def get_data(data_dir, source, target, height, width, batch_size, re=0, workers=8):
dataset = DA(data_dir, source, target)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
num_classes = dataset.num_train_ids
train_transformer = T.Compose([
T.RandomSizedRectCrop(height, width),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalizer,
T.RandomErasing(EPSILON=re),
])
test_transformer = T.Compose([
T.Resize((height, width), interpolation=3),
T.ToTensor(),
normalizer,
])
source_train_loader = DataLoader(
Preprocessor(dataset.source_train, root=osp.join(dataset.source_images_dir, dataset.source_train_path),
transform=train_transformer),
batch_size=batch_size, num_workers=workers,
shuffle=True, pin_memory=True, drop_last=True)
target_train_loader = DataLoader(
UnsupervisedCamStylePreprocessor(dataset.target_train,
root=osp.join(dataset.target_images_dir, dataset.target_train_path),
camstyle_root=osp.join(dataset.target_images_dir,
dataset.target_train_camstyle_path),
num_cam=dataset.target_num_cam, transform=train_transformer),
batch_size=batch_size, num_workers=workers,
shuffle=True, pin_memory=True, drop_last=True)
query_loader = DataLoader(
Preprocessor(dataset.query,
root=osp.join(dataset.target_images_dir, dataset.query_path), transform=test_transformer),
batch_size=batch_size, num_workers=workers,
shuffle=False, pin_memory=True)
gallery_loader = DataLoader(
Preprocessor(dataset.gallery,
root=osp.join(dataset.target_images_dir, dataset.gallery_path), transform=test_transformer),
batch_size=batch_size, num_workers=workers,
shuffle=False, pin_memory=True)
return dataset, num_classes, source_train_loader, target_train_loader, query_loader, gallery_loader
def get_data(data_dir, source, target, height, width, batch_size, num_instance=2, workers=8):
dataset = DA(data_dir, source, target)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
num_classes = dataset.num_train_ids
train_transformer = T.Compose([
T.Resize((256, 128), interpolation=3),
T.Pad(10),
T.RandomCrop((256,128)),
T.RandomHorizontalFlip(0.5),
T.RandomRotation(5),
T.ToTensor(),
normalizer,
])
test_transformer = T.Compose([
T.Resize((256, 128), interpolation=3),
T.ToTensor(),
normalizer,
])
source_train_loader = DataLoader(
Preprocessor_occluded(dataset.source_train, root=osp.join(dataset.source_images_dir, dataset.source_train_path),
transform=train_transformer, train=True),
batch_size=batch_size, num_workers=workers,
sampler=IdentitySampler(dataset.source_train, num_instance),
pin_memory=True, drop_last=True)
query_loader = DataLoader(
Preprocessor_occluded(dataset.query,
root=osp.join(dataset.target_images_dir, dataset.query_path), transform=test_transformer),
batch_size=42, num_workers=workers,
shuffle=False, pin_memory=True)
gallery_loader = DataLoader(
Preprocessor_occluded(dataset.gallery,
root=osp.join(dataset.target_images_dir, dataset.gallery_path), transform=test_transformer),
batch_size=42, num_workers=workers,
shuffle=False, pin_memory=True)
return dataset, num_classes, source_train_loader, query_loader, gallery_loader
def get_data(data_dir, height, width, batch_size, num_instances, re=0, workers=8):
dataset = DA(data_dir)
test_dataset = TotalData(data_dir)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
num_classes = dataset.num_source_ids
train_transformer = T.Compose([
T.Resize((256, 128), interpolation=3),
T.Pad(10),
T.RandomCrop((256,128)),
T.RandomHorizontalFlip(0.5),
T.RandomRotation(5),
T.ColorJitter(brightness=(0.5, 2.0), saturation=(0.5, 2.0), hue=(-0.1, 0.1)),
T.ToTensor(),
normalizer,
# T.RandomErasing(EPSILON=re),
])
test_transformer = T.Compose([
T.Resize((256, 128), interpolation=3),
T.ToTensor(),
normalizer,
])
# Train
source_train_loader = DataLoader(
Preprocessor(dataset.source_train,
transform=train_transformer),
batch_size=batch_size, num_workers=workers,
# shuffle=True, pin_memory=True, drop_last=True)
sampler=RandomIdentitySampler(dataset.source_train, batch_size, num_instances),
pin_memory=True, drop_last=True)
# Test
grid_query_loader = DataLoader(
Preprocessor(test_dataset.grid_query,
root=osp.join(test_dataset.grid_images_dir, test_dataset.query_path), transform=test_transformer),
batch_size=64, num_workers=4,
shuffle=False, pin_memory=True)
grid_gallery_loader = DataLoader(
Preprocessor(test_dataset.grid_gallery,
root=osp.join(test_dataset.grid_images_dir, test_dataset.gallery_path), transform=test_transformer),
batch_size=64, num_workers=4,
shuffle=False, pin_memory=True)
prid_query_loader = DataLoader(
Preprocessor(test_dataset.prid_query,
root=osp.join(test_dataset.prid_images_dir, test_dataset.query_path), transform=test_transformer),
batch_size=64, num_workers=4,
shuffle=False, pin_memory=True)
prid_gallery_loader = DataLoader(
Preprocessor(test_dataset.prid_gallery,
root=osp.join(test_dataset.prid_images_dir, test_dataset.gallery_path), transform=test_transformer),
batch_size=64, num_workers=4,
shuffle=False, pin_memory=True)
viper_query_loader = DataLoader(
Preprocessor(test_dataset.viper_query,
root=osp.join(test_dataset.viper_images_dir, test_dataset.query_path), transform=test_transformer),
batch_size=64, num_workers=4,
shuffle=False, pin_memory=True)
viper_gallery_loader = DataLoader(
Preprocessor(test_dataset.viper_gallery,
root=osp.join(test_dataset.viper_images_dir, test_dataset.gallery_path), transform=test_transformer),
batch_size=64, num_workers=4,
shuffle=False, pin_memory=True)
ilid_query_loader = DataLoader(
Preprocessor(test_dataset.ilid_query,
root=osp.join(test_dataset.ilid_images_dir, "images"), transform=test_transformer),
batch_size=64, num_workers=4,
shuffle=False, pin_memory=True)
ilid_gallery_loader = DataLoader(
Preprocessor(test_dataset.ilid_gallery,
root=osp.join(test_dataset.ilid_images_dir, "images"), transform=test_transformer),
batch_size=64, num_workers=4,
shuffle=False, pin_memory=True)
return dataset, test_dataset, num_classes, source_train_loader, grid_query_loader, grid_gallery_loader,prid_query_loader, prid_gallery_loader,viper_query_loader, viper_gallery_loader, ilid_query_loader, ilid_gallery_loader
def get_data(data_dir, source, target, height, width, batch_size, re=0, workers=8):
dataset = DA(data_dir, source, target)
dataset_2 = DA(data_dir, target, source)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
num_classes = dataset.num_train_ids
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(EPSILON=re),
T.RandomErasing(probability=0.4, mean=[0.485, 0.456, 0.406])
])
train_transformer_2 = 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(EPSILON=re),
T.RandomErasing(probability=0.4, mean=[0.485, 0.456, 0.406])
])
test_transformer = T.Compose([
T.Resize((height, width), interpolation=3),
T.ToTensor(),
normalizer,
])
'''
num_instances=4
rmgs_flag = num_instances > 0
if rmgs_flag:
sampler = RandomIdentitySampler(dataset.target_train, num_instances)
else:
sampler = None
'''
source_train_loader = DataLoader(
Preprocessor(dataset.source_train, root=osp.join(dataset.source_images_dir, dataset.source_train_path),
transform=train_transformer),
batch_size=batch_size, num_workers=workers,
shuffle=True, pin_memory=True, drop_last=True)
num_instances=0
rmgs_flag = num_instances > 0
if rmgs_flag:
sampler = RandomIdentitySampler(dataset.target_train, num_instances)
else:
sampler = None
target_train_loader = DataLoader(
UnsupervisedCamStylePreprocessor(dataset.target_train,
root=osp.join(dataset.target_images_dir, dataset.target_train_path),
camstyle_root=osp.join(dataset.target_images_dir,
dataset.target_train_camstyle_path),
num_cam=dataset.target_num_cam, transform=train_transformer),
batch_size=batch_size, num_workers=workers,
shuffle=True, pin_memory=True, drop_last=True)
query_loader = DataLoader(
Preprocessor(dataset.query,
root=osp.join(dataset.target_images_dir, dataset.query_path), transform=test_transformer),
batch_size=batch_size, num_workers=workers,
shuffle=False, pin_memory=True)
query_loader_2 = DataLoader(
Preprocessor(dataset_2.query,
root=osp.join(dataset_2.target_images_dir, dataset_2.query_path), transform=test_transformer),
batch_size=batch_size, num_workers=workers,
shuffle=False, pin_memory=True)
gallery_loader = DataLoader(
Preprocessor(dataset.gallery,
root=osp.join(dataset.target_images_dir, dataset.gallery_path), transform=test_transformer),
batch_size=batch_size, num_workers=workers,
shuffle=False, pin_memory=True)
gallery_loader_2 = DataLoader(
Preprocessor(dataset_2.gallery,
root=osp.join(dataset_2.target_images_dir, dataset_2.gallery_path), transform=test_transformer),
batch_size=batch_size, num_workers=workers,
shuffle=False, pin_memory=True)
return dataset,dataset_2, num_classes, source_train_loader, target_train_loader, query_loader, gallery_loader, query_loader_2, gallery_loader_2