Update.

[mygptrnn.git] / main.py

diff --git a/main.py b/main.py
index c22ae57..79841f3 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -133,6 +133,10 @@ 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
 
@@ -465,15 +469,15 @@ with os.popen("sha256sum *.py") as f:
         log_string(f"sha256sum {l.strip()}")
 
 now = time.strftime("%Y%m%d-%H%M%S", time.localtime())
-os.system(f"tar --ignore-failed-read zcvf {args.result_dir}/src-{now}.tgz *.py *.sh")
+os.system(f"tar zcvf {args.result_dir}/src-{now}.tgz *.py *.sh")
 
 log_string(f"argv {' '.join(sys.argv)}")
 
 for n in vars(args):
     log_string(f"args.{n} {getattr(args, n)}")
 
-for n in vars(sup_args):
-    log_string(f"sup_args.{n} {getattr(sup_args, n)}")
+for k, v in sup_args.items():
+    log_string(f'sup_args["{k}"] "{v}"')
 
 
 ######################################################################
@@ -701,6 +705,8 @@ 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,
     )
@@ -835,6 +841,25 @@ 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: