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217 lines
8.8 KiB
Python
217 lines
8.8 KiB
Python
# 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 json
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import os
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import sys
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import tempfile
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import unittest
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import unittest.mock as mock
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from pathlib import Path
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from huggingface_hub import HfFolder, Repository, delete_repo, set_access_token
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from requests.exceptions import HTTPError
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from transformers import AutoFeatureExtractor, Wav2Vec2FeatureExtractor
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from transformers.testing_utils import TOKEN, USER, check_json_file_has_correct_format, get_tests_dir, is_staging_test
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from transformers.utils import is_torch_available, is_vision_available
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sys.path.append(str(Path(__file__).parent.parent / "utils"))
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from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
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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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SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = get_tests_dir("fixtures")
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def prepare_image_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False):
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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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"""
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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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if equal_resolution:
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image_inputs = []
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for i in range(feature_extract_tester.batch_size):
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image_inputs.append(
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np.random.randint(
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255,
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size=(
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feature_extract_tester.num_channels,
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feature_extract_tester.max_resolution,
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feature_extract_tester.max_resolution,
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),
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dtype=np.uint8,
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)
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)
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else:
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image_inputs = []
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# To avoid getting image width/height 0
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min_resolution = feature_extract_tester.min_resolution
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if getattr(feature_extract_tester, "size_divisor", 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(feature_extract_tester.size_divisor, min_resolution)
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for i in range(feature_extract_tester.batch_size):
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width, height = np.random.choice(np.arange(min_resolution, feature_extract_tester.max_resolution), 2)
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image_inputs.append(
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np.random.randint(255, size=(feature_extract_tester.num_channels, width, height), dtype=np.uint8)
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)
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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(x, 0, -1)) for x in image_inputs]
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if torchify:
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image_inputs = [torch.from_numpy(x) for x in image_inputs]
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return image_inputs
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class FeatureExtractionSavingTestMixin:
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def test_feat_extract_to_json_string(self):
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feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
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obj = json.loads(feat_extract.to_json_string())
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for key, value in self.feat_extract_dict.items():
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self.assertEqual(obj[key], value)
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def test_feat_extract_to_json_file(self):
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feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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json_file_path = os.path.join(tmpdirname, "feat_extract.json")
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feat_extract_first.to_json_file(json_file_path)
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feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)
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self.assertEqual(feat_extract_second.to_dict(), feat_extract_first.to_dict())
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def test_feat_extract_from_and_save_pretrained(self):
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feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
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check_json_file_has_correct_format(saved_file)
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feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)
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self.assertEqual(feat_extract_second.to_dict(), feat_extract_first.to_dict())
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def test_init_without_params(self):
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feat_extract = self.feature_extraction_class()
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self.assertIsNotNone(feat_extract)
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class FeatureExtractorUtilTester(unittest.TestCase):
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def test_cached_files_are_used_when_internet_is_down(self):
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# A mock response for an HTTP head request to emulate server down
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response_mock = mock.Mock()
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response_mock.status_code = 500
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response_mock.headers = []
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response_mock.raise_for_status.side_effect = HTTPError
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# Download this model to make sure it's in the cache.
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_ = Wav2Vec2FeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
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# Under the mock environment we get a 500 error when trying to reach the model.
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with mock.patch("transformers.utils.hub.requests.head", return_value=response_mock) as mock_head:
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_ = Wav2Vec2FeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
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# This check we did call the fake head request
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mock_head.assert_called()
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@is_staging_test
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class FeatureExtractorPushToHubTester(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls._token = TOKEN
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set_access_token(TOKEN)
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HfFolder.save_token(TOKEN)
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@classmethod
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def tearDownClass(cls):
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try:
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delete_repo(token=cls._token, repo_id="test-feature-extractor")
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except HTTPError:
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pass
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try:
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delete_repo(token=cls._token, repo_id="valid_org/test-feature-extractor-org")
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except HTTPError:
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pass
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try:
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delete_repo(token=cls._token, repo_id="test-dynamic-feature-extractor")
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except HTTPError:
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pass
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def test_push_to_hub(self):
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feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
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with tempfile.TemporaryDirectory() as tmp_dir:
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feature_extractor.save_pretrained(
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os.path.join(tmp_dir, "test-feature-extractor"), push_to_hub=True, use_auth_token=self._token
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)
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new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor")
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for k, v in feature_extractor.__dict__.items():
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self.assertEqual(v, getattr(new_feature_extractor, k))
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def test_push_to_hub_in_organization(self):
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feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
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with tempfile.TemporaryDirectory() as tmp_dir:
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feature_extractor.save_pretrained(
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os.path.join(tmp_dir, "test-feature-extractor-org"),
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push_to_hub=True,
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use_auth_token=self._token,
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organization="valid_org",
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)
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new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org")
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for k, v in feature_extractor.__dict__.items():
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self.assertEqual(v, getattr(new_feature_extractor, k))
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def test_push_to_hub_dynamic_feature_extractor(self):
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CustomFeatureExtractor.register_for_auto_class()
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feature_extractor = CustomFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
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with tempfile.TemporaryDirectory() as tmp_dir:
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repo = Repository(tmp_dir, clone_from=f"{USER}/test-dynamic-feature-extractor", use_auth_token=self._token)
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feature_extractor.save_pretrained(tmp_dir)
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# This has added the proper auto_map field to the config
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self.assertDictEqual(
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feature_extractor.auto_map,
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{"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"},
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)
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# The code has been copied from fixtures
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self.assertTrue(os.path.isfile(os.path.join(tmp_dir, "custom_feature_extraction.py")))
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repo.push_to_hub()
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new_feature_extractor = AutoFeatureExtractor.from_pretrained(
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f"{USER}/test-dynamic-feature-extractor", trust_remote_code=True
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)
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# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
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self.assertEqual(new_feature_extractor.__class__.__name__, "CustomFeatureExtractor")
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