Update.

[picoclvr.git] / snake.py

diff --git a/snake.py b/snake.py
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+++ b/snake.py
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+#!/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
+import torch.nn.functional as F
+
+
+def generate_sequences(
+    nb, height, width, nb_colors, length, prompt_length, device=torch.device("cpu")
+):
+    worlds = torch.randint(nb_colors, (nb, height, width), device=device)
+    nb_prior_visits = torch.zeros(nb, height, width, device=device)
+
+    # nb x 2
+    snake_position = torch.cat(
+        (
+            torch.randint(height, (nb, 1), device=device),
+            torch.randint(width, (nb, 1), device=device),
+        ),
+        1,
+    )
+    snake_direction = torch.randint(4, (nb,), device=device)
+    sequences = torch.empty(nb, 2 * length, device=device, dtype=torch.int64)
+    sequences_prior_visits = torch.zeros(
+        nb, 2 * length, device=device, dtype=torch.int64
+    )
+    i = torch.arange(nb, device=device)  # [:,None]
+
+    for l in range(length):
+        # nb x 3
+        snake_next_direction = torch.cat(
+            (
+                (snake_direction[:, None] - 1) % 4,
+                snake_direction[:, None],
+                (snake_direction[:, None] + 1) % 4,
+            ),
+            1,
+        )
+
+        # nb x 3
+        vh = (snake_next_direction + 1) % 2 * (snake_next_direction - 1)
+        vw = snake_next_direction % 2 * (snake_next_direction - 2)
+
+        # nb x 3 x 2
+        snake_next_speed = torch.cat((vh[:, :, None], vw[:, :, None]), 2)
+        snake_next_position = snake_position[:, None, :] + snake_next_speed
+
+        # nb x 3
+        val = torch.logical_and(
+            torch.logical_and(
+                snake_next_position[:, :, 0] >= 0, snake_next_position[:, :, 0] < height
+            ),
+            torch.logical_and(
+                snake_next_position[:, :, 1] >= 0, snake_next_position[:, :, 1] < width
+            ),
+        ).float()
+        val = (
+            # The multiplicative factors bias toward moving forward
+            torch.rand_like(val)
+            * val
+            * torch.tensor([[1.0, 2.0, 1.0]], device=device)
+        )
+
+        # nb
+        j = val.argmax(1)
+        snake_direction = snake_next_direction[i, j]
+
+        sequences[:, 2 * l] = worlds[i, snake_position[:, 0], snake_position[:, 1]] + 4
+        sequences_prior_visits[:, 2 * l] = nb_prior_visits[
+            i, snake_position[:, 0], snake_position[:, 1]
+        ]
+        if l < prompt_length:
+            nb_prior_visits[i, snake_position[:, 0], snake_position[:, 1]] += 1
+        sequences[:, 2 * l + 1] = snake_direction
+
+        # nb x 2
+        snake_position = snake_next_position[i, j]
+
+    return sequences, sequences_prior_visits
+
+
+# generate_snake_sequences(nb=1, height=4, width=6, nb_colors=3, length=20)
+# exit(0)
+
+
+def solver(input, ar_mask):
+    for n in range(input.size(0)):
+        i, j, memory = 0, 0, {}
+        # print(input[n])
+        # print(ar_mask[n])
+        for l in range(input.size(1) // 2):
+            if ar_mask[n, 2 * l] == 1:
+                if memory.get((i, j)) is None:
+                    input[n, 2 * l] = -1
+                else:
+                    input[n, 2 * l] = memory[(i, j)]
+            else:
+                # print(f'@3 {memory=}')
+                if memory.get((i, j)) is None:
+                    memory[(i, j)] = input[n, 2 * l]
+                else:
+                    assert memory[(i, j)] == input[n, 2 * l], f"n={n} l={l}"
+            # print(f'@1 {i=} {j=}')
+            d = input[n, 2 * l + 1].item()
+            i += (d + 1) % 2 * (d - 1)
+            j += d % 2 * (d - 2)
+            # print(f'@2 {i=} {j=}')
+
+
+######################################################################
+
+if __name__ == "__main__":
+    for n in range(16):
+        descr = generate(nb=1, height=12, width=16)
+
+        print(nb_properties(descr, height=12, width=16))
+
+        with open(f"picoclvr_example_{n:02d}.txt", "w") as f:
+            for d in descr:
+                f.write(f"{d}\n\n")
+
+        img = descr2img(descr, height=12, width=16)
+        if img.size(0) == 1:
+            img = F.pad(img, (1, 1, 1, 1), value=64)
+
+        torchvision.utils.save_image(
+            img / 255.0,
+            f"picoclvr_example_{n:02d}.png",
+            padding=1,
+            nrow=4,
+            pad_value=0.8,
+        )
+
+    import time
+
+    start_time = time.perf_counter()
+    descr = generate(nb=1000, height=12, width=16)
+    end_time = time.perf_counter()
+    print(f"{len(descr) / (end_time - start_time):.02f} samples per second")
+
+######################################################################