from torch import nn
from torch.nn import functional as F
+import ffutils
import mygpt, tasks
######################################################################
parser.add_argument(
"--task",
type=str,
- default="picoclvr",
- help="picoclvr, mnist, maze, snake, stack, expr",
+ default="sandbox",
+ help="sandbox, picoclvr, mnist, maze, snake, stack, expr, world",
)
parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
##############################
# picoclvr options
+parser.add_argument("--sandbox_level", type=int, default=0)
+
+parser.add_argument("--sandbox_levels_nb_items", type=int, default=25)
+
+parser.add_argument("--sandbox_levels_len_source", type=int, default=5)
+
+parser.add_argument("--sandbox_levels_len_result", type=int, default=8)
+
+##############################
+# picoclvr options
+
parser.add_argument("--picoclvr_nb_colors", type=int, default=5)
parser.add_argument("--picoclvr_height", type=int, default=12)
parser.add_argument("--snake_length", type=int, default=200)
##############################
-# Snake options
+# Stack options
parser.add_argument("--stack_nb_steps", type=int, default=100)
parser.add_argument("--expr_input_file", type=str, default=None)
+##############################
+# World options
+
+parser.add_argument("--world_vqae_nb_epochs", type=int, default=25)
+
######################################################################
args = parser.parse_args()
######################################################################
default_args = {
+ "sandbox": {
+ "nb_epochs": 10,
+ "batch_size": 25,
+ "nb_train_samples": 25000,
+ "nb_test_samples": 10000,
+ },
"picoclvr": {
"nb_epochs": 25,
"batch_size": 25,
"nb_train_samples": 1000000,
"nb_test_samples": 10000,
},
+ "world": {
+ "nb_epochs": 10,
+ "batch_size": 25,
+ "nb_train_samples": 25000,
+ "nb_test_samples": 1000,
+ },
}
if args.task in default_args:
######################################################################
-if args.task == "picoclvr":
+if args.task == "sandbox":
+ if args.sandbox_level == 0:
+ problem = tasks.ProblemLevel0(
+ nb_sentences=args.sandbox_levels_nb_items,
+ len_prompt=args.sandbox_levels_len_source,
+ len_result=args.sandbox_levels_len_result,
+ )
+ elif args.sandbox_level == 1:
+ problem = tasks.ProblemLevel1(
+ nb_operators=args.sandbox_levels_nb_items,
+ len_source=args.sandbox_levels_len_source,
+ len_result=args.sandbox_levels_len_result,
+ )
+ elif args.sandbox_level == 2:
+ problem = tasks.ProblemLevel2(
+ len_source=args.sandbox_levels_len_source,
+ len_result=args.sandbox_levels_len_result,
+ )
+ else:
+ raise ValueError(f"Unknown sandbox level {args.sandbox_level}")
+
+ task = tasks.SandBox(
+ problem,
+ # tasks.ProblemAddition(zero_padded=False, inverted_result=False),
+ 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 == "picoclvr":
task = tasks.PicoCLVR(
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
device=device,
)
+elif args.task == "world":
+ task = tasks.World(
+ 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,
+ logger=log_string,
+ device=device,
+ )
+
else:
raise ValueError(f"Unknown task {args.task}")