X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=world.py;h=aad0bfb9727a3757dd90a0bfcb56e74040c6e011;hb=d2844d7a2d09ef38dc6f62d5e131059cccc872c5;hp=bac9e761e248bb64547aada2bac7109f0099d38d;hpb=e38b98574f1966ea3a91ffb8fd9042f10a75ca88;p=picoclvr.git

diff --git a/world.py b/world.py
index bac9e76..aad0bfb 100755
--- a/world.py
+++ b/world.py
@@ -1,6 +1,11 @@
 #!/usr/bin/env python
 
-import math
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
+
+import math, sys, tqdm
 
 import torch, torchvision
 
@@ -8,8 +13,12 @@ from torch import nn
 from torch.nn import functional as F
 import cairo
 
+######################################################################
+
 
 class Box:
+    nb_rgb_levels = 10
+
     def __init__(self, x, y, w, h, r, g, b):
         self.x = x
         self.y = y
@@ -30,7 +39,189 @@ class Box:
         return False
 
 
-def scene2tensor(xh, yh, scene, size=512):
+######################################################################
+
+
+class Normalizer(nn.Module):
+    def __init__(self, mu, std):
+        super().__init__()
+        self.register_buffer("mu", mu)
+        self.register_buffer("log_var", 2 * torch.log(std))
+
+    def forward(self, x):
+        return (x - self.mu) / torch.exp(self.log_var / 2.0)
+
+
+class SignSTE(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        # torch.sign() takes three values
+        s = (x >= 0).float() * 2 - 1
+
+        if self.training:
+            u = torch.tanh(x)
+            return s + u - u.detach()
+        else:
+            return s
+
+
+class DiscreteSampler2d(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        s = (x >= x.max(-3, keepdim=True).values).float()
+
+        if self.training:
+            u = x.softmax(dim=-3)
+            return s + u - u.detach()
+        else:
+            return s
+
+
+def loss_H(binary_logits, h_threshold=1):
+    p = binary_logits.sigmoid().mean(0)
+    h = (-p.xlogy(p) - (1 - p).xlogy(1 - p)) / math.log(2)
+    h.clamp_(max=h_threshold)
+    return h_threshold - h.mean()
+
+
+def train_encoder(
+    train_input,
+    test_input,
+    depth,
+    nb_bits_per_token,
+    dim_hidden=48,
+    lambda_entropy=0.0,
+    lr_start=1e-3,
+    lr_end=1e-4,
+    nb_epochs=10,
+    batch_size=25,
+    logger=None,
+    device=torch.device("cpu"),
+):
+    mu, std = train_input.float().mean(), train_input.float().std()
+
+    def encoder_core(depth, dim):
+        l = [
+            [
+                nn.Conv2d(
+                    dim * 2**k, dim * 2**k, kernel_size=5, stride=1, padding=2
+                ),
+                nn.ReLU(),
+                nn.Conv2d(dim * 2**k, dim * 2 ** (k + 1), kernel_size=2, stride=2),
+                nn.ReLU(),
+            ]
+            for k in range(depth)
+        ]
+
+        return nn.Sequential(*[x for m in l for x in m])
+
+    def decoder_core(depth, dim):
+        l = [
+            [
+                nn.ConvTranspose2d(
+                    dim * 2 ** (k + 1), dim * 2**k, kernel_size=2, stride=2
+                ),
+                nn.ReLU(),
+                nn.ConvTranspose2d(
+                    dim * 2**k, dim * 2**k, kernel_size=5, stride=1, padding=2
+                ),
+                nn.ReLU(),
+            ]
+            for k in range(depth - 1, -1, -1)
+        ]
+
+        return nn.Sequential(*[x for m in l for x in m])
+
+    encoder = nn.Sequential(
+        Normalizer(mu, std),
+        nn.Conv2d(3, dim_hidden, kernel_size=1, stride=1),
+        nn.ReLU(),
+        # 64x64
+        encoder_core(depth=depth, dim=dim_hidden),
+        # 8x8
+        nn.Conv2d(dim_hidden * 2**depth, nb_bits_per_token, kernel_size=1, stride=1),
+    )
+
+    quantizer = SignSTE()
+
+    decoder = nn.Sequential(
+        nn.Conv2d(nb_bits_per_token, dim_hidden * 2**depth, kernel_size=1, stride=1),
+        # 8x8
+        decoder_core(depth=depth, dim=dim_hidden),
+        # 64x64
+        nn.ConvTranspose2d(dim_hidden, 3 * Box.nb_rgb_levels, kernel_size=1, stride=1),
+    )
+
+    model = nn.Sequential(encoder, decoder)
+
+    nb_parameters = sum(p.numel() for p in model.parameters())
+
+    logger(f"vqae nb_parameters {nb_parameters}")
+
+    model.to(device)
+
+    for k in range(nb_epochs):
+        lr = math.exp(
+            math.log(lr_start) + math.log(lr_end / lr_start) / (nb_epochs - 1) * k
+        )
+        optimizer = torch.optim.Adam(model.parameters(), lr=lr)
+
+        acc_train_loss = 0.0
+
+        for input in tqdm.tqdm(train_input.split(batch_size), desc="vqae-train"):
+            input = input.to(device)
+            z = encoder(input)
+            zq = quantizer(z)
+            output = decoder(zq)
+
+            output = output.reshape(
+                output.size(0), -1, 3, output.size(2), output.size(3)
+            )
+
+            train_loss = F.cross_entropy(output, input)
+
+            if lambda_entropy > 0:
+                train_loss = train_loss + lambda_entropy * loss_H(z, h_threshold=0.5)
+
+            acc_train_loss += train_loss.item() * input.size(0)
+
+            optimizer.zero_grad()
+            train_loss.backward()
+            optimizer.step()
+
+        acc_test_loss = 0.0
+
+        for input in tqdm.tqdm(test_input.split(batch_size), desc="vqae-test"):
+            input = input.to(device)
+            z = encoder(input)
+            zq = quantizer(z)
+            output = decoder(zq)
+
+            output = output.reshape(
+                output.size(0), -1, 3, output.size(2), output.size(3)
+            )
+
+            test_loss = F.cross_entropy(output, input)
+
+            acc_test_loss += test_loss.item() * input.size(0)
+
+        train_loss = acc_train_loss / train_input.size(0)
+        test_loss = acc_test_loss / test_input.size(0)
+
+        logger(f"vqae train {k} lr {lr} train_loss {train_loss} test_loss {test_loss}")
+        sys.stdout.flush()
+
+    return encoder, quantizer, decoder
+
+
+######################################################################
+
+
+def scene2tensor(xh, yh, scene, size):
     width, height = size, size
     pixel_map = torch.ByteTensor(width, height, 4).fill_(255)
     data = pixel_map.numpy()
@@ -47,12 +238,15 @@ def scene2tensor(xh, yh, scene, size=512):
         ctx.rel_line_to(0, b.h * size)
         ctx.rel_line_to(-b.w * size, 0)
         ctx.close_path()
-        ctx.set_source_rgba(b.r, b.g, b.b, 1.0)
-        ctx.fill_preserve()
-        ctx.set_source_rgba(0, 0, 0, 1.0)
-        ctx.stroke()
+        ctx.set_source_rgba(
+            b.r / (Box.nb_rgb_levels - 1),
+            b.g / (Box.nb_rgb_levels - 1),
+            b.b / (Box.nb_rgb_levels - 1),
+            1.0,
+        )
+        ctx.fill()
 
-    hs = size * 0.05
+    hs = size * 0.1
     ctx.set_source_rgba(0.0, 0.0, 0.0, 1.0)
     ctx.move_to(xh * size - hs / 2, yh * size - hs / 2)
     ctx.rel_line_to(hs, 0)
@@ -61,20 +255,31 @@ def scene2tensor(xh, yh, scene, size=512):
     ctx.close_path()
     ctx.fill()
 
-    return pixel_map[None, :, :, :3].flip(-1).permute(0, 3, 1, 2).float() / 255
+    return (
+        pixel_map[None, :, :, :3]
+        .flip(-1)
+        .permute(0, 3, 1, 2)
+        .long()
+        .mul(Box.nb_rgb_levels)
+        .floor_divide(256)
+    )
 
 
-def random_scene():
+def random_scene(nb_insert_attempts=3):
     scene = []
     colors = [
-        (1.00, 0.00, 0.00),
-        (0.00, 1.00, 0.00),
-        (0.00, 0.00, 1.00),
-        (1.00, 1.00, 0.00),
-        (0.75, 0.75, 0.75),
+        ((Box.nb_rgb_levels - 1), 0, 0),
+        (0, (Box.nb_rgb_levels - 1), 0),
+        (0, 0, (Box.nb_rgb_levels - 1)),
+        ((Box.nb_rgb_levels - 1), (Box.nb_rgb_levels - 1), 0),
+        (
+            (Box.nb_rgb_levels * 2) // 3,
+            (Box.nb_rgb_levels * 2) // 3,
+            (Box.nb_rgb_levels * 2) // 3,
+        ),
     ]
 
-    for k in range(10):
+    for k in range(nb_insert_attempts):
         wh = torch.rand(2) * 0.2 + 0.2
         xy = torch.rand(2) * (1 - wh)
         c = colors[torch.randint(len(colors), (1,))]
@@ -87,7 +292,7 @@ def random_scene():
     return scene
 
 
-def sequence(length=10):
+def generate_episode(steps, size=64):
     delta = 0.1
     effects = [
         (False, 0, 0),
@@ -102,17 +307,21 @@ def sequence(length=10):
     ]
 
     while True:
+        frames = []
+
         scene = random_scene()
         xh, yh = tuple(x.item() for x in torch.rand(2))
 
-        frame_start = scene2tensor(xh, yh, scene)
+        actions = torch.randint(len(effects), (len(steps),))
+        nb_changes = 0
+
+        for s, a in zip(steps, actions):
+            if s:
+                frames.append(scene2tensor(xh, yh, scene, size=size))
 
-        actions = torch.randint(len(effects), (length,))
-        change = False
+            grasp, dx, dy = effects[a]
 
-        for a in actions:
-            g, dx, dy = effects[a]
-            if g:
+            if grasp:
                 for b in scene:
                     if b.x <= xh and b.x + b.w >= xh and b.y <= yh and b.y + b.h >= yh:
                         x, y = b.x, b.y
@@ -129,7 +338,7 @@ def sequence(length=10):
                         else:
                             xh += dx
                             yh += dy
-                            change = True
+                            nb_changes += 1
             else:
                 x, y = xh, yh
                 xh += dx
@@ -137,14 +346,140 @@ def sequence(length=10):
                 if xh < 0 or xh > 1 or yh < 0 or yh > 1:
                     xh, yh = x, y
 
-        frame_end = scene2tensor(xh, yh, scene)
-        if change:
+        if nb_changes > len(steps) // 3:
             break
 
-    return frame_start, frame_end, actions
+    return frames, actions
+
+
+######################################################################
+
+
+def generate_episodes(nb, steps):
+    all_frames, all_actions = [], []
+    for n in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world-data"):
+        frames, actions = generate_episode(steps)
+        all_frames += frames
+        all_actions += [actions[None, :]]
+    return torch.cat(all_frames, 0).contiguous(), torch.cat(all_actions, 0)
 
 
+def create_data_and_processors(
+    nb_train_samples,
+    nb_test_samples,
+    mode,
+    nb_steps,
+    depth=3,
+    nb_bits_per_token=8,
+    nb_epochs=10,
+    device=torch.device("cpu"),
+    device_storage=torch.device("cpu"),
+    logger=None,
+):
+    assert mode in ["first_last"]
+
+    if mode == "first_last":
+        steps = [True] + [False] * (nb_steps + 1) + [True]
+
+    if logger is None:
+        logger = lambda s: print(s)
+
+    train_input, train_actions = generate_episodes(nb_train_samples, steps)
+    train_input, train_actions = train_input.to(device_storage), train_actions.to(
+        device_storage
+    )
+    test_input, test_actions = generate_episodes(nb_test_samples, steps)
+    test_input, test_actions = test_input.to(device_storage), test_actions.to(
+        device_storage
+    )
+
+    encoder, quantizer, decoder = train_encoder(
+        train_input,
+        test_input,
+        depth=depth,
+        nb_bits_per_token=nb_bits_per_token,
+        lambda_entropy=1.0,
+        nb_epochs=nb_epochs,
+        logger=logger,
+        device=device,
+    )
+    encoder.train(False)
+    quantizer.train(False)
+    decoder.train(False)
+
+    z = encoder(train_input[:1].to(device))
+    pow2 = (2 ** torch.arange(z.size(1), device=device))[None, None, :]
+    z_h, z_w = z.size(2), z.size(3)
+
+    logger(f"vqae input {train_input[0].size()} output {z[0].size()}")
+
+    def frame2seq(input, batch_size=25):
+        seq = []
+        p = pow2.to(device)
+        for x in input.split(batch_size):
+            x = x.to(device)
+            z = encoder(x)
+            ze_bool = (quantizer(z) >= 0).long()
+            output = (
+                ze_bool.permute(0, 2, 3, 1).reshape(
+                    ze_bool.size(0), -1, ze_bool.size(1)
+                )
+                * p
+            ).sum(-1)
+
+            seq.append(output)
+
+        return torch.cat(seq, dim=0)
+
+    def seq2frame(input, batch_size=25, T=1e-2):
+        frames = []
+        p = pow2.to(device)
+        for seq in input.split(batch_size):
+            seq = seq.to(device)
+            zd_bool = (seq[:, :, None] // p) % 2
+            zd_bool = zd_bool.reshape(zd_bool.size(0), z_h, z_w, -1).permute(0, 3, 1, 2)
+            logits = decoder(zd_bool * 2.0 - 1.0)
+            logits = logits.reshape(
+                logits.size(0), -1, 3, logits.size(2), logits.size(3)
+            ).permute(0, 2, 3, 4, 1)
+            output = torch.distributions.categorical.Categorical(
+                logits=logits / T
+            ).sample()
+
+            frames.append(output)
+
+        return torch.cat(frames, dim=0)
+
+    return train_input, train_actions, test_input, test_actions, frame2seq, seq2frame
+
+
+######################################################################
+
 if __name__ == "__main__":
-    frame_start, frame_end, actions = sequence()
-    torchvision.utils.save_image(frame_start, "world_start.png")
-    torchvision.utils.save_image(frame_end, "world_end.png")
+    (
+        train_input,
+        train_actions,
+        test_input,
+        test_actions,
+        frame2seq,
+        seq2frame,
+    ) = create_data_and_processors(
+        25000,
+        1000,
+        nb_epochs=5,
+        mode="first_last",
+        nb_steps=20,
+    )
+
+    input = test_input[:256]
+
+    seq = frame2seq(input)
+    output = seq2frame(seq)
+
+    torchvision.utils.save_image(
+        input.float() / (Box.nb_rgb_levels - 1), "orig.png", nrow=16
+    )
+
+    torchvision.utils.save_image(
+        output.float() / (Box.nb_rgb_levels - 1), "qtiz.png", nrow=16
+    )