chore: add [CCCV] prefix to error and log messages (#3)

* chore: only test PyTorch >= 2.0

* chore: add CCCV prefix to error and log messages

Author: Tohrusky

.github/workflows/CI-test.yml

@@ -24,7 +24,7 @@ jobs:
       matrix:
         os-version: ["windows-latest", "ubuntu-latest", "macos-14"]
         python-version: ["3.9"]
-        pytorch-version: ["2.7.1", "2.1.2", "2.0.0", "1.13.0"]
+        pytorch-version: ["2.7.1", "2.1.2", "2.0.0"]
 
     runs-on: ${{ matrix.os-version }}
     steps:
@@ -58,11 +58,6 @@ jobs:
         run: |
           uv pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1
 
-      - name: Install PyTorch 1.13.0
-        if: matrix.pytorch-version == '1.13.0'
-        run: |
-          uv pip install torch==1.13.0 torchvision==0.14.0 torchaudio==0.13.0
-
       - name: Show environment
         run: |
           uv pip list

README.md

@@ -10,7 +10,7 @@ an inference lib for image/video restoration and video frame interpolation with
 
 ### Install
 
-Make sure you have Python >= 3.9 and PyTorch >= 1.13 installed
+Make sure you have Python >= 3.9 and PyTorch >= 2.0 installed
 
 ```bash
 pip install cccv

cccv/cache_models/__init__.py

@@ -53,19 +53,24 @@ def load_file_from_url(
     :return:
     """
 
+    CCCV_REMOTE_MODEL_ZOO = os.environ.get(
+        "CCCV_REMOTE_MODEL_ZOO", "https://github.com/EutropicAI/cccv/releases/download/model_zoo/"
+    )
+    CCCV_CACHE_MODEL_DIR = os.environ.get("CCCV_CACHE_MODEL_DIR", str(CACHE_PATH))
+
     if model_dir is None:
-        model_dir = str(CACHE_PATH)
+        model_dir = str(CCCV_CACHE_MODEL_DIR)
+        print(
+            f"[CCCV] Using default cache model path {model_dir}, override it by setting environment variable CCCV_CACHE_MODEL_DIR"
+        )
 
     cached_file_path = os.path.abspath(os.path.join(model_dir, config.name))
 
     if config.url is not None:
         _url: str = str(config.url)
     else:
-        CCCV_REMOTE_MODEL_ZOO = os.environ.get(
-            "CCCV_REMOTE_MODEL_ZOO", "https://github.com/EutropicAI/cccv/releases/download/model_zoo/"
-        )
         print(
-            f"Fetch models from {CCCV_REMOTE_MODEL_ZOO}, override it by setting environment variable CCCV_REMOTE_MODEL_ZOO"
+            f"[CCCV] Fetching models from {CCCV_REMOTE_MODEL_ZOO}, override it by setting environment variable CCCV_REMOTE_MODEL_ZOO"
         )
         if not CCCV_REMOTE_MODEL_ZOO.endswith("/"):
             CCCV_REMOTE_MODEL_ZOO += "/"
@@ -79,15 +84,15 @@ def load_file_from_url(
 
     if not os.path.exists(cached_file_path) or force_download:
         if _gh_proxy is not None:
-            print(f"Using github proxy: {_gh_proxy}")
-        print(f"Downloading: {_url} to {cached_file_path}\n")
+            print(f"[CCCV] Using github proxy: {_gh_proxy}")
+        print(f"[CCCV] Downloading: {_url} to {cached_file_path}\n")
 
         @retry(wait=wait_random(min=3, max=5), stop=stop_after_delay(10) | stop_after_attempt(30))
         def _download() -> None:
             try:
                 download_url_to_file(url=_url, dst=cached_file_path, hash_prefix=None, progress=progress)
             except Exception as e:
-                print(f"Download failed: {e}, retrying...")
+                print(f"[CCCV] Download failed: {e}, retrying...")
                 raise e
 
         _download()
@@ -96,7 +101,7 @@ def _download() -> None:
         get_hash = get_file_sha256(cached_file_path)
         if get_hash != config.hash:
             raise ValueError(
-                f"File {cached_file_path} hash mismatched with config hash {config.hash}, compare with {get_hash}"
+                f"[CCCV] File {cached_file_path} hash mismatched with config hash {config.hash}, compare with {get_hash}"
             )
 
     return cached_file_path
@@ -110,4 +115,4 @@ def _download() -> None:
                 continue
             file_path = os.path.join(root, file)
             name = os.path.basename(file_path)
-            print(f"{name}: {get_file_sha256(file_path)}")
+            print(f"[CCCV] {name}: {get_file_sha256(file_path)}")

cccv/model/auxiliary_base_model.py

@@ -8,4 +8,4 @@
 @MODEL_REGISTRY.register(name=ModelType.AuxiliaryBaseModel)
 class AuxiliaryBaseModel(CCBaseModel):
     def inference(self, *args: Any, **kwargs: Any) -> Any:
-        raise NotImplementedError("Auxiliary model should use self.model to load in the main model")
+        raise NotImplementedError("[CCCV] Auxiliary model should use self.model to load in the main model")

cccv/model/base_model.py

@@ -69,7 +69,7 @@ def __init__(
             try:
                 self.model = self.model.half()
             except Exception as e:
-                print(f"Error: {e}, fp16 is not supported on this model.")
+                print(f"[CCCV] Warning: {e}. \nfp16 is not supported on this model, fallback to fp32.")
                 self.fp16 = False
                 self.model = self.load_model()
 
@@ -83,7 +83,7 @@ def __init__(
                         self.compile_backend = "inductor"
                 self.model = torch.compile(self.model, backend=self.compile_backend)
             except Exception as e:
-                print(f"Error: {e}, compile is not supported on this model.")
+                print(f"[CCCV] Error: {e}, compile is not supported on this model.")
 
     def post_init_hook(self) -> None:
         """
@@ -109,7 +109,7 @@ def get_state_dict(self) -> Any:
                     config=cfg, force_download=False, model_dir=self.model_dir, gh_proxy=self.gh_proxy
                 )
             except Exception as e:
-                print(f"Error: {e}, try force download the model...")
+                print(f"[CCCV] Error: {e}, try force download the model...")
                 state_dict_path = load_file_from_url(
                     config=cfg, force_download=True, model_dir=self.model_dir, gh_proxy=self.gh_proxy
                 )
@@ -150,9 +150,9 @@ def load_model(self) -> Any:
         try:
             net_kw = {k: cfg_dict[k] for k in signature(net).parameters}
         except (KeyError, TypeError) as e:
-            raise RuntimeError(f"Config missing or mismatch required param for {net.__name__}: {e}") from e
+            raise RuntimeError(f"[CCCV] Config missing or mismatch required param for {net.__name__}: {e}") from e
 
-        # print(f"net_kw: {net_kw}")
+        # print(f"[CCCV] net_kw: {net_kw}")
         model = net(**net_kw)
 
         model.load_state_dict(state_dict)

cccv/model/vsr_base_model.py

@@ -70,7 +70,7 @@ def inference_image_list(self, img_list: List[np.ndarray], *args: Any, **kwargs:
 
             return [img]
         else:
-            raise ValueError(f"Unexpected output shape: {out.shape}")
+            raise ValueError(f"[CCCV] Unexpected output shape: {out.shape}")
 
     @torch.inference_mode()  # type: ignore
     def inference_video(self, clip: Any, *args: Any, **kwargs: Any) -> Any:

cccv/util/color.py

@@ -26,10 +26,10 @@ def rgb_to_yuv(image: Tensor) -> Tensor:
         >>> output = rgb_to_yuv(input)  # 2x3x4x5
     """
     if not isinstance(image, Tensor):
-        raise TypeError(f"Input type is not a Tensor. Got {type(image)}")
+        raise TypeError(f"[CCCV] Input type is not a Tensor. Got {type(image)}")
 
     if len(image.shape) < 3 or image.shape[-3] != 3:
-        raise ValueError(f"Input size must have a shape of (*, 3, H, W). Got {image.shape}")
+        raise ValueError(f"[CCCV] Input size must have a shape of (*, 3, H, W). Got {image.shape}")
 
     r: Tensor = image[..., 0, :, :]
     g: Tensor = image[..., 1, :, :]
@@ -65,10 +65,10 @@ def yuv_to_rgb(image: Tensor) -> Tensor:
         >>> output = yuv_to_rgb(input)  # 2x3x4x5
     """
     if not isinstance(image, Tensor):
-        raise TypeError(f"Input type is not a Tensor. Got {type(image)}")
+        raise TypeError(f"[CCCV] Input type is not a Tensor. Got {type(image)}")
 
     if image.dim() < 3 or image.shape[-3] != 3:
-        raise ValueError(f"Input size must have a shape of (*, 3, H, W). Got {image.shape}")
+        raise ValueError(f"[CCCV] Input size must have a shape of (*, 3, H, W). Got {image.shape}")
 
     y: Tensor = image[..., 0, :, :]
     u: Tensor = image[..., 1, :, :]

cccv/util/device.py

@@ -10,7 +10,7 @@ def default_device() -> torch.device:
         try:
             return torch.device("mps" if torch.backends.mps.is_available() else "cpu")
         except Exception as e:
-            print(f"Err: {e}, MPS is not available, use CPU instead.")
+            print(f"[CCCV] Error: {e}, MPS is not available, use CPU instead.")
             return torch.device("cpu")
 
 

cccv/util/registry.py

@@ -41,7 +41,7 @@ def __init__(self, name: str) -> None:
 
     def _do_register(self, name: str, obj: Any) -> None:
         if name in self._obj_map:
-            print("An object named '{}' was already registered in '{}' registry!".format(name, self._name))
+            print("[CCCV] An object named '{}' was already registered in '{}' registry!".format(name, self._name))
         else:
             self._obj_map[name] = obj
 
@@ -69,7 +69,7 @@ def deco(func_or_class: Any) -> Any:
     def get(self, name: str) -> Any:
         ret = self._obj_map.get(name)
         if ret is None:
-            raise KeyError("No object named '{}' found in '{}' registry!".format(name, self._name))
+            raise KeyError("[CCCV] No object named '{}' found in '{}' registry!".format(name, self._name))
         return ret
 
     def __contains__(self, name: str) -> bool:

cccv/vs/sr.py

@@ -30,7 +30,7 @@ def inference_sr(
     """
 
     if clip.format.id not in [vs.RGBH, vs.RGBS]:
-        raise vs.Error("Only vs.RGBH and vs.RGBS formats are supported")
+        raise vs.Error("[CCCV] Only vs.RGBH and vs.RGBS formats are supported")
 
     if device.type == torch.device("cuda").type:
         return inference_sr_cuda(inference, clip, scale, device)