2022-12-10 15:06:15 +01:00
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from pathlib import Path
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import cv2
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import pytest
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import torch
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from PIL import Image
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from lama_cleaner.model_manager import ModelManager
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from lama_cleaner.schema import HDStrategy
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from lama_cleaner.tests.test_model import get_config, get_data
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current_dir = Path(__file__).parent.absolute().resolve()
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save_dir = current_dir / 'result'
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save_dir.mkdir(exist_ok=True, parents=True)
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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device = torch.device(device)
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def assert_equal(
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model, config, gt_name,
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fx: float = 1, fy: float = 1,
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img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
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mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
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2022-12-11 15:03:36 +01:00
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example_p=current_dir / "bunny.jpeg",
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2022-12-10 15:06:15 +01:00
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):
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img, mask = get_data(fx=fx, fy=fy, img_p=img_p, mask_p=mask_p)
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example_image = cv2.imread(str(example_p))
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example_image = cv2.cvtColor(example_image, cv2.COLOR_BGRA2RGB)
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example_image = cv2.resize(example_image, None, fx=fx, fy=fy, interpolation=cv2.INTER_AREA)
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print(f"Input image shape: {img.shape}, example_image: {example_image.shape}")
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config.paint_by_example_example_image = Image.fromarray(example_image)
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res = model(img, mask, config)
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cv2.imwrite(str(save_dir / gt_name), res)
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@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
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def test_paint_by_example(strategy):
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2023-01-18 11:34:10 +01:00
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model = ModelManager(name="paint_by_example", device=device, disable_nsfw=True)
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2023-01-05 15:07:39 +01:00
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cfg = get_config(strategy, paint_by_example_steps=30)
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2022-12-10 15:06:15 +01:00
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assert_equal(
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model,
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cfg,
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f"paint_by_example_{strategy.capitalize()}.png",
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img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
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mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
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fy=0.9,
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2023-01-05 15:07:39 +01:00
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fx=1.3,
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)
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2023-01-18 11:34:10 +01:00
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@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
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def test_paint_by_example_disable_nsfw(strategy):
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model = ModelManager(name="paint_by_example", device=device, disable_nsfw=False)
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cfg = get_config(strategy, paint_by_example_steps=30)
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assert_equal(
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model,
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cfg,
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f"paint_by_example_{strategy.capitalize()}_disable_nsfw.png",
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img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
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mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
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)
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2023-01-05 15:07:39 +01:00
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@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
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def test_paint_by_example_sd_scale(strategy):
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2023-01-18 11:34:10 +01:00
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model = ModelManager(name="paint_by_example", device=device, disable_nsfw=True)
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2023-01-05 15:07:39 +01:00
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cfg = get_config(strategy, paint_by_example_steps=30, sd_scale=0.85)
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assert_equal(
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model,
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cfg,
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f"paint_by_example_{strategy.capitalize()}_sdscale.png",
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img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
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mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
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fy=0.9,
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fx=1.3
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)
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@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
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def test_paint_by_example_cpu_offload(strategy):
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2023-01-18 11:34:10 +01:00
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model = ModelManager(name="paint_by_example", device=device, cpu_offload=True, disable_nsfw=False)
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2023-01-05 15:07:39 +01:00
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cfg = get_config(strategy, paint_by_example_steps=30, sd_scale=0.85)
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assert_equal(
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model,
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cfg,
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f"paint_by_example_{strategy.capitalize()}_cpu_offload.png",
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img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
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mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
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2022-12-10 15:06:15 +01:00
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)
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2023-01-07 01:52:11 +01:00
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@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
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def test_paint_by_example_cpu_offload_cpu_device(strategy):
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2023-01-18 11:34:10 +01:00
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model = ModelManager(name="paint_by_example", device=torch.device('cpu'), cpu_offload=True, disable_nsfw=True)
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2023-01-07 01:52:11 +01:00
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cfg = get_config(strategy, paint_by_example_steps=1, sd_scale=0.85)
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assert_equal(
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model,
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cfg,
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f"paint_by_example_{strategy.capitalize()}_cpu_offload_cpu_device.png",
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img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
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mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
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fy=0.9,
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fx=1.3
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)
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