def dd_helper(image, layer, iterations, lr):
input = Variable(preprocess(image).unsqueeze(0).cuda(), requires_grad=True)
vgg.zero_grad()
for i in range(iterations):
# print('Iteration: ', i)
out = input
for j in range(layer):
out = module_list[j + 1](out)
loss = out.norm()
loss.backward()
input.data = input.data + lr * input.grad.data
input = input.data.squeeze()
input.transpose_(0, 1)
input.transpose_(1, 2)
input = np.clip(deprocess(input), 0, 1)
im = Image.fromarray(np.uint8(input * 255))
return im
def image_from_array(self, image):
if self.scheme == 'color':
return Image.fromarray(image, mode="RGB")
return Image.fromarray(image, mode="L")
