parser.add_argument("--maze_nb_walls", type=int, default=15)
+parser.add_argument("--oneshot_mode", type=str, default="head")
+
######################################################################
args = parser.parse_args()
def one_shot(gpt, task):
t = gpt.training
gpt.eval()
-
+ dim_in = args.dim_model * (args.nb_blocks * 2 if args.oneshot_mode == "deep" else 1)
model = nn.Sequential(
- nn.Linear(args.dim_model, args.dim_model),
+ nn.Linear(dim_in, args.dim_model),
nn.ReLU(),
nn.Linear(args.dim_model, args.dim_model),
nn.ReLU(),
acc_train_loss, nb_train_samples = 0, 0
for input, targets in task.policy_batches(split="train"):
- output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
+ output_gpt = gpt(mygpt.BracketedSequence(input), mode=args.oneshot_mode).x
output = model(output_gpt)
targets = targets * (input.unsqueeze(-1) == maze.v_empty)
output = output * (input.unsqueeze(-1) == maze.v_empty)
- loss = (output.softmax(-1) - targets).abs().max(-1).values.mean()
- # loss = (
- # -(output.log_softmax(-1) * targets).sum()
- # / (input == maze.v_empty).sum()
- # + targets.xlogy(targets).sum() / (input == maze.v_empty).sum()
- # )
+ # loss = (output.softmax(-1) - targets).abs().max(-1).values.mean()
+ loss = (
+ -(output.log_softmax(-1) * targets).sum()
+ / (input == maze.v_empty).sum()
+ )
acc_train_loss += loss.item() * input.size(0)
nb_train_samples += input.size(0)
acc_test_loss, nb_test_samples = 0, 0
for input, targets in task.policy_batches(split="test"):
- output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
+ output_gpt = gpt(mygpt.BracketedSequence(input), mode=args.oneshot_mode).x
output = model(output_gpt)
targets = targets * (input.unsqueeze(-1) == maze.v_empty)
output = output * (input.unsqueeze(-1) == maze.v_empty)
- loss = (output.softmax(-1) - targets).abs().max(-1).values.mean()
- # loss = (
- # -(output.log_softmax(-1) * targets).sum()
- # / (input == maze.v_empty).sum()
- # + targets.xlogy(targets).sum() / (input == maze.v_empty).sum()
- # )
+ # loss = (output.softmax(-1) - targets).abs().max(-1).values.mean()
+ loss = (
+ -(output.log_softmax(-1) * targets).sum()
+ / (input == maze.v_empty).sum()
+ )
acc_test_loss += loss.item() * input.size(0)
nb_test_samples += input.size(0)
# -------------------
input = task.test_input[:32, : task.height * task.width]
targets = task.test_policies[:32]
- output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
+ output_gpt = gpt(mygpt.BracketedSequence(input), mode=args.oneshot_mode).x
output = model(output_gpt)
# losses = (-output.log_softmax(-1) * targets + targets.xlogy(targets)).sum(-1)
# losses = losses * (input == maze.v_empty)
# losses = losses / losses.max()
- losses = (output.softmax(-1) - targets).abs().max(-1).values
- losses = (losses >= 0.05).float()
+ # losses = (output.softmax(-1) - targets).abs().max(-1).values
+ # losses = (losses >= 0.05).float()
+ losses = (
+ (F.one_hot(output.argmax(-1), num_classes=4) * targets).sum(-1) == 0
+ ).float()
losses = losses.reshape(-1, args.maze_height, args.maze_width)
input = input.reshape(-1, args.maze_height, args.maze_width)
maze.save_image(
- os.path.join(args.result_dir, f"oneshot_{n_epoch:04d}.png"),
+ os.path.join(
+ args.result_dir, f"oneshot_{args.oneshot_mode}_{n_epoch:04d}.png"
+ ),
mazes=input,
score_paths=losses,
)