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# coding=utf-8
# Copyright 2021 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 os
import tempfile
import unittest
import datasets
import numpy as np
import pytest
from huggingface_hub.file_download import http_get
from requests import ConnectTimeout, ReadTimeout
from tests.pipelines.test_pipelines_document_question_answering import INVOICE_URL
from transformers import is_torch_available, is_vision_available
from transformers.image_utils import ChannelDimension, get_channel_dimension_axis, make_list_of_images
from transformers.testing_utils import is_flaky, require_torch, require_vision
if is_torch_available():
import torch
if is_vision_available():
import PIL.Image
from transformers import ImageFeatureExtractionMixin
from transformers.image_utils import get_image_size, infer_channel_dimension_format, load_image
def get_random_image(height, width):
random_array = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
return PIL.Image.fromarray(random_array)
@require_vision
class ImageFeatureExtractionTester(unittest.TestCase):
def test_conversion_image_to_array(self):
feature_extractor = ImageFeatureExtractionMixin()
image = get_random_image(16, 32)
# Conversion with defaults (rescale + channel first)
array1 = feature_extractor.to_numpy_array(image)
self.assertTrue(array1.dtype, np.float32)
self.assertEqual(array1.shape, (3, 16, 32))
# Conversion with rescale and not channel first
array2 = feature_extractor.to_numpy_array(image, channel_first=False)
self.assertTrue(array2.dtype, np.float32)
self.assertEqual(array2.shape, (16, 32, 3))
self.assertTrue(np.array_equal(array1, array2.transpose(2, 0, 1)))
# Conversion with no rescale and channel first
array3 = feature_extractor.to_numpy_array(image, rescale=False)
self.assertTrue(array3.dtype, np.uint8)
self.assertEqual(array3.shape, (3, 16, 32))
self.assertTrue(np.array_equal(array1, array3.astype(np.float32) * (1 / 255.0)))
# Conversion with no rescale and not channel first
array4 = feature_extractor.to_numpy_array(image, rescale=False, channel_first=False)
self.assertTrue(array4.dtype, np.uint8)
self.assertEqual(array4.shape, (16, 32, 3))
self.assertTrue(np.array_equal(array2, array4.astype(np.float32) * (1 / 255.0)))
def test_conversion_array_to_array(self):
feature_extractor = ImageFeatureExtractionMixin()
array = np.random.randint(0, 256, (16, 32, 3), dtype=np.uint8)
# By default, rescale (for an array of ints) and channel permute
array1 = feature_extractor.to_numpy_array(array)
self.assertTrue(array1.dtype, np.float32)
self.assertEqual(array1.shape, (3, 16, 32))
self.assertTrue(np.array_equal(array1, array.transpose(2, 0, 1).astype(np.float32) * (1 / 255.0)))
# Same with no permute
array2 = feature_extractor.to_numpy_array(array, channel_first=False)
self.assertTrue(array2.dtype, np.float32)
self.assertEqual(array2.shape, (16, 32, 3))
self.assertTrue(np.array_equal(array2, array.astype(np.float32) * (1 / 255.0)))
# Force rescale to False
array3 = feature_extractor.to_numpy_array(array, rescale=False)
self.assertTrue(array3.dtype, np.uint8)
self.assertEqual(array3.shape, (3, 16, 32))
self.assertTrue(np.array_equal(array3, array.transpose(2, 0, 1)))
# Force rescale to False and no channel permute
array4 = feature_extractor.to_numpy_array(array, rescale=False, channel_first=False)
self.assertTrue(array4.dtype, np.uint8)
self.assertEqual(array4.shape, (16, 32, 3))
self.assertTrue(np.array_equal(array4, array))
# Now test the default rescale for a float array (defaults to False)
array5 = feature_extractor.to_numpy_array(array2)
self.assertTrue(array5.dtype, np.float32)
self.assertEqual(array5.shape, (3, 16, 32))
self.assertTrue(np.array_equal(array5, array1))
def test_make_list_of_images_numpy(self):
# Test a single image is converted to a list of 1 image
images = np.random.randint(0, 256, (16, 32, 3))
images_list = make_list_of_images(images)
self.assertEqual(len(images_list), 1)
self.assertTrue(np.array_equal(images_list[0], images))
self.assertIsInstance(images_list, list)
# Test a batch of images is converted to a list of images
images = np.random.randint(0, 256, (4, 16, 32, 3))
images_list = make_list_of_images(images)
self.assertEqual(len(images_list), 4)
self.assertTrue(np.array_equal(images_list[0], images[0]))
self.assertIsInstance(images_list, list)
# Test a list of images is not modified
images = [np.random.randint(0, 256, (16, 32, 3)) for _ in range(4)]
images_list = make_list_of_images(images)
self.assertEqual(len(images_list), 4)
self.assertTrue(np.array_equal(images_list[0], images[0]))
self.assertIsInstance(images_list, list)
# Test batched masks with no channel dimension are converted to a list of masks
masks = np.random.randint(0, 2, (4, 16, 32))
masks_list = make_list_of_images(masks, expected_ndims=2)
self.assertEqual(len(masks_list), 4)
self.assertTrue(np.array_equal(masks_list[0], masks[0]))
self.assertIsInstance(masks_list, list)
@require_torch
def test_make_list_of_images_torch(self):
# Test a single image is converted to a list of 1 image
images = torch.randint(0, 256, (16, 32, 3))
images_list = make_list_of_images(images)
self.assertEqual(len(images_list), 1)
self.assertTrue(np.array_equal(images_list[0], images))
self.assertIsInstance(images_list, list)
# Test a batch of images is converted to a list of images
images = torch.randint(0, 256, (4, 16, 32, 3))
images_list = make_list_of_images(images)
self.assertEqual(len(images_list), 4)
self.assertTrue(np.array_equal(images_list[0], images[0]))
self.assertIsInstance(images_list, list)
# Test a list of images is left unchanged
images = [torch.randint(0, 256, (16, 32, 3)) for _ in range(4)]
images_list = make_list_of_images(images)
self.assertEqual(len(images_list), 4)
self.assertTrue(np.array_equal(images_list[0], images[0]))
self.assertIsInstance(images_list, list)
@require_torch
def test_conversion_torch_to_array(self):
feature_extractor = ImageFeatureExtractionMixin()
tensor = torch.randint(0, 256, (16, 32, 3))
array = tensor.numpy()
# By default, rescale (for a tensor of ints) and channel permute
array1 = feature_extractor.to_numpy_array(array)
self.assertTrue(array1.dtype, np.float32)
self.assertEqual(array1.shape, (3, 16, 32))
self.assertTrue(np.array_equal(array1, array.transpose(2, 0, 1).astype(np.float32) * (1 / 255.0)))
# Same with no permute
array2 = feature_extractor.to_numpy_array(array, channel_first=False)
self.assertTrue(array2.dtype, np.float32)
self.assertEqual(array2.shape, (16, 32, 3))
self.assertTrue(np.array_equal(array2, array.astype(np.float32) * (1 / 255.0)))
# Force rescale to False
array3 = feature_extractor.to_numpy_array(array, rescale=False)
self.assertTrue(array3.dtype, np.uint8)
self.assertEqual(array3.shape, (3, 16, 32))
self.assertTrue(np.array_equal(array3, array.transpose(2, 0, 1)))
# Force rescale to False and no channel permute
array4 = feature_extractor.to_numpy_array(array, rescale=False, channel_first=False)
self.assertTrue(array4.dtype, np.uint8)
self.assertEqual(array4.shape, (16, 32, 3))
self.assertTrue(np.array_equal(array4, array))
# Now test the default rescale for a float tensor (defaults to False)
array5 = feature_extractor.to_numpy_array(array2)
self.assertTrue(array5.dtype, np.float32)
self.assertEqual(array5.shape, (3, 16, 32))
self.assertTrue(np.array_equal(array5, array1))
def test_conversion_image_to_image(self):
feature_extractor = ImageFeatureExtractionMixin()
image = get_random_image(16, 32)
# On an image, `to_pil_image1` is a noop.
image1 = feature_extractor.to_pil_image(image)
self.assertTrue(isinstance(image, PIL.Image.Image))
self.assertTrue(np.array_equal(np.array(image), np.array(image1)))
def test_conversion_array_to_image(self):
feature_extractor = ImageFeatureExtractionMixin()
array = np.random.randint(0, 256, (16, 32, 3), dtype=np.uint8)
# By default, no rescale (for an array of ints)
image1 = feature_extractor.to_pil_image(array)
self.assertTrue(isinstance(image1, PIL.Image.Image))
self.assertTrue(np.array_equal(np.array(image1), array))
# If the array is channel-first, proper reordering of the channels is done.
image2 = feature_extractor.to_pil_image(array.transpose(2, 0, 1))
self.assertTrue(isinstance(image2, PIL.Image.Image))
self.assertTrue(np.array_equal(np.array(image2), array))
# If the array has floating type, it's rescaled by default.
image3 = feature_extractor.to_pil_image(array.astype(np.float32) * (1 / 255.0))
self.assertTrue(isinstance(image3, PIL.Image.Image))
self.assertTrue(np.array_equal(np.array(image3), array))
# You can override the default to rescale.
image4 = feature_extractor.to_pil_image(array.astype(np.float32), rescale=False)
self.assertTrue(isinstance(image4, PIL.Image.Image))
self.assertTrue(np.array_equal(np.array(image4), array))
# And with floats + channel first.
image5 = feature_extractor.to_pil_image(array.transpose(2, 0, 1).astype(np.float32) * (1 / 255.0))
self.assertTrue(isinstance(image5, PIL.Image.Image))
self.assertTrue(np.array_equal(np.array(image5), array))
@require_torch
def test_conversion_tensor_to_image(self):
feature_extractor = ImageFeatureExtractionMixin()
tensor = torch.randint(0, 256, (16, 32, 3))
array = tensor.numpy()
# By default, no rescale (for a tensor of ints)
image1 = feature_extractor.to_pil_image(tensor)
self.assertTrue(isinstance(image1, PIL.Image.Image))
self.assertTrue(np.array_equal(np.array(image1), array))
# If the tensor is channel-first, proper reordering of the channels is done.
image2 = feature_extractor.to_pil_image(tensor.permute(2, 0, 1))
self.assertTrue(isinstance(image2, PIL.Image.Image))
self.assertTrue(np.array_equal(np.array(image2), array))
# If the tensor has floating type, it's rescaled by default.
image3 = feature_extractor.to_pil_image(tensor.float() / 255.0)
self.assertTrue(isinstance(image3, PIL.Image.Image))
self.assertTrue(np.array_equal(np.array(image3), array))
# You can override the default to rescale.
image4 = feature_extractor.to_pil_image(tensor.float(), rescale=False)
self.assertTrue(isinstance(image4, PIL.Image.Image))
self.assertTrue(np.array_equal(np.array(image4), array))
# And with floats + channel first.
image5 = feature_extractor.to_pil_image(tensor.permute(2, 0, 1).float() * (1 / 255.0))
self.assertTrue(isinstance(image5, PIL.Image.Image))
self.assertTrue(np.array_equal(np.array(image5), array))
def test_resize_image_and_array(self):
feature_extractor = ImageFeatureExtractionMixin()
image = get_random_image(16, 32)
array = np.array(image)
# Size can be an int or a tuple of ints.
resized_image = feature_extractor.resize(image, 8)
self.assertTrue(isinstance(resized_image, PIL.Image.Image))
self.assertEqual(resized_image.size, (8, 8))
resized_image1 = feature_extractor.resize(image, (8, 16))
self.assertTrue(isinstance(resized_image1, PIL.Image.Image))
self.assertEqual(resized_image1.size, (8, 16))
# Passing an array converts it to a PIL Image.
resized_image2 = feature_extractor.resize(array, 8)
self.assertTrue(isinstance(resized_image2, PIL.Image.Image))
self.assertEqual(resized_image2.size, (8, 8))
self.assertTrue(np.array_equal(np.array(resized_image), np.array(resized_image2)))
resized_image3 = feature_extractor.resize(image, (8, 16))
self.assertTrue(isinstance(resized_image3, PIL.Image.Image))
self.assertEqual(resized_image3.size, (8, 16))
self.assertTrue(np.array_equal(np.array(resized_image1), np.array(resized_image3)))
def test_resize_image_and_array_non_default_to_square(self):
feature_extractor = ImageFeatureExtractionMixin()
heights_widths = [
# height, width
# square image
(28, 28),
(27, 27),
# rectangular image: h < w
(28, 34),
(29, 35),
# rectangular image: h > w
(34, 28),
(35, 29),
]
# single integer or single integer in tuple/list
sizes = [22, 27, 28, 36, [22], (27,)]
for (height, width), size in zip(heights_widths, sizes):
for max_size in (None, 37, 1000):
image = get_random_image(height, width)
array = np.array(image)
size = size[0] if isinstance(size, (list, tuple)) else size
# Size can be an int or a tuple of ints.
# If size is an int, smaller edge of the image will be matched to this number.
# i.e, if height > width, then image will be rescaled to (size * height / width, size).
if height < width:
exp_w, exp_h = (int(size * width / height), size)
if max_size is not None and max_size < exp_w:
exp_w, exp_h = max_size, int(max_size * exp_h / exp_w)
elif width < height:
exp_w, exp_h = (size, int(size * height / width))
if max_size is not None and max_size < exp_h:
exp_w, exp_h = int(max_size * exp_w / exp_h), max_size
else:
exp_w, exp_h = (size, size)
if max_size is not None and max_size < size:
exp_w, exp_h = max_size, max_size
resized_image = feature_extractor.resize(image, size=size, default_to_square=False, max_size=max_size)
self.assertTrue(isinstance(resized_image, PIL.Image.Image))
self.assertEqual(resized_image.size, (exp_w, exp_h))
# Passing an array converts it to a PIL Image.
resized_image2 = feature_extractor.resize(array, size=size, default_to_square=False, max_size=max_size)
self.assertTrue(isinstance(resized_image2, PIL.Image.Image))
self.assertEqual(resized_image2.size, (exp_w, exp_h))
self.assertTrue(np.array_equal(np.array(resized_image), np.array(resized_image2)))
@require_torch
def test_resize_tensor(self):
feature_extractor = ImageFeatureExtractionMixin()
tensor = torch.randint(0, 256, (16, 32, 3))
array = tensor.numpy()
# Size can be an int or a tuple of ints.
resized_image = feature_extractor.resize(tensor, 8)
self.assertTrue(isinstance(resized_image, PIL.Image.Image))
self.assertEqual(resized_image.size, (8, 8))
resized_image1 = feature_extractor.resize(tensor, (8, 16))
self.assertTrue(isinstance(resized_image1, PIL.Image.Image))
self.assertEqual(resized_image1.size, (8, 16))
# Check we get the same results as with NumPy arrays.
resized_image2 = feature_extractor.resize(array, 8)
self.assertTrue(np.array_equal(np.array(resized_image), np.array(resized_image2)))
resized_image3 = feature_extractor.resize(array, (8, 16))
self.assertTrue(np.array_equal(np.array(resized_image1), np.array(resized_image3)))
def test_normalize_image(self):
feature_extractor = ImageFeatureExtractionMixin()
image = get_random_image(16, 32)
array = np.array(image)
mean = [0.1, 0.5, 0.9]
std = [0.2, 0.4, 0.6]
# PIL Image are converted to NumPy arrays for the normalization
normalized_image = feature_extractor.normalize(image, mean, std)
self.assertTrue(isinstance(normalized_image, np.ndarray))
self.assertEqual(normalized_image.shape, (3, 16, 32))
# During the conversion rescale and channel first will be applied.
expected = array.transpose(2, 0, 1).astype(np.float32) * (1 / 255.0)
np_mean = np.array(mean).astype(np.float32)[:, None, None]
np_std = np.array(std).astype(np.float32)[:, None, None]
expected = (expected - np_mean) / np_std
self.assertTrue(np.array_equal(normalized_image, expected))
def test_normalize_array(self):
feature_extractor = ImageFeatureExtractionMixin()
array = np.random.random((16, 32, 3))
mean = [0.1, 0.5, 0.9]
std = [0.2, 0.4, 0.6]
# mean and std can be passed as lists or NumPy arrays.
expected = (array - np.array(mean)) / np.array(std)
normalized_array = feature_extractor.normalize(array, mean, std)
self.assertTrue(np.array_equal(normalized_array, expected))
normalized_array = feature_extractor.normalize(array, np.array(mean), np.array(std))
self.assertTrue(np.array_equal(normalized_array, expected))
# Normalize will detect automatically if channel first or channel last is used.
array = np.random.random((3, 16, 32))
expected = (array - np.array(mean)[:, None, None]) / np.array(std)[:, None, None]
normalized_array = feature_extractor.normalize(array, mean, std)
self.assertTrue(np.array_equal(normalized_array, expected))
normalized_array = feature_extractor.normalize(array, np.array(mean), np.array(std))
self.assertTrue(np.array_equal(normalized_array, expected))
@require_torch
def test_normalize_tensor(self):
feature_extractor = ImageFeatureExtractionMixin()
tensor = torch.rand(16, 32, 3)
mean = [0.1, 0.5, 0.9]
std = [0.2, 0.4, 0.6]
# mean and std can be passed as lists or tensors.
expected = (tensor - torch.tensor(mean)) / torch.tensor(std)
normalized_tensor = feature_extractor.normalize(tensor, mean, std)
self.assertTrue(torch.equal(normalized_tensor, expected))
normalized_tensor = feature_extractor.normalize(tensor, torch.tensor(mean), torch.tensor(std))
self.assertTrue(torch.equal(normalized_tensor, expected))
# Normalize will detect automatically if channel first or channel last is used.
tensor = torch.rand(3, 16, 32)
expected = (tensor - torch.tensor(mean)[:, None, None]) / torch.tensor(std)[:, None, None]
normalized_tensor = feature_extractor.normalize(tensor, mean, std)
self.assertTrue(torch.equal(normalized_tensor, expected))
normalized_tensor = feature_extractor.normalize(tensor, torch.tensor(mean), torch.tensor(std))
self.assertTrue(torch.equal(normalized_tensor, expected))
def test_center_crop_image(self):
feature_extractor = ImageFeatureExtractionMixin()
image = get_random_image(16, 32)
# Test various crop sizes: bigger on all dimensions, on one of the dimensions only and on both dimensions.
crop_sizes = [8, (8, 64), 20, (32, 64)]
for size in crop_sizes:
cropped_image = feature_extractor.center_crop(image, size)
self.assertTrue(isinstance(cropped_image, PIL.Image.Image))
# PIL Image.size is transposed compared to NumPy or PyTorch (width first instead of height first).
expected_size = (size, size) if isinstance(size, int) else (size[1], size[0])
self.assertEqual(cropped_image.size, expected_size)
def test_center_crop_array(self):
feature_extractor = ImageFeatureExtractionMixin()
image = get_random_image(16, 32)
array = feature_extractor.to_numpy_array(image)
# Test various crop sizes: bigger on all dimensions, on one of the dimensions only and on both dimensions.
crop_sizes = [8, (8, 64), 20, (32, 64)]
for size in crop_sizes:
cropped_array = feature_extractor.center_crop(array, size)
self.assertTrue(isinstance(cropped_array, np.ndarray))
expected_size = (size, size) if isinstance(size, int) else size
self.assertEqual(cropped_array.shape[-2:], expected_size)
# Check result is consistent with PIL.Image.crop
cropped_image = feature_extractor.center_crop(image, size)
self.assertTrue(np.array_equal(cropped_array, feature_extractor.to_numpy_array(cropped_image)))
@require_torch
def test_center_crop_tensor(self):
feature_extractor = ImageFeatureExtractionMixin()
image = get_random_image(16, 32)
array = feature_extractor.to_numpy_array(image)
tensor = torch.tensor(array)
# Test various crop sizes: bigger on all dimensions, on one of the dimensions only and on both dimensions.
crop_sizes = [8, (8, 64), 20, (32, 64)]
for size in crop_sizes:
cropped_tensor = feature_extractor.center_crop(tensor, size)
self.assertTrue(isinstance(cropped_tensor, torch.Tensor))
expected_size = (size, size) if isinstance(size, int) else size
self.assertEqual(cropped_tensor.shape[-2:], expected_size)
# Check result is consistent with PIL.Image.crop
cropped_image = feature_extractor.center_crop(image, size)
self.assertTrue(torch.equal(cropped_tensor, torch.tensor(feature_extractor.to_numpy_array(cropped_image))))
@require_vision
class LoadImageTester(unittest.TestCase):
def test_load_img_url(self):
img = load_image(INVOICE_URL)
img_arr = np.array(img)
self.assertEqual(img_arr.shape, (1061, 750, 3))
@is_flaky()
def test_load_img_url_timeout(self):
with self.assertRaises((ReadTimeout, ConnectTimeout)):
load_image(INVOICE_URL, timeout=0.001)
def test_load_img_local(self):
img = load_image("./tests/fixtures/tests_samples/COCO/000000039769.png")
img_arr = np.array(img)
self.assertEqual(
img_arr.shape,
(480, 640, 3),
)
def test_load_img_base64_prefix(self):
try:
tmp_file = tempfile.mktemp()
with open(tmp_file, "wb") as f:
http_get(
"https://huggingface.co/datasets/hf-internal-testing/dummy-base64-images/raw/main/image_0.txt", f
)
with open(tmp_file, encoding="utf-8") as b64:
img = load_image(b64.read())
img_arr = np.array(img)
finally:
os.remove(tmp_file)
self.assertEqual(img_arr.shape, (64, 32, 3))
def test_load_img_base64(self):
try:
tmp_file = tempfile.mktemp()
with open(tmp_file, "wb") as f:
http_get(
"https://huggingface.co/datasets/hf-internal-testing/dummy-base64-images/raw/main/image_1.txt", f
)
with open(tmp_file, encoding="utf-8") as b64:
img = load_image(b64.read())
img_arr = np.array(img)
finally:
os.remove(tmp_file)
self.assertEqual(img_arr.shape, (64, 32, 3))
def test_load_img_rgba(self):
dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test")
img = load_image(dataset[0]["file"]) # img with mode RGBA
img_arr = np.array(img)
self.assertEqual(
img_arr.shape,
(512, 512, 3),
)
def test_load_img_la(self):
dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test")
img = load_image(dataset[1]["file"]) # img with mode LA
img_arr = np.array(img)
self.assertEqual(
img_arr.shape,
(512, 768, 3),
)
def test_load_img_l(self):
dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test")
img = load_image(dataset[2]["file"]) # img with mode L
img_arr = np.array(img)
self.assertEqual(
img_arr.shape,
(381, 225, 3),
)
def test_load_img_exif_transpose(self):
dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test")
img_file = dataset[3]["file"]
img_without_exif_transpose = PIL.Image.open(img_file)
img_arr_without_exif_transpose = np.array(img_without_exif_transpose)
self.assertEqual(
img_arr_without_exif_transpose.shape,
(333, 500, 3),
)
img_with_exif_transpose = load_image(img_file)
img_arr_with_exif_transpose = np.array(img_with_exif_transpose)
self.assertEqual(
img_arr_with_exif_transpose.shape,
(500, 333, 3),
)
class UtilFunctionTester(unittest.TestCase):
def test_get_image_size(self):
# Test we can infer the size and channel dimension of an image.
image = np.random.randint(0, 256, (32, 64, 3))
self.assertEqual(get_image_size(image), (32, 64))
image = np.random.randint(0, 256, (3, 32, 64))
self.assertEqual(get_image_size(image), (32, 64))
# Test the channel dimension can be overriden
image = np.random.randint(0, 256, (3, 32, 64))
self.assertEqual(get_image_size(image, channel_dim=ChannelDimension.LAST), (3, 32))
def test_infer_channel_dimension(self):
# Test we fail with invalid input
with pytest.raises(ValueError):
infer_channel_dimension_format(np.random.randint(0, 256, (10, 10)))
with pytest.raises(ValueError):
infer_channel_dimension_format(np.random.randint(0, 256, (10, 10, 10, 10, 10)))
# Test we fail if neither first not last dimension is of size 3 or 1
with pytest.raises(ValueError):
infer_channel_dimension_format(np.random.randint(0, 256, (10, 1, 50)))
# But if we explicitly set one of the number of channels to 50 it works
inferred_dim = infer_channel_dimension_format(np.random.randint(0, 256, (10, 1, 50)), num_channels=50)
self.assertEqual(inferred_dim, ChannelDimension.LAST)
# Test we correctly identify the channel dimension
image = np.random.randint(0, 256, (3, 4, 5))
inferred_dim = infer_channel_dimension_format(image)
self.assertEqual(inferred_dim, ChannelDimension.FIRST)
image = np.random.randint(0, 256, (1, 4, 5))
inferred_dim = infer_channel_dimension_format(image)
self.assertEqual(inferred_dim, ChannelDimension.FIRST)
image = np.random.randint(0, 256, (4, 5, 3))
inferred_dim = infer_channel_dimension_format(image)
self.assertEqual(inferred_dim, ChannelDimension.LAST)
image = np.random.randint(0, 256, (4, 5, 1))
inferred_dim = infer_channel_dimension_format(image)
self.assertEqual(inferred_dim, ChannelDimension.LAST)
# We can take a batched array of images and find the dimension
image = np.random.randint(0, 256, (1, 3, 4, 5))
inferred_dim = infer_channel_dimension_format(image)
self.assertEqual(inferred_dim, ChannelDimension.FIRST)
def test_get_channel_dimension_axis(self):
# Test we correctly identify the channel dimension
image = np.random.randint(0, 256, (3, 4, 5))
inferred_axis = get_channel_dimension_axis(image)
self.assertEqual(inferred_axis, 0)
image = np.random.randint(0, 256, (1, 4, 5))
inferred_axis = get_channel_dimension_axis(image)
self.assertEqual(inferred_axis, 0)
image = np.random.randint(0, 256, (4, 5, 3))
inferred_axis = get_channel_dimension_axis(image)
self.assertEqual(inferred_axis, 2)
image = np.random.randint(0, 256, (4, 5, 1))
inferred_axis = get_channel_dimension_axis(image)
self.assertEqual(inferred_axis, 2)
# We can take a batched array of images and find the dimension
image = np.random.randint(0, 256, (1, 3, 4, 5))
inferred_axis = get_channel_dimension_axis(image)
self.assertEqual(inferred_axis, 1)
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