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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 gc | |
| import random | |
| import unittest | |
| import numpy as np | |
| import torch | |
| from PIL import Image | |
| from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer | |
| from diffusers import ( | |
| AutoencoderKL, | |
| DPMSolverMultistepScheduler, | |
| LMSDiscreteScheduler, | |
| PNDMScheduler, | |
| StableDiffusionInpaintPipeline, | |
| UNet2DConditionModel, | |
| ) | |
| from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint import prepare_mask_and_masked_image | |
| from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device | |
| from diffusers.utils.testing_utils import require_torch_gpu | |
| from ...pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS | |
| from ...test_pipelines_common import PipelineTesterMixin | |
| torch.backends.cuda.matmul.allow_tf32 = False | |
| class StableDiffusionInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase): | |
| pipeline_class = StableDiffusionInpaintPipeline | |
| params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS | |
| batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS | |
| def get_dummy_components(self): | |
| torch.manual_seed(0) | |
| unet = UNet2DConditionModel( | |
| block_out_channels=(32, 64), | |
| layers_per_block=2, | |
| sample_size=32, | |
| in_channels=9, | |
| out_channels=4, | |
| down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), | |
| up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), | |
| cross_attention_dim=32, | |
| ) | |
| scheduler = PNDMScheduler(skip_prk_steps=True) | |
| torch.manual_seed(0) | |
| vae = AutoencoderKL( | |
| block_out_channels=[32, 64], | |
| in_channels=3, | |
| out_channels=3, | |
| down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], | |
| up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], | |
| latent_channels=4, | |
| ) | |
| torch.manual_seed(0) | |
| text_encoder_config = CLIPTextConfig( | |
| bos_token_id=0, | |
| eos_token_id=2, | |
| hidden_size=32, | |
| intermediate_size=37, | |
| layer_norm_eps=1e-05, | |
| num_attention_heads=4, | |
| num_hidden_layers=5, | |
| pad_token_id=1, | |
| vocab_size=1000, | |
| ) | |
| text_encoder = CLIPTextModel(text_encoder_config) | |
| tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
| components = { | |
| "unet": unet, | |
| "scheduler": scheduler, | |
| "vae": vae, | |
| "text_encoder": text_encoder, | |
| "tokenizer": tokenizer, | |
| "safety_checker": None, | |
| "feature_extractor": None, | |
| } | |
| return components | |
| def get_dummy_inputs(self, device, seed=0): | |
| # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched | |
| image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) | |
| image = image.cpu().permute(0, 2, 3, 1)[0] | |
| init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64)) | |
| mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((64, 64)) | |
| if str(device).startswith("mps"): | |
| generator = torch.manual_seed(seed) | |
| else: | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| inputs = { | |
| "prompt": "A painting of a squirrel eating a burger", | |
| "image": init_image, | |
| "mask_image": mask_image, | |
| "generator": generator, | |
| "num_inference_steps": 2, | |
| "guidance_scale": 6.0, | |
| "output_type": "numpy", | |
| } | |
| return inputs | |
| def test_stable_diffusion_inpaint(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| components = self.get_dummy_components() | |
| sd_pipe = StableDiffusionInpaintPipeline(**components) | |
| sd_pipe = sd_pipe.to(device) | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_dummy_inputs(device) | |
| image = sd_pipe(**inputs).images | |
| image_slice = image[0, -3:, -3:, -1] | |
| assert image.shape == (1, 64, 64, 3) | |
| expected_slice = np.array([0.4723, 0.5731, 0.3939, 0.5441, 0.5922, 0.4392, 0.5059, 0.4651, 0.4474]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
| def test_stable_diffusion_inpaint_image_tensor(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| components = self.get_dummy_components() | |
| sd_pipe = StableDiffusionInpaintPipeline(**components) | |
| sd_pipe = sd_pipe.to(device) | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_dummy_inputs(device) | |
| output = sd_pipe(**inputs) | |
| out_pil = output.images | |
| inputs = self.get_dummy_inputs(device) | |
| inputs["image"] = torch.tensor(np.array(inputs["image"]) / 127.5 - 1).permute(2, 0, 1).unsqueeze(0) | |
| inputs["mask_image"] = torch.tensor(np.array(inputs["mask_image"]) / 255).permute(2, 0, 1)[:1].unsqueeze(0) | |
| output = sd_pipe(**inputs) | |
| out_tensor = output.images | |
| assert out_pil.shape == (1, 64, 64, 3) | |
| assert np.abs(out_pil.flatten() - out_tensor.flatten()).max() < 5e-2 | |
| class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase): | |
| def setUp(self): | |
| super().setUp() | |
| def tearDown(self): | |
| super().tearDown() | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): | |
| generator = torch.Generator(device=generator_device).manual_seed(seed) | |
| init_image = load_image( | |
| "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
| "/stable_diffusion_inpaint/input_bench_image.png" | |
| ) | |
| mask_image = load_image( | |
| "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
| "/stable_diffusion_inpaint/input_bench_mask.png" | |
| ) | |
| inputs = { | |
| "prompt": "Face of a yellow cat, high resolution, sitting on a park bench", | |
| "image": init_image, | |
| "mask_image": mask_image, | |
| "generator": generator, | |
| "num_inference_steps": 3, | |
| "guidance_scale": 7.5, | |
| "output_type": "numpy", | |
| } | |
| return inputs | |
| def test_stable_diffusion_inpaint_ddim(self): | |
| pipe = StableDiffusionInpaintPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-inpainting", safety_checker=None | |
| ) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing() | |
| inputs = self.get_inputs(torch_device) | |
| image = pipe(**inputs).images | |
| image_slice = image[0, 253:256, 253:256, -1].flatten() | |
| assert image.shape == (1, 512, 512, 3) | |
| expected_slice = np.array([0.0427, 0.0460, 0.0483, 0.0460, 0.0584, 0.0521, 0.1549, 0.1695, 0.1794]) | |
| assert np.abs(expected_slice - image_slice).max() < 1e-4 | |
| def test_stable_diffusion_inpaint_fp16(self): | |
| pipe = StableDiffusionInpaintPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, safety_checker=None | |
| ) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing() | |
| inputs = self.get_inputs(torch_device, dtype=torch.float16) | |
| image = pipe(**inputs).images | |
| image_slice = image[0, 253:256, 253:256, -1].flatten() | |
| assert image.shape == (1, 512, 512, 3) | |
| expected_slice = np.array([0.1350, 0.1123, 0.1350, 0.1641, 0.1328, 0.1230, 0.1289, 0.1531, 0.1687]) | |
| assert np.abs(expected_slice - image_slice).max() < 5e-2 | |
| def test_stable_diffusion_inpaint_pndm(self): | |
| pipe = StableDiffusionInpaintPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-inpainting", safety_checker=None | |
| ) | |
| pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing() | |
| inputs = self.get_inputs(torch_device) | |
| image = pipe(**inputs).images | |
| image_slice = image[0, 253:256, 253:256, -1].flatten() | |
| assert image.shape == (1, 512, 512, 3) | |
| expected_slice = np.array([0.0425, 0.0273, 0.0344, 0.1694, 0.1727, 0.1812, 0.3256, 0.3311, 0.3272]) | |
| assert np.abs(expected_slice - image_slice).max() < 1e-4 | |
| def test_stable_diffusion_inpaint_k_lms(self): | |
| pipe = StableDiffusionInpaintPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-inpainting", safety_checker=None | |
| ) | |
| pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing() | |
| inputs = self.get_inputs(torch_device) | |
| image = pipe(**inputs).images | |
| image_slice = image[0, 253:256, 253:256, -1].flatten() | |
| assert image.shape == (1, 512, 512, 3) | |
| expected_slice = np.array([0.9314, 0.7575, 0.9432, 0.8885, 0.9028, 0.7298, 0.9811, 0.9667, 0.7633]) | |
| assert np.abs(expected_slice - image_slice).max() < 1e-4 | |
| def test_stable_diffusion_inpaint_with_sequential_cpu_offloading(self): | |
| torch.cuda.empty_cache() | |
| torch.cuda.reset_max_memory_allocated() | |
| torch.cuda.reset_peak_memory_stats() | |
| pipe = StableDiffusionInpaintPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-inpainting", safety_checker=None, torch_dtype=torch.float16 | |
| ) | |
| pipe = pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing(1) | |
| pipe.enable_sequential_cpu_offload() | |
| inputs = self.get_inputs(torch_device, dtype=torch.float16) | |
| _ = pipe(**inputs) | |
| mem_bytes = torch.cuda.max_memory_allocated() | |
| # make sure that less than 2.2 GB is allocated | |
| assert mem_bytes < 2.2 * 10**9 | |
| class StableDiffusionInpaintPipelineNightlyTests(unittest.TestCase): | |
| def tearDown(self): | |
| super().tearDown() | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): | |
| generator = torch.Generator(device=generator_device).manual_seed(seed) | |
| init_image = load_image( | |
| "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
| "/stable_diffusion_inpaint/input_bench_image.png" | |
| ) | |
| mask_image = load_image( | |
| "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
| "/stable_diffusion_inpaint/input_bench_mask.png" | |
| ) | |
| inputs = { | |
| "prompt": "Face of a yellow cat, high resolution, sitting on a park bench", | |
| "image": init_image, | |
| "mask_image": mask_image, | |
| "generator": generator, | |
| "num_inference_steps": 50, | |
| "guidance_scale": 7.5, | |
| "output_type": "numpy", | |
| } | |
| return inputs | |
| def test_inpaint_ddim(self): | |
| sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting") | |
| sd_pipe.to(torch_device) | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_inputs(torch_device) | |
| image = sd_pipe(**inputs).images[0] | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
| "/stable_diffusion_inpaint/stable_diffusion_inpaint_ddim.npy" | |
| ) | |
| max_diff = np.abs(expected_image - image).max() | |
| assert max_diff < 1e-3 | |
| def test_inpaint_pndm(self): | |
| sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting") | |
| sd_pipe.scheduler = PNDMScheduler.from_config(sd_pipe.scheduler.config) | |
| sd_pipe.to(torch_device) | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_inputs(torch_device) | |
| image = sd_pipe(**inputs).images[0] | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
| "/stable_diffusion_inpaint/stable_diffusion_inpaint_pndm.npy" | |
| ) | |
| max_diff = np.abs(expected_image - image).max() | |
| assert max_diff < 1e-3 | |
| def test_inpaint_lms(self): | |
| sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting") | |
| sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config) | |
| sd_pipe.to(torch_device) | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_inputs(torch_device) | |
| image = sd_pipe(**inputs).images[0] | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
| "/stable_diffusion_inpaint/stable_diffusion_inpaint_lms.npy" | |
| ) | |
| max_diff = np.abs(expected_image - image).max() | |
| assert max_diff < 1e-3 | |
| def test_inpaint_dpm(self): | |
| sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting") | |
| sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config) | |
| sd_pipe.to(torch_device) | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_inputs(torch_device) | |
| inputs["num_inference_steps"] = 30 | |
| image = sd_pipe(**inputs).images[0] | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
| "/stable_diffusion_inpaint/stable_diffusion_inpaint_dpm_multi.npy" | |
| ) | |
| max_diff = np.abs(expected_image - image).max() | |
| assert max_diff < 1e-3 | |
| class StableDiffusionInpaintingPrepareMaskAndMaskedImageTests(unittest.TestCase): | |
| def test_pil_inputs(self): | |
| im = np.random.randint(0, 255, (32, 32, 3), dtype=np.uint8) | |
| im = Image.fromarray(im) | |
| mask = np.random.randint(0, 255, (32, 32), dtype=np.uint8) > 127.5 | |
| mask = Image.fromarray((mask * 255).astype(np.uint8)) | |
| t_mask, t_masked = prepare_mask_and_masked_image(im, mask) | |
| self.assertTrue(isinstance(t_mask, torch.Tensor)) | |
| self.assertTrue(isinstance(t_masked, torch.Tensor)) | |
| self.assertEqual(t_mask.ndim, 4) | |
| self.assertEqual(t_masked.ndim, 4) | |
| self.assertEqual(t_mask.shape, (1, 1, 32, 32)) | |
| self.assertEqual(t_masked.shape, (1, 3, 32, 32)) | |
| self.assertTrue(t_mask.dtype == torch.float32) | |
| self.assertTrue(t_masked.dtype == torch.float32) | |
| self.assertTrue(t_mask.min() >= 0.0) | |
| self.assertTrue(t_mask.max() <= 1.0) | |
| self.assertTrue(t_masked.min() >= -1.0) | |
| self.assertTrue(t_masked.min() <= 1.0) | |
| self.assertTrue(t_mask.sum() > 0.0) | |
| def test_np_inputs(self): | |
| im_np = np.random.randint(0, 255, (32, 32, 3), dtype=np.uint8) | |
| im_pil = Image.fromarray(im_np) | |
| mask_np = np.random.randint(0, 255, (32, 32), dtype=np.uint8) > 127.5 | |
| mask_pil = Image.fromarray((mask_np * 255).astype(np.uint8)) | |
| t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np) | |
| t_mask_pil, t_masked_pil = prepare_mask_and_masked_image(im_pil, mask_pil) | |
| self.assertTrue((t_mask_np == t_mask_pil).all()) | |
| self.assertTrue((t_masked_np == t_masked_pil).all()) | |
| def test_torch_3D_2D_inputs(self): | |
| im_tensor = torch.randint(0, 255, (3, 32, 32), dtype=torch.uint8) | |
| mask_tensor = torch.randint(0, 255, (32, 32), dtype=torch.uint8) > 127.5 | |
| im_np = im_tensor.numpy().transpose(1, 2, 0) | |
| mask_np = mask_tensor.numpy() | |
| t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor) | |
| t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np) | |
| self.assertTrue((t_mask_tensor == t_mask_np).all()) | |
| self.assertTrue((t_masked_tensor == t_masked_np).all()) | |
| def test_torch_3D_3D_inputs(self): | |
| im_tensor = torch.randint(0, 255, (3, 32, 32), dtype=torch.uint8) | |
| mask_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8) > 127.5 | |
| im_np = im_tensor.numpy().transpose(1, 2, 0) | |
| mask_np = mask_tensor.numpy()[0] | |
| t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor) | |
| t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np) | |
| self.assertTrue((t_mask_tensor == t_mask_np).all()) | |
| self.assertTrue((t_masked_tensor == t_masked_np).all()) | |
| def test_torch_4D_2D_inputs(self): | |
| im_tensor = torch.randint(0, 255, (1, 3, 32, 32), dtype=torch.uint8) | |
| mask_tensor = torch.randint(0, 255, (32, 32), dtype=torch.uint8) > 127.5 | |
| im_np = im_tensor.numpy()[0].transpose(1, 2, 0) | |
| mask_np = mask_tensor.numpy() | |
| t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor) | |
| t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np) | |
| self.assertTrue((t_mask_tensor == t_mask_np).all()) | |
| self.assertTrue((t_masked_tensor == t_masked_np).all()) | |
| def test_torch_4D_3D_inputs(self): | |
| im_tensor = torch.randint(0, 255, (1, 3, 32, 32), dtype=torch.uint8) | |
| mask_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8) > 127.5 | |
| im_np = im_tensor.numpy()[0].transpose(1, 2, 0) | |
| mask_np = mask_tensor.numpy()[0] | |
| t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor) | |
| t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np) | |
| self.assertTrue((t_mask_tensor == t_mask_np).all()) | |
| self.assertTrue((t_masked_tensor == t_masked_np).all()) | |
| def test_torch_4D_4D_inputs(self): | |
| im_tensor = torch.randint(0, 255, (1, 3, 32, 32), dtype=torch.uint8) | |
| mask_tensor = torch.randint(0, 255, (1, 1, 32, 32), dtype=torch.uint8) > 127.5 | |
| im_np = im_tensor.numpy()[0].transpose(1, 2, 0) | |
| mask_np = mask_tensor.numpy()[0][0] | |
| t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor) | |
| t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np) | |
| self.assertTrue((t_mask_tensor == t_mask_np).all()) | |
| self.assertTrue((t_masked_tensor == t_masked_np).all()) | |
| def test_torch_batch_4D_3D(self): | |
| im_tensor = torch.randint(0, 255, (2, 3, 32, 32), dtype=torch.uint8) | |
| mask_tensor = torch.randint(0, 255, (2, 32, 32), dtype=torch.uint8) > 127.5 | |
| im_nps = [im.numpy().transpose(1, 2, 0) for im in im_tensor] | |
| mask_nps = [mask.numpy() for mask in mask_tensor] | |
| t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor) | |
| nps = [prepare_mask_and_masked_image(i, m) for i, m in zip(im_nps, mask_nps)] | |
| t_mask_np = torch.cat([n[0] for n in nps]) | |
| t_masked_np = torch.cat([n[1] for n in nps]) | |
| self.assertTrue((t_mask_tensor == t_mask_np).all()) | |
| self.assertTrue((t_masked_tensor == t_masked_np).all()) | |
| def test_torch_batch_4D_4D(self): | |
| im_tensor = torch.randint(0, 255, (2, 3, 32, 32), dtype=torch.uint8) | |
| mask_tensor = torch.randint(0, 255, (2, 1, 32, 32), dtype=torch.uint8) > 127.5 | |
| im_nps = [im.numpy().transpose(1, 2, 0) for im in im_tensor] | |
| mask_nps = [mask.numpy()[0] for mask in mask_tensor] | |
| t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor) | |
| nps = [prepare_mask_and_masked_image(i, m) for i, m in zip(im_nps, mask_nps)] | |
| t_mask_np = torch.cat([n[0] for n in nps]) | |
| t_masked_np = torch.cat([n[1] for n in nps]) | |
| self.assertTrue((t_mask_tensor == t_mask_np).all()) | |
| self.assertTrue((t_masked_tensor == t_masked_np).all()) | |
| def test_shape_mismatch(self): | |
| # test height and width | |
| with self.assertRaises(AssertionError): | |
| prepare_mask_and_masked_image(torch.randn(3, 32, 32), torch.randn(64, 64)) | |
| # test batch dim | |
| with self.assertRaises(AssertionError): | |
| prepare_mask_and_masked_image(torch.randn(2, 3, 32, 32), torch.randn(4, 64, 64)) | |
| # test batch dim | |
| with self.assertRaises(AssertionError): | |
| prepare_mask_and_masked_image(torch.randn(2, 3, 32, 32), torch.randn(4, 1, 64, 64)) | |
| def test_type_mismatch(self): | |
| # test tensors-only | |
| with self.assertRaises(TypeError): | |
| prepare_mask_and_masked_image(torch.rand(3, 32, 32), torch.rand(3, 32, 32).numpy()) | |
| # test tensors-only | |
| with self.assertRaises(TypeError): | |
| prepare_mask_and_masked_image(torch.rand(3, 32, 32).numpy(), torch.rand(3, 32, 32)) | |
| def test_channels_first(self): | |
| # test channels first for 3D tensors | |
| with self.assertRaises(AssertionError): | |
| prepare_mask_and_masked_image(torch.rand(32, 32, 3), torch.rand(3, 32, 32)) | |
| def test_tensor_range(self): | |
| # test im <= 1 | |
| with self.assertRaises(ValueError): | |
| prepare_mask_and_masked_image(torch.ones(3, 32, 32) * 2, torch.rand(32, 32)) | |
| # test im >= -1 | |
| with self.assertRaises(ValueError): | |
| prepare_mask_and_masked_image(torch.ones(3, 32, 32) * (-2), torch.rand(32, 32)) | |
| # test mask <= 1 | |
| with self.assertRaises(ValueError): | |
| prepare_mask_and_masked_image(torch.rand(3, 32, 32), torch.ones(32, 32) * 2) | |
| # test mask >= 0 | |
| with self.assertRaises(ValueError): | |
| prepare_mask_and_masked_image(torch.rand(3, 32, 32), torch.ones(32, 32) * -1) | |