parser.add_argument("--maze_nb_walls", type=int, default=15)
+##############################
+# one-shot prediction
+
parser.add_argument("--oneshot", action="store_true", default=False)
parser.add_argument("--oneshot_input", type=str, default="head")
-parser.add_argument("--oneshot_output", type=str, default="policy")
+parser.add_argument("--oneshot_output", type=str, default="trace")
######################################################################
######################################################################
-def oneshot_policy_loss(output, policies, mask):
- targets = policies.permute(0, 2, 1) * mask.unsqueeze(-1)
- output = output * mask.unsqueeze(-1)
- return -(output.log_softmax(-1) * targets).sum() / mask.sum()
+def oneshot_policy_loss(mazes, output, policies, height, width):
+ masks = (mazes == maze.v_empty).unsqueeze(-1)
+ targets = policies.permute(0, 2, 1) * masks
+ output = output * masks
+ return -(output.log_softmax(-1) * targets).sum() / masks.sum()
-# loss = (output.softmax(-1) - targets).abs().max(-1).values.mean()
+def oneshot_trace_loss(mazes, output, policies, height, width):
+ masks = mazes == maze.v_empty
+ targets = maze.stationary_densities(
+ mazes.view(-1, height, width), policies.view(-1, 4, height, width)
+ ).flatten(-2)
+ targets = targets * masks
+ output = output.squeeze(-1) * masks
+ return (output - targets).abs().sum() / masks.sum()
def oneshot(gpt, task):
compute_loss = oneshot_policy_loss
elif args.oneshot_output == "trace":
dim_out = 1
+ compute_loss = oneshot_trace_loss
else:
raise ValueError(f"{args.oneshot_output=}")
nn.ReLU(),
nn.Linear(args.dim_model, args.dim_model),
nn.ReLU(),
- nn.Linear(args.dim_model, 4),
+ nn.Linear(args.dim_model, dim_out),
).to(device)
for n_epoch in range(args.nb_epochs):
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
acc_train_loss, nb_train_samples = 0, 0
- for input, policies in task.policy_batches(split="train"):
- ####
- # print(f'{input.size()=} {policies.size()=}')
- # s = maze.stationary_densities(
- # exit(0)
- ####
- mask = input == maze.v_empty
- output_gpt = gpt(mygpt.BracketedSequence(input), mode=args.oneshot_input).x
+ for mazes, policies in task.policy_batches(split="train"):
+ output_gpt = gpt(mygpt.BracketedSequence(mazes), mode=args.oneshot_input).x
output = model(output_gpt)
- loss = compute_loss(output, policies, mask)
- acc_train_loss += loss.item() * input.size(0)
- nb_train_samples += input.size(0)
+ loss = compute_loss(mazes, output, policies, task.height, task.width)
+ acc_train_loss += loss.item() * mazes.size(0)
+ nb_train_samples += mazes.size(0)
optimizer.zero_grad()
loss.backward()
optimizer.step()
acc_test_loss, nb_test_samples = 0, 0
- for input, policies in task.policy_batches(split="test"):
- mask = input == maze.v_empty
- output_gpt = gpt(mygpt.BracketedSequence(input), mode=args.oneshot_input).x
+ for mazes, policies in task.policy_batches(split="test"):
+ output_gpt = gpt(mygpt.BracketedSequence(mazes), mode=args.oneshot_input).x
output = model(output_gpt)
- loss = compute_loss(output, policies, mask)
- acc_test_loss += loss.item() * input.size(0)
- nb_test_samples += input.size(0)
+ loss = compute_loss(mazes, output, policies, task.height, task.width)
+ acc_test_loss += loss.item() * mazes.size(0)
+ nb_test_samples += mazes.size(0)
log_string(
f"diff_ce {n_epoch} train {acc_train_loss/nb_train_samples} test {acc_test_loss/nb_test_samples}"
)
# -------------------
- input = task.test_input[:32, : task.height * task.width]
- targets = task.test_policies[:32].permute(0, 2, 1)
- output_gpt = gpt(mygpt.BracketedSequence(input), mode=args.oneshot_input).x
+ mazes = task.test_input[:32, : task.height * task.width]
+ policies = task.test_policies[:32]
+ output_gpt = gpt(mygpt.BracketedSequence(mazes), mode=args.oneshot_input).x
output = model(output_gpt)
- scores = (
- (F.one_hot(output.argmax(-1), num_classes=4) * targets).sum(-1) == 0
- ).float()
+ if args.oneshot_output == "policy":
+ targets = policies.permute(0, 2, 1)
+ scores = (
+ (F.one_hot(output.argmax(-1), num_classes=4) * targets).sum(-1) == 0
+ ).float()
+ elif args.oneshot_output == "trace":
+ targets = maze.stationary_densities(
+ mazes.view(-1, task.height, task.width),
+ policies.view(-1, 4, task.height, task.width),
+ ).flatten(-2)
+ scores = output
+ else:
+ raise ValueError(f"{args.oneshot_output=}")
+
scores = scores.reshape(-1, task.height, task.width)
- input = input.reshape(-1, task.height, task.width)
+ mazes = mazes.reshape(-1, task.height, task.width)
+ targets = targets.reshape(-1, task.height, task.width)
maze.save_image(
os.path.join(
args.result_dir,
f"oneshot_{args.oneshot_input}_{args.oneshot_output}_{n_epoch:04d}.png",
),
- mazes=input,
+ mazes=mazes,
score_paths=scores,
+ score_truth=targets,
)
# -------------------
##############################
-if args.oneshot:
- oneshot(model, task)
- exit(0)
-
-##############################
-
if nb_epochs_finished >= args.nb_epochs:
n_epoch = nb_epochs_finished
train_perplexity = compute_perplexity(model, split="train")
log_string(f"saved checkpoint {checkpoint_name}")
######################################################################
+
+if args.oneshot:
+ oneshot(model, task)
+
+######################################################################
projects / beaver.git / blobdiff