Apply amp.autocast only when CUDA is available

lama_cleaner/model/ldm.py

@@ -1,26 +1,30 @@
 import os
+from functools import wraps
 
 import numpy as np
 import torch
-from loguru import logger
+import torch.nn as nn
 
+from lama_cleaner.helper import get_cache_path_by_url, load_jit_model, norm_img
 from lama_cleaner.model.base import InpaintModel
 from lama_cleaner.model.ddim_sampler import DDIMSampler
 from lama_cleaner.model.plms_sampler import PLMSSampler
+from lama_cleaner.model.utils import make_beta_schedule, timestep_embedding
 from lama_cleaner.schema import Config, LDMSampler
 
-torch.manual_seed(42)
+# torch.manual_seed(42)
-import torch.nn as nn
+
-from lama_cleaner.helper import (
+
-    download_model,
+def conditional_autocast(func):
-    norm_img,
+    @wraps(func)
-    get_cache_path_by_url,
+    def wrapper(*args, **kwargs):
-    load_jit_model,
+        if torch.cuda.is_available():
-)
+            with torch.cuda.amp.autocast():
-from lama_cleaner.model.utils import (
+                return func(*args, **kwargs)
-    make_beta_schedule,
+        else:
-    timestep_embedding,
+            return func(*args, **kwargs)
-)
+    return wrapper
+
 
 LDM_ENCODE_MODEL_URL = os.environ.get(
     "LDM_ENCODE_MODEL_URL",
@@ -110,7 +114,7 @@ class DDPM(nn.Module):
             alphas_cumprod.shape[0] == self.num_timesteps
         ), "alphas have to be defined for each timestep"
 
-        to_torch = lambda x: torch.tensor(x, dtype=torch.float32).to(self.device)
+        def to_torch(x): return torch.tensor(x, dtype=torch.float32).to(self.device)
 
         self.register_buffer("betas", to_torch(betas))
         self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
@@ -269,7 +273,7 @@ class LDM(InpaintModel):
         ]
         return all([os.path.exists(it) for it in model_paths])
 
-    @torch.cuda.amp.autocast()
+    @conditional_autocast
     def forward(self, image, mask, config: Config):
         """
         image: [H, W, C] RGB