545 lines
22 KiB
Python
545 lines
22 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 gc
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import random
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import unittest
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import numpy as np
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import torch
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from diffusers import (
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AutoencoderKL,
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DPMSolverMultistepScheduler,
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LMSDiscreteScheduler,
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PNDMScheduler,
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StableDiffusionInpaintPipeline,
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UNet2DConditionModel,
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)
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from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint import prepare_mask_and_masked_image
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from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
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from diffusers.utils.testing_utils import require_torch_gpu
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from PIL import Image
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from transformers import CLIPImageProcessor, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
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from ...test_pipelines_common import PipelineTesterMixin
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torch.backends.cuda.matmul.allow_tf32 = False
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class StableDiffusionInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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pipeline_class = StableDiffusionInpaintPipeline
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def get_dummy_components(self):
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torch.manual_seed(0)
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unet = UNet2DConditionModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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in_channels=9,
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out_channels=4,
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down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
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up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
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cross_attention_dim=32,
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)
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scheduler = PNDMScheduler(skip_prk_steps=True)
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torch.manual_seed(0)
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vae = AutoencoderKL(
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block_out_channels=[32, 64],
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in_channels=3,
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out_channels=3,
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down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
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up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
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latent_channels=4,
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)
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torch.manual_seed(0)
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text_encoder_config = CLIPTextConfig(
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bos_token_id=0,
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eos_token_id=2,
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hidden_size=32,
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intermediate_size=37,
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layer_norm_eps=1e-05,
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num_attention_heads=4,
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num_hidden_layers=5,
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pad_token_id=1,
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vocab_size=1000,
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)
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text_encoder = CLIPTextModel(text_encoder_config)
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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feature_extractor = CLIPImageProcessor(crop_size=32, size=32)
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components = {
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"unet": unet,
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"scheduler": scheduler,
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"vae": vae,
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"text_encoder": text_encoder,
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"tokenizer": tokenizer,
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"safety_checker": None,
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"feature_extractor": feature_extractor,
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}
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return components
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def get_dummy_inputs(self, device, seed=0):
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# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
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image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
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image = image.cpu().permute(0, 2, 3, 1)[0]
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init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
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mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((64, 64))
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if str(device).startswith("mps"):
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generator = torch.manual_seed(seed)
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else:
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generator = torch.Generator(device=device).manual_seed(seed)
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inputs = {
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"prompt": "A painting of a squirrel eating a burger",
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"image": init_image,
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"mask_image": mask_image,
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"generator": generator,
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"num_inference_steps": 2,
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"guidance_scale": 6.0,
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"output_type": "numpy",
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}
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return inputs
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def test_stable_diffusion_inpaint(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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sd_pipe = StableDiffusionInpaintPipeline(**components)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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image = sd_pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array([0.4723, 0.5731, 0.3939, 0.5441, 0.5922, 0.4392, 0.5059, 0.4651, 0.4474])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_inpaint_image_tensor(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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sd_pipe = StableDiffusionInpaintPipeline(**components)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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output = sd_pipe(**inputs)
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out_pil = output.images
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inputs = self.get_dummy_inputs(device)
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inputs["image"] = torch.tensor(np.array(inputs["image"]) / 127.5 - 1).permute(2, 0, 1).unsqueeze(0)
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inputs["mask_image"] = torch.tensor(np.array(inputs["mask_image"]) / 255).permute(2, 0, 1)[:1].unsqueeze(0)
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output = sd_pipe(**inputs)
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out_tensor = output.images
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assert out_pil.shape == (1, 64, 64, 3)
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assert np.abs(out_pil.flatten() - out_tensor.flatten()).max() < 5e-2
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def test_stable_diffusion_inpaint_with_num_images_per_prompt(self):
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device = "cpu"
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components = self.get_dummy_components()
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sd_pipe = StableDiffusionInpaintPipeline(**components)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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images = sd_pipe(**inputs, num_images_per_prompt=2).images
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# check if the output is a list of 2 images
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assert len(images) == 2
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@slow
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@require_torch_gpu
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class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):
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def setUp(self):
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super().setUp()
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def tearDown(self):
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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def get_inputs(self, device, dtype=torch.float32, seed=0):
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generator = torch.Generator(device=device).manual_seed(seed)
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init_image = load_image(
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"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
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"/stable_diffusion_inpaint/input_bench_image.png"
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)
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mask_image = load_image(
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"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
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"/stable_diffusion_inpaint/input_bench_mask.png"
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)
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inputs = {
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"prompt": "Face of a yellow cat, high resolution, sitting on a park bench",
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"image": init_image,
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"mask_image": mask_image,
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"generator": generator,
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"num_inference_steps": 3,
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"guidance_scale": 7.5,
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"output_type": "numpy",
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}
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return inputs
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def test_stable_diffusion_inpaint_ddim(self):
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pipe = StableDiffusionInpaintPipeline.from_pretrained(
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"runwayml/stable-diffusion-inpainting", safety_checker=None
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)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing()
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inputs = self.get_inputs(torch_device)
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image = pipe(**inputs).images
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image_slice = image[0, 253:256, 253:256, -1].flatten()
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.05978, 0.10983, 0.10514, 0.07922, 0.08483, 0.08587, 0.05302, 0.03218, 0.01636])
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assert np.abs(expected_slice - image_slice).max() < 1e-4
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def test_stable_diffusion_inpaint_fp16(self):
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pipe = StableDiffusionInpaintPipeline.from_pretrained(
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"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, safety_checker=None
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)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing()
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inputs = self.get_inputs(torch_device, dtype=torch.float16)
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image = pipe(**inputs).images
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image_slice = image[0, 253:256, 253:256, -1].flatten()
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.06152, 0.11060, 0.10449, 0.07959, 0.08643, 0.08496, 0.05420, 0.03247, 0.01831])
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assert np.abs(expected_slice - image_slice).max() < 1e-2
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def test_stable_diffusion_inpaint_pndm(self):
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pipe = StableDiffusionInpaintPipeline.from_pretrained(
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"runwayml/stable-diffusion-inpainting", safety_checker=None
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)
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pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing()
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inputs = self.get_inputs(torch_device)
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image = pipe(**inputs).images
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image_slice = image[0, 253:256, 253:256, -1].flatten()
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.06892, 0.06994, 0.07905, 0.05366, 0.04709, 0.04890, 0.04107, 0.05083, 0.04180])
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assert np.abs(expected_slice - image_slice).max() < 1e-4
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def test_stable_diffusion_inpaint_k_lms(self):
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pipe = StableDiffusionInpaintPipeline.from_pretrained(
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"runwayml/stable-diffusion-inpainting", safety_checker=None
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)
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pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing()
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inputs = self.get_inputs(torch_device)
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image = pipe(**inputs).images
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image_slice = image[0, 253:256, 253:256, -1].flatten()
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.23513, 0.22413, 0.29442, 0.24243, 0.26214, 0.30329, 0.26431, 0.25025, 0.25197])
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assert np.abs(expected_slice - image_slice).max() < 1e-4
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def test_stable_diffusion_inpaint_with_sequential_cpu_offloading(self):
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torch.cuda.empty_cache()
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torch.cuda.reset_max_memory_allocated()
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torch.cuda.reset_peak_memory_stats()
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pipe = StableDiffusionInpaintPipeline.from_pretrained(
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"runwayml/stable-diffusion-inpainting", safety_checker=None, torch_dtype=torch.float16
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)
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pipe = pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing(1)
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pipe.enable_sequential_cpu_offload()
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inputs = self.get_inputs(torch_device, dtype=torch.float16)
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_ = pipe(**inputs)
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mem_bytes = torch.cuda.max_memory_allocated()
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# make sure that less than 2.2 GB is allocated
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assert mem_bytes < 2.2 * 10**9
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@nightly
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@require_torch_gpu
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class StableDiffusionInpaintPipelineNightlyTests(unittest.TestCase):
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def tearDown(self):
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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def get_inputs(self, device, dtype=torch.float32, seed=0):
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generator = torch.Generator(device=device).manual_seed(seed)
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init_image = load_image(
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"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
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"/stable_diffusion_inpaint/input_bench_image.png"
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)
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mask_image = load_image(
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"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
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"/stable_diffusion_inpaint/input_bench_mask.png"
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)
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inputs = {
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"prompt": "Face of a yellow cat, high resolution, sitting on a park bench",
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"image": init_image,
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"mask_image": mask_image,
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"generator": generator,
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"num_inference_steps": 50,
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"guidance_scale": 7.5,
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"output_type": "numpy",
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}
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return inputs
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def test_inpaint_ddim(self):
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sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
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sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_inputs(torch_device)
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image = sd_pipe(**inputs).images[0]
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expected_image = load_numpy(
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"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
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"/stable_diffusion_inpaint/stable_diffusion_inpaint_ddim.npy"
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)
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max_diff = np.abs(expected_image - image).max()
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assert max_diff < 1e-3
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def test_inpaint_pndm(self):
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sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
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sd_pipe.scheduler = PNDMScheduler.from_config(sd_pipe.scheduler.config)
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sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_inputs(torch_device)
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image = sd_pipe(**inputs).images[0]
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expected_image = load_numpy(
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"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
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"/stable_diffusion_inpaint/stable_diffusion_inpaint_pndm.npy"
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)
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max_diff = np.abs(expected_image - image).max()
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assert max_diff < 1e-3
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def test_inpaint_lms(self):
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sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
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sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
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sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_inputs(torch_device)
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image = sd_pipe(**inputs).images[0]
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expected_image = load_numpy(
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"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
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"/stable_diffusion_inpaint/stable_diffusion_inpaint_lms.npy"
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)
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max_diff = np.abs(expected_image - image).max()
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assert max_diff < 1e-3
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def test_inpaint_dpm(self):
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sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
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sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
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sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_inputs(torch_device)
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inputs["num_inference_steps"] = 30
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image = sd_pipe(**inputs).images[0]
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expected_image = load_numpy(
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"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
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"/stable_diffusion_inpaint/stable_diffusion_inpaint_dpm_multi.npy"
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)
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max_diff = np.abs(expected_image - image).max()
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assert max_diff < 1e-3
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class StableDiffusionInpaintingPrepareMaskAndMaskedImageTests(unittest.TestCase):
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def test_pil_inputs(self):
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im = np.random.randint(0, 255, (32, 32, 3), dtype=np.uint8)
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im = Image.fromarray(im)
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mask = np.random.randint(0, 255, (32, 32), dtype=np.uint8) > 127.5
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mask = Image.fromarray((mask * 255).astype(np.uint8))
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t_mask, t_masked = prepare_mask_and_masked_image(im, mask)
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self.assertTrue(isinstance(t_mask, torch.Tensor))
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self.assertTrue(isinstance(t_masked, torch.Tensor))
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self.assertEqual(t_mask.ndim, 4)
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self.assertEqual(t_masked.ndim, 4)
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self.assertEqual(t_mask.shape, (1, 1, 32, 32))
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self.assertEqual(t_masked.shape, (1, 3, 32, 32))
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self.assertTrue(t_mask.dtype == torch.float32)
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self.assertTrue(t_masked.dtype == torch.float32)
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self.assertTrue(t_mask.min() >= 0.0)
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self.assertTrue(t_mask.max() <= 1.0)
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self.assertTrue(t_masked.min() >= -1.0)
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self.assertTrue(t_masked.min() <= 1.0)
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self.assertTrue(t_mask.sum() > 0.0)
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def test_np_inputs(self):
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im_np = np.random.randint(0, 255, (32, 32, 3), dtype=np.uint8)
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im_pil = Image.fromarray(im_np)
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mask_np = np.random.randint(0, 255, (32, 32), dtype=np.uint8) > 127.5
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mask_pil = Image.fromarray((mask_np * 255).astype(np.uint8))
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t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
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t_mask_pil, t_masked_pil = prepare_mask_and_masked_image(im_pil, mask_pil)
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self.assertTrue((t_mask_np == t_mask_pil).all())
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self.assertTrue((t_masked_np == t_masked_pil).all())
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def test_torch_3D_2D_inputs(self):
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im_tensor = torch.randint(0, 255, (3, 32, 32), dtype=torch.uint8)
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mask_tensor = torch.randint(0, 255, (32, 32), dtype=torch.uint8) > 127.5
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im_np = im_tensor.numpy().transpose(1, 2, 0)
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mask_np = mask_tensor.numpy()
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t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
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t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
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self.assertTrue((t_mask_tensor == t_mask_np).all())
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self.assertTrue((t_masked_tensor == t_masked_np).all())
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def test_torch_3D_3D_inputs(self):
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im_tensor = torch.randint(0, 255, (3, 32, 32), dtype=torch.uint8)
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mask_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8) > 127.5
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im_np = im_tensor.numpy().transpose(1, 2, 0)
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mask_np = mask_tensor.numpy()[0]
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t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
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t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
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self.assertTrue((t_mask_tensor == t_mask_np).all())
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self.assertTrue((t_masked_tensor == t_masked_np).all())
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def test_torch_4D_2D_inputs(self):
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im_tensor = torch.randint(0, 255, (1, 3, 32, 32), dtype=torch.uint8)
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mask_tensor = torch.randint(0, 255, (32, 32), dtype=torch.uint8) > 127.5
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im_np = im_tensor.numpy()[0].transpose(1, 2, 0)
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mask_np = mask_tensor.numpy()
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t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
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t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
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self.assertTrue((t_mask_tensor == t_mask_np).all())
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self.assertTrue((t_masked_tensor == t_masked_np).all())
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def test_torch_4D_3D_inputs(self):
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im_tensor = torch.randint(0, 255, (1, 3, 32, 32), dtype=torch.uint8)
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mask_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8) > 127.5
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im_np = im_tensor.numpy()[0].transpose(1, 2, 0)
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mask_np = mask_tensor.numpy()[0]
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t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
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t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
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self.assertTrue((t_mask_tensor == t_mask_np).all())
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self.assertTrue((t_masked_tensor == t_masked_np).all())
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def test_torch_4D_4D_inputs(self):
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im_tensor = torch.randint(0, 255, (1, 3, 32, 32), dtype=torch.uint8)
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mask_tensor = torch.randint(0, 255, (1, 1, 32, 32), dtype=torch.uint8) > 127.5
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im_np = im_tensor.numpy()[0].transpose(1, 2, 0)
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mask_np = mask_tensor.numpy()[0][0]
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|
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t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
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t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np)
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|
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self.assertTrue((t_mask_tensor == t_mask_np).all())
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self.assertTrue((t_masked_tensor == t_masked_np).all())
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def test_torch_batch_4D_3D(self):
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im_tensor = torch.randint(0, 255, (2, 3, 32, 32), dtype=torch.uint8)
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mask_tensor = torch.randint(0, 255, (2, 32, 32), dtype=torch.uint8) > 127.5
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|
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im_nps = [im.numpy().transpose(1, 2, 0) for im in im_tensor]
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mask_nps = [mask.numpy() for mask in mask_tensor]
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|
|
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t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor)
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nps = [prepare_mask_and_masked_image(i, m) for i, m in zip(im_nps, mask_nps)]
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t_mask_np = torch.cat([n[0] for n in nps])
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t_masked_np = torch.cat([n[1] for n in nps])
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|
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self.assertTrue((t_mask_tensor == t_mask_np).all())
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self.assertTrue((t_masked_tensor == t_masked_np).all())
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|
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def test_torch_batch_4D_4D(self):
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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)
|