total=input.size(0) // batch_size,
)
- for input, ar_mask in batches:
- model.masked_inplace_autoregression(
- input, ar_mask, forbidden_tokens, deterministic_synthesis
- )
+ with torch.autograd.no_grad():
+ t = model.training
+ model.eval()
+
+ for input, ar_mask in batches:
+ model.masked_inplace_autoregression(
+ input, ar_mask, forbidden_tokens, deterministic_synthesis
+ )
+
+ model.train(t)
+
+
+######################################################################
class Task:
def produce_results(
self, n_epoch, model, result_dir, logger, deterministic_synthesis
):
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
-
- train_nb_total, train_nb_correct, count = self.compute_error(
- model,
- "train",
- nb_to_use=1000,
- deterministic_synthesis=deterministic_synthesis,
- )
- logger(
- f"accuracy_train {n_epoch} nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
- )
-
- test_nb_total, test_nb_correct, count = self.compute_error(
- model,
- "test",
- nb_to_use=1000,
- deterministic_synthesis=deterministic_synthesis,
- )
- logger(
- f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
- )
+ train_nb_total, train_nb_correct, count = self.compute_error(
+ model,
+ "train",
+ nb_to_use=1000,
+ deterministic_synthesis=deterministic_synthesis,
+ )
+ logger(
+ f"accuracy_train {n_epoch} nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
+ )
- if count is not None:
- proportion_optimal = count.diagonal().sum().float() / count.sum()
- logger(f"proportion_optimal_test {proportion_optimal*100:.02f}%")
- with open(
- os.path.join(result_dir, f"maze_result_{n_epoch:04d}.txt"), "w"
- ) as f:
- for i in range(count.size(0)):
- for j in range(count.size(1)):
- eol = " " if j < count.size(1) - 1 else "\n"
- f.write(f"{count[i,j]}{eol}")
-
- input = self.test_input[:48]
- result = input.clone()
- ar_mask = result.new_zeros(result.size())
- ar_mask[:, self.height * self.width :] = 1
- result *= 1 - ar_mask
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
+ test_nb_total, test_nb_correct, count = self.compute_error(
+ model,
+ "test",
+ nb_to_use=1000,
+ deterministic_synthesis=deterministic_synthesis,
+ )
+ logger(
+ f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
+ )
- mazes, paths = self.seq2map(input)
- _, predicted_paths = self.seq2map(result)
-
- filename = os.path.join(result_dir, f"maze_result_{n_epoch:04d}.png")
- maze.save_image(
- filename,
- mazes=mazes,
- target_paths=paths,
- predicted_paths=predicted_paths,
- path_correct=maze.path_correctness(mazes, predicted_paths),
- path_optimal=maze.path_optimality(paths, predicted_paths),
- )
- logger(f"wrote {filename}")
+ if count is not None:
+ proportion_optimal = count.diagonal().sum().float() / count.sum()
+ logger(f"proportion_optimal_test {proportion_optimal*100:.02f}%")
+ with open(
+ os.path.join(result_dir, f"maze_result_{n_epoch:04d}.txt"), "w"
+ ) as f:
+ for i in range(count.size(0)):
+ for j in range(count.size(1)):
+ eol = " " if j < count.size(1) - 1 else "\n"
+ f.write(f"{count[i,j]}{eol}")
+
+ input = self.test_input[:48]
+ result = input.clone()
+ ar_mask = result.new_zeros(result.size())
+ ar_mask[:, self.height * self.width :] = 1
+ result *= 1 - ar_mask
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
- model.train(t)
+ mazes, paths = self.seq2map(input)
+ _, predicted_paths = self.seq2map(result)
+
+ filename = os.path.join(result_dir, f"maze_result_{n_epoch:04d}.png")
+ maze.save_image(
+ filename,
+ mazes=mazes,
+ target_paths=paths,
+ predicted_paths=predicted_paths,
+ path_correct=maze.path_correctness(mazes, predicted_paths),
+ path_optimal=maze.path_optimality(paths, predicted_paths),
+ )
+ logger(f"wrote {filename}")
######################################################################
def produce_results(
self, n_epoch, model, result_dir, logger, deterministic_synthesis
):
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
-
- def compute_nb_correct(input, prior_visits):
- result = input.clone()
- i = torch.arange(result.size(1), device=result.device)[None, :]
- ar_mask = (
- torch.logical_and(i >= self.prompt_length * 2, i % 2 == 0)
- .long()
- .expand_as(result)
- )
- result *= 1 - ar_mask
-
- # snake.solver(result,ar_mask)
+ def compute_nb_correct(input, prior_visits):
+ result = input.clone()
+ i = torch.arange(result.size(1), device=result.device)[None, :]
+ ar_mask = (
+ torch.logical_and(i >= self.prompt_length * 2, i % 2 == 0)
+ .long()
+ .expand_as(result)
+ )
+ result *= 1 - ar_mask
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
+ # snake.solver(result,ar_mask)
- nb_total = ((prior_visits > 0) * ar_mask).sum()
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
- nb_correct = (
- (result == input).long() * (prior_visits > 0) * ar_mask
- ).sum()
+ nb_total = ((prior_visits > 0) * ar_mask).sum()
- # nb_total = result.size(0)
- # nb_correct = ((result - input).abs().sum(1) == 0).sum()
+ nb_correct = ((result == input).long() * (prior_visits > 0) * ar_mask).sum()
- return nb_total, nb_correct
+ # nb_total = result.size(0)
+ # nb_correct = ((result - input).abs().sum(1) == 0).sum()
- # train_nb_total, train_nb_correct = compute_nb_correct(
- # self.train_input, self.train_prior_visits
- # )
+ return nb_total, nb_correct
- # logger(
- # f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
- # )
+ # train_nb_total, train_nb_correct = compute_nb_correct(
+ # self.train_input, self.train_prior_visits
+ # )
- test_nb_total, test_nb_correct = compute_nb_correct(
- self.test_input[:1000], self.test_prior_visits[:1000]
- )
+ # logger(
+ # f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
+ # )
- logger(
- f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
- )
+ test_nb_total, test_nb_correct = compute_nb_correct(
+ self.test_input[:1000], self.test_prior_visits[:1000]
+ )
- model.train(t)
+ logger(
+ f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
+ )
######################################################################
def produce_results(
self, n_epoch, model, result_dir, logger, deterministic_synthesis
):
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
-
- def compute_nb_correct(input):
- result = input.clone()
- stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
- ar_mask = (result != input).long()
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
-
- errors = ((result != input).long() * ar_mask).reshape(
- -1, 1 + self.nb_digits
- )
- ar_mask = ar_mask.reshape(-1, 1 + self.nb_digits)
-
- nb_total = ar_mask.max(1).values.sum()
- nb_correct = nb_total - errors.max(1).values.sum()
-
- return nb_total, nb_correct
-
- test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
-
- logger(
- f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
- )
-
- ##############################################################
- # Log a few generated sequences
- input = self.test_input[:10, : 12 * (1 + self.nb_digits)]
+ def compute_nb_correct(input):
result = input.clone()
stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
ar_mask = (result != input).long()
-
- # for n in range(result.size(0)):
- # logger(
- # f"test_before {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
- # )
-
masked_inplace_autoregression(
model,
self.batch_size,
device=self.device,
)
- 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)}"
- )
- ##############################################################
+ errors = ((result != input).long() * ar_mask).reshape(
+ -1, 1 + self.nb_digits
+ )
+ ar_mask = ar_mask.reshape(-1, 1 + self.nb_digits)
+
+ nb_total = ar_mask.max(1).values.sum()
+ nb_correct = nb_total - errors.max(1).values.sum()
+
+ return nb_total, nb_correct
+
+ test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
+
+ logger(
+ f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
+ )
+
+ ##############################################################
+ # Log a few generated sequences
+ input = self.test_input[:10, : 12 * (1 + self.nb_digits)]
+ result = input.clone()
+ stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
+ ar_mask = (result != input).long()
+
+ # for n in range(result.size(0)):
+ # logger(
+ # f"test_before {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
+ # )
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
- model.train(t)
+ 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)}"
+ )
+ ##############################################################
######################################################################
nb_test_samples,
nb_variables,
sequence_length,
+ operand_max,
+ result_max,
batch_size,
device=torch.device("cpu"),
):
nb_train_samples,
nb_variables=nb_variables,
length=sequence_length,
+ operand_max=operand_max,
+ result_max=result_max,
)
test_sequences = expr.generate_sequences(
nb_test_samples,
nb_variables=nb_variables,
length=sequence_length,
+ operand_max=operand_max,
+ result_max=result_max,
)
symbols = list(set("#" + "".join(train_sequences + test_sequences)))
deterministic_synthesis,
input_file=None,
):
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
-
- def compute_nb_correct(input):
- result = input.clone()
- s = (result == self.space).long()
- ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
- result = (1 - ar_mask) * result + ar_mask * self.filler
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
+ def compute_nb_correct(input):
+ result = input.clone()
+ s = (result == self.space).long()
+ ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
+ result = (1 - ar_mask) * result + ar_mask * self.filler
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
- nb_total = input.size(0)
- nb_correct = (input == result).long().min(1).values.sum()
+ nb_total = input.size(0)
+ nb_correct = (input == result).long().min(1).values.sum()
- #######################################################################
- # Comput predicted vs. true variable values
+ #######################################################################
+ # Comput predicted vs. true variable values
- nb_delta = torch.zeros(5, dtype=torch.int64)
- nb_missed = 0
+ nb_delta = torch.zeros(5, dtype=torch.int64)
+ nb_missed = 0
- values_input = expr.extract_results([self.seq2str(s) for s in input])
- values_result = expr.extract_results([self.seq2str(s) for s in result])
+ values_input = expr.extract_results([self.seq2str(s) for s in input])
+ values_result = expr.extract_results([self.seq2str(s) for s in result])
- for i, r in zip(values_input, values_result):
- for n, vi in i.items():
- vr = r.get(n)
- if vr is None or vr < 0:
+ for i, r in zip(values_input, values_result):
+ for n, vi in i.items():
+ vr = r.get(n)
+ if vr is None or vr < 0:
+ nb_missed += 1
+ else:
+ d = abs(vr - vi)
+ if d >= nb_delta.size(0):
nb_missed += 1
else:
- d = abs(vr - vi)
- if d >= nb_delta.size(0):
- nb_missed += 1
- else:
- nb_delta[d] += 1
+ nb_delta[d] += 1
- ######################################################################
+ ######################################################################
- return nb_total, nb_correct, nb_delta, nb_missed
+ return nb_total, nb_correct, nb_delta, nb_missed
- (
- test_nb_total,
- test_nb_correct,
- test_nb_delta,
- test_nb_missed,
- ) = compute_nb_correct(self.test_input[:10000])
+ (
+ test_nb_total,
+ test_nb_correct,
+ test_nb_delta,
+ test_nb_missed,
+ ) = compute_nb_correct(self.test_input[:10000])
- logger(
- f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
- )
+ logger(
+ f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
+ )
- nb_total = test_nb_delta.sum() + test_nb_missed
- for d in range(test_nb_delta.size(0)):
- logger(
- f"error_value {n_epoch} delta {d} {test_nb_delta[d]} {test_nb_delta[d]*100/nb_total:.02f}%"
- )
+ nb_total = test_nb_delta.sum() + test_nb_missed
+ for d in range(test_nb_delta.size(0)):
logger(
- f"error_value {n_epoch} missed {test_nb_missed} {test_nb_missed*100/nb_total:.02f}%"
+ f"error_value {n_epoch} delta {d} {test_nb_delta[d]} {test_nb_delta[d]*100/nb_total:.02f}%"
)
+ logger(
+ f"error_value {n_epoch} missed {test_nb_missed} {test_nb_missed*100/nb_total:.02f}%"
+ )
- ##############################################################
- # Log a few generated sequences
- if input_file is None:
- input = self.test_input[:10]
- else:
- with open(input_file, "r") as f:
- sequences = [e.strip() for e in f.readlines()]
- sequences = [s + " " + "#" * 50 for s in sequences]
- input = self.tensorize(sequences)
-
- result = input.clone()
- s = (result == self.space).long()
- ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
- result = (1 - ar_mask) * result + ar_mask * self.filler
+ ##############################################################
+ # Log a few generated sequences
+ if input_file is None:
+ input = self.test_input[:10]
+ else:
+ with open(input_file, "r") as f:
+ sequences = [e.strip() for e in f.readlines()]
+ sequences = [s + " " + "#" * 50 for s in sequences]
+ input = self.tensorize(sequences)
- for n in range(result.size(0)):
- logger(f"test_before {self.seq2str(result[n])}")
+ result = input.clone()
+ s = (result == self.space).long()
+ ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
+ result = (1 - ar_mask) * result + ar_mask * self.filler
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
+ for n in range(result.size(0)):
+ logger(f"test_before {self.seq2str(result[n])}")
- correct = (1 - ar_mask) * self.space + ar_mask * input
- for n in range(result.size(0)):
- comment = "GOOD" if (result[n] - input[n]).abs().max() == 0 else ""
- logger(f"test_after {self.seq2str(result[n])} {comment}")
- logger(f"truth {self.seq2str(correct[n])}")
- ##############################################################
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
- model.train(t)
+ correct = (1 - ar_mask) * self.space + ar_mask * input
+ for n in range(result.size(0)):
+ comment = "GOOD" if (result[n] - input[n]).abs().max() == 0 else ""
+ logger(f"test_after {self.seq2str(result[n])} {comment}")
+ logger(f"truth {self.seq2str(correct[n])}")
+ ##############################################################
######################################################################
projects / picoclvr.git / blobdiff