wehub-resource-sync
87 lines
3.7 KiB
Python
87 lines
3.7 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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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 tempfile
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import numpy as np
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import paddle
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from PIL import Image
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from ..transformers.test_utils import check_json_file_has_correct_format
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def prepare_image_inputs(image_processor_tester, equal_resolution=False, numpify=False, paddlefy=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 PaddlePaddle tensors if one specifies paddlefy=True.
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One can specify whether the images are of the same resolution or not.
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"""
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assert not (numpify and paddlefy), "You cannot specify both numpy and PaddlePaddle tensors at the same time"
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image_inputs = []
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for i in range(image_processor_tester.batch_size):
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if equal_resolution:
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width = height = image_processor_tester.max_resolution
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else:
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# To avoid getting image width/height 0
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min_resolution = image_processor_tester.min_resolution
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if getattr(image_processor_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(image_processor_tester.size_divisor, min_resolution)
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width, height = np.random.choice(np.arange(min_resolution, image_processor_tester.max_resolution), 2)
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image_inputs.append(
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np.random.randint(255, size=(image_processor_tester.num_channels, width, height), dtype=np.uint8)
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)
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if not numpify and not paddlefy:
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# PIL expects the channel dimension as last dimension
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image_inputs = [Image.fromarray(np.moveaxis(image, 0, -1)) for image in image_inputs]
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if paddlefy:
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image_inputs = [paddle.to_tensor(image) for image in image_inputs]
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return image_inputs
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class ImageProcessingSavingTestMixin:
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test_cast_dtype = None
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def test_image_processor_to_json_string(self):
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image_processor = self.image_processing_class(**self.image_processor_dict)
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obj = json.loads(image_processor.to_json_string())
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for key, value in self.image_processor_dict.items():
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self.assertEqual(obj[key], value)
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def test_image_processor_to_json_file(self):
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image_processor_first = self.image_processing_class(**self.image_processor_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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json_file_path = os.path.join(tmpdirname, "image_processor.json")
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image_processor_first.to_json_file(json_file_path)
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image_processor_second = self.image_processing_class.from_json_file(json_file_path)
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self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())
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def test_image_processor_from_and_save_pretrained(self):
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image_processor_first = self.image_processing_class(**self.image_processor_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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saved_file = image_processor_first.save_pretrained(tmpdirname)[0]
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check_json_file_has_correct_format(saved_file)
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image_processor_second = self.image_processing_class.from_pretrained(tmpdirname)
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self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())
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