Rename test_feature_extraction files (#21140)
* Rename files * Update file names in tests
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amyeroberts
260
tests/models/vilt/test_image_processing_vilt.py
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260
tests/models/vilt/test_image_processing_vilt.py
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# coding=utf-8
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# Copyright 2021 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 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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from PIL import Image
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from transformers import ViltFeatureExtractor
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class ViltFeatureExtractionTester(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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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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size_divisor=2,
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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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):
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size = size if size is not None else {"shortest_edge": 30}
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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.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.size_divisor = size_divisor
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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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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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"size_divisor": self.size_divisor,
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}
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def get_expected_values(self, image_inputs, batched=False):
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"""
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This function computes the expected height and width when providing images to ViltFeatureExtractor,
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assuming do_resize is set to True with a scalar size and size_divisor.
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"""
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if not batched:
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size = self.size["shortest_edge"]
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image = image_inputs[0]
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if isinstance(image, Image.Image):
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w, h = image.size
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else:
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h, w = image.shape[1], image.shape[2]
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scale = size / min(w, h)
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if h < w:
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newh, neww = size, scale * w
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else:
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newh, neww = scale * h, size
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max_size = int((1333 / 800) * size)
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if max(newh, neww) > max_size:
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scale = max_size / max(newh, neww)
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newh = newh * scale
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neww = neww * scale
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newh, neww = int(newh + 0.5), int(neww + 0.5)
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expected_height, expected_width = (
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newh // self.size_divisor * self.size_divisor,
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neww // self.size_divisor * self.size_divisor,
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)
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else:
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expected_values = []
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for image in image_inputs:
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expected_height, expected_width = self.get_expected_values([image])
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expected_values.append((expected_height, expected_width))
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expected_height = max(expected_values, key=lambda item: item[0])[0]
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expected_width = max(expected_values, key=lambda item: item[1])[1]
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return expected_height, expected_width
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@require_torch
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@require_vision
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class ViltFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase):
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feature_extraction_class = ViltFeatureExtractor if is_vision_available() else None
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def setUp(self):
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self.feature_extract_tester = ViltFeatureExtractionTester(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, "size"))
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self.assertTrue(hasattr(feature_extractor, "size_divisor"))
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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, {"shortest_edge": 30})
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feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict, size=42)
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self.assertEqual(feature_extractor.size, {"shortest_edge": 42})
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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, Image.Image)
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# Test not batched input
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encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
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expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs)
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self.assertEqual(
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encoded_images.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").pixel_values
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expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True)
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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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def test_call_numpy(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 numpy tensors
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image_inputs = prepare_image_inputs(self.feature_extract_tester, 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 = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
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expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs)
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self.assertEqual(
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encoded_images.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").pixel_values
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expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True)
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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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def test_call_pytorch(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 PyTorch tensors
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image_inputs = prepare_image_inputs(self.feature_extract_tester, 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 = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
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expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs)
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self.assertEqual(
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encoded_images.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").pixel_values
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expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True)
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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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def test_equivalence_pad_and_create_pixel_mask(self):
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# Initialize feature_extractors
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feature_extractor_1 = self.feature_extraction_class(**self.feat_extract_dict)
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feature_extractor_2 = self.feature_extraction_class(do_resize=False, do_normalize=False, do_rescale=False)
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# create random PyTorch tensors
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image_inputs = prepare_image_inputs(self.feature_extract_tester, 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 whether the method "pad_and_return_pixel_mask" and calling the feature extractor return the same tensors
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encoded_images_with_method = feature_extractor_1.pad_and_create_pixel_mask(image_inputs, return_tensors="pt")
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encoded_images = feature_extractor_2(image_inputs, return_tensors="pt")
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self.assertTrue(
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torch.allclose(encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], atol=1e-4)
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)
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self.assertTrue(
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torch.allclose(encoded_images_with_method["pixel_mask"], encoded_images["pixel_mask"], atol=1e-4)
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)
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