X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=inline;f=sky.py;h=cc5bd4fd5cfedec896b88d7549d1b831ae2ef265;hb=5f5c6c079c2751a76887444c211c5c464e875ed0;hp=abcd394580c8bd443e1d44599ec1a92de57606f3;hpb=bee6e628aabc1380772409f6aabffb024c0e70ab;p=culture.git diff --git a/sky.py b/sky.py index abcd394..cc5bd4f 100755 --- a/sky.py +++ b/sky.py @@ -5,7 +5,7 @@ # Written by Francois Fleuret -import math, sys, tqdm, os +import math, sys, tqdm, os, warnings import torch, torchvision @@ -37,8 +37,6 @@ class Sky(problem.Problem): 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)]) + "><" @@ -52,7 +50,11 @@ class Sky(problem.Problem): speed=2, nb_iterations=2, avoid_collision=True, + max_nb_cached_chunks=None, + chunk_size=None, + nb_threads=-1, ): + super().__init__(max_nb_cached_chunks, chunk_size, nb_threads) self.height = height self.width = width self.nb_birds = nb_birds @@ -60,9 +62,6 @@ class Sky(problem.Problem): 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 = [] @@ -112,11 +111,20 @@ class Sky(problem.Problem): break result = torch.zeros( - self.nb_iterations, self.height, self.width, dtype=torch.int64 + self.nb_iterations * self.speed, + self.height, + self.width, + dtype=torch.int64, + ) + + fine = torch.empty(self.nb_iterations * self.speed) + + t_to_keep = ( + torch.arange(self.nb_iterations, device=result.device) * self.speed ) - for l in range(self.nb_iterations): - fine = collision_okay() + for l in range(self.nb_iterations * self.speed): + fine[l] = collision_okay() for n in range(self.nb_birds): c = col[n] result[l, i[n], j[n]] = c @@ -136,7 +144,10 @@ class Sky(problem.Problem): i[n] += vi[n] j[n] += vj[n] - if fine: + result = result[t_to_keep] + fine = fine[t_to_keep] + + if fine[-1]: break frame_sequences.append(result) @@ -145,32 +156,8 @@ class Sky(problem.Problem): ###################################################################### - 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): - x = x.reshape(-1, self.height, self.width) + x = x.reshape(x.size(0), self.height, -1) m = torch.logical_and( x >= 0, x < self.first_bird_token + self.nb_bird_tokens ).long() @@ -196,92 +183,140 @@ class Sky(problem.Problem): 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 - separator = torch.full((seq.size(0), 3, self.height * scale - 1, 1), 0) + def save_image( + self, + result_dir, + filename, + prompts, + answers, + predicted_prompts=None, + predicted_answers=None, + ): + if predicted_prompts is None: + predicted_prompts = 255 - t = self.height * self.width + if predicted_answers is None: + predicted_answers = 255 - while t < seq.size(1): - direction_tokens = seq[:, t] - t += 1 + def add_frame(x, c, margin, bottom=False): + if bottom: + h, w, di, dj = x.size(2) + margin, x.size(3), 0, 0 + else: + h, w, di, dj = ( + x.size(2) + 2 * margin, + x.size(3) + 2 * margin, + margin, + margin, + ) - direction_images = self.colors[ - torch.full( - (direction_tokens.size(0), self.height * scale - 1, scale), 0 + y = x.new_full((x.size(0), x.size(1), h, w), 0) + + if type(c) is int: + y[...] = c + else: + c = c.long()[:, None] + c = ( + (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) ) - ].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) + y[...] = c[:, :, None, None] - def seq2str(self, seq): - result = [] - for s in seq: - result.append("".join([self.token2char[v] for v in s])) - return result + y[:, :, di : di + x.size(2), dj : dj + x.size(3)] = x + + return y + + margin = 4 + + img_prompts = add_frame(self.frame2img(prompts.to("cpu")), c=0, margin=1) + h = img_prompts.size(2) + img_answers = add_frame(self.frame2img(answers.to("cpu")), c=0, margin=1) + + img_prompts = add_frame(img_prompts, c=255, margin=margin, bottom=True) + img_answers = add_frame(img_answers, c=255, margin=margin, bottom=True) + + img_prompts = add_frame( + img_prompts, c=predicted_prompts, margin=margin, bottom=True + ) + img_answers = add_frame( + img_answers, c=predicted_answers, margin=margin, bottom=True + ) + + marker_size = 16 + + separator = img_prompts.new_full( + ( + img_prompts.size(0), + img_prompts.size(1), + img_prompts.size(2), + marker_size, + ), + 255, + ) + + separator[:, :, 0] = 0 + separator[:, :, h - 1] = 0 + + for k in range(1, 2 * marker_size - 8): + i = k - (marker_size - 4) + j = marker_size - 5 - abs(i) + separator[:, :, h // 2 - 1 + i, 2 + j] = 0 + separator[:, :, h // 2 - 1 + i + 1, 2 + j] = 0 + + img = torch.cat([img_prompts, separator, 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) - 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 * 4, pad_value=1.0 + ) + + ###################################################################### + + def nb_token_values(self): + return len(self.colors) - def save_quizzes(self, input, result_dir, filename_prefix): - self.save_image(input, result_dir, filename_prefix + ".png") + def generate_prompts_and_answers(self, nb): + frame_sequences = self.generate_frame_sequences(nb) + 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) + + # warnings.warn("dirty test with longer answer", RuntimeWarning) + # answers = torch.cat( + # [ + # frame_sequences[:, frame_sequences.size(1) // 2 :], + # frame_sequences[:, frame_sequences.size(1) // 2 :], + # ], + # dim=3, + # ).flatten(1) + + return prompts, answers + + def save_quiz_illustrations( + 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, + ) ###################################################################### @@ -289,12 +324,21 @@ class Sky(problem.Problem): if __name__ == "__main__": import time - sky = Sky(height=6, width=8, speed=2, nb_iterations=2) + sky = Sky(height=6, width=8, speed=1, nb_iterations=4) - 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.randint(3, (prompts.size(0),)) - 1 + predicted_answers = torch.randint(3, (prompts.size(0),)) - 1 + + sky.save_quiz_illustrations( + "/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])) @@ -312,9 +356,9 @@ if __name__ == "__main__": # seq = (1 - m) * seq + m * 23 # print(seq.size()) - img = sky.seq2img(token_sequences) + # img = sky.seq2img(token_sequences) # 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 + # )