X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=world.py;h=fb8609d82109ce2f29d0612a553bfe77d36a74c5;hb=2192d72289bbf2cd069f67d3e93daf7934f886af;hp=5c21fadcc95c5d21e328b5d9cbc37c2987e148dd;hpb=8e23dd068df00df61c690ffa89ecc8cb9db4b32d;p=picoclvr.git

diff --git a/world.py b/world.py
index 5c21fad..fb8609d 100755
--- a/world.py
+++ b/world.py
@@ -72,8 +72,12 @@ def train_encoder(
     lr_end=1e-4,
     nb_epochs=10,
     batch_size=25,
+    logger=None,
     device=torch.device("cpu"),
 ):
+    if logger is None:
+        logger = lambda s: print(s)
+
     mu, std = train_input.float().mean(), train_input.float().std()
 
     def encoder_core(depth, dim):
@@ -132,7 +136,7 @@ def train_encoder(
 
     nb_parameters = sum(p.numel() for p in model.parameters())
 
-    print(f"nb_parameters {nb_parameters}")
+    logger(f"nb_parameters {nb_parameters}")
 
     model.to(device)
 
@@ -179,7 +183,7 @@ def train_encoder(
         train_loss = acc_train_loss / train_input.size(0)
         test_loss = acc_test_loss / test_input.size(0)
 
-        print(f"train_ae {k} lr {lr} train_loss {train_loss} test_loss {test_loss}")
+        logger(f"train_ae {k} lr {lr} train_loss {train_loss} test_loss {test_loss}")
         sys.stdout.flush()
 
     return encoder, quantizer, decoder
@@ -326,7 +330,7 @@ def generate_episodes(nb, steps):
     for n in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world-data"):
         frames, actions = generate_episode(steps)
         all_frames += frames
-        all_actions += [actions]
+        all_actions += [actions[None, :]]
     return torch.cat(all_frames, 0).contiguous(), torch.cat(all_actions, 0)
 
 
@@ -337,6 +341,7 @@ def create_data_and_processors(
     nb_steps,
     nb_epochs=10,
     device=torch.device("cpu"),
+    logger=None,
 ):
     assert mode in ["first_last"]
 
@@ -349,7 +354,7 @@ def create_data_and_processors(
     test_input, test_actions = test_input.to(device), test_actions.to(device)
 
     encoder, quantizer, decoder = train_encoder(
-        train_input, test_input, nb_epochs=nb_epochs, device=device
+        train_input, test_input, nb_epochs=nb_epochs, logger=logger, device=device
     )
     encoder.train(False)
     quantizer.train(False)