From: François Fleuret <francois@fleuret.org>
Date: Fri, 21 Jun 2024 06:29:04 +0000 (+0200)
Subject: Update.
X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=6bd776c5842485db888d81e756e22623e8dc949f;p=culture.git

Update.
---

diff --git a/tasks.py b/tasks.py
index 7894fcd..b4e6f67 100755
--- a/tasks.py
+++ b/tasks.py
@@ -500,38 +500,26 @@ class World(Task):
 
         logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}")
 
-        if save_attention_image is not None:
-            for k in range(10):
-                ns = torch.randint(self.test_input.size(0), (1,)).item()
-                input = self.test_input[ns : ns + 1].clone()
+        ##############################
 
-                with torch.autograd.no_grad():
-                    t = model.training
-                    model.eval()
-                    # model.record_attention(True)
-                    model(BracketedSequence(input))
-                    model.train(t)
-                    # ram = model.retrieve_attention()
-                    # model.record_attention(False)
+        input, ar_mask = self.test_input[:64], self.test_ar_mask[:64]
+        result = input.clone() * (1 - ar_mask)
 
-                # tokens_output = [c for c in self.problem.seq2str(input[0])]
-                # tokens_input = ["n/a"] + tokens_output[:-1]
-                # for n_head in range(ram[0].size(1)):
-                # filename = os.path.join(
-                # result_dir, f"sandbox_attention_{k}_h{n_head}.pdf"
-                # )
-                # attention_matrices = [m[0, n_head] for m in ram]
-                # save_attention_image(
-                # filename,
-                # tokens_input,
-                # tokens_output,
-                # attention_matrices,
-                # k_top=10,
-                ##min_total_attention=0.9,
-                # token_gap=12,
-                # layer_gap=50,
-                # )
-                # logger(f"wrote {filename}")
+        masked_inplace_autoregression(
+            model,
+            self.batch_size,
+            result,
+            ar_mask,
+            deterministic_synthesis,
+            progress_bar_desc=None,
+            device=self.device,
+        )
+
+        img = world.sample2img(result.to("cpu"), self.height, self.width)
+
+        image_name = os.path.join(result_dir, f"world_result_{n_epoch:04d}.png")
+        torchvision.utils.save_image(img.float() / 255.0, image_name, nrow=8, padding=2)
+        logger(f"wrote {image_name}")
 
 
 ######################################################################
diff --git a/world.py b/world.py
index 0392940..ac201e7 100755
--- a/world.py
+++ b/world.py
@@ -27,76 +27,75 @@ colors = torch.tensor(
     ]
 )
 
-token2char = "_X01234>"
+token2char = "_X" + "".join([str(n) for n in range(len(colors) - 2)]) + ">"
 
 
 def generate(
     nb,
     height,
     width,
-    obj_length=6,
-    mask_height=3,
-    mask_width=3,
-    nb_obj=3,
+    max_nb_obj=len(colors) - 2,
+    nb_iterations=2,
 ):
-    intact = torch.zeros(nb, height, width, dtype=torch.int64)
-    n = torch.arange(intact.size(0))
+    f_start = torch.zeros(nb, height, width, dtype=torch.int64)
+    f_end = torch.zeros(nb, height, width, dtype=torch.int64)
+    n = torch.arange(f_start.size(0))
 
     for n in range(nb):
-        for c in torch.randperm(colors.size(0) - 2)[:nb_obj] + 2:
-            z = intact[n].flatten()
-            m = (torch.rand(z.size()) * (z == 0)).argmax(dim=0)
-            i, j = m // width, m % width
+        nb_fish = torch.randint(max_nb_obj, (1,)).item() + 1
+        for c in range(nb_fish):
+            i, j = (
+                torch.randint(height - 2, (1,))[0] + 1,
+                torch.randint(width - 2, (1,))[0] + 1,
+            )
             vm = torch.randint(4, (1,))[0]
             vi, vj = (vm // 2) * (2 * (vm % 2) - 1), (1 - vm // 2) * (2 * (vm % 2) - 1)
-            for l in range(obj_length):
-                intact[n, i, j] = c
+
+            f_start[n, i, j] = c + 2
+            f_start[n, i - vi, j - vj] = c + 2
+            f_start[n, i + vj, j - vi] = c + 2
+            f_start[n, i - vj, j + vi] = c + 2
+
+            for l in range(nb_iterations):
                 i += vi
                 j += vj
-                if i < 0 or i >= height or j < 0 or j >= width or intact[n, i, j] != 0:
+                if i < 0 or i >= height or j < 0 or j >= width:
                     i -= vi
                     j -= vj
-                    vi, vj = -vj, vi
+                    vi, vj = -vi, -vj
                     i += vi
                     j += vj
-                    if (
-                        i < 0
-                        or i >= height
-                        or j < 0
-                        or j >= width
-                        or intact[n, i, j] != 0
-                    ):
-                        break
 
-    masked = intact.clone()
-
-    for n in range(nb):
-        i = torch.randint(height - mask_height + 1, (1,))[0]
-        j = torch.randint(width - mask_width + 1, (1,))[0]
-        masked[n, i : i + mask_height, j : j + mask_width] = 1
+            f_end[n, i, j] = c + 2
+            f_end[n, i - vi, j - vj] = c + 2
+            f_end[n, i + vj, j - vi] = c + 2
+            f_end[n, i - vj, j + vi] = c + 2
 
     return torch.cat(
         [
-            masked.flatten(1),
-            torch.full((masked.size(0), 1), len(colors)),
-            intact.flatten(1),
+            f_end.flatten(1),
+            torch.full((f_end.size(0), 1), len(colors)),
+            f_start.flatten(1),
         ],
         dim=1,
     )
 
 
 def sample2img(seq, height, width):
-    intact = seq[:, : height * width].reshape(-1, height, width)
-    masked = seq[:, height * width + 1 :].reshape(-1, height, width)
-    img_intact, img_masked = colors[intact], colors[masked]
+    f_start = seq[:, : height * width].reshape(-1, height, width)
+    f_start = (f_start >= len(colors)).long() + (f_start < len(colors)).long() * f_start
+    f_end = seq[:, height * width + 1 :].reshape(-1, height, width)
+    f_end = (f_end >= len(colors)).long() + (f_end < len(colors)).long() * f_end
+
+    img_f_start, img_f_end = colors[f_start], colors[f_end]
 
     img = torch.cat(
         [
-            img_intact,
+            img_f_start,
             torch.full(
-                (img_intact.size(0), img_intact.size(1), 1, img_intact.size(3)), 1
+                (img_f_start.size(0), img_f_start.size(1), 1, img_f_start.size(3)), 1
             ),
-            img_masked,
+            img_f_end,
         ],
         dim=2,
     )