% epoch_item,
file=F_txt)
adjust_learning_rate(optimizer, epoch_item)
# ======================================= Folder of Datasets =======================================
# image transform & normalization
ImgTransform = transforms.Compose([
transforms.Resize((opt.imageSize, opt.imageSize)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
trainset = Imagefolder_csv(data_dir=opt.dataset_dir,
mode=opt.mode,
image_size=opt.imageSize,
transform=ImgTransform,
episode_num=opt.episode_train_num,
way_num=opt.way_num,
shot_num=opt.shot_num,
query_num=opt.query_num)
valset = Imagefolder_csv(data_dir=opt.dataset_dir,
mode='val',
image_size=opt.imageSize,
transform=ImgTransform,
episode_num=opt.episode_val_num,
way_num=opt.way_num,
shot_num=opt.shot_num,
query_num=opt.query_num)
testset = Imagefolder_csv(data_dir=opt.dataset_dir,
mode='test',
image_size=opt.imageSize,
transform=ImgTransform,
total_h = np.zeros(repeat_num)
for r in range(repeat_num):
# =================== Folder of Datasets =====================
# image transform & normalization
ImgTransform = transforms.Compose([
transforms.Resize((opt.imageSize, opt.imageSize)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
testset = Imagefolder_csv(data_dir=opt.dataset_dir,
mode=opt.mode,
image_size=opt.imageSize,
transform=ImgTransform,
episode_num=opt.episode_num,
way_num=opt.way_num,
shot_num=opt.shot_num,
query_num=opt.query_num)
print('.........The %d-th round.........' % r)
print('.........The %d-th round.........' % r, file=F_txt)
print('Testset: %d-------------%d' % (len(testset), r), file=F_txt)
# ===================== Load Datasets =======================
test_loader = torch.utils.data.DataLoader(testset,
batch_size=opt.testepisodeSize,
shuffle=True,
num_workers=int(opt.workers),
drop_last=True,
pin_memory=True)
