def get_data(name,
data_dir,
height,
width,
batch_size,
workers,
num_instances,
iters=200):
root = osp.join(data_dir)
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.AugMix(),
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_data(name, data_dir, l=1):
root = osp.join(data_dir)
dataset = datasets.create(name, root, l)
label_dict = {}
for i, item_l in enumerate(dataset.train):
# dataset.train[i]=(item_l[0],0,item_l[2])
if item_l[1] in label_dict:
label_dict[item_l[1]].append(i)
else:
label_dict[item_l[1]] = [i]
return dataset, label_dict