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| # coding=utf-8 | |
| # Copyright 2023 HuggingFace Inc. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import unittest | |
| import numpy as np | |
| import PIL | |
| import torch | |
| from diffusers.image_processor import VaeImageProcessor | |
| class ImageProcessorTest(unittest.TestCase): | |
| def dummy_sample(self): | |
| batch_size = 1 | |
| num_channels = 3 | |
| height = 8 | |
| width = 8 | |
| sample = torch.rand((batch_size, num_channels, height, width)) | |
| return sample | |
| def to_np(self, image): | |
| if isinstance(image[0], PIL.Image.Image): | |
| return np.stack([np.array(i) for i in image], axis=0) | |
| elif isinstance(image, torch.Tensor): | |
| return image.cpu().numpy().transpose(0, 2, 3, 1) | |
| return image | |
| def test_vae_image_processor_pt(self): | |
| image_processor = VaeImageProcessor(do_resize=False, do_normalize=False) | |
| input_pt = self.dummy_sample | |
| input_np = self.to_np(input_pt) | |
| for output_type in ["pt", "np", "pil"]: | |
| out = image_processor.postprocess( | |
| image_processor.preprocess(input_pt), | |
| output_type=output_type, | |
| ) | |
| out_np = self.to_np(out) | |
| in_np = (input_np * 255).round() if output_type == "pil" else input_np | |
| assert ( | |
| np.abs(in_np - out_np).max() < 1e-6 | |
| ), f"decoded output does not match input for output_type {output_type}" | |
| def test_vae_image_processor_np(self): | |
| image_processor = VaeImageProcessor(do_resize=False, do_normalize=False) | |
| input_np = self.dummy_sample.cpu().numpy().transpose(0, 2, 3, 1) | |
| for output_type in ["pt", "np", "pil"]: | |
| out = image_processor.postprocess(image_processor.preprocess(input_np), output_type=output_type) | |
| out_np = self.to_np(out) | |
| in_np = (input_np * 255).round() if output_type == "pil" else input_np | |
| assert ( | |
| np.abs(in_np - out_np).max() < 1e-6 | |
| ), f"decoded output does not match input for output_type {output_type}" | |
| def test_vae_image_processor_pil(self): | |
| image_processor = VaeImageProcessor(do_resize=False, do_normalize=False) | |
| input_np = self.dummy_sample.cpu().numpy().transpose(0, 2, 3, 1) | |
| input_pil = image_processor.numpy_to_pil(input_np) | |
| for output_type in ["pt", "np", "pil"]: | |
| out = image_processor.postprocess(image_processor.preprocess(input_pil), output_type=output_type) | |
| for i, o in zip(input_pil, out): | |
| in_np = np.array(i) | |
| out_np = self.to_np(out) if output_type == "pil" else (self.to_np(out) * 255).round() | |
| assert ( | |
| np.abs(in_np - out_np).max() < 1e-6 | |
| ), f"decoded output does not match input for output_type {output_type}" | |
| def test_preprocess_input_3d(self): | |
| image_processor = VaeImageProcessor(do_resize=False, do_normalize=False) | |
| input_pt_4d = self.dummy_sample | |
| input_pt_3d = input_pt_4d.squeeze(0) | |
| out_pt_4d = image_processor.postprocess( | |
| image_processor.preprocess(input_pt_4d), | |
| output_type="np", | |
| ) | |
| out_pt_3d = image_processor.postprocess( | |
| image_processor.preprocess(input_pt_3d), | |
| output_type="np", | |
| ) | |
| input_np_4d = self.to_np(self.dummy_sample) | |
| input_np_3d = input_np_4d.squeeze(0) | |
| out_np_4d = image_processor.postprocess( | |
| image_processor.preprocess(input_np_4d), | |
| output_type="np", | |
| ) | |
| out_np_3d = image_processor.postprocess( | |
| image_processor.preprocess(input_np_3d), | |
| output_type="np", | |
| ) | |
| assert np.abs(out_pt_4d - out_pt_3d).max() < 1e-6 | |
| assert np.abs(out_np_4d - out_np_3d).max() < 1e-6 | |
| def test_preprocess_input_list(self): | |
| image_processor = VaeImageProcessor(do_resize=False, do_normalize=False) | |
| input_pt_4d = self.dummy_sample | |
| input_pt_list = list(input_pt_4d) | |
| out_pt_4d = image_processor.postprocess( | |
| image_processor.preprocess(input_pt_4d), | |
| output_type="np", | |
| ) | |
| out_pt_list = image_processor.postprocess( | |
| image_processor.preprocess(input_pt_list), | |
| output_type="np", | |
| ) | |
| input_np_4d = self.to_np(self.dummy_sample) | |
| list(input_np_4d) | |
| out_np_4d = image_processor.postprocess( | |
| image_processor.preprocess(input_pt_4d), | |
| output_type="np", | |
| ) | |
| out_np_list = image_processor.postprocess( | |
| image_processor.preprocess(input_pt_list), | |
| output_type="np", | |
| ) | |
| assert np.abs(out_pt_4d - out_pt_list).max() < 1e-6 | |
| assert np.abs(out_np_4d - out_np_list).max() < 1e-6 | |