def test_random_crop(self):
trans = transforms.Compose([
transforms.RandomCrop(200),
transforms.RandomCrop((140, 160)),
])
self.do_transform(trans)
trans_random_crop1 = transforms.RandomCrop(224)
trans_random_crop2 = transforms.RandomCrop((140, 160))
fake_img = self.create_image((500, 400, 3))
fake_img_crop1 = trans_random_crop1(fake_img)
fake_img_crop2 = trans_random_crop2(fake_img_crop1)
np.testing.assert_equal(self.get_shape(fake_img_crop1), (3, 224, 224))
np.testing.assert_equal(self.get_shape(fake_img_crop2), (3, 140, 160))
trans_random_crop_same = transforms.RandomCrop((140, 160))
img = trans_random_crop_same(fake_img_crop2)
trans_random_crop_bigger = transforms.RandomCrop((180, 200),
pad_if_needed=True)
img = trans_random_crop_bigger(img)
trans_random_crop_pad = transforms.RandomCrop((224, 256), 2, True)
img = trans_random_crop_pad(img)
def test_random_crop(self):
trans = transforms.Compose([
transforms.RandomCrop(200),
transforms.RandomCrop((140, 160)),
])
self.do_transform(trans)
trans_random_crop1 = transforms.RandomCrop(224)
trans_random_crop2 = transforms.RandomCrop((140, 160))
fake_img = np.random.rand(500, 400, 3).astype('float32')
fake_img_crop1 = trans_random_crop1(fake_img)
fake_img_crop2 = trans_random_crop2(fake_img_crop1)
np.testing.assert_equal(fake_img_crop1.shape, (224, 224, 3))
np.testing.assert_equal(fake_img_crop2.shape, (140, 160, 3))
trans_random_crop_same = transforms.RandomCrop((140, 160))
img = trans_random_crop_same(fake_img_crop2)
trans_random_crop_bigger = transforms.RandomCrop((180, 200))
img = trans_random_crop_bigger(img)
trans_random_crop_pad = transforms.RandomCrop((224, 256), 2, True)
img = trans_random_crop_pad(img)
def build_model(self):
""" DataLoader """
pad = int(30 * self.img_size // 256)
train_transform = T.Compose([
T.RandomHorizontalFlip(),
T.Resize((self.img_size + pad, self.img_size + pad)),
T.RandomCrop(self.img_size),
T.ToTensor(),
T.Normalize(mean=0.5, std=0.5),
])
test_transform = T.Compose([
T.Resize((self.img_size, self.img_size)),
T.ToTensor(),
T.Normalize(mean=0.5, std=0.5)
])
self.trainA = ImageFolder('dataset/photo2cartoon/trainA', self.img_size, train_transform)
self.trainB = ImageFolder('dataset/photo2cartoon/trainB', self.img_size, train_transform)
self.testA = ImageFolder('dataset/photo2cartoon/testA', self.img_size, test_transform)
self.testB = ImageFolder('dataset/photo2cartoon/testB', self.img_size, test_transform)
self.trainA_loader = DataLoader(self.trainA, batch_size=self.batch_size, shuffle=True)
self.trainB_loader = DataLoader(self.trainB, batch_size=self.batch_size, shuffle=True)
self.testA_loader = DataLoader(self.testA, batch_size=1, shuffle=False)
self.testB_loader = DataLoader(self.testB, batch_size=1, shuffle=False)
""" Define Generator, Discriminator """
self.genA2B = ResnetGenerator(ngf=self.ch, img_size=self.img_size, light=self.light)
self.genB2A = ResnetGenerator(ngf=self.ch, img_size=self.img_size, light=self.light)
self.disGA = Discriminator(input_nc=3, ndf=self.ch, n_layers=7)
self.disGB = Discriminator(input_nc=3, ndf=self.ch, n_layers=7)
self.disLA = Discriminator(input_nc=3, ndf=self.ch, n_layers=5)
self.disLB = Discriminator(input_nc=3, ndf=self.ch, n_layers=5)
""" Define Loss """
self.L1_loss = nn.loss.L1Loss()
self.MSE_loss = nn.loss.MSELoss()
self.BCE_loss = nn.loss.BCEWithLogitsLoss()
self.G_optim = paddle.optimizer.Adam(
learning_rate=self.lr, beta1=0.5, beta2=0.999, weight_decay=0.0001,
parameters=self.genA2B.parameters()+self.genB2A.parameters()
)
self.D_optim = paddle.optimizer.Adam(
learning_rate=self.lr, beta1=0.5, beta2=0.999, weight_decay=0.0001,
parameters=self.disGA.parameters()+self.disGB.parameters()+self.disLA.parameters()+self.disLB.parameters()
)
self.Rho_clipper = RhoClipper(0, self.rho_clipper)
self.W_clipper = WClipper(0, self.w_clipper)
def __len__(self):
return len(self.img_names)
if __name__ == '__main__':
from paddle.vision.transforms import transforms as T
from paddle.io import DataLoader
img_size = 256
pad = 30
train_transform = T.Compose([
T.RandomHorizontalFlip(),
T.Resize((img_size + pad, img_size + pad)),
T.RandomCrop(img_size),
T.ToTensor(),
T.Normalize(mean=0.5, std=0.5)
])
dataloader = ImageFolder('dataset/photo2cartoon/trainB',
transform=train_transform)
train_loader = DataLoader(dataloader, batch_size=1, shuffle=True)
print('num: ', len(train_loader))
for i in range(300):
print(i)
try:
real_A, _ = next(trainA_iter)
except:
trainA_iter = iter(train_loader())