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* Add transforms for object detection * Update src/transformers/image_transforms.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Better var names & docstring * Remove unused var desc in docstring * Update src/transformers/image_transforms.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
292 lines
12 KiB
Python
292 lines
12 KiB
Python
# coding=utf-8
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# Copyright 2022 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from parameterized import parameterized
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from transformers.testing_utils import require_flax, require_tf, require_torch, require_vision
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from transformers.utils.import_utils import is_flax_available, is_tf_available, is_torch_available, is_vision_available
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if is_torch_available():
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import torch
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if is_tf_available():
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import tensorflow as tf
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if is_flax_available():
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import jax
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if is_vision_available():
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import PIL.Image
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from transformers.image_transforms import (
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center_crop,
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center_to_corners_format,
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corners_to_center_format,
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get_resize_output_image_size,
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id_to_rgb,
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normalize,
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resize,
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rgb_to_id,
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to_channel_dimension_format,
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to_pil_image,
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)
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def get_random_image(height, width, num_channels=3, channels_first=True):
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shape = (num_channels, height, width) if channels_first else (height, width, num_channels)
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random_array = np.random.randint(0, 256, shape, dtype=np.uint8)
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return random_array
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@require_vision
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class ImageTransformsTester(unittest.TestCase):
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@parameterized.expand(
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[
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("numpy_float_channels_first", (3, 4, 5), np.float32),
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("numpy_float_channels_last", (4, 5, 3), np.float32),
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("numpy_int_channels_first", (3, 4, 5), np.int32),
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("numpy_uint_channels_first", (3, 4, 5), np.uint8),
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]
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)
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@require_vision
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def test_to_pil_image(self, name, image_shape, dtype):
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image = np.random.randint(0, 256, image_shape).astype(dtype)
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pil_image = to_pil_image(image)
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self.assertIsInstance(pil_image, PIL.Image.Image)
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self.assertEqual(pil_image.size, (5, 4))
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@require_tf
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def test_to_pil_image_from_tensorflow(self):
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# channels_first
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image = tf.random.uniform((3, 4, 5))
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pil_image = to_pil_image(image)
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self.assertIsInstance(pil_image, PIL.Image.Image)
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self.assertEqual(pil_image.size, (5, 4))
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# channels_last
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image = tf.random.uniform((4, 5, 3))
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pil_image = to_pil_image(image)
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self.assertIsInstance(pil_image, PIL.Image.Image)
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self.assertEqual(pil_image.size, (5, 4))
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@require_torch
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def test_to_pil_image_from_torch(self):
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# channels first
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image = torch.rand((3, 4, 5))
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pil_image = to_pil_image(image)
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self.assertIsInstance(pil_image, PIL.Image.Image)
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self.assertEqual(pil_image.size, (5, 4))
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# channels last
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image = torch.rand((4, 5, 3))
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pil_image = to_pil_image(image)
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self.assertIsInstance(pil_image, PIL.Image.Image)
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self.assertEqual(pil_image.size, (5, 4))
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@require_flax
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def test_to_pil_image_from_jax(self):
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key = jax.random.PRNGKey(0)
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# channel first
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image = jax.random.uniform(key, (3, 4, 5))
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pil_image = to_pil_image(image)
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self.assertIsInstance(pil_image, PIL.Image.Image)
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self.assertEqual(pil_image.size, (5, 4))
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# channel last
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image = jax.random.uniform(key, (4, 5, 3))
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pil_image = to_pil_image(image)
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self.assertIsInstance(pil_image, PIL.Image.Image)
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self.assertEqual(pil_image.size, (5, 4))
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def test_to_channel_dimension_format(self):
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# Test that function doesn't reorder if channel dim matches the input.
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image = np.random.rand(3, 4, 5)
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image = to_channel_dimension_format(image, "channels_first")
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self.assertEqual(image.shape, (3, 4, 5))
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image = np.random.rand(4, 5, 3)
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image = to_channel_dimension_format(image, "channels_last")
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self.assertEqual(image.shape, (4, 5, 3))
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# Test that function reorders if channel dim doesn't match the input.
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image = np.random.rand(3, 4, 5)
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image = to_channel_dimension_format(image, "channels_last")
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self.assertEqual(image.shape, (4, 5, 3))
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image = np.random.rand(4, 5, 3)
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image = to_channel_dimension_format(image, "channels_first")
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self.assertEqual(image.shape, (3, 4, 5))
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def test_get_resize_output_image_size(self):
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image = np.random.randint(0, 256, (3, 224, 224))
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# Test the output size defaults to (x, x) if an int is given.
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self.assertEqual(get_resize_output_image_size(image, 10), (10, 10))
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self.assertEqual(get_resize_output_image_size(image, [10]), (10, 10))
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self.assertEqual(get_resize_output_image_size(image, (10,)), (10, 10))
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# Test the output size is the same as the input if a two element tuple/list is given.
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self.assertEqual(get_resize_output_image_size(image, (10, 20)), (10, 20))
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self.assertEqual(get_resize_output_image_size(image, [10, 20]), (10, 20))
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self.assertEqual(get_resize_output_image_size(image, (10, 20), default_to_square=True), (10, 20))
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# To match pytorch behaviour, max_size is only relevant if size is an int
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self.assertEqual(get_resize_output_image_size(image, (10, 20), max_size=5), (10, 20))
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# Test output size = (int(size * height / width), size) if size is an int and height > width
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image = np.random.randint(0, 256, (3, 50, 40))
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self.assertEqual(get_resize_output_image_size(image, 20, default_to_square=False), (25, 20))
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# Test output size = (size, int(size * width / height)) if size is an int and width <= height
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image = np.random.randint(0, 256, (3, 40, 50))
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self.assertEqual(get_resize_output_image_size(image, 20, default_to_square=False), (20, 25))
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# Test size is resized if longer size > max_size
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image = np.random.randint(0, 256, (3, 50, 40))
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self.assertEqual(get_resize_output_image_size(image, 20, default_to_square=False, max_size=22), (22, 17))
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def test_resize(self):
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image = np.random.randint(0, 256, (3, 224, 224))
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# Check the channel order is the same by default
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resized_image = resize(image, (30, 40))
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self.assertIsInstance(resized_image, np.ndarray)
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self.assertEqual(resized_image.shape, (3, 30, 40))
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# Check channel order is changed if specified
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resized_image = resize(image, (30, 40), data_format="channels_last")
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self.assertIsInstance(resized_image, np.ndarray)
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self.assertEqual(resized_image.shape, (30, 40, 3))
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# Check PIL.Image.Image is return if return_numpy=False
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resized_image = resize(image, (30, 40), return_numpy=False)
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self.assertIsInstance(resized_image, PIL.Image.Image)
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# PIL size is in (width, height) order
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self.assertEqual(resized_image.size, (40, 30))
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def test_normalize(self):
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image = np.random.randint(0, 256, (224, 224, 3)) / 255
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# Test that exception is raised if inputs are incorrect
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# Not a numpy array image
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with self.assertRaises(ValueError):
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normalize(5, 5, 5)
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# Number of mean values != number of channels
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with self.assertRaises(ValueError):
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normalize(image, mean=(0.5, 0.6), std=1)
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# Number of std values != number of channels
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with self.assertRaises(ValueError):
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normalize(image, mean=1, std=(0.5, 0.6))
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# Test result is correct - output data format is channels_first and normalization
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# correctly computed
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mean = (0.5, 0.6, 0.7)
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std = (0.1, 0.2, 0.3)
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expected_image = ((image - mean) / std).transpose((2, 0, 1))
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normalized_image = normalize(image, mean=mean, std=std, data_format="channels_first")
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self.assertIsInstance(normalized_image, np.ndarray)
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self.assertEqual(normalized_image.shape, (3, 224, 224))
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self.assertTrue(np.allclose(normalized_image, expected_image))
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def test_center_crop(self):
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image = np.random.randint(0, 256, (3, 224, 224))
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# Test that exception is raised if inputs are incorrect
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with self.assertRaises(ValueError):
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center_crop(image, 10)
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# Test result is correct - output data format is channels_first and center crop
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# correctly computed
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expected_image = image[:, 52:172, 82:142].transpose(1, 2, 0)
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cropped_image = center_crop(image, (120, 60), data_format="channels_last")
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self.assertIsInstance(cropped_image, np.ndarray)
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self.assertEqual(cropped_image.shape, (120, 60, 3))
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self.assertTrue(np.allclose(cropped_image, expected_image))
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# Test that image is padded with zeros if crop size is larger than image size
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expected_image = np.zeros((300, 260, 3))
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expected_image[38:262, 18:242, :] = image.transpose((1, 2, 0))
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cropped_image = center_crop(image, (300, 260), data_format="channels_last")
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self.assertIsInstance(cropped_image, np.ndarray)
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self.assertEqual(cropped_image.shape, (300, 260, 3))
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self.assertTrue(np.allclose(cropped_image, expected_image))
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def test_center_to_corners_format(self):
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bbox_center = np.array([[10, 20, 4, 8], [15, 16, 3, 4]])
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expected = np.array([[8, 16, 12, 24], [13.5, 14, 16.5, 18]])
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self.assertTrue(np.allclose(center_to_corners_format(bbox_center), expected))
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# Check that the function and inverse function are inverse of each other
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self.assertTrue(np.allclose(corners_to_center_format(center_to_corners_format(bbox_center)), bbox_center))
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def test_corners_to_center_format(self):
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bbox_corners = np.array([[8, 16, 12, 24], [13.5, 14, 16.5, 18]])
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expected = np.array([[10, 20, 4, 8], [15, 16, 3, 4]])
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self.assertTrue(np.allclose(corners_to_center_format(bbox_corners), expected))
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# Check that the function and inverse function are inverse of each other
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self.assertTrue(np.allclose(center_to_corners_format(corners_to_center_format(bbox_corners)), bbox_corners))
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def test_rgb_to_id(self):
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# test list input
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rgb = [125, 4, 255]
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self.assertEqual(rgb_to_id(rgb), 16712829)
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# test numpy array input
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color = np.array(
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[
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[
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[213, 54, 165],
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[88, 207, 39],
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[156, 108, 128],
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],
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[
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[183, 194, 46],
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[137, 58, 88],
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[114, 131, 233],
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],
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]
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)
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expected = np.array([[10827477, 2608984, 8416412], [3064503, 5782153, 15303538]])
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self.assertTrue(np.allclose(rgb_to_id(color), expected))
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def test_id_to_rgb(self):
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# test int input
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self.assertEqual(id_to_rgb(16712829), [125, 4, 255])
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# test array input
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id_array = np.array([[10827477, 2608984, 8416412], [3064503, 5782153, 15303538]])
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color = np.array(
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[
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[
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[213, 54, 165],
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[88, 207, 39],
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[156, 108, 128],
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],
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[
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[183, 194, 46],
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[137, 58, 88],
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[114, 131, 233],
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],
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]
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)
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self.assertTrue(np.allclose(id_to_rgb(id_array), color))
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