def compute_error(
self, model, split="train", nb_to_use=-1, deterministic_synthesis=False
):
+ model_device = next(model.parameters()).device
nb_total, nb_correct = 0, 0
count = torch.zeros(
self.width * self.height,
self.width * self.height,
- device=self.device,
+ device=model_device,
dtype=torch.int64,
)
for input in self.batches(split, nb_to_use):
+ input = input.to(model_device)
result = input.clone()
ar_mask = result.new_zeros(result.size())
ar_mask[:, self.height * self.width :] = 1
eol = " " if j < count.size(1) - 1 else "\n"
f.write(f"{count[i,j]}{eol}")
- input = self.test_input[:48]
+ input = self.test_input[:48].to(next(model.parameters()).device)
result = input.clone()
ar_mask = result.new_zeros(result.size())
ar_mask[:, self.height * self.width :] = 1
device=self.device,
)
+ #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+ for label, input in [
+ ("train", self.train_input[:32]),
+ ("test", self.test_input[:32]),
+ ]:
+ output = model(BracketedSequence(input)).x
+ output = output.log_softmax(dim=-1)
+ filename = os.path.join(
+ result_dir, f"stack_with_crossentropy_{n_epoch:04d}_{label}.txt"
+ )
+ with open(filename, "w") as f:
+ for n in range(input.size(0)):
+ s = stack.seq_to_str(
+ input[n], nb_stacks=self.nb_stacks, nb_digits=self.nb_digits
+ )
+ for t, k, w in zip(range(input[n].size(0)), input[n], s.split(" ")):
+ u = (
+ " " * (10 - len(w))
+ + w
+ + " "
+ + str(output[n][t][k].exp().item())
+ + "\n"
+ )
+ f.write(u)
+ f.write("\n")
+ logger(f"wrote {filename}")
+ #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+
for n in range(result.size(0)):
logger(
f"test_after {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"