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Runtime error
| import hashlib | |
| import os | |
| import time | |
| from lama_cleaner.plugins.anime_seg import AnimeSeg | |
| os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" | |
| from pathlib import Path | |
| import cv2 | |
| import pytest | |
| import torch.cuda | |
| from lama_cleaner.plugins import ( | |
| RemoveBG, | |
| RealESRGANUpscaler, | |
| GFPGANPlugin, | |
| RestoreFormerPlugin, | |
| InteractiveSeg, | |
| ) | |
| current_dir = Path(__file__).parent.absolute().resolve() | |
| save_dir = current_dir / "result" | |
| save_dir.mkdir(exist_ok=True, parents=True) | |
| img_p = current_dir / "bunny.jpeg" | |
| img_bytes = open(img_p, "rb").read() | |
| bgr_img = cv2.imread(str(img_p)) | |
| rgb_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB) | |
| def _save(img, name): | |
| cv2.imwrite(str(save_dir / name), img) | |
| def test_remove_bg(): | |
| model = RemoveBG() | |
| res = model.forward(bgr_img) | |
| res = cv2.cvtColor(res, cv2.COLOR_RGBA2BGRA) | |
| _save(res, "test_remove_bg.png") | |
| def test_anime_seg(): | |
| model = AnimeSeg() | |
| img = cv2.imread(str(current_dir / "anime_test.png")) | |
| res = model.forward(img) | |
| assert len(res.shape) == 3 | |
| assert res.shape[-1] == 4 | |
| _save(res, "test_anime_seg.png") | |
| def test_upscale(device): | |
| if device == "cuda" and not torch.cuda.is_available(): | |
| return | |
| if device == "mps" and not torch.backends.mps.is_available(): | |
| return | |
| model = RealESRGANUpscaler("realesr-general-x4v3", device) | |
| res = model.forward(bgr_img, 2) | |
| _save(res, f"test_upscale_x2_{device}.png") | |
| res = model.forward(bgr_img, 4) | |
| _save(res, f"test_upscale_x4_{device}.png") | |
| def test_gfpgan(device): | |
| if device == "cuda" and not torch.cuda.is_available(): | |
| return | |
| if device == "mps" and not torch.backends.mps.is_available(): | |
| return | |
| model = GFPGANPlugin(device) | |
| res = model(rgb_img, None, None) | |
| _save(res, f"test_gfpgan_{device}.png") | |
| def test_restoreformer(device): | |
| if device == "cuda" and not torch.cuda.is_available(): | |
| return | |
| if device == "mps" and not torch.backends.mps.is_available(): | |
| return | |
| model = RestoreFormerPlugin(device) | |
| res = model(rgb_img, None, None) | |
| _save(res, f"test_restoreformer_{device}.png") | |
| def test_segment_anything(device): | |
| if device == "cuda" and not torch.cuda.is_available(): | |
| return | |
| if device == "mps" and not torch.backends.mps.is_available(): | |
| return | |
| img_md5 = hashlib.md5(img_bytes).hexdigest() | |
| model = InteractiveSeg("vit_l", device) | |
| new_mask = model.forward(rgb_img, [[448 // 2, 394 // 2, 1]], img_md5) | |
| save_name = f"test_segment_anything_{device}.png" | |
| _save(new_mask, save_name) | |
| start = time.time() | |
| model.forward(rgb_img, [[448 // 2, 394 // 2, 1]], img_md5) | |
| print(f"Time for {save_name}: {time.time() - start:.2f}s") | |