def __init__(self, root, lmdir, maskdir, cmaskdir, mode="test", load_h=512, load_w=512):
super().__init__()
transform_ = [
transform.Resize((load_h, load_w), Image.BICUBIC),
transform.ImageNormalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
]
self.transform = transform.Compose(transform_)
transform_mask_ = [
transform.Resize((load_h, load_w), Image.BICUBIC),
transform.Gray(),
]
self.transform_mask = transform.Compose(transform_mask_)
self.files_A = sorted(glob.glob(root + "/*.*"))
self.total_len = len(self.files_A)
self.batch_size = None
self.shuffle = False
self.drop_last = False
self.num_workers = None
self.buffer_size = 512*1024*1024
self.lmdir = lmdir
self.maskdir = maskdir
self.cmaskdir = cmaskdir
self.load_h = load_h
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--epochs', type=int, default=50)
parser.add_argument('--num_classes', type=int, default=130)
parser.add_argument('--lr', type=float, default=2e-3)
parser.add_argument('--weight_decay', type=float, default=1e-5)
parser.add_argument('--resume', type=bool, default=False)
parser.add_argument('--eval', type=bool, default=False)
parser.add_argument('--dataroot', type=str, default='/content/drive/MyDrive/dogflg/data2/')
parser.add_argument('--model_path', type=str, default='./best_model.bin')
parser.add_argument('--sampleratio', type=float, default=0.8)
args = parser.parse_args()
transform_train = transform.Compose([
transform.Resize((256, 256)),
transform.CenterCrop(224),
transform.RandomHorizontalFlip(),
transform.ToTensor(),
transform.ImageNormalize(0.485, 0.229),
# transform.ImageNormalize(0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
root_dir = args.dataroot
train_loader = TsinghuaDog(root_dir, batch_size=args.batch_size, train=True, part='train', shuffle=True, transform=transform_train, sample_rate=args.sampleratio)
transform_test = transform.Compose([
transform.Resize((256, 256)),
transform.CenterCrop(224),
transform.ToTensor(),
transform.ImageNormalize(0.485, 0.229),
# transform.ImageNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
val_loader = TsinghuaDog(root_dir, batch_size=args.batch_size, train=False, part='val', shuffle=False, transform=transform_test, sample_rate=args.sampleratio)
epochs = args.epochs
model = Net(num_classes=args.num_classes)
lr = args.lr
weight_decay = args.weight_decay
optimizer = SGD(model.parameters(), lr=lr, momentum=0.99)
if args.resume:
model.load(args.model_path)
print('model loaded', args.model_path)
#random save for test
#model.save(args.model_path)
if args.eval:
evaluate(model, val_loader, save_path=args.model_path)
return
for epoch in range(epochs):
train(model, train_loader, optimizer, epoch)
evaluate(model, val_loader, epoch, save_path=args.model_path)
def test_not_pil_image(self):
img = jt.random((30, 40, 3))
result = transform.Compose([
transform.RandomAffine(20),
transform.ToTensor(),
])(img)
img = jt.random((30, 40, 3))
result = transform.Compose([
transform.ToPILImage(),
transform.Gray(),
transform.Resize(20),
transform.ToTensor(),
])(img)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=8)
parser.add_argument('--epochs', type=int, default=50)
parser.add_argument('--num_classes', type=int, default=130)
parser.add_argument('--lr', type=float, default=2e-3)
parser.add_argument('--weight_decay', type=float, default=1e-5)
parser.add_argument('--resume', type=bool, default=False)
parser.add_argument('--eval', type=bool, default=False)
parser.add_argument('--dataroot', type=str, default='/home/gmh/dataset/TsinghuaDog/')
parser.add_argument('--model_path', type=str, default='./best_model.pkl')
args = parser.parse_args()
transform_train = transform.Compose([
transform.Resize((512, 512)),
transform.RandomCrop(448),
transform.RandomHorizontalFlip(),
transform.ToTensor(),
transform.ImageNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
root_dir = args.dataroot
train_loader = TsinghuaDog(root_dir, batch_size=16, train=True, part='train', shuffle=True, transform=transform_train)
transform_test = transform.Compose([
transform.Resize((512, 512)),
transform.CenterCrop(448),
transform.ToTensor(),
transform.ImageNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
val_loader = TsinghuaDog(root_dir, batch_size=16, train=False, part='val', shuffle=False, transform=transform_test)
epochs = args.epochs
model = Net(num_classes=args.num_classes)
lr = args.lr
weight_decay = args.weight_decay
optimizer = SGD(model.parameters(), lr=lr, momentum=0.9)
if args.resume:
model.load(args.model_path)
if args.eval:
evaluate(model, val_loader)
return
for epoch in range(epochs):
train(model, train_loader, optimizer, epoch)
evaluate(model, val_loader, epoch)
def test_crop(self):
height = random.randint(10, 32) * 2
width = random.randint(10, 32) * 2
oheight = random.randint(5, (height - 2) / 2) * 2
owidth = random.randint(5, (width - 2) / 2) * 2
img = np.ones([height, width, 3])
oh1 = (height - oheight) // 2
ow1 = (width - owidth) // 2
# imgnarrow = img[oh1:oh1 + oheight, ow1:ow1 + owidth, :]
# imgnarrow.fill(0)
img[oh1:oh1 + oheight, ow1:ow1 + owidth, :] = 0
# img = jt.array(img)
result = transform.Compose([
transform.ToPILImage(),
transform.CenterCrop((oheight, owidth)),
transform.ToTensor(),
])(img)
self.assertEqual(
result.sum(), 0,
f"height: {height} width: {width} oheight: {oheight} owdith: {owidth}"
)
oheight += 1
owidth += 1
result = transform.Compose([
transform.ToPILImage(),
transform.CenterCrop((oheight, owidth)),
transform.ToTensor(),
])(img)
sum1 = result.sum()
# TODO: not pass
# self.assertGreater(sum1, 1,
# f"height: {height} width: {width} oheight: {oheight} owdith: {owidth}")
oheight += 1
owidth += 1
result = transform.Compose([
transform.ToPILImage(),
transform.CenterCrop((oheight, owidth)),
transform.ToTensor(),
])(img)
sum2 = result.sum()
self.assertGreater(
sum2, 0,
f"height: {height} width: {width} oheight: {oheight} owdith: {owidth}"
)
self.assertGreaterEqual(
sum2, sum1,
f"height: {height} width: {width} oheight: {oheight} owdith: {owidth}"
)
def build_transform(self):
"""
Creates a basic transformation that was used to train the models
"""
cfg = self.cfg
# we are loading images with OpenCV, so we don't need to convert them
# to BGR, they are already! So all we need to do is to normalize
# by 255 if we want to convert to BGR255 format, or flip the channels
# if we want it to be in RGB in [0-1] range.
if cfg.INPUT.TO_BGR255:
to_bgr_transform = T.Lambda(lambda x: x * 255)
else:
to_bgr_transform = T.Lambda(lambda x: x[[2, 1, 0]])
normalize_transform = T.ImageNormalize(
mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD
)
min_size = cfg.INPUT.MIN_SIZE_TEST
max_size = cfg.INPUT.MAX_SIZE_TEST
transform = T.Compose(
[
T.ToPILImage(),
Resize(min_size, max_size),
T.ToTensor(),
to_bgr_transform,
normalize_transform,
]
)
return transform
def main():
best_acc1 = 0
#TODO multi gpu
if args.model == 'TNet26':
model = TNet.Resnet26()
elif args.model == 'TNet38':
model = TNet.Resnet38()
elif args.model == 'TNet50':
model = TNet.Resnet50()
elif args.model == 'Resnet26':
model = jtmodels.__dict__['resnet26']()
elif args.model == 'Resnet38':
model = jtmodels.__dict__['resnet38']()
elif args.model == 'Resnet50':
model = jtmodels.__dict__['resnet50']()
elif args.model == 'SAN10':
model = san(sa_type = 0, layers=[2, 1, 2, 4, 1], kernels=[3, 7, 7, 7, 7], num_classes = 1000)
elif args.model == 'SAN_TCN10':
model = san_tcn(sa_type = 0, layers=[2, 1, 2, 4, 1], kernels=[3, 7, 7, 7, 7], num_classes = 1000)
else:
print("Model not found!")
exit(0)
if (args.use_pytorch_conv_init):
pytorch_conv_init(model)
model_path = os.path.join(args.save_path, 'model_best.pk')
model.load(model_path)
mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
# val_transform = transform.Compose([transform.Resize(256), transform.CenterCrop(224), transform.ImageNormalize(mean, std)])
# [transform diff from val!vvvv]
val_transform = transform.Compose([transform.Resize(224), transform.ImageNormalize(mean, std)])
val_loader = ImageFolder('input_images', val_transform).set_attrs(batch_size=args.batch_size_val, shuffle=False, num_workers=args.workers)
test(val_loader, model)
def get_transform(params, gray=False, mask=False):
transform_ = []
# resize
transform_.append(
transform.Resize((params['load_h'], params['load_w']), Image.BICUBIC))
# flip
if params['flip']:
transform_.append(transform.Lambda(lambda img: transform.hflip(img)))
if gray:
transform_.append(transform.Gray())
if mask:
transform_.append(transform.ImageNormalize([
0.,
], [
1.,
]))
else:
if not gray:
transform_.append(
transform.ImageNormalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]))
else:
transform_.append(transform.ImageNormalize([
0.5,
], [
0.5,
]))
return transform.Compose(transform_)
def test_RandomAffine(self):
img = jt.random((30, 40, 3))
result = transform.Compose([
transform.ToPILImage(),
transform.RandomAffine(20),
transform.ToTensor(),
])(img)
def get_loader(root_dir,
label_file,
batch_size,
img_size=0,
num_thread=4,
pin=True,
test=False,
split='train'):
if test is False:
raise NotImplementedError
else:
transform = transforms.Compose([
transforms.Resize((400, 400)),
transforms.ToTensor(),
transforms.ImageNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
dataset = SemanLineDatasetTest(root_dir,
label_file,
transform=transform,
t_transform=None)
if test is False:
raise NotImplementedError
else:
dataset.set_attrs(batch_size=batch_size, shuffle=False)
print('Get dataset success.')
return dataset
def transforms_imagenet_train(
img_size=224,
scale=None,
ratio=None,
hflip=0.5,
vflip=0.,
interpolation='random',
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
):
"""
If separate==True, the transforms are returned as a tuple of 3 separate transforms
for use in a mixing dataset that passes
* all data through the first (primary) transform, called the 'clean' data
* a portion of the data through the secondary transform
* normalizes and converts the branches above with the third, final transform
"""
scale = tuple(scale or (0.08, 1.0)) # default imagenet scale range
ratio = tuple(ratio or (3. / 4., 4. / 3.)) # default imagenet ratio range
primary_tfl = [
RandomResizedCropAndInterpolation(img_size,
scale=scale,
ratio=ratio,
interpolation=interpolation)
]
if hflip > 0.:
primary_tfl += [transforms.RandomHorizontalFlip(p=hflip)]
if vflip > 0.:
primary_tfl += [transforms.RandomVerticalFlip(p=vflip)]
final_tfl = [
transforms.ToTensor(),
transforms.ImageNormalize(mean=mean, std=std)
]
return transforms.Compose(primary_tfl + final_tfl)
def test_TenCrop(self):
img = jt.random((30, 40, 3))
result = transform.Compose([
transform.ToPILImage(),
transform.TenCrop(20),
transform.ToTensor(),
])(img)
def test_RandomPerspective(self):
img = jt.random((30, 40, 3))
result = transform.Compose([
transform.ToPILImage(),
transform.RandomPerspective(p=1),
transform.ToTensor(),
])(img)
def test_imagenet(self):
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=256,
shuffle=False)
random.seed(0)
tc_data = []
for i, data in enumerate(train_loader):
tc_data.append(data)
print("get", data[0].shape)
if i == check_num_batch: break
from jittor.dataset.dataset import ImageFolder
import jittor.transform as transform
dataset = ImageFolder(traindir).set_attrs(batch_size=256,
shuffle=False)
dataset.set_attrs(transform=transform.Compose([
transform.RandomCropAndResize(224),
transform.RandomHorizontalFlip(),
transform.ImageNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]))
random.seed(0)
for i, (images, labels) in enumerate(dataset):
print("compare", i)
assert np.allclose(images.numpy(), tc_data[i][0].numpy())
assert np.allclose(labels.numpy(), tc_data[i][1].numpy())
if i == check_num_batch: break
def __init__(self, root, transforms_=None, img_size=128, mask_size=64, mode="train"):
super().__init__()
self.transform = transform.Compose(transforms_)
self.img_size = img_size
self.mask_size = mask_size
self.mode = mode
self.files = sorted(glob.glob("%s/*.jpg" % root))
self.files = self.files[:-4000] if mode == "train" else self.files[-4000:]
self.set_attrs(total_len=len(self.files))
def get_dataset():
dataset = ImageFolder(traindir).set_attrs(batch_size=256,
shuffle=False)
dataset.set_attrs(transform=transform.Compose([
transform.Resize(224),
transform.ImageNormalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]),
num_workers=0)
return dataset
def test_resize(self):
height = random.randint(24, 32) * 2
width = random.randint(24, 32) * 2
osize = random.randint(5, 12) * 2
img = jt.ones([height, width, 3])
result = transform.Compose([
transform.ToPILImage(),
transform.Resize(osize),
transform.ToTensor(),
])(img)
self.assertIn(osize, result.shape)
if height < width:
self.assertLessEqual(result.shape[1], result.shape[2])
elif width < height:
self.assertGreaterEqual(result.shape[1], result.shape[2])
result = transform.Compose([
transform.ToPILImage(),
transform.Resize([osize, osize]),
transform.ToTensor(),
])(img)
self.assertIn(osize, result.shape)
self.assertEqual(result.shape[1], osize)
self.assertEqual(result.shape[2], osize)
oheight = random.randint(5, 12) * 2
owidth = random.randint(5, 12) * 2
result = transform.Compose([
transform.ToPILImage(),
transform.Resize((oheight, owidth)),
transform.ToTensor(),
])(img)
self.assertEqual(result.shape[1], oheight)
self.assertEqual(result.shape[2], owidth)
result = transform.Compose([
transform.ToPILImage(),
transform.Resize([oheight, owidth]),
transform.ToTensor(),
])(img)
self.assertEqual(result.shape[1], oheight)
self.assertEqual(result.shape[2], owidth)
def __init__(self, root, hr_shape):
hr_height, hr_width = hr_shape
# transform for low resolution images and high resolution images
self.lr_transform = transform.Compose([
transform.Resize((hr_height // 4, hr_height // 4), Image.BICUBIC),
transform.ImageNormalize(mean, std),
])
self.hr_transform = transform.Compose([
transform.Resize((hr_height, hr_height), Image.BICUBIC),
transform.ImageNormalize(mean, std),
])
self.files = sorted(glob.glob(root + "/*.*"))
self.total_len = len(self.files)
self.batch_size = None
self.shuffle = False
self.drop_last = False
self.num_workers = None
self.buffer_size = 512 * 1024 * 1024
def __init__(self, root, transform_=None, mode="train", attributes=None):
super().__init__()
self.transform = transform.Compose(transform_)
self.selected_attrs = attributes
self.files = sorted(glob.glob("%s/images/*.jpg" % root))
self.files = self.files[:-2000] if mode == "train" else self.files[
-2000:]
self.label_path = glob.glob("%s/*.txt" % root)[0]
self.annotations = self.get_annotations()
self.set_attrs(total_len=len(self.files))
def __init__(self, root, input_shape, mode="train"):
self.transform = transform.Compose([
transform.Resize(input_shape[-2:]),
transform.ImageNormalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
])
self.files = sorted(glob.glob(os.path.join(root, mode) + "/*.*"))
self.total_len = len(self.files)
self.batch_size = None
self.shuffle = False
self.drop_last = False
self.num_workers = None
self.buffer_size = 512 * 1024 * 1024
def __init__(self, root, transforms_=None, unaligned=False, mode="train"):
self.transform = transform.Compose(transforms_)
self.unaligned = unaligned
self.files_A = sorted(
glob.glob(os.path.join(root, "%s/A" % mode) + "/*.*"))
self.files_B = sorted(
glob.glob(os.path.join(root, "%s/B" % mode) + "/*.*"))
self.total_len = max(len(self.files_A), len(self.files_B))
self.batch_size = None
self.shuffle = False
self.drop_last = False
self.num_workers = None
self.buffer_size = 512 * 1024 * 1024
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=8)
parser.add_argument('--epochs', type=int, default=50)
parser.add_argument('--num_classes', type=int, default=130)
parser.add_argument('--lr', type=float, default=2e-3)
parser.add_argument('--weight_decay', type=float, default=1e-5)
parser.add_argument('--resume', type=bool, default=True)
parser.add_argument('--eval', type=bool, default=False)
parser.add_argument('--dataroot',
type=str,
default='/content/drive/MyDrive/dogfl/data/TEST_A/')
parser.add_argument('--model_path', type=str, default='./best_model.bin')
parser.add_argument('--out_file', type=str, default='./result.json')
args = parser.parse_args()
root_dir = args.dataroot
transform_test = transform.Compose([
transform.Resize((512, 512)),
transform.CenterCrop(448),
transform.ToTensor(),
transform.ImageNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
name_list = []
for _, _, _name_list in os.walk(root_dir):
name_list = _name_list
val_loader = TsinghuaDogExam(root_dir,
batch_size=args.batch_size,
train=False,
name_list=name_list,
shuffle=False,
transform=transform_test)
model = Net(num_classes=args.num_classes)
if args.resume:
model.load(args.model_path)
print('model loaded', args.model_path)
top5_class_list = evaluate(model, val_loader)
# label start from 1, however it doesn't
pred_result = dict(zip(name_list, top5_class_list))
with open(args.out_file, 'w') as fout:
json.dump(pred_result, fout, ensure_ascii=False, indent=4)
def get_transform(new_size=None):
"""
obtain the image transforms required for the input data
:param new_size: size of the resized images
:return: image_transform => transform object from TorchVision
"""
# from torchvision.transforms import ToTensor, Normalize, Compose, Resize, RandomHorizontalFlip
if new_size is not None:
image_transform = transform.Compose([
transform.RandomHorizontalFlip(),
transform.Resize(new_size),
transform.ToTensor(),
transform.ImageNormalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
else:
image_transform = transform.Compose([
transform.RandomHorizontalFlip(),
transform.ToTensor(),
transform.ImageNormalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
return image_transform
def im_detect_bbox(model, images, target_scale, target_max_size):
"""
Performs bbox detection on the original image.
"""
transform = TT.Compose([
T.Resize(target_scale, target_max_size),
TT.ToTensor(),
T.Normalize(mean=cfg.INPUT.PIXEL_MEAN,
std=cfg.INPUT.PIXEL_STD,
to_bgr255=cfg.INPUT.TO_BGR255)
])
images = [transform(image) for image in images]
images = to_image_list(images, cfg.DATALOADER.SIZE_DIVISIBILITY)
return model(images)
def test_random_crop(self):
height = random.randint(10, 32) * 2
width = random.randint(10, 32) * 2
oheight = random.randint(5, (height - 2) / 2) * 2
owidth = random.randint(5, (width - 2) / 2) * 2
img = np.ones((height, width, 3))
result = transform.Compose([
transform.ToPILImage(),
transform.RandomCrop((oheight, owidth)),
transform.ToTensor(),
])(img)
self.assertEqual(result.shape[1], oheight)
self.assertEqual(result.shape[2], owidth)
result = transform.Compose([
transform.ToPILImage(),
transform.RandomCrop((oheight, owidth)),
transform.ToTensor(),
])(img)
self.assertEqual(result.shape[1], oheight)
self.assertEqual(result.shape[2], owidth)
result = transform.Compose([
transform.ToPILImage(),
transform.RandomCrop((height, width)),
transform.ToTensor()
])(img)
self.assertEqual(result.shape[1], height)
self.assertEqual(result.shape[2], width)
self.assertTrue(np.allclose(img, result.transpose(1, 2, 0)))
with self.assertRaises(AssertionError):
result = transform.Compose([
transform.ToPILImage(),
transform.RandomCrop((height + 1, width + 1)),
transform.ToTensor(),
])(img)
def build_transform():
if cfg.INPUT.TO_BGR255:
to_bgr_transform = T.Lambda(lambda x: x * 255)
else:
to_bgr_transform = T.Lambda(lambda x: x[[2, 1, 0]])
normalize_transform = T.Normalize(mean=cfg.INPUT.PIXEL_MEAN,
std=cfg.INPUT.PIXEL_STD)
min_size = cfg.INPUT.MIN_SIZE_TEST
max_size = cfg.INPUT.MAX_SIZE_TEST
transform = T.Compose([
T.ToPILImage(),
Resize(min_size, max_size),
T.ToTensor(),
to_bgr_transform,
normalize_transform,
])
return transform
def im_detect_bbox_hflip(model, images, target_scale, target_max_size):
"""
Performs bbox detection on the horizontally flipped image.
Function signature is the same as for im_detect_bbox.
"""
transform = TT.Compose([
T.Resize(target_scale, target_max_size),
TT.RandomHorizontalFlip(1.0),
TT.ToTensor(),
T.Normalize(mean=cfg.INPUT.PIXEL_MEAN,
std=cfg.INPUT.PIXEL_STD,
to_bgr255=cfg.INPUT.TO_BGR255)
])
images = [transform(image) for image in images]
images = to_image_list(images, cfg.DATALOADER.SIZE_DIVISIBILITY)
boxlists = model(images)
# Invert the detections computed on the flipped image
boxlists_inv = [boxlist.transpose(0) for boxlist in boxlists]
return boxlists_inv
def transforms_imagenet_eval(img_size=224,
crop_pct=0.9,
interpolation=Image.BICUBIC,
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)):
crop_pct = crop_pct or 0.875
if isinstance(img_size, tuple):
assert len(img_size) == 2
if img_size[-1] == img_size[-2]:
# fall-back to older behaviour so Resize scales to shortest edge if target is square
scale_size = int(math.floor(img_size[0] / crop_pct))
else:
scale_size = tuple([int(x / crop_pct) for x in img_size])
else:
scale_size = int(math.floor(img_size / crop_pct))
return transforms.Compose([
Resize(scale_size, interpolation),
transforms.CenterCrop(img_size),
transforms.ToTensor(),
transforms.ImageNormalize(mean=mean, std=std)
])
def test_dataset():
root = '/home/gmh/dataset/TsinghuaDog'
part = 'train'
# from torchvision import transforms
rgb_mean = [0.5, 0.5, 0.5]
rgb_std = [0.5, 0.5, 0.5]
transform_val = transform.Compose([
transform.Resize((299, 299)),
transform.ToTensor(),
transform.ImageNormalize(rgb_mean, rgb_std),
])
dataloader = TsinghuaDog(root,
batch_size=16,
train=False,
part=part,
shuffle=True,
transform=transform_val)
# def __init__(self, root_dir, batch_size, part='train', train=True, shuffle=False, transform=None, num_workers=1):
for images, labels in dataloader:
# print(images.size(),labels.size(),labels)
pass
# 损失函数：平方误差
# 调用方法：adversarial_loss(网络输出A, 分类标签B)
# 计算结果：(A-B)^2
adversarial_loss = nn.MSELoss()
generator = Generator()
discriminator = Discriminator()
# 导入MNIST数据集
from jittor.dataset.mnist import MNIST
import jittor.transform as transform
transform = transform.Compose([
transform.Resize(opt.img_size),
transform.Gray(),
transform.ImageNormalize(mean=[0.5], std=[0.5]),
])
dataloader = MNIST(train=True,
transform=transform).set_attrs(batch_size=opt.batch_size,
shuffle=True)
optimizer_G = nn.Adam(generator.parameters(),
lr=opt.lr,
betas=(opt.b1, opt.b2))
optimizer_D = nn.Adam(discriminator.parameters(),
lr=opt.lr,
betas=(opt.b1, opt.b2))
from PIL import Image