X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=world.py;h=fb8609d82109ce2f29d0612a553bfe77d36a74c5;hb=2192d72289bbf2cd069f67d3e93daf7934f886af;hp=c3eb1019f275d74bcf8a266cac6eecdb7daba25b;hpb=bf48dc69f7f57ad391481c8917570e35f661cc4a;p=picoclvr.git diff --git a/world.py b/world.py index c3eb101..fb8609d 100755 --- a/world.py +++ b/world.py @@ -65,15 +65,19 @@ class SignSTE(nn.Module): def train_encoder( train_input, test_input, - depth=3, + depth=2, dim_hidden=48, nb_bits_per_token=8, lr_start=1e-3, 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 @@ -326,12 +330,18 @@ 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) def create_data_and_processors( - nb_train_samples, nb_test_samples, mode, nb_steps, nb_epochs=10 + nb_train_samples, + nb_test_samples, + mode, + nb_steps, + nb_epochs=10, + device=torch.device("cpu"), + logger=None, ): assert mode in ["first_last"] @@ -339,10 +349,12 @@ def create_data_and_processors( steps = [True] + [False] * (nb_steps + 1) + [True] train_input, train_actions = generate_episodes(nb_train_samples, steps) + train_input, train_actions = train_input.to(device), train_actions.to(device) test_input, test_actions = generate_episodes(nb_test_samples, steps) + 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 + train_input, test_input, nb_epochs=nb_epochs, logger=logger, device=device ) encoder.train(False) quantizer.train(False)