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 make_dataloader(args, epoch=0):
"""
Make train dataloader.
Args:
epoch: current epoch number, used in random erasing data augmentation.
"""
train_list = [
transforms.Resize((args.img_height, args.img_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:
probability = 0.3 + 0.4 * min((float(epoch) / args.num_epochs), 0.8)
s_epoch = 0.1 + 0.3 * min((float(epoch) / args.num_epochs), 0.8)
train_list.append(
RandomErasing(probability=probability,
s_epoch=s_epoch,
mean=[0.0, 0.0, 0.0]))
train_transform = transforms.Compose(train_list)
batch_m = args.num_classes
if 'dmml' in args.loss_type:
batch_k = args.num_support + args.num_query
elif args.loss_type == 'npair':
batch_k = 2
else:
batch_k = args.num_instances
if args.dataset == 'market1501':
train_set = Market1501(args.dataset_root,
train_transform,
split='train')
elif args.dataset == 'duke':
train_set = DukeMTMC_reID(args.dataset_root,
train_transform,
split='train')
else:
raise NotImplementedError
train_loader = dataloader.DataLoader(train_set,
sampler=RandomSampler(
train_set, batch_k),
batch_size=batch_m * batch_k,
num_workers=args.num_workers,
drop_last=True)
return train_loader
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)
def _get_loader(self, indices, **kwargs):
dataset = self._get_dataset(**kwargs)
dataset = Subset(dataset, indices)
shuffle = kwargs.pop("shuffle", False)
if shuffle is True:
sampler = RandomSampler(dataset)
dataloader = DataLoader(dataset,
sampler=sampler,
num_workers=self.num_workers,
pin_memory=self.pin_memory,
**kwargs)
else:
dataloader = DataLoader(dataset,
shuffle=False,
num_workers=self.num_workers,
pin_memory=self.pin_memory,
**kwargs)
return dataloader
def __init__(self, args):
# 重置图像分辨率参数 依概率p水平翻转 转化为tensor 对数据按通道进行标准化
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])
]
#随机擦除图像增强
if args.random_erasing:
train_list.append(RandomErasing(probability=args.probability, mean=[0.0, 0.0, 0.0]))
train_transform = transforms.Compose(train_list)
# 一起组成几个变换
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])
])
# 判断是否仅仅测试
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')
# 从数据集中加载 batch_size个随机选取的图像数据
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']:
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 _get_loader(self, indices, **kwargs):
"""
Takes the "extra" argument from kwargs and zips it together with the original data into a ZipDataset
:param indices: indices of the subset of the data to be extracted
:param kwargs: an arbitrary dictionary
:return: a DataLoader
"""
dataset = self._get_dataset()
dataset = Subset(dataset, indices)
dataset_extra = kwargs.pop("extra", None)
if dataset_extra is not None and isinstance(
dataset_extra, list) and len(dataset_extra) > 0:
datasets = [dataset, dataset_extra]
dataset = ZipDataset(*datasets)
elif dataset_extra is None or (isinstance(dataset_extra, list)
and len(dataset_extra) == 0):
pass
else:
raise NotImplementedError(
"Check that extra is None, an empty list or a non-empty list")
shuffle = kwargs.pop("shuffle", False)
if shuffle is True:
sampler = RandomSampler(dataset)
dataloader = DataLoader(dataset,
sampler=sampler,
num_workers=self.num_workers,
pin_memory=self.pin_memory)
else:
dataloader = DataLoader(dataset,
shuffle=False,
num_workers=self.num_workers,
pin_memory=self.pin_memory)
return dataloader
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)
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.RandomResizedCrop(size=(args.height, args.width),scale=(0.97, 1.0)), #随机剪裁，0.97
transforms.Resize((args.height, args.width), interpolation=3),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(5), #随机角度旋转+-5
transforms.RandomAffine(5),
transforms.ColorJitter(brightness=0.5,
contrast=0.5,
saturation=0.5), #亮度随机变化0.5
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') #调用market1501类，第二个括号里面是参数
# 为了propotypical 服务
# path = [j for j in self.trainset.imgs]
# y = []
# for _, i in enumerate(path):
# y.append(self.trainset._id2label[self.trainset.id(i)])
# y = tuple(y)
# sampler = PrototypicalBatchSampler(labels=y,
# classes_per_it=args.classes_per_it_tr,
# num_samples=args.num_support_tr + args.num_query_tr,
# iterations=args.iterations)
self.train_loader = dataloader.DataLoader(
self.trainset,
sampler=RandomSampler(
self.trainset, args.batchid,
batch_image=args.batchimage), #MGN的smaple
#batch_sampler=sampler, #prototypical的smaple
batch_size=args.batchid * args.batchimage, #MGN
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')
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)