X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=ec50722f7f6f8e295563ff6960d45ad9490e4f0a;hb=6d23462ce76c9020dcd7c4bc8a0e7a0fae9b7971;hp=3e67a73fab1452f0d1f97aff1f890fe3ab70bcab;hpb=2434c00a82ebb0b23f45d891cc9f80324e3200bd;p=mygptrnn.git

diff --git a/main.py b/main.py
index 3e67a73..ec50722 100755
--- a/main.py
+++ b/main.py
@@ -99,7 +99,13 @@ parser.add_argument("--nb_lines", type=int, default=None)
 
 parser.add_argument("--caterpillar_height", type=int, default=None)
 
-parser.add_argument("--rho", type=float, default=0.0)
+parser.add_argument("--gate_dropout_proba", type=float, default=0.0)
+
+parser.add_argument("--gate_dropout_sync", type=str2bool, default=True)
+
+parser.add_argument("--gate_dropout_replace", type=str2bool, default=True)
+
+parser.add_argument("--rho_inner_loss", type=float, default=0.0)
 
 parser.add_argument("--nb_blocks", type=int, default=None)
 
@@ -133,6 +139,10 @@ parser.add_argument("--rpl_no_prog", action="store_true", default=False)
 
 parser.add_argument("--grid_size", type=int, default=6)
 
+parser.add_argument("--grid_nb_colors", type=int, default=6)
+
+parser.add_argument("--grid_nb_shapes", type=int, default=6)
+
 ##############################
 # picoclvr options
 
@@ -701,6 +711,8 @@ elif args.task == "grid":
         nb_test_samples=args.nb_test_samples,
         batch_size=args.batch_size,
         size=args.grid_size,
+        nb_shapes=args.grid_nb_shapes,
+        nb_colors=args.grid_nb_colors,
         logger=log_string,
         device=device_data,
     )
@@ -741,7 +753,7 @@ model = mygpt.MyGPT(
     dropout=args.dropout,
     attention_layer=args.attention,
     logger=log_string,
-    **sup_args,
+    args=args,
 )
 
 model.to(device)
@@ -835,6 +847,25 @@ if args.max_percents_of_test_in_train >= 0:
 
 ##############################
 
+if "calibrate" in sup_args:
+    for input in task.batches(split="train", desc="calibrate"):
+        input = input.to(device)
+        output = model(mygpt.BracketedSequence(input)).x
+
+    for n, m in model.named_modules():
+        for a in dir(m):
+            x = getattr(m, a)
+            if isinstance(x, mygpt.Calibrator):
+                print(f"####### ${n} | ${a} ########################")
+                mean, std = x.moments()
+                print("mean\n", mean, "\n")
+                print("std\n", std, "\n")
+                print(f"############################################\n\n")
+
+    exit(0)
+
+##############################
+
 nb_samples_seen = 0
 
 if nb_epochs_finished >= nb_epochs:
@@ -880,7 +911,9 @@ for n_epoch in range(nb_epochs_finished, nb_epochs):
         nb_train_samples += input.size(0)
         nb_samples_seen += input.size(0)
 
-        total_loss = loss + (args.rho * inner_loss if args.rho > 0 else 0.0)
+        total_loss = loss + (
+            args.rho_inner_loss * inner_loss if args.rho_inner_loss > 0 else 0.0
+        )
 
         it += 1
         lr = get_lr(n_epoch, it)