def __init__(self, dataset, name, root=None, with_pose=False, pose_root=None, is_test=False, test_root = None,
pid_imgs=None, height=256, width=128, pose_aug='no', transform=None):
super(Preprocessor, self).__init__()
self.dataset = dataset
self.root = root
self.with_pose = with_pose
self.pose_root = pose_root
self.is_test = is_test
self.test_root = test_root
self.pid_imgs = pid_imgs
self.height = height
self.width = width
self.pose_aug = pose_aug
self.name = name
normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
if transform==None:
self.transform = transforms.Compose([
transforms.RectScale(height, width),
transforms.RandomSizedEarser(),
#transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalizer,
])
else:
self.transform = transform
self.transform_p = transforms.Compose([
transforms.RectScale(height, width),
transforms.ToTensor(),
normalizer,
])
def get_data(name, data_dir, height, width, batch_size, workers, pose_aug,
skip, rate, eraser):
root = osp.join(data_dir, name)
dataset = datasets.create(name, root)
video_dict = None
if osp.isfile(osp.join(root, 'video.json')):
video_dict = read_json(osp.join(root, 'video.json'))
if eraser:
train_transformer = T.Compose([
T.RectScale(height, width),
T.RandomSizedEarser(),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
else:
train_transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# use combined trainval set for training as default
train_loader = DataLoader(Preprocessor(dataset.trainval,
name,
root=dataset.images_dir,
with_pose=True,
pose_root=dataset.poses_dir,
pid_imgs=dataset.trainval_query,
height=height,
width=width,
pose_aug=pose_aug,
transform=train_transformer),
sampler=RandomTripletSampler(
dataset.trainval,
video_dict=video_dict,
skip_frames=skip,
inter_rate=rate),
batch_size=batch_size,
num_workers=workers,
pin_memory=False)
test_transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
test_loader = DataLoader(Preprocessor(
list(set(dataset.query) | set(dataset.gallery)),
name,
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=False)
return dataset, train_loader, test_loader
def get_data(name, split_id, data_dir, height, width, batch_size,
num_instances, workers, combine_trainval):
root = osp.join(data_dir, name)
dataset = datasets.create(name, root, split_id=split_id)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_set = dataset.trainval if combine_trainval else dataset.train
num_classes = (dataset.num_trainval_ids
if combine_trainval else dataset.num_train_ids)
train_transformer = T.Compose([
T.RectScale(height, width),
T.RandomSizedEarser(),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalizer,
])
test_transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
normalizer,
])
train_loader = DataLoader(Preprocessor(train_set,
root=dataset.images_dir,
transform=train_transformer),
batch_size=batch_size,
num_workers=workers,
sampler=RandomMultipleGallerySampler(
train_set, num_instances),
pin_memory=True,
drop_last=True)
val_loader = DataLoader(Preprocessor(dataset.val,
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=True)
test_loader = DataLoader(Preprocessor(
list(set(dataset.query) | set(dataset.gallery)),
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=True)
return dataset, num_classes, train_loader, val_loader, test_loader
def get_data(name, split_id, data_dir, height, width, batch_size, workers,
combine_trainval, np_ratio):
root = osp.join(data_dir, name)
dataset = datasets.create(name, root, split_id=split_id)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_set = dataset.trainval if combine_trainval else dataset.train
train_transformer = T.Compose([
T.RandomSizedRectCrop(height, width),
T.RandomSizedEarser(),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalizer,
])
test_transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
normalizer,
])
train_loader = DataLoader(Preprocessor(train_set,
root=dataset.images_dir,
transform=train_transformer),
sampler=RandomPairSampler(
train_set, neg_pos_ratio=np_ratio),
batch_size=batch_size,
num_workers=workers,
pin_memory=False)
val_loader = DataLoader(Preprocessor(dataset.val,
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=0,
shuffle=False,
pin_memory=False)
test_loader = DataLoader(Preprocessor(
list(set(dataset.query) | set(dataset.gallery)),
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=0,
shuffle=False,
pin_memory=False)
return dataset, train_loader, val_loader, test_loader
def get_data(name, split_id, data_dir, height, width, batch_size, workers,
combine_trainval, np_ratio, model, instance_mode, eraser):
root = osp.join(data_dir, name)
dataset = datasets.create(name, root, split_id=split_id)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_set = dataset.trainval if combine_trainval else dataset.train
if eraser:
train_transformer = T.Compose([
T.RandomSizedRectCrop(height, width),
T.RandomSizedEarser(),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalizer,
])
else:
train_transformer = T.Compose([
T.RandomSizedRectCrop(height, width),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalizer,
])
test_transformer = T.Compose([
T.RectScale(height, width),
T.ToTensor(),
normalizer,
])
if (model == 'Single'):
video_dict = None
if osp.isfile(osp.join(root, 'video.json')):
video_dict = read_json(osp.join(root, 'video.json'))
sampler = RandomTripletSampler(train_set,
video_dict=None,
skip_frames=10,
inter_rate=0.9,
inst_sample=instance_mode)
elif (model == 'Siamese'):
sampler = RandomPairSampler(train_set, neg_pos_ratio=np_ratio)
else:
raise ValueError('unrecognized mode')
train_loader = DataLoader(Preprocessor(train_set,
name,
root=dataset.images_dir,
transform=train_transformer),
sampler=sampler,
batch_size=batch_size,
num_workers=workers,
pin_memory=False)
val_loader = DataLoader(Preprocessor(dataset.val,
name,
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=False)
test_loader = DataLoader(Preprocessor(
list(set(dataset.query) | set(dataset.gallery)),
name,
root=dataset.images_dir,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=False)
return dataset, train_loader, val_loader, test_loader