Update.

[picoclvr.git] / main.py

diff --git a/main.py b/main.py
index 00e19ac..496a603 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -33,7 +33,7 @@ parser.add_argument(
     "--task",
     type=str,
     default="twotargets",
-    help="byheart, learnop, guessop, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl, grid",
+    help="byheart, learnop, guessop, mixing, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl, grid, qmlp",
 )
 
 parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
@@ -46,7 +46,7 @@ parser.add_argument("--max_percents_of_test_in_train", type=int, default=1)
 
 ########################################
 
-parser.add_argument("--nb_epochs", type=int, default=None)
+parser.add_argument("--nb_epochs", type=int, default=25)
 
 parser.add_argument("--batch_size", type=int, default=None)
 
@@ -99,6 +99,11 @@ parser.add_argument("--rpl_nb_runs", type=int, default=5)
 
 parser.add_argument("--rpl_no_prog", action="store_true", default=False)
 
+##############################
+# grid options
+
+parser.add_argument("--grid_size", type=int, default=6)
+
 ##############################
 # picoclvr options
 
@@ -155,9 +160,9 @@ parser.add_argument("--expr_result_max", type=int, default=99)
 parser.add_argument("--expr_input_file", type=str, default=None)
 
 ##############################
-# World options
+# Misc
 
-parser.add_argument("--world_vqae_nb_epochs", type=int, default=25)
+parser.add_argument("--mixing_hard", action="store_true", default=False)
 
 ######################################################################
 
@@ -171,102 +176,94 @@ if args.result_dir is None:
 ######################################################################
 
 default_task_args = {
+    "addition": {
+        "model": "352M",
+        "batch_size": 25,
+        "nb_train_samples": 250000,
+        "nb_test_samples": 10000,
+    },
     "byheart": {
         "model": "37M",
-        "nb_epochs": 2,
         "batch_size": 25,
         "nb_train_samples": 50000,
         "nb_test_samples": 10000,
     },
-    "learnop": {
+    "expr": {
+        "model": "352M",
+        "batch_size": 25,
+        "nb_train_samples": 2500000,
+        "nb_test_samples": 10000,
+    },
+    "grid": {
         "model": "37M",
-        "nb_epochs": 15,
         "batch_size": 25,
-        "nb_train_samples": 50000,
+        "nb_train_samples": 250000,
         "nb_test_samples": 10000,
     },
+    "qmlp": {
+        "model": "37M",
+        "batch_size": 10,
+        "nb_train_samples": 100000,
+        "nb_test_samples": 1000,
+    },
     "guessop": {
         "model": "352M",
-        "nb_epochs": 5,
         "batch_size": 25,
         "nb_train_samples": 1000000,
         "nb_test_samples": 10000,
     },
-    "twotargets": {
+    "learnop": {
         "model": "37M",
-        "nb_epochs": 10,
         "batch_size": 25,
         "nb_train_samples": 50000,
         "nb_test_samples": 10000,
     },
-    "addition": {
-        "model": "352M",
-        "nb_epochs": 50,
-        "batch_size": 25,
-        "nb_train_samples": 250000,
+    "maze": {
+        "model": "37M",
+        "batch_size": 5,
+        "nb_train_samples": 100000,
         "nb_test_samples": 10000,
     },
     "picoclvr": {
         "model": "37M",
-        "nb_epochs": 25,
         "batch_size": 25,
         "nb_train_samples": 250000,
         "nb_test_samples": 10000,
     },
-    "mnist": {
-        "model": "37M",
-        "nb_epochs": 25,
-        "batch_size": 10,
-        "nb_train_samples": 60000,
-        "nb_test_samples": 10000,
-    },
-    "maze": {
-        "model": "37M",
-        "nb_epochs": 25,
+    "rpl": {
+        "model": "352M",
         "batch_size": 5,
-        "nb_train_samples": 100000,
+        "nb_train_samples": 2500000,
         "nb_test_samples": 10000,
     },
     "snake": {
         "model": "37M",
-        "nb_epochs": 5,
         "batch_size": 25,
         "nb_train_samples": 250000,
         "nb_test_samples": 10000,
     },
     "stack": {
         "model": "37M",
-        "nb_epochs": 15,
         "batch_size": 25,
         "nb_train_samples": 100000,
         "nb_test_samples": 1000,
     },
-    "expr": {
-        "model": "352M",
-        "nb_epochs": 25,
+    "twotargets": {
+        "model": "37M",
         "batch_size": 25,
-        "nb_train_samples": 2500000,
-        "nb_test_samples": 10000,
-    },
-    "rpl": {
-        "model": "122M",
-        "nb_epochs": 50,
-        "batch_size": 5,
-        "nb_train_samples": 1000000,
+        "nb_train_samples": 50000,
         "nb_test_samples": 10000,
     },
-    "world": {
+    "mixing": {
         "model": "37M",
-        "nb_epochs": 10,
         "batch_size": 25,
-        "nb_train_samples": 25000,
-        "nb_test_samples": 1000,
+        "nb_train_samples": 250000,
+        "nb_test_samples": 10000,
     },
-    "grid": {
+    "mnist": {
         "model": "37M",
-        "nb_epochs": 25,
-        "batch_size": 25,
-        "nb_train_samples": 250000,
+        "batch_size": 10,
+        "nb_train_samples": 60000,
         "nb_test_samples": 10000,
     },
 }
@@ -417,6 +414,16 @@ elif args.task == "twotargets":
         device=device,
     )
 
+elif args.task == "mixing":
+    task = tasks.SandBox(
+        problem=problems.ProblemMixing(hard=args.mixing_hard),
+        nb_train_samples=args.nb_train_samples,
+        nb_test_samples=args.nb_test_samples,
+        batch_size=args.batch_size,
+        logger=log_string,
+        device=device,
+    )
+
 elif args.task == "addition":
     task = tasks.SandBox(
         problem=problems.ProblemAddition(),
@@ -517,18 +524,17 @@ elif args.task == "grid":
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
         batch_size=args.batch_size,
-        height=args.picoclvr_height,
-        width=args.picoclvr_width,
+        size=args.grid_size,
         logger=log_string,
         device=device,
     )
 
-elif args.task == "world":
-    task = tasks.World(
+elif args.task == "qmlp":
+    task = tasks.QMLP(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
         batch_size=args.batch_size,
-        vqae_nb_epochs=args.world_vqae_nb_epochs,
+        result_dir=args.result_dir,
         logger=log_string,
         device=device,
     )