writer = SummaryWriter(LOG_ROOT) # writer for buffering intermedium results
train_transform = transforms.Compose([ # refer to https://pytorch.org/docs/stable/torchvision/transforms.html for more build-in online data augmentation
transforms.Resize([int(128 * INPUT_SIZE[0] / 112), int(128 * INPUT_SIZE[0] / 112)]), # smaller side resized
transforms.RandomCrop([INPUT_SIZE[0], INPUT_SIZE[1]]),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean = RGB_MEAN,
std = RGB_STD),
])
dataset_train = datasets.ImageFolder(os.path.join(DATA_ROOT, 'ms1mv2-asia'), train_transform)
# create a weighted random sampler to process imbalanced data
weights = make_weights_for_balanced_classes(dataset_train.imgs, len(dataset_train.classes))
weights = torch.DoubleTensor(weights)
sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights))
train_loader = torch.utils.data.DataLoader(
dataset_train, batch_size = BATCH_SIZE, sampler = sampler, pin_memory = PIN_MEMORY,
num_workers = NUM_WORKERS, drop_last = DROP_LAST
)
NUM_CLASS = len(train_loader.dataset.classes)
print("Number of Training Classes: {}".format(NUM_CLASS))
lfw, cfp_ff, cfp_fp, agedb, calfw, cplfw, vgg2_fp, lfw_issame, cfp_ff_issame, cfp_fp_issame, agedb_issame, calfw_issame, cplfw_issame, vgg2_fp_issame = get_val_data(DATA_ROOT)
#======= model & loss & optimizer =======#
writer = SummaryWriter(LOG_ROOT) # writer for buffering intermedium results
train_transform = transforms.Compose([
# refer to https://pytorch.org/docs/stable/torchvision/transforms.html for more build-in online data augmentation
transforms.Resize([112, 112]), # smaller side resized
# transforms.RandomCrop([INPUT_SIZE[0], INPUT_SIZE[1]]),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=RGB_MEAN,
std=RGB_STD),
])
dataset_train = datasets.ImageFolder(os.path.join(DATA_ROOT, 'imgs'), train_transform)
# create a weighted random sampler to process imbalanced data
weights = make_weights_for_balanced_classes(dataset_train.imgs, len(dataset_train.classes))
weights = torch.DoubleTensor(weights)
sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, len(weights))
train_loader = torch.utils.data.DataLoader(
dataset_train, batch_size=BATCH_SIZE, sampler=sampler, pin_memory=PIN_MEMORY,
num_workers=NUM_WORKERS, drop_last=DROP_LAST
)
NUM_CLASS = len(train_loader.dataset.classes)
print("Number of Training Classes: {}".format(NUM_CLASS))
lfw, cfp_ff, cfp_fp, agedb, calfw, cplfw, vgg2_fp, lfw_issame, cfp_ff_issame, cfp_fp_issame, agedb_issame, calfw_issame, cplfw_issame, vgg2_fp_issame = get_val_data(DATA_ROOT)
# ======= model & loss & optimizer =======#
BACKBONE_DICT = {'ResNet_50': ResNet_50(INPUT_SIZE),
