Update.

diff --git a/main.py b/main.py
index d412e6c..d194a8d 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -452,10 +452,8 @@ def create_c_quizzes(
         q = valid_c_quizzes(recorded, criteria=lambda nb_correct: nb_correct == n)[:72]
 
         if q.size(0) > 0:
         q = valid_c_quizzes(recorded, criteria=lambda nb_correct: nb_correct == n)[:72]
 
         if q.size(0) > 0:

-            quizz_machine.problem.save_quizzes(
-                q,
-                args.result_dir,
-                f"culture_c_quiz_{n_epoch:04d}_N{n}{s}",
+            quizz_machine.save_quizzes(
+                args.result_dir, f"culture_c_quiz_{n_epoch:04d}_N{n}{s}", q

             )
 
 
             )
 
 

diff --git a/problem.py b/problem.py
index 0795de1..0bc83a1 100755 (executable)
--- a/problem.py
+++ b/problem.py
@@ -7,15 +7,21 @@
 
 
 class Problem:
 
 
 class Problem:

-    # returns a nb x (L+1+L) long tensor where L is the length of one
-    # of the two states of a quizz
-    def generate_token_sequences(self, nb):
+    def nb_token_values(self):

         pass
 
         pass
 

-    # save a file to vizualize quizzes, you can save a txt or png file
-    def save_quizzes(self, input, result_dir, filename_prefix):
+    # returns two tensors nb x D and nb x D'
+    def generate_prompts_and_answers(self, nb):

         pass
 
         pass
 

-    # returns a pair (forward_tokens, backward_token)
-    def direction_tokens(self):
+    # save a file to vizualize quizzes, you can save a txt or png file
+    def save_quizzes(
+        self,
+        result_dir,
+        filename_prefix,
+        prompts,
+        answers,
+        predicted_prompt=None,
+        predicted_answers=None,
+    ):

         pass
         pass

diff --git a/quizz_machine.py b/quizz_machine.py
index 697f27e..5f19998 100755 (executable)
--- a/quizz_machine.py
+++ b/quizz_machine.py
@@ -119,6 +119,20 @@ class QuizzMachine:
         b = torch.arange(input.size(1), device=input.device) > input.size(1) // 2
         return b.long()[None, :].expand_as(input)
 
         b = torch.arange(input.size(1), device=input.device) > input.size(1) // 2
         return b.long()[None, :].expand_as(input)
 

+    def generate_token_sequences(self, nb):
+        prompts, answers = self.problem.generate_prompts_and_answers(nb)
+        result = []
+
+        for prompt, answer in zip(prompts, answers):
+            if torch.rand(1) < 0.5:
+                a = [torch.tensor([self.token_forward]), prompt, answer]
+            else:
+                a = [torch.tensor([self.token_backward]), answer, prompt]
+
+            result.append(torch.cat(a, dim=0)[None, :])
+
+        return torch.cat(result, dim=0)
+

     def __init__(
         self,
         problem,
     def __init__(
         self,
         problem,


@@ -131,29 +145,57 @@ class QuizzMachine:
     ):
         super().__init__()
 
     ):
         super().__init__()
 

+        v = problem.nb_token_values()
+        self.token_forward = v
+        self.token_backward = v + 1
+        self.nb_token_values = v + 2
+

         self.problem = problem
         self.batch_size = batch_size
         self.device = device
         self.logger = logger
 
         self.problem = problem
         self.batch_size = batch_size
         self.device = device
         self.logger = logger
 

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

             device
         )
 
             device
         )
 

-        self.nb_codes = max(self.train_w_quizzes.max(), self.test_w_quizzes.max()) + 1
+        self.test_w_quizzes = self.generate_token_sequences(nb_test_samples).to(device)

 
         self.train_c_quizzes = []
         self.test_c_quizzes = []
 
         if result_dir is not None:
 
         self.train_c_quizzes = []
         self.test_c_quizzes = []
 
         if result_dir is not None:

-            self.problem.save_quizzes(
-                self.train_w_quizzes[:72], result_dir, "culture_w_quizzes"
+            self.save_quizzes(
+                result_dir, "culture_w_quizzes", self.train_w_quizzes[:72]

             )
 
             )
 

+    def save_quizzes(self, result_dir, filename_prefix, quizzes, prediction=False):
+        print(f"DEBUG {quizzes.size()=}")
+        l = (quizzes.size(1) - 1) // 2
+        forward = (quizzes[:, 0] == self.token_forward).long()
+        backward = (quizzes[:, 0] == self.token_backward).long()
+        assert forward.equal(1 - backward)
+        first = quizzes[:, 1 : 1 + l]
+        second = quizzes[:, 1 + l : 1 + 2 * l]
+        prompts = forward[:, None] * first + backward[:, None] * second
+        answers = forward[:, None] * second + backward[:, None] * first
+
+        if prediction:
+            predicted_prompts = backward
+            predicted_answers = forward
+        else:
+            predicted_prompts = None
+            predicted_answers = None
+
+        self.problem.save_quizzes(
+            result_dir,
+            filename_prefix,
+            prompts,
+            answers,
+            predicted_prompts,
+            predicted_answers,
+        )
+

     def batches(self, split="train", desc=None):
         assert split in {"train", "test"}
         if split == "train":
     def batches(self, split="train", desc=None):
         assert split in {"train", "test"}
         if split == "train":


@@ -194,7 +236,7 @@ class QuizzMachine:
             yield batch
 
     def vocabulary_size(self):
             yield batch
 
     def vocabulary_size(self):

-        return self.nb_codes
+        return self.nb_token_values

 
     def produce_results(
         self, n_epoch, model, result_dir, deterministic_synthesis, nmax=1000
 
     def produce_results(
         self, n_epoch, model, result_dir, deterministic_synthesis, nmax=1000


@@ -258,8 +300,11 @@ class QuizzMachine:
             device=self.device,
         )
 
             device=self.device,
         )
 

-        self.problem.save_quizzes(
-            result[:72], result_dir, f"culture_prediction_{n_epoch:04d}_{model.id:02d}"
+        self.save_quizzes(
+            result_dir,
+            f"culture_prediction_{n_epoch:04d}_{model.id:02d}",
+            quizzes=result[:72],
+            prediction=True,

         )
 
         return main_test_accuracy
         )
 
         return main_test_accuracy


@@ -268,7 +313,7 @@ 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.problem.generate_token_sequences(nb).to(self.device)
+        input[-nb:] = self.generate_token_sequences(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:


@@ -277,15 +322,15 @@ class QuizzMachine:
             self.test_c_quizzes.append(new_c_quizzes)
 
     def reverse_time(self, c_quizzes):
             self.test_c_quizzes.append(new_c_quizzes)
 
     def reverse_time(self, c_quizzes):

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

         l = (c_quizzes.size(1) - 1) // 2
         l = (c_quizzes.size(1) - 1) // 2

-        direction = c_quizzes[:, l : l + 1]
-        direction = self.problem.token_forward * (
-            direction == self.problem.token_backward
-        ) + self.problem.token_backward * (direction == self.problem.token_forward)
+        direction = c_quizzes[:, 0:1]
+        direction = self.token_forward * (
+            direction == self.token_backward
+        ) + self.token_backward * (direction == self.token_forward)

 
 

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

 
     def compute_correctness(
         self, c_quizzes, models_for_validation, both_directions=True
 
     def compute_correctness(
         self, c_quizzes, models_for_validation, both_directions=True

diff --git a/sky.py b/sky.py
index 4ca4ba7..d2a4568 100755 (executable)
--- a/sky.py
+++ b/sky.py
@@ -37,13 +37,14 @@ class Sky(problem.Problem):
     token_background = 0
     first_bird_token = 1
     nb_bird_tokens = colors.size(0) - 1
     token_background = 0
     first_bird_token = 1
     nb_bird_tokens = colors.size(0) - 1

-    token_forward = first_bird_token + nb_bird_tokens
-    token_backward = token_forward + 1

 
     token2char = (
         "_" + "".join([chr(ord("A") + n) for n in range(len(colors) - 1)]) + "><"
     )
 
 
     token2char = (
         "_" + "".join([chr(ord("A") + n) for n in range(len(colors) - 1)]) + "><"
     )
 

+    def nb_token_values(self):
+        return len(self.colors)
+

     def __init__(
         self,
         height=6,
     def __init__(
         self,
         height=6,


@@ -60,9 +61,6 @@ class Sky(problem.Problem):
         self.nb_iterations = nb_iterations
         self.avoid_collision = avoid_collision
 
         self.nb_iterations = nb_iterations
         self.avoid_collision = avoid_collision
 

-    def direction_tokens(self):
-        return self.token_forward, self.token_backward
-

     def generate_frame_sequences(self, nb):
         frame_sequences = []
 
     def generate_frame_sequences(self, nb):
         frame_sequences = []
 


@@ -159,32 +157,11 @@ class Sky(problem.Problem):
 
     def generate_prompts_and_answers(self, nb):
         frame_sequences = self.generate_frame_sequences(nb)
 
     def generate_prompts_and_answers(self, nb):
         frame_sequences = self.generate_frame_sequences(nb)

-        prompts = frame_sequences[:, : frame_sequences.size(0) // 2].flatten(1)
-        answers = frame_sequences[:, frame_sequences.size(0) // 2 :].flatten(1)
+        frame_sequences = torch.cat([x[None] for x in frame_sequences], dim=0)
+        prompts = frame_sequences[:, : frame_sequences.size(1) // 2].flatten(1)
+        answers = frame_sequences[:, frame_sequences.size(1) // 2 :].flatten(1)

         return prompts, answers
 
         return prompts, answers
 

-    def generate_token_sequences(self, nb):
-        frame_sequences = self.generate_frame_sequences(nb)
-
-        result = []
-
-        for frame_sequence in frame_sequences:
-            a = []
-            if torch.rand(1) < 0.5:
-                for frame in frame_sequence:
-                    if len(a) > 0:
-                        a.append(torch.tensor([self.token_forward]))
-                    a.append(frame.flatten())
-            else:
-                for frame in reversed(frame_sequence):
-                    if len(a) > 0:
-                        a.append(torch.tensor([self.token_backward]))
-                    a.append(frame.flatten())
-
-            result.append(torch.cat(a, dim=0)[None, :])
-
-        return torch.cat(result, dim=0)
-

     ######################################################################
 
     def frame2img(self, x, scale=15):
     ######################################################################
 
     def frame2img(self, x, scale=15):


@@ -214,92 +191,82 @@ class Sky(problem.Problem):
 
         return x
 
 
         return x
 

-    def seq2img(self, seq, scale=15):
-        all = [
-            self.frame2img(
-                seq[:, : self.height * self.width].reshape(-1, self.height, self.width),
-                scale,
+    def seq2str(self, seq):
+        result = []
+        for s in seq:
+            result.append("".join([self.token2char[v] for v in s]))
+        return result
+
+    def save_image(
+        self,
+        result_dir,
+        filename,
+        prompts,
+        answers,
+        predicted_prompts=None,
+        predicted_answers=None,
+    ):
+        if predicted_prompts is None:
+            predicted_prompts = 255
+
+        if predicted_answers is None:
+            predicted_answers = 255
+
+        def add_frame(x, c, margin):
+            y = x.new_full(
+                (x.size(0), x.size(1), x.size(2) + 2 * margin, x.size(3) + 2 * margin),
+                0,

             )
             )

-        ]
+            if type(c) is int:
+                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)
+                y[...] = c[:, :, None, None]
+            y[:, :, margin:-margin, margin:-margin] = x
+            return y

 
 

-        separator = torch.full((seq.size(0), 3, self.height * scale - 1, 1), 0)
+        margin = 4

 
 

-        t = self.height * self.width
+        img_prompts = add_frame(self.frame2img(prompts.to("cpu")), 0, 1)
+        img_answers = add_frame(self.frame2img(answers.to("cpu")), 0, 1)

 
 

-        while t < seq.size(1):
-            direction_tokens = seq[:, t]
-            t += 1
+        # img_prompts = add_frame(img_prompts, 255, margin)
+        # img_answers = add_frame(img_answers, 255, margin)

 
 

-            direction_images = self.colors[
-                torch.full(
-                    (direction_tokens.size(0), self.height * scale - 1, scale), 0
-                )
-            ].permute(0, 3, 1, 2)
-
-            for n in range(direction_tokens.size(0)):
-                if direction_tokens[n] == self.token_forward:
-                    for k in range(scale):
-                        for l in [0, 1]:
-                            direction_images[
-                                n,
-                                :,
-                                (self.height * scale) // 2 - scale // 2 + k - l,
-                                3 + scale // 2 - abs(k - scale // 2),
-                            ] = 0
-                elif direction_tokens[n] == self.token_backward:
-                    for k in range(scale):
-                        for l in [0, 1]:
-                            direction_images[
-                                n,
-                                :,
-                                (self.height * scale) // 2 - scale // 2 + k - l,
-                                3 + abs(k - scale // 2),
-                            ] = 0
-                else:
-                    for k in range(2, scale - 2):
-                        for l in [0, 1]:
-                            direction_images[
-                                n,
-                                :,
-                                (self.height * scale) // 2 - scale // 2 + k - l,
-                                k,
-                            ] = 0
-                            direction_images[
-                                n,
-                                :,
-                                (self.height * scale) // 2 - scale // 2 + k - l,
-                                scale - 1 - k,
-                            ] = 0
-
-            all += [
-                separator,
-                direction_images,
-                separator,
-                self.frame2img(
-                    seq[:, t : t + self.height * self.width].reshape(
-                        -1, self.height, self.width
-                    ),
-                    scale,
-                ),
-            ]
-
-            t += self.height * self.width
-
-        return torch.cat(all, dim=3)
+        img_prompts = add_frame(img_prompts, predicted_prompts, margin)
+        img_answers = add_frame(img_answers, predicted_answers, margin)

 
 

-    def seq2str(self, seq):
-        result = []
-        for s in seq:
-            result.append("".join([self.token2char[v] for v in s]))
-        return result
+        separator = img_prompts.new_full(
+            (img_prompts.size(0), img_prompts.size(1), img_prompts.size(2), margin), 255
+        )
+
+        img = torch.cat([img_prompts, img_answers], dim=3)

 
 

-    def save_image(self, input, result_dir, filename):
-        img = self.seq2img(input.to("cpu"))

         image_name = os.path.join(result_dir, filename)
         image_name = os.path.join(result_dir, filename)

-        torchvision.utils.save_image(img.float() / 255.0, image_name, nrow=6, padding=4)
+        torchvision.utils.save_image(
+            img.float() / 255.0, image_name, nrow=6, padding=margin * 2, pad_value=1.0
+        )

 
 

-    def save_quizzes(self, input, result_dir, filename_prefix):
-        self.save_image(input, result_dir, filename_prefix + ".png")
+    def save_quizzes(
+        self,
+        result_dir,
+        filename_prefix,
+        prompts,
+        answers,
+        predicted_prompts=None,
+        predicted_answers=None,
+    ):
+        self.save_image(
+            result_dir,
+            filename_prefix + ".png",
+            prompts,
+            answers,
+            predicted_prompts,
+            predicted_answers,
+        )

 
 
 ######################################################################
 
 
 ######################################################################


@@ -309,10 +276,19 @@ if __name__ == "__main__":
 
     sky = Sky(height=6, width=8, speed=4, nb_iterations=2)
 
 
     sky = Sky(height=6, width=8, speed=4, nb_iterations=2)
 

-    start_time = time.perf_counter()
-    token_sequences = sky.generate_token_sequences(nb=64)
-    delay = time.perf_counter() - start_time
-    print(f"{token_sequences.size(0)/delay:02f} seq/s")
+    prompts, answers = sky.generate_prompts_and_answers(4)
+
+    predicted_prompts = torch.rand(prompts.size(0)) < 0.5
+    predicted_answers = torch.rand(answers.size(0)) < 0.5
+
+    sky.save_quizzes(
+        "/tmp", "test", prompts, answers, predicted_prompts, predicted_answers
+    )
+
+    # start_time = time.perf_counter()
+    # token_sequences = sky.generate_token_sequences(nb=64)
+    # delay = time.perf_counter() - start_time
+    # print(f"{token_sequences.size(0)/delay:02f} seq/s")

 
     # print(sky.seq2str(seq[:4]))
 
 
     # print(sky.seq2str(seq[:4]))
 


@@ -330,9 +306,9 @@ if __name__ == "__main__":
     # seq = (1 - m) * seq + m * 23
 
     # print(seq.size())
     # seq = (1 - m) * seq + m * 23
 
     # print(seq.size())

-    img = sky.seq2img(token_sequences)
+    # img = sky.seq2img(token_sequences)

     # print(img.size())
 
     # print(img.size())
 

-    torchvision.utils.save_image(
-        img.float() / 255.0, "/tmp/world.png", nrow=6, padding=6, pad_value=0
-    )
+    # torchvision.utils.save_image(
+    # img.float() / 255.0, "/tmp/world.png", nrow=6, padding=6, pad_value=0
+    # )