From: François Fleuret Date: Sun, 25 Jun 2023 08:49:38 +0000 (+0200) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=3c97745cdf9ae30a87903e3039e38c868e136d6e;p=picoclvr.git Update. --- diff --git a/main.py b/main.py index 784474f..0c2ff24 100755 --- a/main.py +++ b/main.py @@ -1,4 +1,4 @@ -!/usr/bin/env python +#!/usr/bin/env python # Any copyright is dedicated to the Public Domain. # https://creativecommons.org/publicdomain/zero/1.0/ @@ -511,7 +511,7 @@ class TaskPicoCLVR(Task): image_name = os.path.join(args.result_dir, f"picoclvr_result_{n_epoch:04d}.png") torchvision.utils.save_image( - img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=1.0 + img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=0.0 ) log_string(f"wrote {image_name}") @@ -622,15 +622,27 @@ class TaskMaze(Task): def compute_error(self, model, split="train", nb_to_use=-1): nb_total, nb_correct = 0, 0 count = torch.zeros( - self.width * self.height, self.width * self.height, device=self.device, dtype=torch.int64 + self.width * self.height, + self.width * self.height, + device=self.device, + dtype=torch.int64, ) - for input in task.batches(split, nb_to_use): + for input in tqdm.tqdm( + task.batches(split, nb_to_use), + dynamic_ncols=True, + desc=f"test-mazes", + ): 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, device=self.device + model, + self.batch_size, + result, + ar_mask, + progress_bar_desc=None, + device=self.device, ) mazes, paths = self.seq2map(result) path_correctness = maze.path_correctness(mazes, paths) @@ -705,6 +717,7 @@ class TaskMaze(Task): target_paths=paths, predicted_paths=predicted_paths, path_correct=maze.path_correctness(mazes, predicted_paths), + path_optimal=maze.path_optimality(paths, predicted_paths), ) log_string(f"wrote {filename}") diff --git a/maze.py b/maze.py index 81afcd9..fd0a1d2 100755 --- a/maze.py +++ b/maze.py @@ -146,8 +146,16 @@ def mark_path(walls, i, j, goal_i, goal_j, policy): assert n < nmax +def path_optimality(ref_paths, paths): + return (ref_paths == v_path).long().flatten(1).sum(1) == ( + paths == v_path + ).long().flatten(1).sum(1) + + def path_correctness(mazes, paths): - still_ok = (mazes - (paths * (paths < 4))).view(mazes.size(0), -1).abs().sum(1) == 0 + still_ok = (mazes - (paths * (paths != v_path))).view(mazes.size(0), -1).abs().sum( + 1 + ) == 0 reached = still_ok.new_zeros(still_ok.size()) current, pred_current = paths.clone(), paths.new_zeros(paths.size()) goal = (mazes == v_goal).long() @@ -214,6 +222,7 @@ def save_image( score_paths=None, score_truth=None, path_correct=None, + path_optimal=None, ): colors = torch.tensor( [ @@ -276,16 +285,26 @@ def save_image( ) imgs = torch.cat((imgs, c_score_paths.unsqueeze(1)), 1) + img = torch.tensor([224, 224, 224]).view(1, -1, 1, 1) + # NxKxCxHxW - if path_correct is None: - path_correct = torch.zeros(imgs.size(0)) <= 1 - path_correct = path_correct.cpu().long().view(-1, 1, 1, 1) - img = torch.tensor([224, 224, 224]).view(1, -1, 1, 1) * path_correct + torch.tensor( - [255, 0, 0] - ).view(1, -1, 1, 1) * (1 - path_correct) + if path_optimal is not None: + path_optimal = path_optimal.cpu().long().view(-1, 1, 1, 1) + img = ( + img * (1 - path_optimal) + + torch.tensor([0, 255, 0]).view(1, -1, 1, 1) * path_optimal + ) + + if path_correct is not None: + path_correct = path_correct.cpu().long().view(-1, 1, 1, 1) + img = img * path_correct + torch.tensor([255, 0, 0]).view(1, -1, 1, 1) * ( + 1 - path_correct + ) + img = img.expand( -1, -1, imgs.size(3) + 2, 1 + imgs.size(1) * (1 + imgs.size(4)) ).clone() + for k in range(imgs.size(1)): img[ :,