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import json |
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import os |
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import tempfile |
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from transformers.file_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(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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for i in range(feature_extract_tester.batch_size): |
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width, height = np.random.choice( |
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np.arange(feature_extract_tester.min_resolution, feature_extract_tester.max_resolution), 2 |
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) |
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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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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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feat_extract_first.save_pretrained(tmpdirname) |
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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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