0710-1056

diffusion.py

@@ -302,6 +302,12 @@ class TrainConfig:
 # @dataclass
 class DDPM21CM:
     def __init__(self, config):
+        print(
+            "torch.cuda.is_available() =", torch.cuda.is_available(), 
+            "torch.cuda.device_count() =", torch.cuda.device_count(),
+            "torch.cuda.is_initialized() =", torch.cuda.is_initialized(),
+            "torch.cuda.current_device() =", torch.cuda.current_device()
+        )
         # config = TrainConfig()
         # date = datetime.datetime.now().strftime("%m%d-%H%M")
         config.run_name = datetime.datetime.now().strftime("%m%d-%H%M") # the unique name of each experiment
@@ -549,17 +555,12 @@ if __name__ == "__main__":
 # torch.cuda.set_device(0)
 
 # %%
-print(
-    "torch.cuda.is_available() =", torch.cuda.is_available(), 
-"torch.cuda.device_count() =", torch.cuda.device_count(),
-"torch.cuda.is_initialized() =", torch.cuda.is_initialized()
-)
 # print(torch.cuda.__dir__())
 
 # %%
 # print("torch.cuda.is_initialized() =", torch.cuda.is_initialized())
 # print("torch.cuda.get_device_name() =", torch.cuda.get_device_name())
-print("torch.cuda.current_device() =", torch.cuda.current_device())
+# print("torch.cuda.current_device() =", torch.cuda.current_device())
 # print("torch.cuda.get_device_capability() =", torch.cuda.get_device_capability())
 # print("torch.cuda.get_device_properties(torch.cuda.device) =", torch.cuda.get_device_properties(torch.cuda.device))
 # print('here')
@@ -573,26 +574,26 @@ print("torch.cuda.current_device() =", torch.cuda.current_device())
 # # Sampling
 
 # %%
-if __name__ == "__main__":
-    # num_image_list = [1600,3200,6400,12800,25600]
-    num_image_list = [1000]
-    # num_image_list = [3200,6400,12800,25600]
-    # args = (config, nn_model, ddpm, optimizer, dataloader, lr_scheduler)
-    repeat = 2
-    config = TrainConfig()
-    for i, num_image in enumerate(num_image_list):
-        config.num_image = num_image
-        ddpm21cm = DDPM21CM(config)
+# if __name__ == "__main__":
+#     # num_image_list = [1600,3200,6400,12800,25600]
+#     num_image_list = [1000]
+#     # num_image_list = [3200,6400,12800,25600]
+#     # args = (config, nn_model, ddpm, optimizer, dataloader, lr_scheduler)
+#     repeat = 2
+#     config = TrainConfig()
+#     for i, num_image in enumerate(num_image_list):
+#         config.num_image = num_image
+#         ddpm21cm = DDPM21CM(config)
 
-        ddpm21cm.sample(f"./outputs/model_state-N{num_image}", params=torch.tensor([4.4, 131.341]), repeat=repeat)
+#         ddpm21cm.sample(f"./outputs/model_state-N{num_image}", params=torch.tensor([4.4, 131.341]), repeat=repeat)
 
-        # ddpm21cm.sample(f"./outputs/model_state-N{num_image}", params=torch.tensor((5.6, 19.037)), repeat=repeat)
+#         # ddpm21cm.sample(f"./outputs/model_state-N{num_image}", params=torch.tensor((5.6, 19.037)), repeat=repeat)
 
-        # ddpm21cm.sample(f"./outputs/model_state-N{num_image}", params=torch.tensor((4.699, 30)), repeat=repeat)
+#         # ddpm21cm.sample(f"./outputs/model_state-N{num_image}", params=torch.tensor((4.699, 30)), repeat=repeat)
 
-        # ddpm21cm.sample(f"./outputs/model_state-N{num_image}", params=torch.tensor((5.477, 200)), repeat=repeat)
+#         # ddpm21cm.sample(f"./outputs/model_state-N{num_image}", params=torch.tensor((5.477, 200)), repeat=repeat)
 
-        # ddpm21cm.sample(f"./outputs/model_state-N{num_image}", params=torch.tensor((4.8, 131.341)), repeat=repeat)
+#         # ddpm21cm.sample(f"./outputs/model_state-N{num_image}", params=torch.tensor((4.8, 131.341)), repeat=repeat)
 
 # %%
 # ls -lth outputs | head