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a6b7759880
* Add CLIP image processor * Crop size as dict too * Update warning * Actually use logger this time * Normalize doesn't change dtype of input * Add perceiver image processor * Tidy up * Add DPT image processor * Add Vilt image processor * Tidy up * Add poolformer image processor * Tidy up * Add LayoutLM v2 and v3 imsge processors * Tidy up * Add Flava image processor * Tidy up * Add deit image processor * Tidy up * Add ConvNext image processor * Tidy up * Add levit image processor * Add segformer image processor * Add in post processing * Fix up * Add ImageGPT image processor * Fixup * Add mobilevit image processor * Tidy up * Add postprocessing * Fixup * Add VideoMAE image processor * Tidy up * Add ImageGPT image processor * Fixup * Add ViT image processor * Tidy up * Add beit image processor * Add mobilevit image processor * Tidy up * Add postprocessing * Fixup * Fix up * Fix flava and remove tree module * Fix image classification pipeline failing tests * Update feature extractor in trainer scripts * Update pad_if_smaller to accept tuple and int size * Update for image segmentation pipeline * Update src/transformers/models/perceiver/image_processing_perceiver.py Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com> * Update src/transformers/image_processing_utils.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/models/beit/image_processing_beit.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * PR comments - docstrings; remove accidentally added resize; var names * Update docstrings * Add exception if size is not in the right format * Fix exception check * Fix up * Use shortest_edge in tuple in script Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com> Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
72 lines
2.8 KiB
Python
72 lines
2.8 KiB
Python
# coding=utf-8
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# Copyright 2022 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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from transformers.image_processing_utils import get_size_dict
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class ImageProcessingUtilsTester(unittest.TestCase):
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def test_get_size_dict(self):
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# Test a dict with the wrong keys raises an error
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inputs = {"wrong_key": 224}
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with self.assertRaises(ValueError):
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get_size_dict(inputs)
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inputs = {"height": 224}
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with self.assertRaises(ValueError):
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get_size_dict(inputs)
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inputs = {"width": 224, "shortest_edge": 224}
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with self.assertRaises(ValueError):
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get_size_dict(inputs)
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# Test a dict with the correct keys is returned as is
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inputs = {"height": 224, "width": 224}
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outputs = get_size_dict(inputs)
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self.assertEqual(outputs, inputs)
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inputs = {"shortest_edge": 224}
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outputs = get_size_dict(inputs)
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self.assertEqual(outputs, {"shortest_edge": 224})
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inputs = {"longest_edge": 224, "shortest_edge": 224}
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outputs = get_size_dict(inputs)
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self.assertEqual(outputs, {"longest_edge": 224, "shortest_edge": 224})
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# Test a single int value which represents (size, size)
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outputs = get_size_dict(224)
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self.assertEqual(outputs, {"height": 224, "width": 224})
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# Test a single int value which represents the shortest edge
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outputs = get_size_dict(224, default_to_square=False)
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self.assertEqual(outputs, {"shortest_edge": 224})
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# Test a tuple of ints which represents (height, width)
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outputs = get_size_dict((150, 200))
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self.assertEqual(outputs, {"height": 150, "width": 200})
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# Test a tuple of ints which represents (width, height)
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outputs = get_size_dict((150, 200), height_width_order=False)
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self.assertEqual(outputs, {"height": 200, "width": 150})
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# Test an int representing the shortest edge and max_size which represents the longest edge
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outputs = get_size_dict(224, max_size=256, default_to_square=False)
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self.assertEqual(outputs, {"shortest_edge": 224, "longest_edge": 256})
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# Test int with default_to_square=True and max_size fails
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with self.assertRaises(ValueError):
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get_size_dict(224, max_size=256, default_to_square=True)
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