def test_4_channel_tensor_to_pil_image(self):
def verify_img_data(img_data, expected_output, mode):
if mode is None:
img = transform.ToPILImage()(img_data)
self.assertEqual(img.mode,
'RGBA') # default should assume RGBA
else:
img = transform.ToPILImage(mode=mode)(img_data)
self.assertEqual(img.mode, mode)
split = img.split()
for i in range(4):
self.assertTrue(
np.allclose(expected_output[:, :, i],
transform.to_tensor(split[i])[0]))
img_data = jt.random((4, 4, 4))
expected_output = img_data.multiply(255).int().float().divide(255)
for mode in [None, 'RGBA', 'CMYK', 'RGBX']:
verify_img_data(img_data, expected_output, mode)
with self.assertRaises(ValueError):
# should raise if we try a mode for 3 or 1 or 2 channel images
transform.ToPILImage(mode='RGB')(img_data)
transform.ToPILImage(mode='P')(img_data)
transform.ToPILImage(mode='LA')(img_data)
def test_2d_tensor_to_pil_image(self):
to_tensor = transform.ToTensor()
img_data_float = jt.array(np.random.rand(4, 4), dtype='float32')
img_data_byte = jt.array(np.random.randint(0, 255, (4, 4)),
dtype='uint8')
img_data_short = jt.array(np.random.randint(0, 32767, (4, 4)),
dtype='int16')
img_data_int = jt.array(np.random.randint(0, 2147483647, (4, 4)),
dtype='int32')
inputs = [img_data_float, img_data_byte, img_data_short, img_data_int]
expected_outputs = [
img_data_float.multiply(255).int().float().divide(255).numpy(),
img_data_byte.float().divide(255.0).numpy(),
img_data_short.numpy(),
img_data_int.numpy()
]
expected_modes = ['F', 'L', 'I;16', 'I']
for img_data, expected_output, mode in zip(inputs, expected_outputs,
expected_modes):
for t in [transform.ToPILImage(), transform.ToPILImage(mode=mode)]:
img = t(img_data)
self.assertEqual(img.mode, mode)
self.assertTrue(
np.allclose(expected_output,
to_tensor(img),
atol=0.01,
rtol=0.01))
def test_1_channel_tensor_to_pil_image(self):
to_tensor = transform.ToTensor()
shape = (4, 4, 1)
img_data_float = jt.array(np.random.rand(*shape), dtype='float32')
img_data_byte = jt.array(np.random.randint(0, 255, shape),
dtype='uint8')
img_data_short = jt.array(np.random.randint(0, 32767, shape),
dtype='int16')
img_data_int = jt.array(np.random.randint(0, 2147483647, shape),
dtype='int32')
inputs = [img_data_float, img_data_byte, img_data_short, img_data_int]
expected_outputs = [
img_data_float.multiply(255).int().float().divide(255).numpy(),
img_data_byte.float().divide(255.0).numpy(),
img_data_short.numpy(),
img_data_int.numpy()
]
expected_modes = ['F', 'L', 'I;16', 'I']
for img_data, expected_output, mode in zip(inputs, expected_outputs,
expected_modes):
for t in [transform.ToPILImage(), transform.ToPILImage(mode=mode)]:
img = t(img_data)
self.assertEqual(img.mode, mode)
np.testing.assert_allclose(expected_output[:, :, 0],
to_tensor(img)[0],
atol=0.01)
# 'F' mode for torch.FloatTensor
img_F_mode = transform.ToPILImage(mode='F')(img_data_float)
self.assertEqual(img_F_mode.mode, 'F')
def test_ndarray_bad_types_to_pil_image(self):
trans = transform.ToPILImage()
with self.assertRaises(TypeError):
trans(np.ones([4, 4, 1], np.int64))
trans(np.ones([4, 4, 1], np.uint16))
trans(np.ones([4, 4, 1], np.uint32))
trans(np.ones([4, 4, 1], np.float64))
with self.assertRaises(ValueError):
transform.ToPILImage()(np.ones([1, 4, 4, 3]))
def verify_img_data(img_data, mode):
if mode is None:
img = transform.ToPILImage()(img_data)
self.assertEqual(img.mode, 'RGB') # default should assume RGB
else:
img = transform.ToPILImage(mode=mode)(img_data)
self.assertEqual(img.mode, mode)
split = img.split()
for i in range(3):
self.assertTrue(np.allclose(img_data[:, :, i], split[i]))
def verify_img_data(img_data, expected_output, mode):
if mode is None:
img = transform.ToPILImage()(img_data)
self.assertEqual(img.mode, 'LA') # default should assume LA
else:
img = transform.ToPILImage(mode=mode)(img_data)
self.assertEqual(img.mode, mode)
split = img.split()
for i in range(2):
self.assertTrue(
np.allclose(expected_output[:, :, i],
transform.to_tensor(split[i])))
def test_2d_ndarray_to_pil_image(self):
img_data_float = np.random.rand(4, 4).astype(np.float32)
img_data_byte = np.random.randint(0, 255, (4, 4)).astype(np.uint8)
img_data_short = np.random.randint(0, 32767, (4, 4)).astype(np.int16)
img_data_int = np.random.randint(0, 2147483647,
(4, 4)).astype(np.int32)
inputs = [img_data_float, img_data_byte, img_data_short, img_data_int]
expected_modes = ['F', 'L', 'I;16', 'I']
for img_data, mode in zip(inputs, expected_modes):
for t in [transform.ToPILImage(), transform.ToPILImage(mode=mode)]:
img = t(img_data)
self.assertEqual(img.mode, mode)
self.assertTrue(np.allclose(img_data, img))
def test_crop(self):
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
img = np.ones([height, width, 3])
oh1 = (height - oheight) // 2
ow1 = (width - owidth) // 2
# imgnarrow = img[oh1:oh1 + oheight, ow1:ow1 + owidth, :]
# imgnarrow.fill(0)
img[oh1:oh1 + oheight, ow1:ow1 + owidth, :] = 0
# img = jt.array(img)
result = transform.Compose([
transform.ToPILImage(),
transform.CenterCrop((oheight, owidth)),
transform.ToTensor(),
])(img)
self.assertEqual(
result.sum(), 0,
f"height: {height} width: {width} oheight: {oheight} owdith: {owidth}"
)
oheight += 1
owidth += 1
result = transform.Compose([
transform.ToPILImage(),
transform.CenterCrop((oheight, owidth)),
transform.ToTensor(),
])(img)
sum1 = result.sum()
# TODO: not pass
# self.assertGreater(sum1, 1,
# f"height: {height} width: {width} oheight: {oheight} owdith: {owidth}")
oheight += 1
owidth += 1
result = transform.Compose([
transform.ToPILImage(),
transform.CenterCrop((oheight, owidth)),
transform.ToTensor(),
])(img)
sum2 = result.sum()
self.assertGreater(
sum2, 0,
f"height: {height} width: {width} oheight: {oheight} owdith: {owidth}"
)
self.assertGreaterEqual(
sum2, sum1,
f"height: {height} width: {width} oheight: {oheight} owdith: {owidth}"
)
def test_random_horizontal_flip(self):
random_state = random.getstate()
random.seed(42)
img = transform.ToPILImage()(jt.random((3, 10, 10)))
himg = img.transpose(Image.FLIP_LEFT_RIGHT)
num_samples = 250
num_horizontal = 0
for _ in range(num_samples):
out = transform.RandomHorizontalFlip()(img)
if out == himg:
num_horizontal += 1
p_value = stats.binom_test(num_horizontal, num_samples, p=0.5)
random.setstate(random_state)
self.assertGreater(p_value, 0.0001)
num_samples = 250
num_horizontal = 0
for _ in range(num_samples):
out = transform.RandomHorizontalFlip(p=0.7)(img)
if out == himg:
num_horizontal += 1
p_value = stats.binom_test(num_horizontal, num_samples, p=0.7)
random.setstate(random_state)
self.assertGreater(p_value, 0.0001)
def build_transform(self):
"""
Creates a basic transformation that was used to train the models
"""
cfg = self.cfg
# we are loading images with OpenCV, so we don't need to convert them
# to BGR, they are already! So all we need to do is to normalize
# by 255 if we want to convert to BGR255 format, or flip the channels
# if we want it to be in RGB in [0-1] range.
if cfg.INPUT.TO_BGR255:
to_bgr_transform = T.Lambda(lambda x: x * 255)
else:
to_bgr_transform = T.Lambda(lambda x: x[[2, 1, 0]])
normalize_transform = T.ImageNormalize(
mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD
)
min_size = cfg.INPUT.MIN_SIZE_TEST
max_size = cfg.INPUT.MAX_SIZE_TEST
transform = T.Compose(
[
T.ToPILImage(),
Resize(min_size, max_size),
T.ToTensor(),
to_bgr_transform,
normalize_transform,
]
)
return transform
def test_TenCrop(self):
img = jt.random((30, 40, 3))
result = transform.Compose([
transform.ToPILImage(),
transform.TenCrop(20),
transform.ToTensor(),
])(img)
def test_RandomAffine(self):
img = jt.random((30, 40, 3))
result = transform.Compose([
transform.ToPILImage(),
transform.RandomAffine(20),
transform.ToTensor(),
])(img)
def test_RandomPerspective(self):
img = jt.random((30, 40, 3))
result = transform.Compose([
transform.ToPILImage(),
transform.RandomPerspective(p=1),
transform.ToTensor(),
])(img)
def test_random_choice(self):
random_state = random.getstate()
random.seed(42)
random_choice_transform = transform.RandomChoice([
transform.Resize(15),
transform.Resize(20),
transform.CenterCrop(10)
])
img = transform.ToPILImage()(jt.random((25, 25, 3)))
num_samples = 250
num_resize_15 = 0
num_resize_20 = 0
num_crop_10 = 0
for _ in range(num_samples):
out = random_choice_transform(img)
if out.size == (15, 15):
num_resize_15 += 1
elif out.size == (20, 20):
num_resize_20 += 1
elif out.size == (10, 10):
num_crop_10 += 1
p_value = stats.binom_test(num_resize_15, num_samples, p=0.33333)
self.assertGreater(p_value, 0.0001)
p_value = stats.binom_test(num_resize_20, num_samples, p=0.33333)
self.assertGreater(p_value, 0.0001)
p_value = stats.binom_test(num_crop_10, num_samples, p=0.33333)
self.assertGreater(p_value, 0.0001)
random.setstate(random_state)
def test_resize(self):
height = random.randint(24, 32) * 2
width = random.randint(24, 32) * 2
osize = random.randint(5, 12) * 2
img = jt.ones([height, width, 3])
result = transform.Compose([
transform.ToPILImage(),
transform.Resize(osize),
transform.ToTensor(),
])(img)
self.assertIn(osize, result.shape)
if height < width:
self.assertLessEqual(result.shape[1], result.shape[2])
elif width < height:
self.assertGreaterEqual(result.shape[1], result.shape[2])
result = transform.Compose([
transform.ToPILImage(),
transform.Resize([osize, osize]),
transform.ToTensor(),
])(img)
self.assertIn(osize, result.shape)
self.assertEqual(result.shape[1], osize)
self.assertEqual(result.shape[2], osize)
oheight = random.randint(5, 12) * 2
owidth = random.randint(5, 12) * 2
result = transform.Compose([
transform.ToPILImage(),
transform.Resize((oheight, owidth)),
transform.ToTensor(),
])(img)
self.assertEqual(result.shape[1], oheight)
self.assertEqual(result.shape[2], owidth)
result = transform.Compose([
transform.ToPILImage(),
transform.Resize([oheight, owidth]),
transform.ToTensor(),
])(img)
self.assertEqual(result.shape[1], oheight)
self.assertEqual(result.shape[2], owidth)
def test_to_tensor(self):
test_channels = [1, 3, 4]
height, width = 4, 4
trans = transform.ToTensor()
with self.assertRaises(TypeError):
trans(np.random.rand(1, height, width).tolist())
with self.assertRaises(ValueError):
trans(np.random.rand(height))
trans(np.random.rand(1, 1, height, width))
for channels in test_channels:
input_data = np.random.randint(
low=0, high=255, size=(height, width, channels)).astype(
np.float32) / np.float32(255.0)
img = transform.ToPILImage()(input_data)
output = trans(img)
expect = input_data.transpose(2, 0, 1)
self.assertTrue(np.allclose(expect, output),
f"{expect.shape}\n{output.shape}")
ndarray = np.random.randint(low=0,
high=255,
size=(channels, height,
width)).astype(np.uint8)
output = trans(ndarray)
expected_output = ndarray / 255.0
np.testing.assert_allclose(output, expected_output)
ndarray = np.random.rand(channels, height,
width).astype(np.float32)
output = trans(ndarray)
expected_output = ndarray
self.assertTrue(np.allclose(output, expected_output))
# separate test for mode '1' PIL images
input_data = np.random.binomial(1, 0.5, size=(height, width,
1)).astype(np.uint8)
img = transform.ToPILImage()(input_data * 255).convert('1')
output = trans(img)
self.assertTrue(np.allclose(input_data[:, :, 0], output[0]),
f"{input_data.shape}\n{output.shape}")
def test_3_channel_ndarray_to_pil_image(self):
def verify_img_data(img_data, mode):
if mode is None:
img = transform.ToPILImage()(img_data)
self.assertEqual(img.mode, 'RGB') # default should assume RGB
else:
img = transform.ToPILImage(mode=mode)(img_data)
self.assertEqual(img.mode, mode)
split = img.split()
for i in range(3):
self.assertTrue(np.allclose(img_data[:, :, i], split[i]))
img_data = np.random.randint(0, 255, (4, 4, 3)).astype(np.uint8)
for mode in [None, 'RGB', 'HSV', 'YCbCr']:
verify_img_data(img_data, mode)
with self.assertRaises(ValueError):
# should raise if we try a mode for 4 or 1 or 2 channel images
transform.ToPILImage(mode='RGBA')(img_data)
transform.ToPILImage(mode='P')(img_data)
transform.ToPILImage(mode='LA')(img_data)
def test_not_pil_image(self):
img = jt.random((30, 40, 3))
result = transform.Compose([
transform.RandomAffine(20),
transform.ToTensor(),
])(img)
img = jt.random((30, 40, 3))
result = transform.Compose([
transform.ToPILImage(),
transform.Gray(),
transform.Resize(20),
transform.ToTensor(),
])(img)
def test_random_crop(self):
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
img = np.ones((height, width, 3))
result = transform.Compose([
transform.ToPILImage(),
transform.RandomCrop((oheight, owidth)),
transform.ToTensor(),
])(img)
self.assertEqual(result.shape[1], oheight)
self.assertEqual(result.shape[2], owidth)
result = transform.Compose([
transform.ToPILImage(),
transform.RandomCrop((oheight, owidth)),
transform.ToTensor(),
])(img)
self.assertEqual(result.shape[1], oheight)
self.assertEqual(result.shape[2], owidth)
result = transform.Compose([
transform.ToPILImage(),
transform.RandomCrop((height, width)),
transform.ToTensor()
])(img)
self.assertEqual(result.shape[1], height)
self.assertEqual(result.shape[2], width)
self.assertTrue(np.allclose(img, result.transpose(1, 2, 0)))
with self.assertRaises(AssertionError):
result = transform.Compose([
transform.ToPILImage(),
transform.RandomCrop((height + 1, width + 1)),
transform.ToTensor(),
])(img)
def test_random_order(self):
random_state = random.getstate()
random.seed(42)
random_order_transform = transform.RandomOrder(
[transform.Resize(20),
transform.CenterCrop(10)])
img = transform.ToPILImage()(jt.random((3, 25, 25)))
num_samples = 250
num_normal_order = 0
resize_crop_out = transform.CenterCrop(10)(transform.Resize(20)(img))
for _ in range(num_samples):
out = random_order_transform(img)
if out == resize_crop_out:
num_normal_order += 1
p_value = stats.binom_test(num_normal_order, num_samples, p=0.5)
random.setstate(random_state)
self.assertGreater(p_value, 0.0001)
def build_transform():
if cfg.INPUT.TO_BGR255:
to_bgr_transform = T.Lambda(lambda x: x * 255)
else:
to_bgr_transform = T.Lambda(lambda x: x[[2, 1, 0]])
normalize_transform = T.Normalize(mean=cfg.INPUT.PIXEL_MEAN,
std=cfg.INPUT.PIXEL_STD)
min_size = cfg.INPUT.MIN_SIZE_TEST
max_size = cfg.INPUT.MAX_SIZE_TEST
transform = T.Compose([
T.ToPILImage(),
Resize(min_size, max_size),
T.ToTensor(),
to_bgr_transform,
normalize_transform,
])
return transform
def test_random_apply(self):
random_state = random.getstate()
random.seed(42)
random_apply_transform = transform.RandomApply([
transform.RandomHorizontalFlip(),
transform.RandomVerticalFlip(),
],
p=0.4)
img = transform.ToPILImage()(jt.random((3, 10, 10)))
num_samples = 250
num_applies = 0
for _ in range(num_samples):
out = random_apply_transform(img)
if out != img:
num_applies += 1
p_value = stats.binom_test(num_applies, num_samples, p=0.3)
random.setstate(random_state)
self.assertGreater(p_value, 0.0001)
init_step = int(math.log2(args.init_size) - 2)
max_step = int(math.log2(max_size) - 2)
nsteps = max_step - init_step + 1
lr = 1e-3
mixing = True
code_size = 512
batch_size = {4: 512, 8: 256, 16: 128, 32: 64, 64: 32, 128: 16}
batch_default = 32
phase = 150_000
max_iter = 100_000
transform = transform.Compose([
transform.ToPILImage(),
transform.RandomHorizontalFlip(),
transform.ToTensor(),
transform.ImageNormalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
netG = StyledGenerator(code_dim=code_size)
netD = Discriminator(from_rgb_activate=True)
g_running = StyledGenerator(code_size)
g_running.eval()
d_optimizer = jt.optim.Adam(netD.parameters(), lr=lr, betas=(0.0, 0.99))
g_optimizer = jt.optim.Adam(netG.generator.parameters(),
lr=lr,
betas=(0.0, 0.99))
g_optimizer.add_param_group({
def test_tensor_bad_types_to_pil_image(self):
with self.assertRaises(ValueError):
transform.ToPILImage()(jt.ones((1, 3, 4, 4)))
