X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=inline;f=quiz_machine.py;h=c39bf7adbf2fc30ddb155578ebcd7b29b245ec6f;hb=2f87c91cf606a068de1450d198660de7e44cd356;hp=ae146147ca1f753f327943b1494811081d261be1;hpb=4a63b2b44bc08cb04b236b35a3d36aa242912d48;p=culture.git

diff --git a/quiz_machine.py b/quiz_machine.py
index ae14614..c39bf7a 100755
--- a/quiz_machine.py
+++ b/quiz_machine.py
@@ -241,7 +241,7 @@ class QuizMachine:
         self.train_c_quizzes = []
         self.test_c_quizzes = []
 
-    def save_quizzes(
+    def save_quiz_illustrations(
         self,
         result_dir,
         filename_prefix,
@@ -266,7 +266,7 @@ class QuizMachine:
             predicted_prompts *= 2
             predicted_answers *= 2
 
-        self.problem.save_quizzes(
+        self.problem.save_quiz_illustrations(
             result_dir,
             filename_prefix,
             quizzes[:, 1 : 1 + self.prompt_len],
@@ -368,11 +368,7 @@ class QuizMachine:
                 backward_nb_total = correct[n_backward].size(0)
 
                 self.logger(
-                    f"{log_prefix}_forward_accuracy {n_epoch} model {model.id} nb_correct {forward_nb_correct} / {forward_nb_total} ({forward_nb_correct*100/forward_nb_total} %)"
-                )
-
-                self.logger(
-                    f"{log_prefix}_backward_accuracy {n_epoch} model {model.id} nb_correct {backward_nb_correct} / {backward_nb_total} ({backward_nb_correct*100/backward_nb_total} %)"
+                    f"{log_prefix}_accuracy {n_epoch} model {model.id} forward {forward_nb_correct} / {forward_nb_total} backward {backward_nb_correct} / {backward_nb_total}"
                 )
 
             return result, correct
@@ -388,7 +384,7 @@ class QuizMachine:
 
         ##############################
 
-        self.save_quizzes(
+        self.save_quiz_illustrations(
             result_dir,
             f"culture_prediction_{n_epoch:04d}_{model.id:02d}",
             quizzes=test_result[:72],
@@ -416,25 +412,37 @@ class QuizMachine:
             else:
                 self.test_c_quizzes.append(new_c_quizzes.to("cpu"))
 
+    def save_c_quizzes(self, filename):
+        torch.save((self.train_c_quizzes, self.test_c_quizzes), filename)
+
+    def load_c_quizzes(self, filename):
+        self.train_c_quizzes, self.test_c_quizzes = torch.load(filename)
+
     ######################################################################
 
     def logproba_of_solutions(self, models, c_quizzes):
         logproba = c_quizzes.new_zeros(
-            c_quizzes.size(0), len(models), device=self.device
+            c_quizzes.size(0), len(models), device=self.device, dtype=torch.float32
         )
 
         for model in models:
-            for input, l in zip(
-                c_quizzes.split(self.batch_size), logproba.split(self.batch_size)
-            ):
-                input = input.to(self.device)
-                ar_mask = self.make_ar_mask(input)
-                output = model(mygpt.BracketedSequence(input)).x
-                ce = (
-                    F.cross_entropy(output.transpose(1, 2), input, reduction="none")
-                    * ar_mask
-                )
-                l[:, model.id] = -ce.sum(dim=-1)
+            with torch.autograd.no_grad():
+                t = model.training
+                model.eval()
+
+                for input, l in zip(
+                    c_quizzes.split(self.batch_size), logproba.split(self.batch_size)
+                ):
+                    input = input.to(self.device)
+                    ar_mask = self.make_ar_mask(input)
+                    output = model(mygpt.BracketedSequence(input)).x
+                    ce = (
+                        F.cross_entropy(output.transpose(1, 2), input, reduction="none")
+                        * ar_mask
+                    )
+                    l[:, model.id] = -ce.sum(dim=-1)
+
+                model.train(t)
 
         return logproba.to("cpu")