deeper"""
|
||||
if not osp.exists(self.dataset_dir):
|
||||
raise RuntimeError("'{}' is not available".format(self.dataset_dir))
|
||||
if not osp.exists(self.data_dir):
|
||||
raise RuntimeError("'{}' is not available".format(self.data_dir))
|
||||
if not osp.exists(self.raw_mat_path):
|
||||
raise RuntimeError("'{}' is not available".format(self.raw_mat_path))
|
||||
if not osp.exists(self.split_new_det_mat_path):
|
||||
raise RuntimeError("'{}' is not available".format(self.split_new_det_mat_path))
|
||||
if not osp.exists(self.split_new_lab_mat_path):
|
||||
raise RuntimeError("'{}' is not available".format(self.split_new_lab_mat_path))
|
||||
|
||||
def _preprocess(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)
|
||||
"""
|
||||
print(
|
||||
"Note: if root path is changed, the previously generated json files need to be re-generated (delete them first)")
|
||||
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)
|
||||
# skip empty cell
|
||||
if img.size == 0 or img.ndim < 3: continue
|
||||
# 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(name):
|
||||
print("Processing {} images (extract and save) ...".format(name))
|
||||
meta_data = []
|
||||
imgs_dir = self.imgs_detected_dir if name == 'detected' else self.imgs_labeled_dir
|
||||
for campid, camp_ref in enumerate(mat[name][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 splits 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,
|
||||
)
|
||||
splits = [{
|
||||
'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(splits, self.split_new_det_json_path)
|
||||
|
||||
print("Creating new splits 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,
|
||||
)
|
||||
splits = [{
|
||||
'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(splits, self.split_new_lab_json_path)
|
||||
|
|
@ -1,45 +0,0 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
@author: liaoxingyu
|
||||
@contact: sherlockliao01@gmail.com
|
||||
"""
|
||||
|
||||
import os.path as osp
|
||||
from PIL import Image
|
||||
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
|
||||
|
|
@ -1,106 +0,0 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
@author: liaoxingyu
|
||||
@contact: liaoxingyu2@jd.com
|
||||
"""
|
||||
|
||||
import glob
|
||||
import re
|
||||
import urllib
|
||||
import zipfile
|
||||
|
||||
import os.path as osp
|
||||
|
||||
from 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='/home/haoluo/data', verbose=True, **kwargs):
|
||||
super(DukeMTMCreID, self).__init__()
|
||||
self.dataset_dir = osp.join(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()
|
||||
self._check_before_run()
|
||||
|
||||
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 verbose:
|
||||
print("=> DukeMTMC-reID loaded")
|
||||
self.print_dataset_statistics(train, query, gallery)
|
||||
|
||||
self.train = train
|
||||
self.query = query
|
||||
self.gallery = gallery
|
||||
|
||||
self.num_train_pids, self.num_train_imgs, self.num_train_cams = self.get_imagedata_info(self.train)
|
||||
self.num_query_pids, self.num_query_imgs, self.num_query_cams = self.get_imagedata_info(self.query)
|
||||
self.num_gallery_pids, self.num_gallery_imgs, self.num_gallery_cams = self.get_imagedata_info(self.gallery)
|
||||
|
||||
def _download_data(self):
|
||||
if osp.exists(self.dataset_dir):
|
||||
print("This dataset has been downloaded.")
|
||||
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 _check_before_run(self):
|
||||
"""Check if all files are available before going deeper"""
|
||||
if not osp.exists(self.dataset_dir):
|
||||
raise RuntimeError("'{}' is not available".format(self.dataset_dir))
|
||||
if not osp.exists(self.train_dir):
|
||||
raise RuntimeError("'{}' is not available".format(self.train_dir))
|
||||
if not osp.exists(self.query_dir):
|
||||
raise RuntimeError("'{}' is not available".format(self.query_dir))
|
||||
if not osp.exists(self.gallery_dir):
|
||||
raise RuntimeError("'{}' is not available".format(self.gallery_dir))
|
||||
|
||||
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
|
||||
|
|
@ -1,63 +0,0 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
@author: liaoxingyu
|
||||
@contact: sherlockliao01@gmail.com
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
def eval_func(distmat, q_pids, g_pids, q_camids, g_camids, max_rank=50):
|
||||
"""Evaluation with market1501 metric
|
||||
Key: for each query identity, its gallery images from the same camera view are discarded.
|
||||
"""
|
||||
num_q, num_g = distmat.shape
|
||||
if num_g < max_rank:
|
||||
max_rank = num_g
|
||||
print("Note: number of gallery samples is quite small, got {}".format(num_g))
|
||||
indices = np.argsort(distmat, axis=1)
|
||||
matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
|
||||
|
||||
# compute cmc curve for each query
|
||||
all_cmc = []
|
||||
all_AP = []
|
||||
num_valid_q = 0. # number of valid query
|
||||
for q_idx in range(num_q):
|
||||
# get query pid and camid
|
||||
q_pid = q_pids[q_idx]
|
||||
q_camid = q_camids[q_idx]
|
||||
|
||||
# remove gallery samples that have the same pid and camid with query
|
||||
order = indices[q_idx]
|
||||
remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
|
||||
keep = np.invert(remove)
|
||||
|
||||
# compute cmc curve
|
||||
# binary vector, positions with value 1 are correct matches
|
||||
orig_cmc = matches[q_idx][keep]
|
||||
if not np.any(orig_cmc):
|
||||
# this condition is true when query identity does not appear in gallery
|
||||
continue
|
||||
|
||||
cmc = orig_cmc.cumsum()
|
||||
cmc[cmc > 1] = 1
|
||||
|
||||
all_cmc.append(cmc[:max_rank])
|
||||
num_valid_q += 1.
|
||||
|
||||
# compute average precision
|
||||
# reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
|
||||
num_rel = orig_cmc.sum()
|
||||
tmp_cmc = orig_cmc.cumsum()
|
||||
tmp_cmc = [x / (i + 1.) for i, x in enumerate(tmp_cmc)]
|
||||
tmp_cmc = np.asarray(tmp_cmc) * orig_cmc
|
||||
AP = tmp_cmc.sum() / num_rel
|
||||
all_AP.append(AP)
|
||||
|
||||
assert num_valid_q > 0, "Error: all query identities do not appear in gallery"
|
||||
|
||||
all_cmc = np.asarray(all_cmc).astype(np.float32)
|
||||
all_cmc = all_cmc.sum(0) / num_valid_q
|
||||
mAP = np.mean(all_AP)
|
||||
|
||||
return all_cmc, mAP
|
||||
|
|
@ -1,85 +0,0 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
@author: sherlock
|
||||
@contact: sherlockliao01@gmail.com
|
||||
"""
|
||||
|
||||
import glob
|
||||
import re
|
||||
|
||||
import os.path as osp
|
||||
|
||||
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='/home/haoluo/data', verbose=True, **kwargs):
|
||||
super(Market1501, self).__init__()
|
||||
self.dataset_dir = osp.join(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._check_before_run()
|
||||
|
||||
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 verbose:
|
||||
print("=> Market1501 loaded")
|
||||
self.print_dataset_statistics(train, query, gallery)
|
||||
|
||||
self.train = train
|
||||
self.query = query
|
||||
self.gallery = gallery
|
||||
|
||||
self.num_train_pids, self.num_train_imgs, self.num_train_cams = self.get_imagedata_info(self.train)
|
||||
self.num_query_pids, self.num_query_imgs, self.num_query_cams = self.get_imagedata_info(self.query)
|
||||
self.num_gallery_pids, self.num_gallery_imgs, self.num_gallery_cams = self.get_imagedata_info(self.gallery)
|
||||
|
||||
def _check_before_run(self):
|
||||
"""Check if all files are available before going deeper"""
|
||||
if not osp.exists(self.dataset_dir):
|
||||
raise RuntimeError("'{}' is not available".format(self.dataset_dir))
|
||||
if not osp.exists(self.train_dir):
|
||||
raise RuntimeError("'{}' is not available".format(self.train_dir))
|
||||
if not osp.exists(self.query_dir):
|
||||
raise RuntimeError("'{}' is not available".format(self.query_dir))
|
||||
if not osp.exists(self.gallery_dir):
|
||||
raise RuntimeError("'{}' is not available".format(self.gallery_dir))
|
||||
|
||||
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
|
||||
|
|
@ -1,83 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Time : 2019/1/17 15:00
|
||||
# @Author : Hao Luo
|
||||
# @File : msmt17.py
|
||||
|
||||
import glob
|
||||
import re
|
||||
|
||||
import os.path as osp
|
||||
|
||||
from .bases import BaseImageDataset
|
||||
|
||||
|
||||
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='/home/haoluo/data', verbose=True, **kwargs):
|
||||
super(MSMT17, self).__init__()
|
||||
self.dataset_dir = osp.join(root, self.dataset_dir)
|
||||
self.train_dir = osp.join(self.dataset_dir, 'MSMT17_V2/mask_train_v2')
|
||||
self.test_dir = osp.join(self.dataset_dir, 'MSMT17_V2/mask_test_v2')
|
||||
self.list_train_path = osp.join(self.dataset_dir, 'MSMT17_V2/list_train.txt')
|
||||
self.list_val_path = osp.join(self.dataset_dir, 'MSMT17_V2/list_val.txt')
|
||||
self.list_query_path = osp.join(self.dataset_dir, 'MSMT17_V2/list_query.txt')
|
||||
self.list_gallery_path = osp.join(self.dataset_dir, 'MSMT17_V2/list_gallery.txt')
|
||||
|
||||
self._check_before_run()
|
||||
train = self._process_dir(self.train_dir, self.list_train_path)
|
||||
#val, num_val_pids, num_val_imgs = 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)
|
||||
if verbose:
|
||||
print("=> MSMT17 loaded")
|
||||
self.print_dataset_statistics(train, query, gallery)
|
||||
|
||||
self.train = train
|
||||
self.query = query
|
||||
self.gallery = gallery
|
||||
|
||||
self.num_train_pids, self.num_train_imgs, self.num_train_cams = self.get_imagedata_info(self.train)
|
||||
self.num_query_pids, self.num_query_imgs, self.num_query_cams = self.get_imagedata_info(self.query)
|
||||
self.num_gallery_pids, self.num_gallery_imgs, self.num_gallery_cams = self.get_imagedata_info(self.gallery)
|
||||
|
||||
def _check_before_run(self):
|
||||
"""Check if all files are available before going deeper"""
|
||||
if not osp.exists(self.dataset_dir):
|
||||
raise RuntimeError("'{}' is not available".format(self.dataset_dir))
|
||||
if not osp.exists(self.train_dir):
|
||||
raise RuntimeError("'{}' is not available".format(self.train_dir))
|
||||
if not osp.exists(self.test_dir):
|
||||
raise RuntimeError("'{}' is not available".format(self.test_dir))
|
||||
|
||||
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])
|
||||
img_path = osp.join(dir_path, img_path)
|
||||
dataset.append((img_path, pid, camid))
|
||||
pid_container.add(pid)
|
||||
|
||||
# check if pid starts from 0 and increments with 1
|
||||
for idx, pid in enumerate(pid_container):
|
||||
assert idx == pid, "See code comment for explanation"
|
||||
return dataset
|
||||
|
|
@ -1,7 +0,0 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
@author: liaoxingyu
|
||||
@contact: sherlockliao01@gmail.com
|
||||
"""
|
||||
|
||||
from .triplet_sampler import RandomIdentitySampler, RandomIdentitySampler_alignedreid # new add by gu
|
||||
Binary file not shown.
Binary file not shown.
|
|
@ -1,110 +0,0 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
@author: liaoxingyu
|
||||
@contact: liaoxingyu2@jd.com
|
||||
"""
|
||||
|
||||
import copy
|
||||
import random
|
||||
import torch
|
||||
from collections import defaultdict
|
||||
|
||||
import numpy as np
|
||||
from torch.utils.data.sampler import Sampler
|
||||
|
||||
|
||||
class RandomIdentitySampler(Sampler):
|
||||
"""
|
||||
Randomly sample N identities, then for each identity,
|
||||
randomly sample K instances, therefore batch size is N*K.
|
||||
Args:
|
||||
- data_source (list): list of (img_path, pid, camid).
|
||||
- num_instances (int): number of instances per identity in a batch.
|
||||
- batch_size (int): number of examples 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
|
||||
|
||||
|
||||
# New add by gu
|
||||
class RandomIdentitySampler_alignedreid(Sampler):
|
||||
"""
|
||||
Randomly sample N identities, then for each identity,
|
||||
randomly sample K instances, therefore batch size is N*K.
|
||||
|
||||
Code imported from https://github.com/Cysu/open-reid/blob/master/reid/utils/data/sampler.py.
|
||||
|
||||
Args:
|
||||
data_source (Dataset): dataset to sample from.
|
||||
num_instances (int): number of instances per identity.
|
||||
"""
|
||||
def __init__(self, data_source, num_instances):
|
||||
self.data_source = data_source
|
||||
self.num_instances = num_instances
|
||||
self.index_dic = defaultdict(list)
|
||||
for index, (_, pid, _) in enumerate(data_source):
|
||||
self.index_dic[pid].append(index)
|
||||
self.pids = list(self.index_dic.keys())
|
||||
self.num_identities = len(self.pids)
|
||||
|
||||
def __iter__(self):
|
||||
indices = torch.randperm(self.num_identities)
|
||||
ret = []
|
||||
for i in indices:
|
||||
pid = self.pids[i]
|
||||
t = self.index_dic[pid]
|
||||
replace = False if len(t) >= self.num_instances else True
|
||||
t = np.random.choice(t, size=self.num_instances, replace=replace)
|
||||
ret.extend(t)
|
||||
return iter(ret)
|
||||
|
||||
def __len__(self):
|
||||
return self.num_identities * self.num_instances
|
||||
|
|
@ -1,7 +0,0 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
@author: sherlock
|
||||
@contact: sherlockliao01@gmail.com
|
||||
"""
|
||||
|
||||
from .build import build_transforms
|
||||
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Binary file not shown.
|
|
@ -1,31 +0,0 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
@author: liaoxingyu
|
||||
@contact: liaoxingyu2@jd.com
|
||||
"""
|
||||
|
||||
import torchvision.transforms as T
|
||||
|
||||
from .transforms import RandomErasing
|
||||
|
||||
|
||||
def build_transforms(cfg, is_train=True):
|
||||
normalize_transform = T.Normalize(mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD)
|
||||
if is_train:
|
||||
transform = T.Compose([
|
||||
T.Resize(cfg.INPUT.SIZE_TRAIN),
|
||||
T.RandomHorizontalFlip(p=cfg.INPUT.PROB),
|
||||
T.Pad(cfg.INPUT.PADDING),
|
||||
T.RandomCrop(cfg.INPUT.SIZE_TRAIN),
|
||||
T.ToTensor(),
|
||||
normalize_transform,
|
||||
RandomErasing(probability=cfg.INPUT.RE_PROB, mean=cfg.INPUT.PIXEL_MEAN)
|
||||
])
|
||||
else:
|
||||
transform = T.Compose([
|
||||
T.Resize(cfg.INPUT.SIZE_TEST),
|
||||
T.ToTensor(),
|
||||
normalize_transform
|
||||
])
|
||||
|
||||
return transform
|
||||
|
|
@ -1,55 +0,0 @@
|
|||
# encoding: utf-8
|
||||
"""
|
||||
@author: liaoxingyu
|
||||
@contact: liaoxingyu2@jd.com
|
||||
"""
|
||||
|
||||
import math
|
||||
import random
|
||||
|
||||
|
||||
class RandomErasing(object):
|
||||
""" Randomly selects a rectangle region in an image and erases its pixels.
|
||||
'Random Erasing Data Augmentation' by Zhong et al.
|
||||
See https://arxiv.org/pdf/1708.04896.pdf
|
||||
Args:
|
||||
probability: The probability that the Random Erasing operation will be performed.
|
||||
sl: Minimum proportion of erased area against input image.
|
||||
sh: Maximum proportion of erased area against input image.
|
||||
r1: Minimum aspect ratio of erased area.
|
||||
mean: Erasing value.
|
||||
"""
|
||||
|
||||
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
|
||||
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Loading…
Reference in New Issue