295 lines
11 KiB
Python
295 lines
11 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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""" Testing suite for the TVLT image processor. """
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import unittest
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import numpy as np
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torch_available, is_vision_available
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from ...test_image_processing_common import ImageProcessingTestMixin
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if is_torch_available():
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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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from transformers import TvltImageProcessor
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def prepare_video(image_processor_tester, 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(image_processor_tester.num_frames):
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video.append(np.random.randint(255, size=(image_processor_tester.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(image_processor_tester, equal_resolution=False, numpify=False, torchify=False):
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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(image_processor_tester.batch_size):
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if equal_resolution:
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width = height = image_processor_tester.max_resolution
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else:
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width, height = np.random.choice(
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np.arange(image_processor_tester.min_resolution, image_processor_tester.max_resolution), 2
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)
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video = prepare_video(
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image_processor_tester=image_processor_tester,
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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 TvltImageProcessorTester(unittest.TestCase):
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def __init__(
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self,
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parent,
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batch_size=7,
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num_channels=3,
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num_frames=4,
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image_size=18,
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min_resolution=30,
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max_resolution=400,
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do_resize=True,
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size=None,
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do_normalize=True,
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image_mean=[0.5, 0.5, 0.5],
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image_std=[0.5, 0.5, 0.5],
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do_center_crop=True,
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crop_size=None,
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):
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size = size if size is not None else {"shortest_edge": 18}
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crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
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self.parent = parent
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self.batch_size = batch_size
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self.num_channels = num_channels
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self.num_frames = num_frames
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self.image_size = image_size
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self.min_resolution = min_resolution
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self.max_resolution = max_resolution
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self.do_resize = do_resize
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self.size = size
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self.do_normalize = do_normalize
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self.image_mean = image_mean
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self.image_std = image_std
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self.do_center_crop = do_center_crop
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self.crop_size = crop_size
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def prepare_image_processor_dict(self):
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return {
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"image_mean": self.image_mean,
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"image_std": self.image_std,
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"do_normalize": self.do_normalize,
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"do_resize": self.do_resize,
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"size": self.size,
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"do_center_crop": self.do_center_crop,
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"crop_size": self.crop_size,
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}
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@require_torch
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@require_vision
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class TvltImageProcessorTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = TvltImageProcessor if is_vision_available() else None
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def setUp(self):
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self.image_processor_tester = TvltImageProcessorTester(self)
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@property
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def image_processor_dict(self):
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return self.image_processor_tester.prepare_image_processor_dict()
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def test_image_processor_properties(self):
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image_processor = self.image_processing_class(**self.image_processor_dict)
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self.assertTrue(hasattr(image_processor, "image_mean"))
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self.assertTrue(hasattr(image_processor, "image_std"))
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self.assertTrue(hasattr(image_processor, "do_normalize"))
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self.assertTrue(hasattr(image_processor, "do_resize"))
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self.assertTrue(hasattr(image_processor, "do_center_crop"))
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self.assertTrue(hasattr(image_processor, "size"))
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def test_call_pil(self):
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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 PIL videos
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video_inputs = prepare_video_inputs(self.image_processor_tester, equal_resolution=False)
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for video in video_inputs:
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self.assertIsInstance(video, list)
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self.assertIsInstance(video[0], Image.Image)
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# Test not batched input
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encoded_videos = image_processor(video_inputs[0], return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_videos.shape,
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(
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1,
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self.image_processor_tester.num_frames,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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# Test batched
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encoded_videos = image_processor(video_inputs, return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_videos.shape,
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(
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self.image_processor_tester.batch_size,
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self.image_processor_tester.num_frames,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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def test_call_numpy(self):
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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 numpy tensors
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video_inputs = prepare_video_inputs(self.image_processor_tester, equal_resolution=False, numpify=True)
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for video in video_inputs:
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self.assertIsInstance(video, list)
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self.assertIsInstance(video[0], np.ndarray)
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# Test not batched input
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encoded_videos = image_processor(video_inputs[0], return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_videos.shape,
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(
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1,
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self.image_processor_tester.num_frames,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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# Test batched
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encoded_videos = image_processor(video_inputs, return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_videos.shape,
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(
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self.image_processor_tester.batch_size,
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self.image_processor_tester.num_frames,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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def test_call_numpy_4_channels(self):
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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 numpy tensors
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self.image_processor_tester.num_channels = 4
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video_inputs = prepare_video_inputs(self.image_processor_tester, equal_resolution=False, numpify=True)
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for video in video_inputs:
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self.assertIsInstance(video, list)
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self.assertIsInstance(video[0], np.ndarray)
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# Test not batched input
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encoded_videos = image_processor(
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video_inputs[0], return_tensors="pt", input_data_format="channels_first", image_mean=0, image_std=1
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).pixel_values
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self.assertEqual(
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encoded_videos.shape,
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(
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1,
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self.image_processor_tester.num_frames,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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# Test batched
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encoded_videos = image_processor(
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video_inputs, return_tensors="pt", input_data_format="channels_first", image_mean=0, image_std=1
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).pixel_values
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self.assertEqual(
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encoded_videos.shape,
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(
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self.image_processor_tester.batch_size,
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self.image_processor_tester.num_frames,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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self.image_processor_tester.num_channels = 3
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def test_call_pytorch(self):
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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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video_inputs = prepare_video_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
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for video in video_inputs:
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self.assertIsInstance(video, list)
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self.assertIsInstance(video[0], torch.Tensor)
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# Test not batched input
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encoded_videos = image_processor(video_inputs[0], return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_videos.shape,
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(
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1,
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self.image_processor_tester.num_frames,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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# Test batched
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encoded_videos = image_processor(video_inputs, return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_videos.shape,
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(
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self.image_processor_tester.batch_size,
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self.image_processor_tester.num_frames,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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