0715-1441

diffusion.py

@@ -238,8 +238,8 @@ class TrainConfig:
     dim = 3
     stride = (2,2) if dim == 2 else (2,2,4)
     num_image = 1000#32000#20000#15000#7000#25600#3000#10000#1000#10000#5000#2560#800#2560
-    batch_size = 2#50#20#2#100 # 10
-    n_epoch = 3# 10#50#20#20#2#5#25 # 120
+    batch_size = 1#2#50#20#2#100 # 10
+    n_epoch = 4# 10#50#20#20#2#5#25 # 120
     HII_DIM = 64
     num_redshift = 512#128#64#512#256#256#64#512#128
     channel = 1
@@ -266,7 +266,7 @@ class TrainConfig:
     # seed = 0
     # save_dir = './outputs/'
 
-    save_period = np.infty#.1 # the period of sampling
+    save_period = 1#np.infty#.1 # the period of sampling
     # general parameters for the name and logger    
     # device = "cuda" if torch.cuda.is_available() else "cpu"
     lrate = 1e-4
@@ -576,7 +576,7 @@ def train(rank, world_size):
 
     ddp_setup(rank, world_size)
     
-    num_image_list = [10]#[200]#[1600,3200,6400,12800,25600]
+    num_image_list = [2000]#[200]#[1600,3200,6400,12800,25600]
     for i, num_image in enumerate(num_image_list):
         config.num_image = num_image
         # config.world_size = world_size