From: François Fleuret Date: Mon, 3 Jul 2023 09:26:22 +0000 (+0200) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=f91736e6e56152746b3c44342748b70ad1c89888;p=culture.git Update. --- diff --git a/main.py b/main.py index 4770a12..c1f4dc7 100755 --- a/main.py +++ b/main.py @@ -123,24 +123,8 @@ args = parser.parse_args() assert args.picocvlr_prune_properties in {"none", "train+eval", "eval"} -if args.result_dir is None: args.result_dir=f"results_{args.task}" - -try: - os.mkdir(args.result_dir) -except FileExistsError: - if not args.overwrite_results: - print(f"result directory {args.result_dir} already exists") - exit(1) - -log_file = open(os.path.join(args.result_dir, args.log_filename), "a") - -if args.seed >= 0: - # torch.backends.cudnn.deterministic = True - # torch.backends.cudnn.benchmark = False - # torch.use_deterministic_algorithms(True) - torch.manual_seed(args.seed) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(args.seed) +if args.result_dir is None: + args.result_dir = f"results_{args.task}" ###################################################################### @@ -184,6 +168,25 @@ if args.task in default_args: ###################################################################### +try: + os.mkdir(args.result_dir) +except FileExistsError: + if not args.overwrite_results: + print(f"result directory {args.result_dir} already exists") + exit(1) + +log_file = open(os.path.join(args.result_dir, args.log_filename), "a") + +if args.seed >= 0: + # torch.backends.cudnn.deterministic = True + # torch.backends.cudnn.benchmark = False + # torch.use_deterministic_algorithms(True) + torch.manual_seed(args.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(args.seed) + +###################################################################### + def log_string(s): t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime()) @@ -706,14 +709,14 @@ class TaskMaze(Task): model, "train", nb_to_use=1000 ) log_string( - f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%" + 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 ) log_string( - f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" + 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}%" ) if count is not None: @@ -859,7 +862,7 @@ class TaskSnake(Task): ) log_string( - f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" + 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}%" ) model.train(t) @@ -916,12 +919,10 @@ class TaskStack(Task): self.device, ) - mask = self.test_input.clone() - stack.remove_popped_values(mask, self.nb_stacks, self.nb_digits) - mask = mask != self.test_input - counts = self.test_stack_counts.flatten()[mask.flatten()] + i = torch.logical_and(self.test_input % 2 == 1, self.test_input < 2 * nb_stacks) + counts = self.test_stack_counts.flatten()[i.flatten()] counts = F.one_hot(counts).sum(0) - log_string(f"stack_count {counts}") + log_string(f"pop_stack_counts {counts}") self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1 @@ -966,13 +967,12 @@ class TaskStack(Task): test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000]) log_string( - f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" + 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}%" ) - #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! - l = 50 - l = l - l % (1 + self.nb_digits) - input = self.test_input[:10, :l] + ############################################################## + # 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() @@ -987,7 +987,7 @@ class TaskStack(Task): log_string( f"test_after {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}" ) - #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + ############################################################## model.train(t) diff --git a/stack.py b/stack.py index 3844161..543f04e 100755 --- a/stack.py +++ b/stack.py @@ -38,7 +38,7 @@ def generate_sequences( (stack_counts[k, st] - 1).clamp(min=0), ] stack[k, st, stack_counts[k, st]] = val_push - recorded_stack_counts[:, (1 + nb_digits) * t + 1] = stack_counts[k, st] + recorded_stack_counts[:, (1 + nb_digits) * t] = stack_counts[k, st] stack_counts[k[op == 0], st[op == 0]] += 1 stack_counts[k[op == 1], st[op == 1]] -= 1 result[:, (1 + nb_digits) * t] = st * 2 + op @@ -64,6 +64,8 @@ def seq_to_str(seq, nb_stacks, nb_digits, recorded_stack_counts=None): n_op = seq[(1 + nb_digits) * t] if t > 0: s += " " + if recorded_stack_counts is not None: + s += f"[{recorded_stack_counts[(1 + nb_digits)*t]}] " s += f"POP" if n_op % 2 == 1 else f"PSH" if nb_stacks > 1: s += f"_{n_op//2}" @@ -72,8 +74,6 @@ def seq_to_str(seq, nb_stacks, nb_digits, recorded_stack_counts=None): s += " ?" else: s += f" {seq[(1 + nb_digits) * t + 1 + d] - 2 * nb_stacks:1d}" - if recorded_stack_counts is not None: - s += f"[{recorded_stack_counts[(1 + nb_digits)*t+1]}] " return s @@ -89,8 +89,15 @@ if __name__ == "__main__": ) for n in range(min(10, seq.size(0))): - # print(seq_to_str(seq[n], recorded_stack_counts[n])) - print(seq_to_str(seq[n], nb_stacks=nb_stacks, nb_digits=nb_digits)) + print( + seq_to_str( + seq[n], + nb_stacks=nb_stacks, + nb_digits=nb_digits, + recorded_stack_counts=recorded_stack_counts[n], + ) + ) + # print(seq_to_str(seq[n], nb_stacks=nb_stacks, nb_digits=nb_digits)) print("-- PREPARED FOR TEST -----------------")