def get_train_loader(dataset, height, width, batch_size, workers,
num_instances, iters):
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_transformer = T.Compose([
T.Resize((height, width), interpolation=3),
T.RandomHorizontalFlip(p=0.5),
T.Pad(10),
T.RandomCrop((height, width)),
T.ToTensor(), normalizer,
T.RandomErasing(probability=0.5, mean=[0.485, 0.456, 0.406])
])
train_set = sorted(dataset.train)
rmgs_flag = num_instances > 0
if rmgs_flag:
sampler = RandomMultipleGallerySampler(train_set, num_instances)
else:
sampler = None
train_loader = IterLoader(DataLoader(Preprocessor(
train_set,
root=dataset.images_dir,
transform=train_transformer,
mutual=False),
batch_size=batch_size,
num_workers=workers,
sampler=sampler,
shuffle=not rmgs_flag,
pin_memory=True,
drop_last=True),
length=iters)
return train_loader
def get_data(name, data_dir, height, width, batch_size, workers, num_instances, iters=200):
root = osp.join(data_dir, name)
dataset = datasets.create(name, root)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_set = dataset.train
num_classes = dataset.num_train_pids
train_transformer = T.Compose([
T.Resize((height, width), interpolation=3),
T.RandomHorizontalFlip(p=0.5),
T.Pad(10),
T.RandomCrop((height, width)),
T.ToTensor(),
normalizer
])
test_transformer = T.Compose([
T.Resize((height, width), interpolation=3),
T.ToTensor(),
normalizer
])
rmgs_flag = num_instances > 0
if rmgs_flag:
sampler = RandomMultipleGallerySampler(train_set, num_instances)
else:
sampler = None
train_loader = IterLoader(
DataLoader(Preprocessor(train_set, root=dataset.images_dir,
transform=train_transformer),
batch_size=batch_size, num_workers=workers, sampler=sampler,
shuffle=not rmgs_flag, pin_memory=True, drop_last=True), length=iters)
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, test_loader
def get_test_loader(dataset, resize_size, batch_size, workers, testset=None):
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
test_transformer = T.Compose(
[T.resize(resize_size, resize_size),
T.ToTensor(), normalize])
test_loader = DataLoader(PreProcessor)
images = datasets.ImageFolder(dataset, test_transformer)
test_loader = DataLoader(images,
batch_size=batch_size,
shuffle=False,
num_workers=workers,
pin_memory=True)
return test_loader
def get_test_data(name, data_dir, height, width, batch_size, workers):
dataset = datasets.create(name, data_dir)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
test_transformer = T.Compose(
[T.Resize((height, width), interpolation=3),
T.ToTensor(), normalizer])
test_loader = DataLoader(Preprocessor(
list(set(dataset.query) | set(dataset.gallery)),
root=None,
transform=test_transformer),
batch_size=batch_size,
num_workers=workers,
shuffle=False,
pin_memory=True)
return dataset, test_loader
def get_test_loader(dataset, height, width, batch_size, workers, testset=None):
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
test_transformer = T.Compose([
T.Resize((height, width), interpolation=3),
T.ToTensor(),
normalizer
])
if (testset is None):
testset = list(set(dataset.query) | set(dataset.gallery))
test_loader = DataLoader(
Preprocessor(testset, root=dataset.images_dir, transform=test_transformer),
batch_size=batch_size, num_workers=workers,
shuffle=False, pin_memory=True)
return test_loader
