X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=reasoning.py;h=5499bdfd6985081a2f8db5bf1ace853fcc354768;hb=30c76210e3ed2704b2a059208f385cb623c1486d;hp=cd726cb83011e5f566d3764f8e156d09afdd4b3f;hpb=cc241681730e50ad149a68c612e3a06f2d4a71be;p=culture.git

diff --git a/reasoning.py b/reasoning.py
index cd726cb..5499bdf 100755
--- a/reasoning.py
+++ b/reasoning.py
@@ -87,6 +87,7 @@ class Reasoning(problem.Problem):
         answers,
         predicted_prompts=None,
         predicted_answers=None,
+        nrow=4,
     ):
         prompts = prompts.reshape(prompts.size(0), self.height, -1)
         answers = answers.reshape(answers.size(0), self.height, -1)
@@ -114,9 +115,13 @@ class Reasoning(problem.Problem):
                 y[...] = c
             else:
                 c = c.long()[:, None]
-                c = c * torch.tensor([192, 192, 192], device=c.device) + (
-                    1 - c
-                ) * torch.tensor([255, 255, 255], device=c.device)
+                c = (
+                    (1 - ((c == 1).long() + (c == 0).long() + (c == -1).long()))
+                    * torch.tensor([192, 192, 192], device=c.device)
+                    + (c == 1).long() * torch.tensor([0, 255, 0], device=c.device)
+                    + (c == 0).long() * torch.tensor([255, 255, 255], device=c.device)
+                    + (c == -1).long() * torch.tensor([255, 0, 0], device=c.device)
+                )
                 y[...] = c[:, :, None, None]
 
             y[:, :, di : di + x.size(2), dj : dj + x.size(3)] = x
@@ -186,7 +191,11 @@ class Reasoning(problem.Problem):
 
         image_name = os.path.join(result_dir, filename)
         torchvision.utils.save_image(
-            img.float() / 255.0, image_name, nrow=4, padding=margin * 4, pad_value=1.0
+            img.float() / 255.0,
+            image_name,
+            nrow=nrow,
+            padding=margin * 4,
+            pad_value=1.0,
         )
 
     ######################################################################
@@ -581,8 +590,8 @@ class Reasoning(problem.Problem):
 
     ######################################################################
 
-    def generate_prompts_and_answers(self, nb, device="cpu"):
-        tasks = [
+    def all_tasks(self):
+        return [
             self.task_replace_color,
             self.task_translate,
             self.task_grow,
@@ -594,6 +603,11 @@ class Reasoning(problem.Problem):
             self.task_bounce,
             self.task_scale,
         ]
+
+    def generate_prompts_and_answers(self, nb, tasks=None, device="cpu"):
+        if tasks is None:
+            tasks = self.all_tasks()
+
         prompts = torch.zeros(nb, self.height, self.width * 3, dtype=torch.int64)
         answers = torch.zeros(nb, self.height, self.width, dtype=torch.int64)
         w = self.width
@@ -621,6 +635,7 @@ class Reasoning(problem.Problem):
         answers,
         predicted_prompts=None,
         predicted_answers=None,
+        nrow=4,
     ):
         self.save_image(
             result_dir,
@@ -629,6 +644,7 @@ class Reasoning(problem.Problem):
             answers,
             predicted_prompts,
             predicted_answers,
+            nrow,
         )
 
 
@@ -637,22 +653,32 @@ class Reasoning(problem.Problem):
 if __name__ == "__main__":
     import time
 
+    nb = 4
+
     reasoning = Reasoning()
 
+    for t in reasoning.all_tasks():
+        print(t.__name__)
+        prompts, answers = reasoning.generate_prompts_and_answers(nb, tasks=[t])
+        reasoning.save_quizzes("/tmp", t.__name__, prompts[:nb], answers[:nb], nrow=1)
+
+    exit(0)
+
     start_time = time.perf_counter()
-    prompts, answers = reasoning.generate_prompts_and_answers(100)
+    prompts, answers = reasoning.generate_prompts_and_answers(nb)
     delay = time.perf_counter() - start_time
     print(f"{prompts.size(0)/delay:02f} seq/s")
 
-    predicted_prompts = torch.rand(prompts.size(0)) < 0.5
-    predicted_answers = torch.logical_not(predicted_prompts)
+    # m = torch.randint(2, (prompts.size(0),))
+    # predicted_prompts = m * (torch.randint(2, (prompts.size(0),)) * 2 - 1)
+    # predicted_answers = (1 - m) * (torch.randint(2, (prompts.size(0),)) * 2 - 1)
 
     reasoning.save_quizzes(
         "/tmp",
         "test",
-        prompts[:64],
-        answers[:64],
+        prompts[:nb],
+        answers[:nb],
         # You can add a bool to put a frame around the predicted parts
-        # predicted_prompts[:64],
-        # predicted_answers[:64],
+        # predicted_prompts[:nb],
+        # predicted_answers[:nb],
     )