Rename test_feature_extraction files (#21140)
* Rename files * Update file names in tests
This commit is contained in:
amyeroberts
373
tests/models/flava/test_image_processing_flava.py
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373
tests/models/flava/test_image_processing_flava.py
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# coding=utf-8
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# Copyright 2022 Meta Platforms authors and 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 random
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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_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
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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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import PIL
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from transformers import FlavaFeatureExtractor
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from transformers.image_utils import PILImageResampling
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from transformers.models.flava.image_processing_flava import (
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FLAVA_CODEBOOK_MEAN,
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FLAVA_CODEBOOK_STD,
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FLAVA_IMAGE_MEAN,
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FLAVA_IMAGE_STD,
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)
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else:
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FLAVA_IMAGE_MEAN = FLAVA_IMAGE_STD = FLAVA_CODEBOOK_MEAN = FLAVA_CODEBOOK_STD = None
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class FlavaFeatureExtractionTester(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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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_center_crop=True,
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crop_size=None,
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resample=None,
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do_rescale=True,
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rescale_factor=1 / 255,
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do_normalize=True,
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image_mean=FLAVA_IMAGE_MEAN,
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image_std=FLAVA_IMAGE_STD,
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input_size_patches=14,
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total_mask_patches=75,
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mask_group_max_patches=None,
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mask_group_min_patches=16,
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mask_group_min_aspect_ratio=0.3,
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mask_group_max_aspect_ratio=None,
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codebook_do_resize=True,
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codebook_size=None,
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codebook_resample=None,
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codebook_do_center_crop=True,
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codebook_crop_size=None,
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codebook_do_map_pixels=True,
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codebook_do_normalize=True,
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codebook_image_mean=FLAVA_CODEBOOK_MEAN,
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codebook_image_std=FLAVA_CODEBOOK_STD,
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):
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size = size if size is not None else {"height": 224, "width": 224}
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crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
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codebook_size = codebook_size if codebook_size is not None else {"height": 112, "width": 112}
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codebook_crop_size = codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112}
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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.do_resize = do_resize
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self.do_rescale = do_rescale
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self.rescale_factor = rescale_factor
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self.min_resolution = min_resolution
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self.max_resolution = max_resolution
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self.size = size
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self.resample = resample if resample is not None else PILImageResampling.BICUBIC
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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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self.input_size_patches = input_size_patches
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self.total_mask_patches = total_mask_patches
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self.mask_group_max_patches = mask_group_max_patches
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self.mask_group_min_patches = mask_group_min_patches
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self.mask_group_min_aspect_ratio = mask_group_min_aspect_ratio
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self.mask_group_max_aspect_ratio = mask_group_max_aspect_ratio
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self.codebook_do_resize = codebook_do_resize
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self.codebook_size = codebook_size
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self.codebook_resample = codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
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self.codebook_do_center_crop = codebook_do_center_crop
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self.codebook_crop_size = codebook_crop_size
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self.codebook_do_map_pixels = codebook_do_map_pixels
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self.codebook_do_normalize = codebook_do_normalize
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self.codebook_image_mean = codebook_image_mean
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self.codebook_image_std = codebook_image_std
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def prepare_feat_extract_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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"resample": self.resample,
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"do_rescale": self.do_rescale,
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"rescale_factor": self.rescale_factor,
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"do_center_crop": self.do_center_crop,
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"crop_size": self.crop_size,
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"input_size_patches": self.input_size_patches,
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"total_mask_patches": self.total_mask_patches,
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"mask_group_max_patches": self.mask_group_max_patches,
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"mask_group_min_patches": self.mask_group_min_patches,
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"mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
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"mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
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"codebook_do_resize": self.codebook_do_resize,
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"codebook_size": self.codebook_size,
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"codebook_resample": self.codebook_resample,
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"codebook_do_center_crop": self.codebook_do_center_crop,
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"codebook_crop_size": self.codebook_crop_size,
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"codebook_do_map_pixels": self.codebook_do_map_pixels,
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"codebook_do_normalize": self.codebook_do_normalize,
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"codebook_image_mean": self.codebook_image_mean,
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"codebook_image_std": self.codebook_image_std,
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}
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def get_expected_image_size(self):
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return (self.size["height"], self.size["width"])
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def get_expected_mask_size(self):
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return (
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(self.input_size_patches, self.input_size_patches)
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if not isinstance(self.input_size_patches, tuple)
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else self.input_size_patches
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)
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def get_expected_codebook_image_size(self):
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return (self.codebook_size["height"], self.codebook_size["width"])
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@require_torch
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@require_vision
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class FlavaFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase):
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feature_extraction_class = FlavaFeatureExtractor if is_vision_available() else None
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maxDiff = None
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def setUp(self):
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self.feature_extract_tester = FlavaFeatureExtractionTester(self)
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@property
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def feat_extract_dict(self):
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return self.feature_extract_tester.prepare_feat_extract_dict()
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def test_feat_extract_properties(self):
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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self.assertTrue(hasattr(feature_extractor, "image_mean"))
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self.assertTrue(hasattr(feature_extractor, "image_std"))
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self.assertTrue(hasattr(feature_extractor, "do_normalize"))
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self.assertTrue(hasattr(feature_extractor, "do_resize"))
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self.assertTrue(hasattr(feature_extractor, "resample"))
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self.assertTrue(hasattr(feature_extractor, "crop_size"))
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self.assertTrue(hasattr(feature_extractor, "do_center_crop"))
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self.assertTrue(hasattr(feature_extractor, "do_rescale"))
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self.assertTrue(hasattr(feature_extractor, "rescale_factor"))
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self.assertTrue(hasattr(feature_extractor, "masking_generator"))
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self.assertTrue(hasattr(feature_extractor, "codebook_do_resize"))
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self.assertTrue(hasattr(feature_extractor, "codebook_size"))
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self.assertTrue(hasattr(feature_extractor, "codebook_resample"))
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self.assertTrue(hasattr(feature_extractor, "codebook_do_center_crop"))
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self.assertTrue(hasattr(feature_extractor, "codebook_crop_size"))
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self.assertTrue(hasattr(feature_extractor, "codebook_do_map_pixels"))
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self.assertTrue(hasattr(feature_extractor, "codebook_do_normalize"))
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self.assertTrue(hasattr(feature_extractor, "codebook_image_mean"))
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self.assertTrue(hasattr(feature_extractor, "codebook_image_std"))
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def test_feat_extract_from_dict_with_kwargs(self):
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feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict)
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self.assertEqual(feature_extractor.size, {"height": 224, "width": 224})
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self.assertEqual(feature_extractor.crop_size, {"height": 224, "width": 224})
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self.assertEqual(feature_extractor.codebook_size, {"height": 112, "width": 112})
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self.assertEqual(feature_extractor.codebook_crop_size, {"height": 112, "width": 112})
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feature_extractor = self.feature_extraction_class.from_dict(
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self.feat_extract_dict, size=42, crop_size=84, codebook_size=33, codebook_crop_size=66
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)
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self.assertEqual(feature_extractor.size, {"height": 42, "width": 42})
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self.assertEqual(feature_extractor.crop_size, {"height": 84, "width": 84})
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self.assertEqual(feature_extractor.codebook_size, {"height": 33, "width": 33})
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self.assertEqual(feature_extractor.codebook_crop_size, {"height": 66, "width": 66})
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def test_batch_feature(self):
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pass
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def test_call_pil(self):
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# Initialize feature_extractor
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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# create random PIL images
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image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False)
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for image in image_inputs:
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self.assertIsInstance(image, PIL.Image.Image)
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# Test not batched input
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encoded_images = feature_extractor(image_inputs[0], return_tensors="pt")
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# Test no bool masked pos
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self.assertFalse("bool_masked_pos" in encoded_images)
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expected_height, expected_width = self.feature_extract_tester.get_expected_image_size()
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self.assertEqual(
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encoded_images.pixel_values.shape,
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(1, self.feature_extract_tester.num_channels, expected_height, expected_width),
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)
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# Test batched
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encoded_images = feature_extractor(image_inputs, return_tensors="pt")
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expected_height, expected_width = self.feature_extract_tester.get_expected_image_size()
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# Test no bool masked pos
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self.assertFalse("bool_masked_pos" in encoded_images)
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self.assertEqual(
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encoded_images.pixel_values.shape,
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(
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self.feature_extract_tester.batch_size,
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self.feature_extract_tester.num_channels,
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expected_height,
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expected_width,
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),
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)
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def _test_call_framework(self, instance_class, prepare_kwargs):
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# Initialize feature_extractor
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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# create random tensors
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image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, **prepare_kwargs)
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for image in image_inputs:
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self.assertIsInstance(image, instance_class)
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# Test not batched input
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encoded_images = feature_extractor(image_inputs[0], return_tensors="pt")
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expected_height, expected_width = self.feature_extract_tester.get_expected_image_size()
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self.assertEqual(
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encoded_images.pixel_values.shape,
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(1, self.feature_extract_tester.num_channels, expected_height, expected_width),
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)
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encoded_images = feature_extractor(image_inputs, return_image_mask=True, return_tensors="pt")
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expected_height, expected_width = self.feature_extract_tester.get_expected_image_size()
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self.assertEqual(
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encoded_images.pixel_values.shape,
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(
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self.feature_extract_tester.batch_size,
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self.feature_extract_tester.num_channels,
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expected_height,
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expected_width,
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),
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)
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expected_height, expected_width = self.feature_extract_tester.get_expected_mask_size()
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self.assertEqual(
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encoded_images.bool_masked_pos.shape,
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(
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self.feature_extract_tester.batch_size,
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expected_height,
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expected_width,
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),
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)
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# Test batched
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encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
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expected_height, expected_width = self.feature_extract_tester.get_expected_image_size()
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self.assertEqual(
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encoded_images.shape,
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(
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self.feature_extract_tester.batch_size,
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self.feature_extract_tester.num_channels,
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expected_height,
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expected_width,
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),
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)
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# Test masking
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encoded_images = feature_extractor(image_inputs, return_image_mask=True, return_tensors="pt")
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expected_height, expected_width = self.feature_extract_tester.get_expected_image_size()
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self.assertEqual(
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encoded_images.pixel_values.shape,
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(
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self.feature_extract_tester.batch_size,
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self.feature_extract_tester.num_channels,
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expected_height,
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expected_width,
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),
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)
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expected_height, expected_width = self.feature_extract_tester.get_expected_mask_size()
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self.assertEqual(
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encoded_images.bool_masked_pos.shape,
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(
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self.feature_extract_tester.batch_size,
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expected_height,
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expected_width,
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),
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)
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def test_call_numpy(self):
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self._test_call_framework(np.ndarray, prepare_kwargs={"numpify": True})
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def test_call_pytorch(self):
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self._test_call_framework(torch.Tensor, prepare_kwargs={"torchify": True})
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def test_masking(self):
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# Initialize feature_extractor
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random.seed(1234)
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
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# Test not batched input
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encoded_images = feature_extractor(image_inputs[0], return_image_mask=True, return_tensors="pt")
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self.assertEqual(encoded_images.bool_masked_pos.sum().item(), 75)
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def test_codebook_pixels(self):
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# Initialize feature_extractor
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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# create random PIL images
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image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False)
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for image in image_inputs:
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self.assertIsInstance(image, PIL.Image.Image)
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# Test not batched input
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encoded_images = feature_extractor(image_inputs[0], return_codebook_pixels=True, return_tensors="pt")
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expected_height, expected_width = self.feature_extract_tester.get_expected_codebook_image_size()
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self.assertEqual(
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encoded_images.codebook_pixel_values.shape,
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(1, self.feature_extract_tester.num_channels, expected_height, expected_width),
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)
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# Test batched
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encoded_images = feature_extractor(image_inputs, return_codebook_pixels=True, return_tensors="pt")
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expected_height, expected_width = self.feature_extract_tester.get_expected_codebook_image_size()
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self.assertEqual(
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encoded_images.codebook_pixel_values.shape,
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(
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self.feature_extract_tester.batch_size,
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self.feature_extract_tester.num_channels,
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expected_height,
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expected_width,
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),
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)
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