Update.

[culture.git] / quizz_machine.py

diff --git a/quizz_machine.py b/quizz_machine.py
index 28b94d1..f799bf1 100755 (executable)
--- a/quizz_machine.py
+++ b/quizz_machine.py
@@ -27,7 +27,7 @@ def masked_inplace_autoregression(
     deterministic_synthesis,
     forbidden_tokens=None,
     logit_biases=None,
-    progress_bar_desc="autoregression",
+    progress_bar_desc=None,
     device=torch.device("cpu"),
 ):
     assert input.size() == ar_mask.size()
@@ -66,8 +66,6 @@ def masked_inplace_autoregression(
 
 ######################################################################
 
-import sky
-
 
 class QuizzMachine:
     def make_ar_mask(self, input):
@@ -76,6 +74,7 @@ class QuizzMachine:
 
     def __init__(
         self,
+        problem,
         nb_train_samples,
         nb_test_samples,
         batch_size,
@@ -85,7 +84,7 @@ class QuizzMachine:
     ):
         super().__init__()
 
-        self.problem = sky.Sky(height=6, width=8, nb_birds=3, nb_iterations=2)
+        self.problem = problem
         self.batch_size = batch_size
         self.device = device
 
@@ -99,7 +98,7 @@ class QuizzMachine:
 
         if result_dir is not None:
             self.problem.save_quizzes(
-                self.train_w_quizzes[:72], result_dir, f"culture_w_quizzes", logger
+                self.train_w_quizzes[:72], result_dir, "culture_w_quizzes"
             )
 
     def batches(self, split="train", desc=None):
@@ -207,10 +206,7 @@ class QuizzMachine:
         )
 
         self.problem.save_quizzes(
-            result[:72],
-            result_dir,
-            f"culture_prediction_{n_epoch:04d}_{model.id:02d}",
-            logger,
+            result[:72], result_dir, f"culture_prediction_{n_epoch:04d}_{model.id:02d}"
         )
 
         return main_test_accuracy
@@ -229,13 +225,13 @@ class QuizzMachine:
 
     def create_c_quizzes(
         self,
+        nb,
+        model_for_generation,
+        models_for_validation,
+        min_ave_seq_logproba,
         n_epoch,
         result_dir,
         logger,
-        nb,
-        model,
-        other_models,
-        min_ave_seq_logproba,
     ):
         ###############################################################
         # Generate quizzes with model
@@ -254,37 +250,35 @@ class QuizzMachine:
             seq_logproba[...] = 0
 
             masked_inplace_autoregression(
-                model=model,
+                model=model_for_generation,
                 batch_size=self.batch_size,
                 input=c_quizzes,
                 ar_mask=ar_mask,
                 seq_logproba=seq_logproba,
                 temperature=temperature,
                 deterministic_synthesis=False,
-                progress_bar_desc="sampling c_quizzes",
+                # progress_bar_desc="sampling c_quizzes",
                 device=self.device,
             )
 
             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 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
-            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
             else:
                 break
 
-            logger(f"chaging temperature to {temperature}")
+            logger(f"changing temperature to {temperature}")
 
         ###############################################################
         # Create the reverse quizzes
@@ -309,18 +303,18 @@ class QuizzMachine:
 
         nb_correct = []
 
-        for m in other_models:
+        for model in models_for_validation:
             result = c_quizzes.clone()
 
             masked_inplace_autoregression(
-                model=m,
+                model=model,
                 batch_size=self.batch_size,
                 input=result,
                 ar_mask=ar_mask,
                 seq_logproba=seq_logproba,
                 temperature=1.0,
                 deterministic_synthesis=True,
-                progress_bar_desc="solving c_quizzes",
+                # progress_bar_desc="solving c_quizzes",
                 device=self.device,
             )
 
@@ -329,14 +323,14 @@ class QuizzMachine:
             reverse_result = reverse_c_quizzes.clone()
 
             masked_inplace_autoregression(
-                model=m,
+                model=model,
                 batch_size=self.batch_size,
                 input=reverse_result,
                 ar_mask=ar_mask,
                 seq_logproba=seq_logproba,
                 temperature=1.0,
                 deterministic_synthesis=True,
-                progress_bar_desc="solving reversed c_quizzes",
+                # progress_bar_desc="solving reversed c_quizzes",
                 device=self.device,
             )