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66e08dba71
transformers/tests/test_image_transforms.py
Matthew Beckers 3b09d3f05f
fix: center_crop occasionally outputs off-by-one dimension matrix (#30934) 
If required padding for a crop larger than input image is odd-numbered,
the padding would be rounded down instead of rounded up, causing the
output dimension to be one smaller than it should be.
2024-05-21 13:56:52 +01:00

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# coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from parameterized import parameterized
from transformers.testing_utils import require_flax, require_tf, require_torch, require_vision
from transformers.utils.import_utils import is_flax_available, is_tf_available, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
if is_flax_available():
import jax
if is_vision_available():
import PIL.Image
from transformers.image_transforms import (
center_crop,
center_to_corners_format,
convert_to_rgb,
corners_to_center_format,
flip_channel_order,
get_resize_output_image_size,
id_to_rgb,
normalize,
pad,
resize,
rgb_to_id,
to_channel_dimension_format,
to_pil_image,
)
def get_random_image(height, width, num_channels=3, channels_first=True):
shape = (num_channels, height, width) if channels_first else (height, width, num_channels)
random_array = np.random.randint(0, 256, shape, dtype=np.uint8)
return random_array
@require_vision
class ImageTransformsTester(unittest.TestCase):
@parameterized.expand(
[
("numpy_float_channels_first", (3, 4, 5), np.float32),
("numpy_float_channels_last", (4, 5, 3), np.float32),
("numpy_float_channels_first", (3, 4, 5), np.float64),
("numpy_float_channels_last", (4, 5, 3), np.float64),
("numpy_int_channels_first", (3, 4, 5), np.int32),
("numpy_uint_channels_first", (3, 4, 5), np.uint8),
]
)
@require_vision
def test_to_pil_image(self, name, image_shape, dtype):
image = np.random.randint(0, 256, image_shape).astype(dtype)
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
# make sure image is correctly rescaled
self.assertTrue(np.abs(np.asarray(pil_image)).sum() > 0)
@parameterized.expand(
[
("numpy_float_channels_first", (3, 4, 5), np.float32),
("numpy_float_channels_first", (3, 4, 5), np.float64),
("numpy_float_channels_last", (4, 5, 3), np.float32),
("numpy_float_channels_last", (4, 5, 3), np.float64),
]
)
@require_vision
def test_to_pil_image_from_float(self, name, image_shape, dtype):
image = np.random.rand(*image_shape).astype(dtype)
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
# make sure image is correctly rescaled
self.assertTrue(np.abs(np.asarray(pil_image)).sum() > 0)
# Make sure that an exception is raised if image is not in [0, 1]
image = np.random.randn(*image_shape).astype(dtype)
with self.assertRaises(ValueError):
to_pil_image(image)
@require_vision
def test_to_pil_image_from_mask(self):
# Make sure binary mask remains a binary mask
image = np.random.randint(0, 2, (3, 4, 5)).astype(np.uint8)
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
np_img = np.asarray(pil_image)
self.assertTrue(np_img.min() == 0)
self.assertTrue(np_img.max() == 1)
image = np.random.randint(0, 2, (3, 4, 5)).astype(np.float32)
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
np_img = np.asarray(pil_image)
self.assertTrue(np_img.min() == 0)
self.assertTrue(np_img.max() == 1)
@require_tf
def test_to_pil_image_from_tensorflow(self):
# channels_first
image = tf.random.uniform((3, 4, 5))
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
# channels_last
image = tf.random.uniform((4, 5, 3))
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
@require_torch
def test_to_pil_image_from_torch(self):
# channels first
image = torch.rand((3, 4, 5))
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
# channels last
image = torch.rand((4, 5, 3))
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
@require_flax
def test_to_pil_image_from_jax(self):
key = jax.random.PRNGKey(0)
# channel first
image = jax.random.uniform(key, (3, 4, 5))
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
# channel last
image = jax.random.uniform(key, (4, 5, 3))
pil_image = to_pil_image(image)
self.assertIsInstance(pil_image, PIL.Image.Image)
self.assertEqual(pil_image.size, (5, 4))
def test_to_channel_dimension_format(self):
# Test that function doesn't reorder if channel dim matches the input.
image = np.random.rand(3, 4, 5)
image = to_channel_dimension_format(image, "channels_first")
self.assertEqual(image.shape, (3, 4, 5))
image = np.random.rand(4, 5, 3)
image = to_channel_dimension_format(image, "channels_last")
self.assertEqual(image.shape, (4, 5, 3))
# Test that function reorders if channel dim doesn't match the input.
image = np.random.rand(3, 4, 5)
image = to_channel_dimension_format(image, "channels_last")
self.assertEqual(image.shape, (4, 5, 3))
image = np.random.rand(4, 5, 3)
image = to_channel_dimension_format(image, "channels_first")
self.assertEqual(image.shape, (3, 4, 5))
# Can pass in input_data_format and works if data format is ambiguous or unknown.
image = np.random.rand(4, 5, 6)
image = to_channel_dimension_format(image, "channels_first", input_channel_dim="channels_last")
self.assertEqual(image.shape, (6, 4, 5))
def test_get_resize_output_image_size(self):
image = np.random.randint(0, 256, (3, 224, 224))
# Test the output size defaults to (x, x) if an int is given.
self.assertEqual(get_resize_output_image_size(image, 10), (10, 10))
self.assertEqual(get_resize_output_image_size(image, [10]), (10, 10))
self.assertEqual(get_resize_output_image_size(image, (10,)), (10, 10))
# Test the output size is the same as the input if a two element tuple/list is given.
self.assertEqual(get_resize_output_image_size(image, (10, 20)), (10, 20))
self.assertEqual(get_resize_output_image_size(image, [10, 20]), (10, 20))
self.assertEqual(get_resize_output_image_size(image, (10, 20), default_to_square=True), (10, 20))
# To match pytorch behaviour, max_size is only relevant if size is an int
self.assertEqual(get_resize_output_image_size(image, (10, 20), max_size=5), (10, 20))
# Test output size = (int(size * height / width), size) if size is an int and height > width
image = np.random.randint(0, 256, (3, 50, 40))
self.assertEqual(get_resize_output_image_size(image, 20, default_to_square=False), (25, 20))
# Test output size = (size, int(size * width / height)) if size is an int and width <= height
image = np.random.randint(0, 256, (3, 40, 50))
self.assertEqual(get_resize_output_image_size(image, 20, default_to_square=False), (20, 25))
# Test size is resized if longer size > max_size
image = np.random.randint(0, 256, (3, 50, 40))
self.assertEqual(get_resize_output_image_size(image, 20, default_to_square=False, max_size=22), (22, 17))
# Test output size = (int(size * height / width), size) if size is an int and height > width and
# input has 4 channels
image = np.random.randint(0, 256, (4, 50, 40))
self.assertEqual(
get_resize_output_image_size(image, 20, default_to_square=False, input_data_format="channels_first"),
(25, 20),
)
# Test correct channel dimension is returned if output size if height == 3
# Defaults to input format - channels first
image = np.random.randint(0, 256, (3, 18, 97))
resized_image = resize(image, (3, 20))
self.assertEqual(resized_image.shape, (3, 3, 20))
# Defaults to input format - channels last
image = np.random.randint(0, 256, (18, 97, 3))
resized_image = resize(image, (3, 20))
self.assertEqual(resized_image.shape, (3, 20, 3))
image = np.random.randint(0, 256, (3, 18, 97))
resized_image = resize(image, (3, 20), data_format="channels_last")
self.assertEqual(resized_image.shape, (3, 20, 3))
image = np.random.randint(0, 256, (18, 97, 3))
resized_image = resize(image, (3, 20), data_format="channels_first")
self.assertEqual(resized_image.shape, (3, 3, 20))
def test_resize(self):
image = np.random.randint(0, 256, (3, 224, 224))
# Check the channel order is the same by default
resized_image = resize(image, (30, 40))
self.assertIsInstance(resized_image, np.ndarray)
self.assertEqual(resized_image.shape, (3, 30, 40))
# Check channel order is changed if specified
resized_image = resize(image, (30, 40), data_format="channels_last")
self.assertIsInstance(resized_image, np.ndarray)
self.assertEqual(resized_image.shape, (30, 40, 3))
# Check PIL.Image.Image is returned if return_numpy=False
resized_image = resize(image, (30, 40), return_numpy=False)
self.assertIsInstance(resized_image, PIL.Image.Image)
# PIL size is in (width, height) order
self.assertEqual(resized_image.size, (40, 30))
# Check an image with float values between 0-1 is returned with values in this range
image = np.random.rand(3, 224, 224)
resized_image = resize(image, (30, 40))
self.assertIsInstance(resized_image, np.ndarray)
self.assertEqual(resized_image.shape, (3, 30, 40))
self.assertTrue(np.all(resized_image >= 0))
self.assertTrue(np.all(resized_image <= 1))
# Check that an image with 4 channels is resized correctly
image = np.random.randint(0, 256, (4, 224, 224))
resized_image = resize(image, (30, 40), input_data_format="channels_first")
self.assertIsInstance(resized_image, np.ndarray)
self.assertEqual(resized_image.shape, (4, 30, 40))
def test_normalize(self):
image = np.random.randint(0, 256, (224, 224, 3)) / 255
# Test that exception is raised if inputs are incorrect
# Not a numpy array image
with self.assertRaises(ValueError):
normalize(5, 5, 5)
# Number of mean values != number of channels
with self.assertRaises(ValueError):
normalize(image, mean=(0.5, 0.6), std=1)
# Number of std values != number of channels
with self.assertRaises(ValueError):
normalize(image, mean=1, std=(0.5, 0.6))
# Test result is correct - output data format is channels_first and normalization
# correctly computed
mean = (0.5, 0.6, 0.7)
std = (0.1, 0.2, 0.3)
expected_image = ((image - mean) / std).transpose((2, 0, 1))
normalized_image = normalize(image, mean=mean, std=std, data_format="channels_first")
self.assertIsInstance(normalized_image, np.ndarray)
self.assertEqual(normalized_image.shape, (3, 224, 224))
self.assertTrue(np.allclose(normalized_image, expected_image, atol=1e-6))
# Test image with 4 channels is normalized correctly
image = np.random.randint(0, 256, (224, 224, 4)) / 255
mean = (0.5, 0.6, 0.7, 0.8)
std = (0.1, 0.2, 0.3, 0.4)
expected_image = (image - mean) / std
self.assertTrue(
np.allclose(
normalize(image, mean=mean, std=std, input_data_format="channels_last"), expected_image, atol=1e-6
)
)
# Test float32 image input keeps float32 dtype
image = np.random.randint(0, 256, (224, 224, 3)).astype(np.float32) / 255
mean = (0.5, 0.6, 0.7)
std = (0.1, 0.2, 0.3)
expected_image = ((image - mean) / std).astype(np.float32)
normalized_image = normalize(image, mean=mean, std=std)
self.assertEqual(normalized_image.dtype, np.float32)
self.assertTrue(np.allclose(normalized_image, expected_image, atol=1e-6))
# Test float16 image input keeps float16 dtype
image = np.random.randint(0, 256, (224, 224, 3)).astype(np.float16) / 255
mean = (0.5, 0.6, 0.7)
std = (0.1, 0.2, 0.3)
# The mean and std are cast to match the dtype of the input image
cast_mean = np.array(mean, dtype=np.float16)
cast_std = np.array(std, dtype=np.float16)
expected_image = (image - cast_mean) / cast_std
normalized_image = normalize(image, mean=mean, std=std)
self.assertEqual(normalized_image.dtype, np.float16)
self.assertTrue(np.allclose(normalized_image, expected_image, atol=1e-6))
# Test int image input is converted to float32
image = np.random.randint(0, 2, (224, 224, 3), dtype=np.uint8)
mean = (0.5, 0.6, 0.7)
std = (0.1, 0.2, 0.3)
expected_image = (image.astype(np.float32) - mean) / std
normalized_image = normalize(image, mean=mean, std=std)
self.assertEqual(normalized_image.dtype, np.float32)
self.assertTrue(np.allclose(normalized_image, expected_image, atol=1e-6))
def test_center_crop(self):
image = np.random.randint(0, 256, (3, 224, 224))
# Test that exception is raised if inputs are incorrect
with self.assertRaises(ValueError):
center_crop(image, 10)
# Test result is correct - output data format is channels_first and center crop
# correctly computed
expected_image = image[:, 52:172, 82:142].transpose(1, 2, 0)
cropped_image = center_crop(image, (120, 60), data_format="channels_last")
self.assertIsInstance(cropped_image, np.ndarray)
self.assertEqual(cropped_image.shape, (120, 60, 3))
self.assertTrue(np.allclose(cropped_image, expected_image))
# Test that image is padded with zeros if crop size is larger than image size
expected_image = np.zeros((300, 260, 3))
expected_image[38:262, 18:242, :] = image.transpose((1, 2, 0))
cropped_image = center_crop(image, (300, 260), data_format="channels_last")
self.assertIsInstance(cropped_image, np.ndarray)
self.assertEqual(cropped_image.shape, (300, 260, 3))
self.assertTrue(np.allclose(cropped_image, expected_image))
# Test that odd numbered padding requirement still leads to correct output dimensions
cropped_image = center_crop(image, (300, 259), data_format="channels_last")
self.assertEqual(cropped_image.shape, (300, 259, 3))
# Test image with 4 channels is cropped correctly
image = np.random.randint(0, 256, (224, 224, 4))
expected_image = image[52:172, 82:142, :]
self.assertTrue(np.allclose(center_crop(image, (120, 60), input_data_format="channels_last"), expected_image))
def test_center_to_corners_format(self):
bbox_center = np.array([[10, 20, 4, 8], [15, 16, 3, 4]])
expected = np.array([[8, 16, 12, 24], [13.5, 14, 16.5, 18]])
self.assertTrue(np.allclose(center_to_corners_format(bbox_center), expected))
# Check that the function and inverse function are inverse of each other
self.assertTrue(np.allclose(corners_to_center_format(center_to_corners_format(bbox_center)), bbox_center))
def test_corners_to_center_format(self):
bbox_corners = np.array([[8, 16, 12, 24], [13.5, 14, 16.5, 18]])
expected = np.array([[10, 20, 4, 8], [15, 16, 3, 4]])
self.assertTrue(np.allclose(corners_to_center_format(bbox_corners), expected))
# Check that the function and inverse function are inverse of each other
self.assertTrue(np.allclose(center_to_corners_format(corners_to_center_format(bbox_corners)), bbox_corners))
def test_rgb_to_id(self):
# test list input
rgb = [125, 4, 255]
self.assertEqual(rgb_to_id(rgb), 16712829)
# test numpy array input
color = np.array(
[
[
[213, 54, 165],
[88, 207, 39],
[156, 108, 128],
],
[
[183, 194, 46],
[137, 58, 88],
[114, 131, 233],
],
]
)
expected = np.array([[10827477, 2608984, 8416412], [3064503, 5782153, 15303538]])
self.assertTrue(np.allclose(rgb_to_id(color), expected))
def test_id_to_rgb(self):
# test int input
self.assertEqual(id_to_rgb(16712829), [125, 4, 255])
# test array input
id_array = np.array([[10827477, 2608984, 8416412], [3064503, 5782153, 15303538]])
color = np.array(
[
[
[213, 54, 165],
[88, 207, 39],
[156, 108, 128],
],
[
[183, 194, 46],
[137, 58, 88],
[114, 131, 233],
],
]
)
self.assertTrue(np.allclose(id_to_rgb(id_array), color))
def test_pad(self):
# fmt: off
image = np.array([[
[0, 1],
[2, 3],
]])
# fmt: on
# Test that exception is raised if unknown padding mode is specified
with self.assertRaises(ValueError):
pad(image, 10, mode="unknown")
# Test that exception is raised if invalid padding is specified
with self.assertRaises(ValueError):
# Cannot pad on channel dimension
pad(image, (5, 10, 10))
# Test image is padded equally on all sides is padding is an int
# fmt: off
expected_image = np.array([
[[0, 0, 0, 0],
[0, 0, 1, 0],
[0, 2, 3, 0],
[0, 0, 0, 0]],
])
# fmt: on
self.assertTrue(np.allclose(expected_image, pad(image, 1)))
# Test the left and right of each axis is padded (pad_left, pad_right)
# fmt: off
expected_image = np.array(
[[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 2, 3, 0],
[0, 0, 0, 0, 0]])
# fmt: on
self.assertTrue(np.allclose(expected_image, pad(image, (2, 1))))
# Test only one axis is padded (pad_left, pad_right)
# fmt: off
expected_image = np.array([[
[9, 9],
[9, 9],
[0, 1],
[2, 3],
[9, 9]
]])
# fmt: on
self.assertTrue(np.allclose(expected_image, pad(image, ((2, 1), (0, 0)), constant_values=9)))
# Test padding with a constant value
# fmt: off
expected_image = np.array([[
[8, 8, 0, 1, 9],
[8, 8, 2, 3, 9],
[8, 8, 7, 7, 9],
[8, 8, 7, 7, 9]
]])
# fmt: on
self.assertTrue(np.allclose(expected_image, pad(image, ((0, 2), (2, 1)), constant_values=((6, 7), (8, 9)))))
# fmt: off
image = np.array([[
[0, 1, 2],
[3, 4, 5],
[6, 7, 8],
]])
# fmt: on
# Test padding with PaddingMode.REFLECT
# fmt: off
expected_image = np.array([[
[2, 1, 0, 1, 2, 1],
[5, 4, 3, 4, 5, 4],
[8, 7, 6, 7, 8, 7],
[5, 4, 3, 4, 5, 4],
[2, 1, 0, 1, 2, 1],
]])
# fmt: on
self.assertTrue(np.allclose(expected_image, pad(image, ((0, 2), (2, 1)), mode="reflect")))
# Test padding with PaddingMode.REPLICATE
# fmt: off
expected_image = np.array([[
[0, 0, 0, 1, 2, 2],
[3, 3, 3, 4, 5, 5],
[6, 6, 6, 7, 8, 8],
[6, 6, 6, 7, 8, 8],
[6, 6, 6, 7, 8, 8],
]])
# fmt: on
self.assertTrue(np.allclose(expected_image, pad(image, ((0, 2), (2, 1)), mode="replicate")))
# Test padding with PaddingMode.SYMMETRIC
# fmt: off
expected_image = np.array([[
[1, 0, 0, 1, 2, 2],
[4, 3, 3, 4, 5, 5],
[7, 6, 6, 7, 8, 8],
[7, 6, 6, 7, 8, 8],
[4, 3, 3, 4, 5, 5],
]])
# fmt: on
self.assertTrue(np.allclose(expected_image, pad(image, ((0, 2), (2, 1)), mode="symmetric")))
# Test we can specify the output data format
# Test padding with PaddingMode.REFLECT
# fmt: off
image = np.array([[
[0, 1],
[2, 3],
]])
expected_image = np.array([
[[0], [1], [0], [1], [0]],
[[2], [3], [2], [3], [2]],
[[0], [1], [0], [1], [0]],
[[2], [3], [2], [3], [2]]
])
# fmt: on
self.assertTrue(
np.allclose(expected_image, pad(image, ((0, 2), (2, 1)), mode="reflect", data_format="channels_last"))
)
# Test we can pad on an image with 2 channels
# fmt: off
image = np.array([
[[0, 1], [2, 3]],
])
expected_image = np.array([
[[0, 0], [0, 1], [2, 3]],
[[0, 0], [0, 0], [0, 0]],
])
# fmt: on
self.assertTrue(
np.allclose(
expected_image, pad(image, ((0, 1), (1, 0)), mode="constant", input_data_format="channels_last")
)
)
@require_vision
def test_convert_to_rgb(self):
# Test that an RGBA image is converted to RGB
image = np.array([[[1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.uint8)
pil_image = PIL.Image.fromarray(image)
self.assertEqual(pil_image.mode, "RGBA")
self.assertEqual(pil_image.size, (2, 1))
# For the moment, numpy images are returned as is
rgb_image = convert_to_rgb(image)
self.assertEqual(rgb_image.shape, (1, 2, 4))
self.assertTrue(np.allclose(rgb_image, image))
# And PIL images are converted
rgb_image = convert_to_rgb(pil_image)
self.assertEqual(rgb_image.mode, "RGB")
self.assertEqual(rgb_image.size, (2, 1))
self.assertTrue(np.allclose(np.array(rgb_image), np.array([[[1, 2, 3], [5, 6, 7]]], dtype=np.uint8)))
# Test that a grayscale image is converted to RGB
image = np.array([[0, 255]], dtype=np.uint8)
pil_image = PIL.Image.fromarray(image)
self.assertEqual(pil_image.mode, "L")
self.assertEqual(pil_image.size, (2, 1))
rgb_image = convert_to_rgb(pil_image)
self.assertEqual(rgb_image.mode, "RGB")
self.assertEqual(rgb_image.size, (2, 1))
self.assertTrue(np.allclose(np.array(rgb_image), np.array([[[0, 0, 0], [255, 255, 255]]], dtype=np.uint8)))
def test_flip_channel_order(self):
# fmt: off
img_channels_first = np.array([
[[ 0, 1, 2, 3],
[ 4, 5, 6, 7]],
[[ 8, 9, 10, 11],
[12, 13, 14, 15]],
[[16, 17, 18, 19],
[20, 21, 22, 23]],
])
# fmt: on
img_channels_last = np.moveaxis(img_channels_first, 0, -1)
# fmt: off
flipped_img_channels_first = np.array([
[[16, 17, 18, 19],
[20, 21, 22, 23]],
[[ 8, 9, 10, 11],
[12, 13, 14, 15]],
[[ 0, 1, 2, 3],
[ 4, 5, 6, 7]],
])
# fmt: on
flipped_img_channels_last = np.moveaxis(flipped_img_channels_first, 0, -1)
self.assertTrue(np.allclose(flip_channel_order(img_channels_first), flipped_img_channels_first))
self.assertTrue(
np.allclose(flip_channel_order(img_channels_first, "channels_last"), flipped_img_channels_last)
)
self.assertTrue(np.allclose(flip_channel_order(img_channels_last), flipped_img_channels_last))
self.assertTrue(
np.allclose(flip_channel_order(img_channels_last, "channels_first"), flipped_img_channels_first)
)
# Can flip when the image has 2 channels
# fmt: off
img_channels_first = np.array([
[[ 0, 1, 2, 3],
[ 4, 5, 6, 7]],
[[ 8, 9, 10, 11],
[12, 13, 14, 15]],
])
# fmt: on
flipped_img_channels_first = img_channels_first[::-1, :, :]
self.assertTrue(
np.allclose(
flip_channel_order(img_channels_first, input_data_format="channels_first"), flipped_img_channels_first
)
)
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