add model md5 check

lama_cleaner/model/manga.py

@@ -11,67 +11,21 @@ from lama_cleaner.helper import get_cache_path_by_url, load_jit_model
 from lama_cleaner.model.base import InpaintModel
 from lama_cleaner.schema import Config
 
-# def norm(np_img):
-#     return np_img / 255 * 2 - 1.0
-#
-#
-# @torch.no_grad()
-# def run():
-#     name = 'manga_1080x740.jpg'
-#     img_p = f'/Users/qing/code/github/MangaInpainting/examples/test/imgs/{name}'
-#     mask_p = f'/Users/qing/code/github/MangaInpainting/examples/test/masks/mask_{name}'
-#     erika_model = torch.jit.load('erika.jit')
-#     manga_inpaintor_model = torch.jit.load('manga_inpaintor.jit')
-#
-#     img = cv2.imread(img_p)
-#     gray_img = cv2.imread(img_p, cv2.IMREAD_GRAYSCALE)
-#     mask = cv2.imread(mask_p, cv2.IMREAD_GRAYSCALE)
-#
-#     kernel = np.ones((9, 9), dtype=np.uint8)
-#     mask = cv2.dilate(mask, kernel, 2)
-#     # cv2.imwrite("mask.jpg", mask)
-#     # cv2.imshow('dilated_mask', cv2.hconcat([mask, dilated_mask]))
-#     # cv2.waitKey(0)
-#     # exit()
-#
-#     # img = pad(img)
-#     gray_img = pad(gray_img).astype(np.float32)
-#     mask = pad(mask)
-#
-#     # pad_mod = 16
-#     import time
-#     start = time.time()
-#     y = erika_model(torch.from_numpy(gray_img[np.newaxis, np.newaxis, :, :]))
-#     y = torch.clamp(y, 0, 255)
-#     lines = y.cpu().numpy()
-#     print(f"erika_model time: {time.time() - start}")
-#
-#     cv2.imwrite('lines.png', lines[0][0])
-#
-#     start = time.time()
-#     masks = torch.from_numpy(mask[np.newaxis, np.newaxis, :, :])
-#     masks = torch.where(masks > 0.5, torch.tensor(1.0), torch.tensor(0.0))
-#     noise = torch.randn_like(masks)
-#
-#     images = torch.from_numpy(norm(gray_img)[np.newaxis, np.newaxis, :, :])
-#     lines = torch.from_numpy(norm(lines))
-#
-#     outputs = manga_inpaintor_model(images, lines, masks, noise)
-#     print(f"manga_inpaintor_model time: {time.time() - start}")
-#
-#     outputs_merged = (outputs * masks) + (images * (1 - masks))
-#     outputs_merged = outputs_merged * 127.5 + 127.5
-#     outputs_merged = outputs_merged.permute(0, 2, 3, 1)[0].detach().cpu().numpy().astype(np.uint8)
-#     cv2.imwrite(f'output_{name}', outputs_merged)
-
 
 MANGA_INPAINTOR_MODEL_URL = os.environ.get(
     "MANGA_INPAINTOR_MODEL_URL",
-    "https://github.com/Sanster/models/releases/download/manga/manga_inpaintor.jit"
+    "https://github.com/Sanster/models/releases/download/manga/manga_inpaintor.jit",
 )
+MANGA_INPAINTOR_MODEL_MD5 = os.environ.get(
+    "MANGA_INPAINTOR_MODEL_MD5", "7d8b269c4613b6b3768af714610da86c"
+)
+
 MANGA_LINE_MODEL_URL = os.environ.get(
     "MANGA_LINE_MODEL_URL",
-    "https://github.com/Sanster/models/releases/download/manga/erika.jit"
+    "https://github.com/Sanster/models/releases/download/manga/erika.jit",
+)
+MANGA_LINE_MODEL_MD5 = os.environ.get(
+    "MANGA_LINE_MODEL_MD5", "8f157c142718f11e233d3750a65e0794"
 )
 
 
@@ -80,8 +34,12 @@ class Manga(InpaintModel):
     pad_mod = 16
 
     def init_model(self, device, **kwargs):
-        self.inpaintor_model = load_jit_model(MANGA_INPAINTOR_MODEL_URL, device)
-        self.line_model = load_jit_model(MANGA_LINE_MODEL_URL, device)
+        self.inpaintor_model = load_jit_model(
+            MANGA_INPAINTOR_MODEL_URL, device, MANGA_INPAINTOR_MODEL_MD5
+        )
+        self.line_model = load_jit_model(
+            MANGA_LINE_MODEL_URL, device, MANGA_LINE_MODEL_MD5
+        )
         self.seed = 42
 
     @staticmethod
@@ -105,7 +63,9 @@ class Manga(InpaintModel):
         torch.cuda.manual_seed_all(seed)
 
         gray_img = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
-        gray_img = torch.from_numpy(gray_img[np.newaxis, np.newaxis, :, :].astype(np.float32)).to(self.device)
+        gray_img = torch.from_numpy(
+            gray_img[np.newaxis, np.newaxis, :, :].astype(np.float32)
+        ).to(self.device)
         start = time.time()
         lines = self.line_model(gray_img)
         torch.cuda.empty_cache()