def test_invert(self):
tf = T.RandomApply(
transforms=[T.Identity(), T.ToTensor()],
p=1,
)
img_tensor = tf(self.img_pil)
self.assertIsInstance(img_tensor, torch.Tensor)
self.assertIsInstance(tf.invert(img_tensor), Image.Image)
def test_replay(self):
tf = T.Compose([
T.RandomCrop(self.crop_size),
T.RandomVerticalFlip(),
T.RandomHorizontalFlip(),
T.ToTensor(),
])
img_tf1 = tf.replay(self.img_pil)
img_tf2 = tf.replay(self.img_pil)
self.assertTrue(torch.allclose(img_tf1, img_tf2))
def setUp(self) -> None:
self.img_size = (256, 320)
self.h, self.w = self.img_size
self.crop_size = (64, 128)
self.img_tensor = torch.randn((1, ) + self.img_size).clamp(0, 1)
self.img_pil = T.ToPILImage()(self.img_tensor)
self.img_tensor = T.ToTensor()(self.img_pil)
self.n = random.randint(0, 1e9)
def __randaug_mnist(self, img_pil, to_tensor, norm):
return T.Compose([
T.RandomAffine(
degrees=10,
translate=(0.17, 0.17),
scale=(0.85, 1.05),
shear=(-10, 10, -10, 10),
resample=PIL.Image.BILINEAR,
),
T.ColorJitter(0.5, 0.5, 0.5, 0.25),
T.TransformIf(T.ToTensor(), to_tensor),
T.TransformIf(T.Normalize(mean=(0.1307, ), std=(0.3081, )),
to_tensor and norm),
])(img_pil)
def test_track(self):
tf = T.Compose([
T.ToPILImage(),
T.RandomVerticalFlip(p=0.5),
# the crop will include the center pixels
T.RandomCrop(size=tuple(
int(0.8 * self.img_size[i]) for i in range(2))),
T.RandomHorizontalFlip(p=0.5),
T.ToTensor(),
])
imgs_tf = [tf.track(self.img_tensor) for _ in range(10)]
for i, img_tf in enumerate(imgs_tf):
n = min(self.img_size) // 10
center_pixels = (0, ) + tuple(
slice(self.img_size[i] // 2 - n, self.img_size[i] // 2 + n)
for i in range(2))
self.assertTrue(
torch.allclose(tf[i](img_tf)[center_pixels],
self.img_tensor[center_pixels]))
def __randaug_imagenet(self, img_pil, to_tensor, norm):
# TODO: turn this code less cryptic...
policy = [('FlipLR', 0.5, random.randint(1, 9)),
('FlipUD', 0.5, random.randint(1, 9)),
('Rotate', 0.5, random.randint(1, 9))] \
+ random.choice(self._all_policies)
img_shape = img_pil.size[::-1] + (3, )
for xform in policy:
assert len(xform) == 3
name, probability, level = xform
xform_fn = NAME_TO_TRANSFORM[name].pil_transformer(
probability, level, img_shape)
img_pil = xform_fn(img_pil)
return T.Compose([
T.Lambda(lambda img: img.convert('RGB')),
T.TransformIf(T.ToTensor(), to_tensor),
T.TransformIf(
T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]), to_tensor and norm),
])(img_pil)
def test_invert(self):
tf = T.RandomChoice([T.ToTensor()] * 10)
self.assertIsInstance(tf(self.img_pil), torch.Tensor)
self.assertIsInstance(tf.inverse(), T.ToPILImage)
def test_invert(self):
tf = T.TransformIf(transform=T.ToTensor(), condition=True)
self.assertIsInstance(tf.inverse(), T.ToPILImage)
def pil_unwrap2(img_pil, mean_std=None):
img = T.ToTensor()(img_pil.convert('RGB'))
if mean_std is not None:
img = T.Normalize(*mean_std)(img)
return img
def main(**kwargs):
########################
# [DATA] some datasets #
########################
dataset_name = kwargs['dataset_name']
tf = T.Compose([
T.ToTensor(),
T.TransformIf(T.Normalize(mean=[0.13], std=[0.31]),
dataset_name == 'mnist'),
T.TransformIf(T.Normalize(mean=[0.5] * 3, std=[0.5] * 3),
dataset_name == 'cifar10'),
])
dataset, tst_dataset = {
'mnist':
lambda: (
MNIST(root='/data/', train=True, transform=tf, download=True),
MNIST(root='/data/', train=False, transform=tf, download=True),
),
'cifar10':
lambda: (
CIFAR10(root='/data/', train=True, transform=tf, download=True),
CIFAR10(root='/data/', train=False, transform=tf, download=True),
)
}[dataset_name]()
tng_dataset, val_dataset = split(dataset,
percentage=kwargs['val_percentage'])
sampler = RandomSampler(tng_dataset,
num_samples=len(val_dataset),
replacement=False)
tng_dataloader = DataLoader(dataset=tng_dataset,
batch_size=kwargs['tng_batch_size'],
sampler=sampler,
num_workers=4)
val_dataloader = DataLoader(dataset=val_dataset,
batch_size=kwargs['val_batch_size'],
shuffle=False,
num_workers=4)
tst_dataloader = DataLoader(dataset=tst_dataset,
batch_size=kwargs['val_batch_size'],
shuffle=False,
num_workers=4)
###########################
# [MODEL] a pytorch model #
###########################
sample_input, _ = dataset[0]
net = SimpLeNet(
input_size=sample_input.size(),
n_classes=10,
)
########################################
# [STRATEGY] it describes the training #
########################################
exp_name = f'{net.__class__.__name__.lower()}/{dataset_name}'
kwargs['log_dir'] = Path(kwargs['log_dir']) / exp_name
classifier = ClassifierStrategy(net=net, **kwargs)
##################################
# [EXECUTOR] it handles the rest #
##################################
executor = Executor(
tng_dataloader=tng_dataloader,
val_dataloader=val_dataloader,
tst_dataloader=tst_dataloader,
exp_name=exp_name,
**kwargs,
)
executor.train_test(strategy=classifier, **kwargs)