Update.

[culture.git] / quizz_machine.py

diff --git a/quizz_machine.py b/quizz_machine.py
index a3da365..d63855c 100755 (executable)
--- a/quizz_machine.py
+++ b/quizz_machine.py
@@ -66,25 +66,15 @@ def masked_inplace_autoregression(
 
 ######################################################################
 
 
 ######################################################################
 

-import sky
-

 
 class QuizzMachine:
 
 class QuizzMachine:

-    def save_image(self, input, result_dir, filename, logger):
-        img = self.sky.seq2img(input.to("cpu"))
-        image_name = os.path.join(result_dir, filename)
-        torchvision.utils.save_image(img.float() / 255.0, image_name, nrow=6, padding=4)
-        logger(f"wrote {image_name}")
-
-    def save_quizzes(self, input, result_dir, filename_prefix, logger):
-        self.save_image(input, result_dir, filename_prefix + ".png", logger)
-

     def make_ar_mask(self, input):
         b = torch.arange(input.size(1), device=input.device) > input.size(1) // 2
         return b.long()[None, :].expand_as(input)
 
     def __init__(
         self,
     def make_ar_mask(self, input):
         b = torch.arange(input.size(1), device=input.device) > input.size(1) // 2
         return b.long()[None, :].expand_as(input)
 
     def __init__(
         self,

+        problem,

         nb_train_samples,
         nb_test_samples,
         batch_size,
         nb_train_samples,
         nb_test_samples,
         batch_size,


@@ -94,12 +84,12 @@ class QuizzMachine:
     ):
         super().__init__()
 
     ):
         super().__init__()
 

-        self.sky = sky.Sky(height=6, width=8, nb_birds=3, nb_iterations=2)
+        self.problem = problem

         self.batch_size = batch_size
         self.device = device
 
         self.batch_size = batch_size
         self.device = device
 

-        self.train_w_quizzes = self.sky.generate_seq(nb_train_samples).to(device)
-        self.test_w_quizzes = self.sky.generate_seq(nb_test_samples).to(device)
+        self.train_w_quizzes = self.problem.generate_seq(nb_train_samples).to(device)
+        self.test_w_quizzes = self.problem.generate_seq(nb_test_samples).to(device)

 
         self.nb_codes = max(self.train_w_quizzes.max(), self.test_w_quizzes.max()) + 1
 
 
         self.nb_codes = max(self.train_w_quizzes.max(), self.test_w_quizzes.max()) + 1
 


@@ -107,7 +97,7 @@ class QuizzMachine:
         self.test_c_quizzes = []
 
         if result_dir is not None:
         self.test_c_quizzes = []
 
         if result_dir is not None:

-            self.save_quizzes(
+            self.problem.save_quizzes(

                 self.train_w_quizzes[:72], result_dir, f"culture_w_quizzes", logger
             )
 
                 self.train_w_quizzes[:72], result_dir, f"culture_w_quizzes", logger
             )
 


@@ -215,7 +205,7 @@ class QuizzMachine:
             device=self.device,
         )
 
             device=self.device,
         )
 

-        self.save_quizzes(
+        self.problem.save_quizzes(

             result[:72],
             result_dir,
             f"culture_prediction_{n_epoch:04d}_{model.id:02d}",
             result[:72],
             result_dir,
             f"culture_prediction_{n_epoch:04d}_{model.id:02d}",


@@ -228,7 +218,7 @@ class QuizzMachine:
         input = self.train_w_quizzes if for_train else self.test_w_quizzes
         nb = min(nb, input.size(0))
         input[:-nb] = input[nb:].clone()
         input = self.train_w_quizzes if for_train else self.test_w_quizzes
         nb = min(nb, input.size(0))
         input[:-nb] = input[nb:].clone()

-        input[-nb:] = self.sky.generate_seq(nb).to(self.device)
+        input[-nb:] = self.problem.generate_seq(nb).to(self.device)

 
     def store_c_quizzes(self, new_c_quizzes, for_train=True):
         if for_train:
 
     def store_c_quizzes(self, new_c_quizzes, for_train=True):
         if for_train:


@@ -276,17 +266,15 @@ class QuizzMachine:
 
             ave_seq_logproba = seq_logproba.mean()
 
 
             ave_seq_logproba = seq_logproba.mean()
 

-            logger(f"{ave_seq_logproba=} {min_ave_seq_logproba=}")
-

             if min_ave_seq_logproba is None:
                 break
 
             # Oh man that's ugly
             if min_ave_seq_logproba is None:
                 break
 
             # Oh man that's ugly

-            if ave_seq_logproba < min_ave_seq_logproba * 1.1:
+            if ave_seq_logproba < min_ave_seq_logproba:

                 if d_temperature > 0:
                     d_temperature *= -1 / 3
                 temperature += d_temperature
                 if d_temperature > 0:
                     d_temperature *= -1 / 3
                 temperature += d_temperature

-            elif ave_seq_logproba > min_ave_seq_logproba:
+            elif ave_seq_logproba > min_ave_seq_logproba * 0.99:

                 if d_temperature < 0:
                     d_temperature *= -1 / 3
                 temperature += d_temperature
                 if d_temperature < 0:
                     d_temperature *= -1 / 3
                 temperature += d_temperature


@@ -298,11 +286,13 @@ class QuizzMachine:
         ###############################################################
         # Create the reverse quizzes
 
         ###############################################################
         # Create the reverse quizzes
 

+        token_forward, token_backward = self.problem.direction_tokens()
+

         l = (c_quizzes.size(1) - 1) // 2
         direction = c_quizzes[:, l : l + 1]
         l = (c_quizzes.size(1) - 1) // 2
         direction = c_quizzes[:, l : l + 1]

-        direction = self.sky.token_forward * (
-            direction == self.sky.token_backward
-        ) + self.sky.token_backward * (direction == self.sky.token_forward)
+        direction = self.problem.token_forward * (
+            direction == self.problem.token_backward
+        ) + self.problem.token_backward * (direction == self.problem.token_forward)

         reverse_c_quizzes = torch.cat(
             [c_quizzes[:, l + 1 :], direction, c_quizzes[:, :l]], dim=1
         )
         reverse_c_quizzes = torch.cat(
             [c_quizzes[:, l + 1 :], direction, c_quizzes[:, :l]], dim=1
         )