def __init__(self, args):
train_list = [
transforms.Resize((args.height, args.width), interpolation=3),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]
if args.random_erasing:
train_list.append(
RandomErasing(probability=args.probability,
mean=[0.0, 0.0, 0.0]))
train_transform = transforms.Compose(train_list)
test_transform = transforms.Compose([
transforms.Resize((args.height, args.width), interpolation=3),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
if not args.test_only:
module_train = import_module('data.' + args.data_train.lower())
self.trainset = getattr(module_train,
args.data_train)(args, train_transform,
'train')
self.train_loader = dataloader.DataLoader(
self.trainset,
sampler=RandomSampler(self.trainset,
args.batchid,
batch_image=args.batchimage),
#shuffle=True,
batch_size=args.batchid * args.batchimage,
num_workers=args.nThread)
else:
self.train_loader = None
if args.data_test in [
'Market1501', 'Market1501_folder', 'MSMT17_folder',
'CUHK03_folder', 'SYSU30K_folder'
]:
module = import_module('data.' + args.data_train.lower())
self.testset = getattr(module,
args.data_test)(args, test_transform,
'test')
self.queryset = getattr(module,
args.data_test)(args, test_transform,
'query')
else:
raise Exception()
self.test_loader = dataloader.DataLoader(self.testset,
batch_size=args.batchtest,
num_workers=args.nThread)
self.query_loader = dataloader.DataLoader(self.queryset,
batch_size=args.batchtest,
num_workers=args.nThread)
def __call__(self, x):
if self.data == 'person':
x = T.Resize((384, 128))(x)
elif self.data == 'car':
x = pad_shorter(x)
x = T.Resize((256, 256))(x)
x = T.RandomCrop((224, 224))(x)
elif self.data == 'cub':
x = pad_shorter(x)
x = T.Resize((256, 256))(x)
x = T.RandomCrop((224, 224))(x)
elif self.data == 'clothes':
x = pad_shorter(x)
x = T.Resize((256, 256))(x)
x = T.RandomCrop((224, 224))(x)
elif self.data == 'product':
x = pad_shorter(x)
x = T.Resize((256, 256))(x)
x = T.RandomCrop((224, 224))(x)
elif self.data == 'cifar':
x = T.Resize((40, 40))(x)
x = T.RandomCrop((32, 32))(x)
x = T.RandomHorizontalFlip()(x)
x = T.ToTensor()(x)
x = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])(x)
if self.data == 'person':
x = Cutout(probability = 0.5, size=64, mean=[0.0, 0.0, 0.0])(x)
else:
x = RandomErasing(probability = 0.5, mean=[0.0, 0.0, 0.0])(x)
return x
def __call__(self, x):
x = T.Resize((cfg.TRAIN.IMG_HEIGHT, cfg.TRAIN.IMG_WIDTH), interpolation=Image.BICUBIC)(x)
x = T.RandomHorizontalFlip()(x)
x = T.RandomVerticalFlip()(x)
x = T.ToTensor()(x)
x = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])(x)
x = RandomErasing(probability=0.5, mean=[0., 0., 0.])(x)
return x
def __call__(self, x):
x = T.Resize((256, 128), interpolation=3)(x)
x = T.RandomHorizontalFlip(0.5)(x)
if self.data_aug is not None:
x = T.Pad(10)(x)
x = T.RandomCrop((256, 128))(x)
x = T.ToTensor()(x)
x = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224,
0.225])(x)
if self.data_aug is not None:
if self.data_aug == 'RandomErase':
x = RandomErasing(mean=[0.485, 0.456, 0.406])(x)
else:
x = Cutout(probability=0.5, size=64, mean=[0.0, 0.0, 0.0])(x)
return x
def __init__(self, args):
train_list = [
transforms.Resize((args.height, args.width), interpolation=3),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], \
std=[0.229, 0.224, 0.225])
]
if args.random_erasing:
train_list.append(RandomErasing(probability=args.probability, \
mean=[0.0, 0.0, 0.0]))
train_transform = transforms.Compose(train_list)
test_transform = transforms.Compose([
transforms.Resize((args.height, args.width), interpolation=3),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], \
std=[0.229, 0.224, 0.225])
])
if not args.test_only:
self.trainset = GeneralDataLoader(args, train_transform, \
args.data_train.lower(), 'train')
self.train_loader = dataloader.DataLoader(self.trainset,
sampler=RandomSampler(self.trainset,args.batchid,batch_image=args.batchimage),
#shuffle=True,
batch_size=args.batchid * args.batchimage,
num_workers=args.nThread)
else:
self.train_loader = None
if args.data_test in ['market1501', 'duke', 'cuhk03', 'rap2']:
self.testset = GeneralDataLoader(args, test_transform, \
args.data_test, 'test')
self.queryset= GeneralDataLoader(args, test_transform, \
args.data_test, 'query')
else:
raise Exception()
self.test_loader = dataloader.DataLoader(self.testset, \
batch_size=args.batchtest, num_workers=args.nThread)
self.query_loader = dataloader.DataLoader(self.queryset, \
batch_size=args.batchtest, num_workers=args.nThread)
######################################################################
# Load Data
# --------
#
normalize_transform = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_transforms = T.Compose([
T.Resize([384, 128]),
T.RandomHorizontalFlip(p=0.5),
T.Pad(10),
T.RandomCrop([384, 128]),
T.ToTensor(), normalize_transform,
RandomErasing(probability=0.5, mean=[0.485, 0.456, 0.406])
])
# val_transforms = T.Compose([
# T.Resize([384, 128]),
# T.ToTensor(),
# normalize_transform
# ])
dataset = init_dataset('mars', root='../')
dataset_sizes = {}
dataset_sizes['train'] = dataset.num_train_imgs
train_set = VideoDataset(dataset.train, opt.seq_len, opt.sample_method,
train_transforms)
dataloaders = {}
dataloaders['train'] = DataLoader(train_set,
transform_train_list = [
transforms.Resize((384, 128), interpolation=3),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
transform_val_list = [
transforms.Resize(size=(384, 128), interpolation=3), # Image.BICUBIC
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
if opt.erasing_p > 0:
transform_train_list = transform_train_list + \
[RandomErasing(probability=opt.erasing_p, mean=[0.0, 0.0, 0.0])]
if opt.color_jitter:
transform_train_list = [
transforms.ColorJitter(
brightness=0.1, contrast=0.1, saturation=0.1, hue=0)
] + transform_train_list
print(transform_train_list)
data_transforms = {
'train': transforms.Compose(transform_train_list),
'val': transforms.Compose(transform_val_list),
}
train_all = ''
gid = int(str_id)
if gid >=0:
gpu_ids.append(gid)
if len(gpu_ids)>0:
torch.cuda.set_device(gpu_ids[0])
######################################################################
# prepossessing
transform_train_list = [transforms.Resize((384,128)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]
if opt.erasing_p>0:
transform_train_list = transform_train_list + [RandomErasing(opt.erasing_p)]
if opt.color_jitter:
transform_train_list = [transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0)] + transform_train_list
data_transforms = {'train': transforms.Compose(transform_train_list)}
train_all = ''
if opt.train_all:
train_all = '_all'
image_datasets = datasets.ImageFolder(os.path.join(data_dir, 'train' + train_all), data_transforms['train'])
Sampler = BalancedBatchSampler(image_datasets, n_classes=opt.n_classes, n_samples=opt.n_images)
dataloaders = torch.utils.data.DataLoader(image_datasets, batch_size=opt.n_classes*opt.n_images, shuffle=False, sampler=Sampler)
dataset_sizes = len(image_datasets)
class_names = image_datasets.classes
def __init__(self, args):
# 1. Training transforms
train_list = []
if args.random_crop:
train_list.append(Random2DTranslation(args.height, args.width,
0.5))
else:
train_list.append(
transforms.Resize((args.height, args.width), interpolation=3))
train_list.append(transforms.RandomHorizontalFlip())
if args.color_jitter:
train_list.append(
transforms.ColorJitter(brightness=0.2,
contrast=0.15,
saturation=0,
hue=0))
train_list.append(transforms.ToTensor())
train_list.append(
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]))
if args.random_erasing:
train_list.append(
RandomErasing(probability=args.probability,
mean=[0.0, 0.0, 0.0]))
train_transform = transforms.Compose(train_list)
# 2. Test transforms
test_transform = transforms.Compose([
transforms.Resize((args.height, args.width), interpolation=3),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
if not args.test_only:
module_train = import_module('data.' + args.data_train.lower())
self.trainset = getattr(module_train,
args.data_train)(args, train_transform,
'train')
self.train_loader = dataloader.DataLoader(
self.trainset,
sampler=RandomSampler(self.trainset,
args.batchid,
batch_image=args.batchimage),
# shuffle=True,
batch_size=args.batchid * args.batchimage,
num_workers=args.nThread)
else:
self.train_loader = None
if args.data_test in ['Market1501', 'Boxes']:
module = import_module('data.' + args.data_train.lower())
self.testset = getattr(module,
args.data_test)(args, test_transform,
'test')
self.queryset = getattr(module,
args.data_test)(args, test_transform,
'query')
else:
raise Exception()
self.test_loader = dataloader.DataLoader(self.testset,
batch_size=args.batchtest,
num_workers=args.nThread)
self.query_loader = dataloader.DataLoader(self.queryset,
batch_size=args.batchtest,
num_workers=args.nThread)
transforms.Resize((288, 144), interpolation=3),
transforms.RandomCrop((256, 128)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
transform_val_list = [
transforms.Resize(size=(256, 128), interpolation=3), #Image.BICUBIC
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
if opt.erasing_p > 0:
transform_train_list = transform_train_list + [
RandomErasing(probability=opt.erasing_p, mean=[0.0, 0.0, 0.0])
]
if opt.color_jitter:
transform_train_list = [
transforms.ColorJitter(
brightness=0.1, contrast=0.1, saturation=0.1, hue=0)
] + transform_train_list
# print(transform_train_list)
data_transforms = {
'train': transforms.Compose(transform_train_list),
'val': transforms.Compose(transform_val_list),
}
train_all = ''
]
transform_train_list2 = [
# transforms.RandomResizedCrop(size=128, scale=(0.75,1.0), ratio=(0.75,1.3333), interpolation=3), #Image.BICUBIC)
transforms.Resize((384, 128), interpolation=3), # resize
# transforms.RandomGrayscale(p=0.2),
# transforms.RandomCrop((256,128)),
# transforms.RandomHorizontalFlip(),#randomly horizon flip image
transforms.ToTensor(), # convert PIL image or numpy.ndarray to tensor
transforms.Normalize(
[0.485, 0.456, 0.406],
[0.229, 0.224, 0.225]) # [m1,m2...mn][s1,s2...sn] for n channels
]
if opt.erasing_p > 0:
transform_train_list = transform_train_list + [
RandomErasing(opt.erasing_p)
] # randomly select a rectangle region in a image and erase its pixels with random values
if opt.color_jitter:
transform_train_list = [
transforms.ColorJitter(
brightness=0.1, contrast=0.1, saturation=0.1, hue=0)
] + transform_train_list
# randomly change the brightness,contrast,saturation
print(transform_train_list)
data_transforms = {
'train': transforms.Compose(
transform_train_list), # compose several transforms together
'none': transforms.Compose(transform_train_list2)
}
image_datasets = datasets.ImageFolder(
transforms.RandomHorizontalFlip(), # randomly horizon flip image
transforms.ToTensor(), # convert PIL image or numpy.ndarray to tensor
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # [m1,m2...mn][s1,s2...sn] for n channels
]
transform_train_list2 = [
# transforms.RandomResizedCrop(size=128, scale=(0.75,1.0), ratio=(0.75,1.3333), interpolation=3), #Image.BICUBIC)
transforms.Resize((384, 128), interpolation=3), # resize
# transforms.RandomGrayscale(p=0.2),
# transforms.RandomCrop((256,128)),
# transforms.RandomHorizontalFlip(),#randomly horizon flip image
transforms.ToTensor(), # convert PIL image or numpy.ndarray to tensor
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # [m1,m2...mn][s1,s2...sn] for n channels
]
if opt.erasing_p > 0:
transform_train_list = transform_train_list + [RandomErasing(
opt.erasing_p)] # randomly select a rectangle region in a image and erase its pixels with random values
if opt.color_jitter:
transform_train_list = [transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1,
hue=0)] + transform_train_list
# randomly change the brightness,contrast,saturation
print(transform_train_list)
data_transforms = {
'train': transforms.Compose(transform_train_list), # compose several transforms together
'none': transforms.Compose(transform_train_list2)
}
image_datasets = datasets.ImageFolder(os.path.join(data_dir), data_transforms[
'train']) # return several image folders indicating class including images
# image_datasets=ImageDataset(filelist='./train_set/train_list.txt',source='./train_set',transform1=data_transforms['train'],transform2=data_transforms['none'])
print(len(image_datasets))
# batch sampling for dataset
def __init__(self, args):
self.args = args
self.random_erasing = RandomErasing(probability=args.probability,
mean=[0.0, 0.0, 0.0])
def __init__(self, args):
train_list = [
transforms.Resize((args.height, args.width), interpolation=3),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]
if args.random_erasing:
train_list.append(
RandomErasing(probability=args.probability,
mean=[0.0, 0.0, 0.0]))
train_transform = transforms.Compose(train_list)
self.train_transform = train_transform
test_transform = transforms.Compose([
transforms.Resize((args.height, args.width), interpolation=3),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
self.test_transform = test_transform
if args.data_train in ['Market1501']:
module_train = import_module('data.' + args.data_train.lower())
self.trainset = getattr(module_train,
args.data_train)(args, train_transform,
'train')
# a = self.trainset[1]
self.train_loader = dataloader.DataLoader(
self.trainset,
sampler=RandomSampler(self.trainset,
args.batchid,
batch_image=args.batchimage),
# shuffle=True,
batch_size=args.batchid * args.batchimage,
num_workers=args.nThread)
elif args.data_train in ['SYSU']:
module_train = import_module('data.' + args.data_train.lower())
data_path = args.datadir
self.trainset = getattr(module_train,
args.data_train)(data_path,
train_transform)
color_pos, thermal_pos = module_train.GenIdx(
self.trainset.train_color_label,
self.trainset.train_thermal_label)
self.train_loader = dataloader.DataLoader(
self.trainset,
sampler=IdentitySampler(self.trainset.train_color_label,
self.trainset.train_thermal_label,
color_pos, thermal_pos,
args.batchimage, args.batchid),
# shuffle=True,
batch_size=args.batchid * args.batchimage,
num_workers=args.nThread)
self.trainset.cIndex = self.train_loader.sampler.index1 # RGB index
self.trainset.tIndex = self.train_loader.sampler.index2 # IR index
# a = self.trainset[10]
# embed()
else:
self.train_loader = None
# for get sysu attribute
# args.data_test = 'SYSU'
if args.data_test in ['Market1501']:
module = import_module('data.' + args.data_test.lower())
self.queryset = getattr(module,
args.data_test)(args, test_transform,
'query')
self.testset = getattr(module,
args.data_test)(args, test_transform,
'test')
print(" Dataset statistics: {}".format(args.data_test))
print(" ------------------------------")
print(" subset | # ids | # images")
print(" ------------------------------")
print(' train | {:5d} | {:8d}'.format(
len(self.trainset.unique_ids), len(self.trainset)))
print(' ------------------------------')
print(" query | {:5d} | {:8d}".format(
len(self.queryset.unique_ids), len(self.queryset)))
print(" gallery | {:5d} | {:8d}".format(
len(self.testset.unique_ids), len(self.testset)))
print(" ------------------------------")
elif args.data_test in ['SYSU']:
module = import_module('data.' + args.data_test.lower())
data_path = args.datadir
n_class = len(np.unique(self.trainset.train_color_label))
# rgb --> ir
query_img, query_label, query_cam = module.process_query_sysu(
data_path, mode=args.mode, img_mode="rgb")
gall_img, gall_label, gall_cam = module.process_gallery_sysu(
data_path, mode=args.mode, img_mode="ir")
nquery_rgb2ir = len(query_label)
ngall_rgb2ir = len(gall_label)
self.queryset = module.TestData(query_img,
query_label,
transform=test_transform)
self.testset = module.TestData(gall_img,
gall_label,
transform=test_transform)
# ir --> rgb
query_img, query_label, query_cam = module.process_query_sysu(
data_path, mode=args.mode, img_mode="ir")
gall_img, gall_label, gall_cam = module.process_gallery_sysu(
data_path, mode=args.mode, img_mode="rgb")
nquery_ir2rgb = len(query_label)
ngall_ir2rgb = len(gall_label)
self.queryset = module.TestData(query_img,
query_label,
transform=test_transform)
self.testset = module.TestData(gall_img,
gall_label,
transform=test_transform)
print(" Dataset statistics: {}".format(args.data_test))
print(" ------------------------------")
print(" subset | # ids | # images")
print(" ------------------------------")
print(' rgb_train | {:5d} | {:8d}'.format(
n_class, len(self.trainset.train_color_label)))
print(' ir_train | {:5d} | {:8d}'.format(
n_class, len(self.trainset.train_thermal_label)))
print(' ------------------------------')
print(" rgb_query | {:5d} | {:8d}".format(
len(np.unique(query_label)), nquery_rgb2ir))
print(" ir_gallery | {:5d} | {:8d}".format(
len(np.unique(gall_label)), ngall_rgb2ir))
print(" ------------------------------")
print(" ir_query | {:5d} | {:8d}".format(
len(np.unique(query_label)), nquery_ir2rgb))
print(" rgb_gallery | {:5d} | {:8d}".format(
len(np.unique(gall_label)), ngall_ir2rgb))
print(" ------------------------------")
# for get sysu attribute
# data_path = "/home/zzz/pytorch/ECNU_TXD/shz/data/sysu"
# args.mode = "all"
# module = import_module('data.sysu')
# self.testset = module.SYSU_INFERENCE(data_path, test_transform)
# self.queryset = module.SYSU_INFERENCE(data_path, test_transform)
else:
raise Exception()
self.query_loader = dataloader.DataLoader(self.queryset,
batch_size=args.batchtest,
num_workers=args.nThread)
self.test_loader = dataloader.DataLoader(self.testset,
batch_size=args.batchtest,
num_workers=args.nThread)
self.args = args
def __init__(self, args):
print('[INFO] Making Data...')
train_list = [
transforms.Resize((args.height, args.width), interpolation=3),
# resize the picture （interpolation=3为选择插值方法）
transforms.RandomHorizontalFlip(), # 依概率p水平翻转
transforms.ToTensor(), # 转Tensor
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]) # 归一化
]
if args.random_erasing: # 随机擦除
train_list.append(
RandomErasing(probability=args.probability,
mean=[0.0, 0.0, 0.0]))
train_transform = transforms.Compose(train_list) # 组合步骤
test_transform = transforms.Compose([
transforms.Resize((args.height, args.width), interpolation=3),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
if not args.test_only:
# 加载data/market1501.py，cuhk03.py, dukemtmcreid.py
module_train = import_module(
'data.' + args.data_train.lower()) # lower() aid : A to a
# getattr(对象，属性)为获取某一对象的某个属性的属性值
# def __init__(self, args = args, transform = train_transform, dtype = 'train'):
self.trainset = getattr(module_train,
args.data_train)(args, train_transform,
'train')
self.train_loader = dataloader.DataLoader(
self.trainset, # 传入的数据集
# 自定义从数据集中取样本的策略，如果指定这个参数，那么shuffle必须为False
sampler=RandomSampler(self.trainset,
args.batchid,
batch_image=args.batchimage),
shuffle=False, # 在每个epoch开始的时候，是否对数据进行重新排序
batch_size=args.batchid * args.batchimage, # 每个batch有多少个样本
# 这个参数决定了有几个进程来处理data loading。0意味着所有的数据都会被load进主进程
num_workers=args.nThread)
else:
self.train_loader = None
if args.data_test in ['Market1501']:
module = import_module('data.' + args.data_train.lower())
self.testset = getattr(module,
args.data_test)(args, test_transform,
'test')
self.queryset = getattr(module,
args.data_test)(args, test_transform,
'query')
elif args.data_test in ['DukeMTMCreID']:
module = import_module('data.' + args.data_train.lower())
self.testset = getattr(module,
args.data_test)(args, test_transform,
'test')
self.queryset = getattr(module,
args.data_test)(args, test_transform,
'query')
else:
raise Exception()
self.test_loader = dataloader.DataLoader(self.testset,
batch_size=args.batchtest,
num_workers=args.nThread)
self.query_loader = dataloader.DataLoader(self.queryset,
batch_size=args.batchtest,
num_workers=args.nThread)
def __init__(self, args):
# train_list = [
# transforms.Resize((args.height, args.width), interpolation=3),
# transforms.RandomHorizontalFlip(),
# transforms.ToTensor(),
# transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[
# 0.229, 0.224, 0.225])
# ]
train_list = [
transforms.Resize((args.height, args.width), interpolation=3),
transforms.Pad(10),
transforms.RandomCrop((args.height, args.width)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
if args.random_erasing:
train_list.append(
RandomErasing(probability=args.probability,
mean=[0.485, 0.456, 0.406]))
print('Using random_erasing augmentation.')
if args.cutout:
train_list.append(Cutout(mean=[0.485, 0.456, 0.406]))
print('Using cutout augmentation.')
train_transform = transforms.Compose(train_list)
test_transform = transforms.Compose([
transforms.Resize((args.height, args.width), interpolation=3),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
if not args.test_only and args.model == 'MGN':
module_train = import_module('data.' + args.data_train.lower())
self.trainset = getattr(module_train,
args.data_train)(args, train_transform,
'train')
self.train_loader = dataloader.DataLoader(
self.trainset,
sampler=RandomIdentitySampler(self.trainset,
args.batchid * args.batchimage,
args.batchimage),
# shuffle=True,
batch_size=args.batchid * args.batchimage,
num_workers=args.nThread)
# elif not args.test_only and args.model in ['ResNet50','PCB'] and args.loss.split('*')[1]=='CrossEntropy':
# module_train = import_module('data.' + args.data_train.lower())
# self.trainset = getattr(module_train, args.data_train)(
# args, train_transform, 'train')
# self.train_loader = dataloader.DataLoader(self.trainset,
# shuffle=True,
# batch_size=args.batchid * args.batchimage,
# num_workers=args.nThread)
elif not args.test_only and args.model in [
'ResNet50', 'PCB', 'PCB_v', 'PCB_conv', 'BB_2_db', 'BB',
'MGDB', 'MGDB_v2', 'MGDB_v3', 'BB_2_v3', 'BB_2',
'PCB_conv_modi_2', 'BB_2_conv', 'BB_2_cat', 'BB_4_cat',
'PCB_conv_modi', 'Pyramid', 'PLR'
] and bool(args.sampler):
module_train = import_module('data.' + args.data_train.lower())
self.trainset = getattr(module_train,
args.data_train)(args, train_transform,
'train')
# self.train_loader = dataloader.DataLoader(self.trainset,
# sampler=RandomSampler(
# self.trainset, args.batchid, batch_image=args.batchimage),
# # shuffle=True,
# batch_size=args.batchid * args.batchimage,
# num_workers=args.nThread,
# drop_last=True)
self.train_loader = dataloader.DataLoader(
self.trainset,
sampler=RandomIdentitySampler(self.trainset,
args.batchid * args.batchimage,
args.batchimage),
# shuffle=True,
batch_size=args.batchid * args.batchimage,
num_workers=args.nThread)
elif not args.test_only and args.model not in [
'MGN', 'ResNet50', 'PCB', 'BB_2_db', 'PCB_v', 'PCB_conv',
'MGDB', 'PCB_conv_modi_2', 'PCB_conv_modi', 'BB', 'BB_2',
'BB_2_cat', 'BB_4_cat', 'PLR'
]:
raise Exception('DataLoader for {} not designed'.format(
args.model))
else:
self.train_loader = None
if args.data_test in ['Market1501', 'DukeMTMC', 'GTA']:
module = import_module('data.' + args.data_train.lower())
self.galleryset = getattr(module,
args.data_test)(args, test_transform,
'test')
self.queryset = getattr(module,
args.data_test)(args, test_transform,
'query')
else:
raise Exception()
# print(len(self.trainset))
self.test_loader = dataloader.DataLoader(self.galleryset,
batch_size=args.batchtest,
num_workers=args.nThread)
self.query_loader = dataloader.DataLoader(self.queryset,
batch_size=args.batchtest,
num_workers=args.nThread)