self.cnt = 0
def __call__(self, image):
if self.cnt % 1000 == 0:
print(self.cnt)
self.cnt += 1
image = self.to_tensor(image) * 255
image.unsqueeze_(0)
image = self.filter(image)
image = sf.local_normalization(image, 8)
temporal_image = self.temporal_transform(image)
return temporal_image.sign()
kernels = [
utils.DoGKernel(3, 3 / 9, 6 / 9),
utils.DoGKernel(3, 6 / 9, 3 / 9),
utils.DoGKernel(7, 7 / 9, 14 / 9),
utils.DoGKernel(7, 14 / 9, 7 / 9),
utils.DoGKernel(13, 13 / 9, 26 / 9),
utils.DoGKernel(13, 26 / 9, 13 / 9)
]
filter = utils.Filter(kernels, padding=6, thresholds=50)
s1c1 = S1C1Transform(filter)
data_root = "data"
MNIST_train = utils.CacheDataset(
torchvision.datasets.MNIST(root=data_root,
train=True,
download=True,
transform=s1c1))
def preprocess(x, xtest):
x = sample_zero_mean(x)
x = gcn(x)
xtest = sample_zero_mean(xtest)
xtest = gcn(xtest)
return x, xtest
if __name__ == "__main__":
# kernels = [ utils.DoGKernel(3,1,2), utils.DoGKernel(3,2,1),
# utils.OnCenter(3), utils.OffCenter(3)]
kernels = [utils.DoGKernel(3,1,2), utils.DoGKernel(3,2,1)]
filter = utils.Filter(kernels, padding = 6, thresholds = 50)
transform = InputTransform(filter)
data_root = 'data/'
MNIST_train = utils.CacheDataset(MNIST(root=data_root, train=True, download=True, transform=transform)) # 60000 x 30 x 30
MNIST_test = utils.CacheDataset(MNIST(root=data_root, train=True, download=True, transform=transform)) # 10000 x 30
MNIST_loader = DataLoader(MNIST_train, batch_size=1000, shuffle=True)
MNIST_test_loader = DataLoader(MNIST_test, batch_size=1000, shuffle=False)
self.cnt = 0
def __call__(self, image):
if self.cnt % 1000 == 0:
print(self.cnt)
self.cnt += 1
image = self.to_tensor(image) * 255
image.unsqueeze_(0)
image = self.filter(image)
image = sf.local_normalization(image, 8)
temporal_image = self.temporal_transform(image)
return temporal_image.sign().byte()
kernels = [
utils.DoGKernel(7, 1, 2),
utils.DoGKernel(7, 2, 1),
]
filter = utils.Filter(kernels, padding=3, thresholds=50)
s1 = S1Transform(filter)
data_root = "data"
MNIST_train = utils.CacheDataset(
torchvision.datasets.MNIST(root=data_root,
train=True,
download=True,
transform=s1))
MNIST_test = utils.CacheDataset(
torchvision.datasets.MNIST(root=data_root,
train=False,
download=True,