From: François Fleuret <francois@fleuret.org>
Date: Mon, 19 Jun 2023 16:10:36 +0000 (+0200)
Subject: Update.
X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=c921b95d0ea5b94a893447fbd4792e5047ba6e99;p=picoclvr.git

Update.
---

diff --git a/main.py b/main.py
index 08afb66..ae42544 100755
--- a/main.py
+++ b/main.py
@@ -30,6 +30,8 @@ parser = argparse.ArgumentParser(
     description="An implementation of GPT with cache to solve a toy geometric reasoning task."
 )
 
+parser.add_argument("--task", type=str, default="picoclvr")
+
 parser.add_argument("--log_filename", type=str, default="train.log")
 
 parser.add_argument("--result_dir", type=str, default="results_default")
@@ -73,19 +75,28 @@ parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
 ##############################
 # picoclvr options
 
-parser.add_argument("--nb_colors", type=int, default=5)
+parser.add_argument("--picoclvr_nb_colors", type=int, default=5)
+
+parser.add_argument("--picoclvr_height", type=int, default=12)
+
+parser.add_argument("--picoclvr_width", type=int, default=16)
+
+parser.add_argument("--picocvlr_prune_properties", type=str, default="none")
+
+##############################
+# Maze options
 
-parser.add_argument("--height", type=int, default=12)
+parser.add_argument("--maze_height", type=int, default=13)
 
-parser.add_argument("--width", type=int, default=16)
+parser.add_argument("--maze_width", type=int, default=21)
 
-parser.add_argument("--prune_properties", type=str, default="none")
+parser.add_argument("--maze_nb_walls", type=int, default=15)
 
 ######################################################################
 
 args = parser.parse_args()
 
-assert args.prune_properties in {"none", "train+eval", "eval"}
+assert args.picocvlr_prune_properties in {"none", "train+eval", "eval"}
 
 try:
     os.mkdir(args.result_dir)
@@ -311,8 +322,12 @@ class TaskPicoCLVR(Task):
             "rng_state": list(torch.get_rng_state()),
         }
 
-        log_string(f"generating {nb_train_samples+nb_test_samples} samples (can take some time)")
-        self.train_descr = generate_descr(nb_train_samples, "train", pruner=self.pruner_train)
+        log_string(
+            f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
+        )
+        self.train_descr = generate_descr(
+            nb_train_samples, "train", pruner=self.pruner_train
+        )
         self.test_descr = generate_descr(nb_test_samples, "test", pruner=None)
 
         # Build the tokenizer
@@ -445,29 +460,200 @@ class TaskPicoCLVR(Task):
 
 ######################################################################
 
-log_string(f"device {device}")
+import maze
+
 
+class TaskMaze(Task):
+    def map2seq(self, *m):
+        return torch.cat([x.flatten(1) for x in m], 1)
 
-def pruner_horizontal_green(p):
+    def seq2map(self, s):
+        s = s.reshape(s.size(0), -1, self.height, self.width)
+        return (s[:, k] for k in range(s.size(1)))
+
+    def __init__(
+        self,
+        nb_train_samples,
+        nb_test_samples,
+        batch_size,
+        height,
+        width,
+        nb_walls,
+        device=torch.device("cpu"),
+    ):
+        self.batch_size = batch_size
+        self.height = height
+        self.width = width
+        self.device = device
+
+        train_mazes, train_paths, train_policies = maze.create_maze_data(
+            nb_train_samples,
+            height=height,
+            width=width,
+            nb_walls=nb_walls,
+            progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
+        )
+        self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
+        self.train_policies = train_policies.flatten(-2).to(device)
+
+        test_mazes, test_paths, test_policies = maze.create_maze_data(
+            nb_test_samples,
+            height=height,
+            width=width,
+            nb_walls=nb_walls,
+            progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
+        )
+        self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
+        self.test_policies = test_policies.flatten(-2).to(device)
+
+        self.nb_codes = self.train_input.max() + 1
+
+    def batches(self, split="train", nb_to_use=-1, desc=None):
+        assert split in {"train", "test"}
+        input = self.train_input if split == "train" else self.test_input
+        if nb_to_use > 0:
+            input = input[:nb_to_use]
+        if desc is None:
+            desc = f"epoch-{split}"
+        for batch in tqdm.tqdm(
+            input.split(self.batch_size), dynamic_ncols=True, desc=desc
+        ):
+            yield batch
+
+    def policy_batches(self, split="train", nb_to_use=-1, desc=None):
+        assert split in {"train", "test"}
+        input = self.train_input if split == "train" else self.test_input
+        policies = self.train_policies if split == "train" else self.test_policies
+        input = input[:, : self.height * self.width]
+        policies = policies * (input != maze.v_wall)[:, None]
+
+        if nb_to_use > 0:
+            input = input[:nb_to_use]
+            policies = policies[:nb_to_use]
+
+        if desc is None:
+            desc = f"epoch-{split}"
+        for batch in tqdm.tqdm(
+            zip(input.split(self.batch_size), policies.split(self.batch_size)),
+            dynamic_ncols=True,
+            desc=desc,
+        ):
+            yield batch
+
+    def vocabulary_size(self):
+        return self.nb_codes
+
+    def compute_error(self, model, split="train", nb_to_use=-1):
+        nb_total, nb_correct = 0, 0
+        for input in task.batches(split, nb_to_use):
+            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
+            )
+            mazes, paths = self.seq2map(result)
+            nb_correct += maze.path_correctness(mazes, paths).long().sum()
+            nb_total += mazes.size(0)
+
+        return nb_total, nb_correct
+
+    def produce_results(self, n_epoch, model):
+        with torch.autograd.no_grad():
+            t = model.training
+            model.eval()
+
+            train_nb_total, train_nb_correct = self.compute_error(
+                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}%"
+            )
+
+            test_nb_total, test_nb_correct = 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}%"
+            )
+
+            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, device=self.device
+            )
+
+            mazes, paths = self.seq2map(input)
+            _, predicted_paths = self.seq2map(result)
+            filename = f"result_{n_epoch:04d}.png"
+            maze.save_image(
+                os.path.join(args.result_dir, filename),
+                mazes=mazes,
+                target_paths=paths,
+                predicted_paths=predicted_paths,
+                path_correct=maze.path_correctness(mazes, predicted_paths),
+            )
+            log_string(f"wrote {filename}")
+
+            model.train(t)
+
+
+######################################################################
+
+
+def picoclvr_pruner_horizontal_green(p):
     return not ("green" in p and ("left" in p or "right" in p))
 
 
-task = TaskPicoCLVR(
-    nb_train_samples=args.nb_train_samples,
-    nb_test_samples=args.nb_test_samples,
-    batch_size=args.batch_size,
-    height=args.height,
-    width=args.width,
-    nb_colors=args.nb_colors,
-    device=device,
-    pruner_train=pruner_horizontal_green
-    if args.prune_properties in {"train+eval"}
-    else None,
-    pruner_eval=(lambda p: not pruner_horizontal_green(p))
-    if args.prune_properties in {"train+eval", "eval"}
-    else None,
+picoclvr_pruner_train = (
+    picoclvr_pruner_horizontal_green
+    if args.picocvlr_prune_properties in {"train+eval"}
+    else None
+)
+
+picoclvr_pruner_eval = (
+    (lambda p: not picoclvr_pruner_horizontal_green(p))
+    if args.picocvlr_prune_properties in {"train+eval", "eval"}
+    else None
 )
 
+######################################################################
+
+if args.task == "picoclvr":
+    task = TaskPicoCLVR(
+        nb_train_samples=args.nb_train_samples,
+        nb_test_samples=args.nb_test_samples,
+        batch_size=args.batch_size,
+        height=args.picoclvr_height,
+        width=args.picoclvr_width,
+        nb_colors=args.picoclvr_nb_colors,
+        device=device,
+        pruner_train=picoclvr_pruner_train,
+        pruner_eval=picoclvr_pruner_eval,
+    )
+
+elif args.task == "maze":
+    task = TaskMaze(
+        nb_train_samples=args.nb_train_samples,
+        nb_test_samples=args.nb_test_samples,
+        batch_size=args.batch_size,
+        height=args.maze_height,
+        width=args.maze_width,
+        nb_walls=args.maze_nb_walls,
+        device=device,
+    )
+
+else:
+    raise ValueError(f"Unknown task {args.task}")
+
+######################################################################
+
+log_string(f"device {device}")
+
 vocabulary_size = task.vocabulary_size()
 
 log_string(f"vocabulary_size {vocabulary_size}")
diff --git a/maze.py b/maze.py
new file mode 100755
index 0000000..81afcd9
--- /dev/null
+++ b/maze.py
@@ -0,0 +1,311 @@
+#!/usr/bin/env python
+
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
+
+import torch, torchvision
+
+######################################################################
+
+v_empty, v_wall, v_start, v_goal, v_path = 0, 1, 2, 3, 4
+
+
+def create_maze(h=11, w=17, nb_walls=8):
+    a, k = 0, 0
+
+    while k < nb_walls:
+        while True:
+            if a == 0:
+                m = torch.zeros(h, w, dtype=torch.int64)
+                m[0, :] = 1
+                m[-1, :] = 1
+                m[:, 0] = 1
+                m[:, -1] = 1
+
+            r = torch.rand(4)
+
+            if r[0] <= 0.5:
+                i1, i2, j = (
+                    int((r[1] * h).item()),
+                    int((r[2] * h).item()),
+                    int((r[3] * w).item()),
+                )
+                i1, i2, j = i1 - i1 % 2, i2 - i2 % 2, j - j % 2
+                i1, i2 = min(i1, i2), max(i1, i2)
+                if i2 - i1 > 1 and i2 - i1 <= h / 2 and m[i1 : i2 + 1, j].sum() <= 1:
+                    m[i1 : i2 + 1, j] = 1
+                    break
+            else:
+                i, j1, j2 = (
+                    int((r[1] * h).item()),
+                    int((r[2] * w).item()),
+                    int((r[3] * w).item()),
+                )
+                i, j1, j2 = i - i % 2, j1 - j1 % 2, j2 - j2 % 2
+                j1, j2 = min(j1, j2), max(j1, j2)
+                if j2 - j1 > 1 and j2 - j1 <= w / 2 and m[i, j1 : j2 + 1].sum() <= 1:
+                    m[i, j1 : j2 + 1] = 1
+                    break
+            a += 1
+
+            if a > 10 * nb_walls:
+                a, k = 0, 0
+
+        k += 1
+
+    return m
+
+
+######################################################################
+
+
+def compute_distance(walls, goal_i, goal_j):
+    max_length = walls.numel()
+    dist = torch.full_like(walls, max_length)
+
+    dist[goal_i, goal_j] = 0
+    pred_dist = torch.empty_like(dist)
+
+    while True:
+        pred_dist.copy_(dist)
+        d = (
+            torch.cat(
+                (
+                    dist[None, 1:-1, 0:-2],
+                    dist[None, 2:, 1:-1],
+                    dist[None, 1:-1, 2:],
+                    dist[None, 0:-2, 1:-1],
+                ),
+                0,
+            ).min(dim=0)[0]
+            + 1
+        )
+
+        dist[1:-1, 1:-1] = torch.min(dist[1:-1, 1:-1], d)
+        dist = walls * max_length + (1 - walls) * dist
+
+        if dist.equal(pred_dist):
+            return dist * (1 - walls)
+
+
+######################################################################
+
+
+def compute_policy(walls, goal_i, goal_j):
+    distance = compute_distance(walls, goal_i, goal_j)
+    distance = distance + walls.numel() * walls
+
+    value = distance.new_full((4,) + distance.size(), walls.numel())
+    value[0, :, 1:] = distance[:, :-1]  # <
+    value[1, :, :-1] = distance[:, 1:]  # >
+    value[2, 1:, :] = distance[:-1, :]  # ^
+    value[3, :-1, :] = distance[1:, :]  # v
+
+    proba = (value.min(dim=0)[0][None] == value).float()
+    proba = proba / proba.sum(dim=0)[None]
+    proba = proba * (1 - walls) + walls.float() / 4
+
+    return proba
+
+
+def stationary_densities(mazes, policies):
+    policies = policies * (mazes != v_goal)[:, None]
+    start = (mazes == v_start).nonzero(as_tuple=True)
+    probas = mazes.new_zeros(mazes.size(), dtype=torch.float32)
+    pred_probas = probas.clone()
+    probas[start] = 1.0
+
+    while not pred_probas.equal(probas):
+        pred_probas.copy_(probas)
+        probas.zero_()
+        probas[:, 1:, :] += pred_probas[:, :-1, :] * policies[:, 3, :-1, :]
+        probas[:, :-1, :] += pred_probas[:, 1:, :] * policies[:, 2, 1:, :]
+        probas[:, :, 1:] += pred_probas[:, :, :-1] * policies[:, 1, :, :-1]
+        probas[:, :, :-1] += pred_probas[:, :, 1:] * policies[:, 0, :, 1:]
+        probas[start] = 1.0
+
+    return probas
+
+
+######################################################################
+
+
+def mark_path(walls, i, j, goal_i, goal_j, policy):
+    action = torch.distributions.categorical.Categorical(
+        policy.permute(1, 2, 0)
+    ).sample()
+    n, nmax = 0, walls.numel()
+    while i != goal_i or j != goal_j:
+        di, dj = [(0, -1), (0, 1), (-1, 0), (1, 0)][action[i, j]]
+        i, j = i + di, j + dj
+        assert walls[i, j] == 0
+        walls[i, j] = v_path
+        n += 1
+        assert n < nmax
+
+
+def path_correctness(mazes, paths):
+    still_ok = (mazes - (paths * (paths < 4))).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()
+    while not pred_current.equal(current):
+        pred_current.copy_(current)
+        u = (current == v_start).long()
+        possible_next = (
+            u[:, 2:, 1:-1] + u[:, 0:-2, 1:-1] + u[:, 1:-1, 2:] + u[:, 1:-1, 0:-2] > 0
+        ).long()
+        u = u[:, 1:-1, 1:-1]
+        reached += ((goal[:, 1:-1, 1:-1] * possible_next).sum((1, 2)) == 1) * (
+            (current == v_path).sum((1, 2)) == 0
+        )
+        current[:, 1:-1, 1:-1] = (1 - u) * current[:, 1:-1, 1:-1] + (
+            v_start - v_path
+        ) * (possible_next * (current[:, 1:-1, 1:-1] == v_path))
+        still_ok *= (current == v_start).sum((1, 2)) <= 1
+
+    return still_ok * reached
+
+
+######################################################################
+
+
+def create_maze_data(
+    nb, height=11, width=17, nb_walls=8, dist_min=10, progress_bar=lambda x: x
+):
+    mazes = torch.empty(nb, height, width, dtype=torch.int64)
+    paths = torch.empty(nb, height, width, dtype=torch.int64)
+    policies = torch.empty(nb, 4, height, width)
+
+    for n in progress_bar(range(nb)):
+        maze = create_maze(height, width, nb_walls)
+        i = (maze == v_empty).nonzero()
+        while True:
+            start, goal = i[torch.randperm(i.size(0))[:2]]
+            if (start - goal).abs().sum() >= dist_min:
+                break
+        start_i, start_j, goal_i, goal_j = start[0], start[1], goal[0], goal[1]
+
+        policy = compute_policy(maze, goal_i, goal_j)
+        path = maze.clone()
+        mark_path(path, start_i, start_j, goal_i, goal_j, policy)
+        maze[start_i, start_j] = v_start
+        maze[goal_i, goal_j] = v_goal
+        path[start_i, start_j] = v_start
+        path[goal_i, goal_j] = v_goal
+
+        mazes[n] = maze
+        paths[n] = path
+        policies[n] = policy
+
+    return mazes, paths, policies
+
+
+######################################################################
+
+
+def save_image(
+    name,
+    mazes,
+    target_paths=None,
+    predicted_paths=None,
+    score_paths=None,
+    score_truth=None,
+    path_correct=None,
+):
+    colors = torch.tensor(
+        [
+            [255, 255, 255],  # empty
+            [0, 0, 0],  # wall
+            [0, 255, 0],  # start
+            [127, 127, 255],  # goal
+            [255, 0, 0],  # path
+        ]
+    )
+
+    mazes = mazes.cpu()
+
+    c_mazes = (
+        colors[mazes.reshape(-1)].reshape(mazes.size() + (-1,)).permute(0, 3, 1, 2)
+    )
+
+    if score_truth is not None:
+        score_truth = score_truth.cpu()
+        c_score_truth = score_truth.unsqueeze(1).expand(-1, 3, -1, -1)
+        c_score_truth = (
+            c_score_truth * colors[4].reshape(1, 3, 1, 1)
+            + (1 - c_score_truth) * colors[0].reshape(1, 3, 1, 1)
+        ).long()
+        c_mazes = (mazes.unsqueeze(1) != v_empty) * c_mazes + (
+            mazes.unsqueeze(1) == v_empty
+        ) * c_score_truth
+
+    imgs = c_mazes.unsqueeze(1)
+
+    if target_paths is not None:
+        target_paths = target_paths.cpu()
+
+        c_target_paths = (
+            colors[target_paths.reshape(-1)]
+            .reshape(target_paths.size() + (-1,))
+            .permute(0, 3, 1, 2)
+        )
+
+        imgs = torch.cat((imgs, c_target_paths.unsqueeze(1)), 1)
+
+    if predicted_paths is not None:
+        predicted_paths = predicted_paths.cpu()
+        c_predicted_paths = (
+            colors[predicted_paths.reshape(-1)]
+            .reshape(predicted_paths.size() + (-1,))
+            .permute(0, 3, 1, 2)
+        )
+        imgs = torch.cat((imgs, c_predicted_paths.unsqueeze(1)), 1)
+
+    if score_paths is not None:
+        score_paths = score_paths.cpu()
+        c_score_paths = score_paths.unsqueeze(1).expand(-1, 3, -1, -1)
+        c_score_paths = (
+            c_score_paths * colors[4].reshape(1, 3, 1, 1)
+            + (1 - c_score_paths) * colors[0].reshape(1, 3, 1, 1)
+        ).long()
+        c_score_paths = c_score_paths * (mazes.unsqueeze(1) == v_empty) + c_mazes * (
+            mazes.unsqueeze(1) != v_empty
+        )
+        imgs = torch.cat((imgs, c_score_paths.unsqueeze(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)
+    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[
+            :,
+            :,
+            1 : 1 + imgs.size(3),
+            1 + k * (1 + imgs.size(4)) : 1 + k * (1 + imgs.size(4)) + imgs.size(4),
+        ] = imgs[:, k]
+
+    img = img.float() / 255.0
+
+    torchvision.utils.save_image(img, name, nrow=4, padding=1, pad_value=224.0 / 256)
+
+
+######################################################################
+
+if __name__ == "__main__":
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    mazes, paths = create_maze_data(8)
+    mazes, paths = mazes.to(device), paths.to(device)
+    save_image("test.png", mazes, paths, paths)
+    print(path_correctness(mazes, paths))
+
+######################################################################