def test_center_crop_video(self):
numFrames = random.randint(4, 128)
height = random.randint(10, 32) * 2
width = random.randint(10, 32) * 2
oheight = random.randint(5, (height - 2) / 2) * 2
owidth = random.randint(5, (width - 2) / 2) * 2
clip = torch.ones(
(numFrames, height, width, 3), dtype=torch.uint8) * 255
oh1 = (height - oheight) // 2
ow1 = (width - owidth) // 2
clipNarrow = clip[:, oh1:oh1 + oheight, ow1:ow1 + owidth, :]
clipNarrow.fill_(0)
result = transforms.Compose([
transforms.ToTensorVideo(),
transforms.CenterCropVideo((oheight, owidth)),
])(clip)
msg = "height: " + str(height) + " width: " \
+ str(width) + " oheight: " + str(oheight) + " owidth: " + str(owidth)
self.assertEqual(result.sum().item(), 0, msg)
oheight += 1
owidth += 1
result = transforms.Compose([
transforms.ToTensorVideo(),
transforms.CenterCropVideo((oheight, owidth)),
])(clip)
sum1 = result.sum()
msg = "height: " + str(height) + " width: " \
+ str(width) + " oheight: " + str(oheight) + " owidth: " + str(owidth)
self.assertEqual(sum1.item() > 1, True, msg)
oheight += 1
owidth += 1
result = transforms.Compose([
transforms.ToTensorVideo(),
transforms.CenterCropVideo((oheight, owidth)),
])(clip)
sum2 = result.sum()
msg = "height: " + str(height) + " width: " \
+ str(width) + " oheight: " + str(oheight) + " owidth: " + str(owidth)
self.assertTrue(sum2.item() > 1, msg)
self.assertTrue(sum2.item() > sum1.item(), msg)
def test_random_resized_crop_video(self):
numFrames = random.randint(4, 128)
height = random.randint(10, 32) * 2
width = random.randint(10, 32) * 2
oheight = random.randint(5, (height - 2) / 2) * 2
owidth = random.randint(5, (width - 2) / 2) * 2
clip = torch.randint(0,
256, (numFrames, height, width, 3),
dtype=torch.uint8)
result = transforms.Compose([
transforms.ToTensorVideo(),
transforms.RandomResizedCropVideo((oheight, owidth)),
])(clip)
assert result.size(2) == oheight
assert result.size(3) == owidth
transforms.RandomResizedCropVideo((oheight, owidth)).__repr__()
def test_to_tensor_video(self):
numFrames, height, width = 64, 4, 4
trans = transforms.ToTensorVideo()
with self.assertRaises(TypeError):
trans(np.random.rand(numFrames, height, width, 1).tolist())
trans(torch.rand((numFrames, height, width, 1), dtype=torch.float))
with self.assertRaises(ValueError):
trans(
torch.ones((3, numFrames, height, width, 3),
dtype=torch.uint8))
trans(torch.ones((height, width, 3), dtype=torch.uint8))
trans(torch.ones((width, 3), dtype=torch.uint8))
trans(torch.ones((3), dtype=torch.uint8))
trans.__repr__()