def test_crop(self):
img_tensor = torch.randint(0, 255, (3, 16, 16), dtype=torch.uint8)
top = random.randint(0, 15)
left = random.randint(0, 15)
height = random.randint(1, 16 - top)
width = random.randint(1, 16 - left)
img_cropped = F_t.crop(img_tensor, top, left, height, width)
img_PIL = transforms.ToPILImage()(img_tensor)
img_PIL_cropped = F.crop(img_PIL, top, left, height, width)
img_cropped_GT = transforms.ToTensor()(img_PIL_cropped)
self.assertTrue(
torch.equal(img_cropped, (img_cropped_GT * 255).to(torch.uint8)),
"functional_tensor crop not working")
def test_crop(self):
script_crop = torch.jit.script(F_t.crop)
img_tensor = torch.randint(0, 255, (3, 16, 16), dtype=torch.uint8)
img_tensor_clone = img_tensor.clone()
top = random.randint(0, 15)
left = random.randint(0, 15)
height = random.randint(1, 16 - top)
width = random.randint(1, 16 - left)
img_cropped = F_t.crop(img_tensor, top, left, height, width)
img_PIL = transforms.ToPILImage()(img_tensor)
img_PIL_cropped = F.crop(img_PIL, top, left, height, width)
img_cropped_GT = transforms.ToTensor()(img_PIL_cropped)
self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
self.assertTrue(torch.equal(img_cropped, (img_cropped_GT * 255).to(torch.uint8)),
"functional_tensor crop not working")
# scriptable function test
cropped_img_script = script_crop(img_tensor, top, left, height, width)
self.assertTrue(torch.equal(img_cropped, cropped_img_script))
