mirror of
https://github.com/huggingface/transformers.git
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6bca43bb90
* Add copied from statements for image processors * Move out rescale and normalize to base image processor * Remove rescale and normalize from vit (post rebase) * Update docstrings and tidy up * PR comments * Add input_data_format as preprocess argument * Resolve tests and tidy up * Remove num_channels argument * Update doc strings -> default ints not in code formatting
288 lines
12 KiB
Python
288 lines
12 KiB
Python
# coding=utf-8
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# Copyright 2023 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 json
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import os
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import tempfile
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from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
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from transformers.utils import is_torch_available, is_vision_available
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if is_torch_available():
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import numpy as np
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import torch
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if is_vision_available():
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from PIL import Image
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def prepare_image_inputs(
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batch_size,
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min_resolution,
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max_resolution,
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num_channels,
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size_divisor=None,
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equal_resolution=False,
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numpify=False,
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torchify=False,
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):
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"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
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or a list of PyTorch tensors if one specifies torchify=True.
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One can specify whether the images are of the same resolution or not.
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"""
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assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
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image_inputs = []
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for i in range(batch_size):
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if equal_resolution:
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width = height = max_resolution
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else:
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# To avoid getting image width/height 0
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if size_divisor is not None:
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# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
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min_resolution = max(size_divisor, min_resolution)
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width, height = np.random.choice(np.arange(min_resolution, max_resolution), 2)
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image_inputs.append(np.random.randint(255, size=(num_channels, width, height), dtype=np.uint8))
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if not numpify and not torchify:
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# PIL expects the channel dimension as last dimension
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image_inputs = [Image.fromarray(np.moveaxis(image, 0, -1)) for image in image_inputs]
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if torchify:
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image_inputs = [torch.from_numpy(image) for image in image_inputs]
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return image_inputs
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def prepare_video(num_frames, num_channels, width=10, height=10, numpify=False, torchify=False):
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"""This function prepares a video as a list of PIL images/NumPy arrays/PyTorch tensors."""
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video = []
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for i in range(num_frames):
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video.append(np.random.randint(255, size=(num_channels, width, height), dtype=np.uint8))
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if not numpify and not torchify:
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# PIL expects the channel dimension as last dimension
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video = [Image.fromarray(np.moveaxis(frame, 0, -1)) for frame in video]
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if torchify:
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video = [torch.from_numpy(frame) for frame in video]
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return video
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def prepare_video_inputs(
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batch_size,
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num_frames,
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num_channels,
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min_resolution,
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max_resolution,
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equal_resolution=False,
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numpify=False,
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torchify=False,
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):
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"""This function prepares a batch of videos: a list of list of PIL images, or a list of list of numpy arrays if
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one specifies numpify=True, or a list of list of PyTorch tensors if one specifies torchify=True.
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One can specify whether the videos are of the same resolution or not.
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"""
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assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
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video_inputs = []
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for i in range(batch_size):
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if equal_resolution:
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width = height = max_resolution
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else:
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width, height = np.random.choice(np.arange(min_resolution, max_resolution), 2)
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video = prepare_video(
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num_frames=num_frames,
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num_channels=num_channels,
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width=width,
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height=height,
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numpify=numpify,
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torchify=torchify,
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)
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video_inputs.append(video)
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return video_inputs
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class ImageProcessingTestMixin:
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test_cast_dtype = None
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def test_image_processor_to_json_string(self):
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image_processor = self.image_processing_class(**self.image_processor_dict)
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obj = json.loads(image_processor.to_json_string())
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for key, value in self.image_processor_dict.items():
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self.assertEqual(obj[key], value)
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def test_image_processor_to_json_file(self):
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image_processor_first = self.image_processing_class(**self.image_processor_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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json_file_path = os.path.join(tmpdirname, "image_processor.json")
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image_processor_first.to_json_file(json_file_path)
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image_processor_second = self.image_processing_class.from_json_file(json_file_path)
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self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())
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def test_image_processor_from_and_save_pretrained(self):
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image_processor_first = self.image_processing_class(**self.image_processor_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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saved_file = image_processor_first.save_pretrained(tmpdirname)[0]
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check_json_file_has_correct_format(saved_file)
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image_processor_second = self.image_processing_class.from_pretrained(tmpdirname)
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self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())
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def test_init_without_params(self):
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image_processor = self.image_processing_class()
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self.assertIsNotNone(image_processor)
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@require_torch
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@require_vision
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def test_cast_dtype_device(self):
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if self.test_cast_dtype is not None:
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# Initialize image_processor
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image_processor = self.image_processing_class(**self.image_processor_dict)
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# create random PyTorch tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
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encoding = image_processor(image_inputs, return_tensors="pt")
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# for layoutLM compatiblity
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self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
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self.assertEqual(encoding.pixel_values.dtype, torch.float32)
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encoding = image_processor(image_inputs, return_tensors="pt").to(torch.float16)
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self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
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self.assertEqual(encoding.pixel_values.dtype, torch.float16)
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encoding = image_processor(image_inputs, return_tensors="pt").to("cpu", torch.bfloat16)
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self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
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self.assertEqual(encoding.pixel_values.dtype, torch.bfloat16)
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with self.assertRaises(TypeError):
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_ = image_processor(image_inputs, return_tensors="pt").to(torch.bfloat16, "cpu")
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# Try with text + image feature
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encoding = image_processor(image_inputs, return_tensors="pt")
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encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]])})
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encoding = encoding.to(torch.float16)
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self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
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self.assertEqual(encoding.pixel_values.dtype, torch.float16)
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self.assertEqual(encoding.input_ids.dtype, torch.long)
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def test_call_pil(self):
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random PIL images
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
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for image in image_inputs:
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self.assertIsInstance(image, Image.Image)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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self.assertEqual(
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tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
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)
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def test_call_numpy(self):
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
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for image in image_inputs:
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self.assertIsInstance(image, np.ndarray)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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self.assertEqual(
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tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
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)
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def test_call_pytorch(self):
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random PyTorch tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
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for image in image_inputs:
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self.assertIsInstance(image, torch.Tensor)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test batched
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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self.assertEqual(
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tuple(encoded_images.shape),
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(self.image_processor_tester.batch_size, *expected_output_image_shape),
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)
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def test_call_numpy_4_channels(self):
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# Test that can process images which have an arbitrary number of channels
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# Initialize image_processing
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image_processor = self.image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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self.image_processor_tester.num_channels = 4
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
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# Test not batched input
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encoded_images = image_processor(
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image_inputs[0],
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return_tensors="pt",
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input_data_format="channels_first",
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image_mean=0,
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image_std=1,
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).pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test batched
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encoded_images = image_processor(
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image_inputs,
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return_tensors="pt",
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input_data_format="channels_first",
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image_mean=0,
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image_std=1,
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).pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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self.assertEqual(
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tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
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)
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