From 2286652f558746313af4f2917541133ce5430919 Mon Sep 17 00:00:00 2001 From: =?utf8?q?Fran=C3=A7ois=20Fleuret?= Date: Mon, 15 Jul 2024 00:10:06 +0200 Subject: [PATCH] Update. --- grids.py | 73 ++++++++++++++++++++++++-------------------------------- main.py | 15 ++++++++---- 2 files changed, 42 insertions(+), 46 deletions(-) diff --git a/grids.py b/grids.py index e651940..eea8c6c 100755 --- a/grids.py +++ b/grids.py @@ -143,7 +143,7 @@ class Grids(problem.Problem): self.task_scale, self.task_symbols, self.task_isometry, - self.task_islands, + # self.task_islands, ] if tasks is None: @@ -877,7 +877,7 @@ class Grids(problem.Problem): ): break - def compute_distance(self, walls, goal_i, goal_j, start_i, start_j): + def compute_distance(self, walls, goal_i, goal_j): max_length = walls.numel() dist = torch.full_like(walls, max_length) @@ -886,9 +886,10 @@ class Grids(problem.Problem): while True: pred_dist.copy_(dist) - d = ( + dist[1:-1, 1:-1] = ( torch.cat( ( + dist[None, 1:-1, 1:-1], dist[None, 1:-1, 0:-2], dist[None, 2:, 1:-1], dist[None, 1:-1, 2:], @@ -899,16 +900,16 @@ class Grids(problem.Problem): + 1 ) - dist[1:-1, 1:-1].minimum_(d) # = torch.min(dist[1:-1, 1:-1], d) dist = walls * max_length + (1 - walls) * dist - if dist[start_i, start_j] < max_length or dist.equal(pred_dist): + if dist.equal(pred_dist): return dist * (1 - walls) # @torch.compile - def task_path(self, A, f_A, B, f_B): + def task_distance(self, A, f_A, B, f_B): c = torch.randperm(len(self.colors) - 1)[:3] + 1 - dist = torch.empty(self.height + 2, self.width + 2) + dist0 = torch.empty(self.height + 2, self.width + 2) + dist1 = torch.empty(self.height + 2, self.width + 2) for X, f_X in [(A, f_A), (B, f_B)]: nb_rec = torch.randint(3, (1,)).item() + 1 while True: @@ -933,43 +934,31 @@ class Grids(problem.Problem): ) if X[i1, j1] == 0: break - dist[...] = 1 - dist[1:-1, 1:-1] = (X != 0).long() - dist[...] = self.compute_distance(dist, i1 + 1, j1 + 1, i0 + 1, j0 + 1) - if dist[i0 + 1, j0 + 1] >= 1 and dist[i0 + 1, j0 + 1] < self.height * 4: + dist1[...] = 1 + dist1[1:-1, 1:-1] = (X != 0).long() + dist1[...] = self.compute_distance(dist1, i1 + 1, j1 + 1) + if ( + dist1[i0 + 1, j0 + 1] >= 1 + and dist1[i0 + 1, j0 + 1] < self.height * 4 + ): break - dist[1:-1, 1:-1] += (X != 0).long() * self.height * self.width - dist[0, :] = self.height * self.width - dist[-1, :] = self.height * self.width - dist[:, 0] = self.height * self.width - dist[:, -1] = self.height * self.width - # dist += torch.rand(dist.size()) - - i, j = i0 + 1, j0 + 1 - while i != i1 + 1 or j != j1 + 1: - f_X[i - 1, j - 1] = c[2] - r, s, t, u = ( - dist[i - 1, j], - dist[i, j - 1], - dist[i + 1, j], - dist[i, j + 1], - ) - m = min(r, s, t, u) - if r == m: - i = i - 1 - elif t == m: - i = i + 1 - elif s == m: - j = j - 1 - else: - j = j + 1 + dist0[...] = 1 + dist0[1:-1, 1:-1] = (X != 0).long() + dist0[...] = self.compute_distance(dist0, i0 + 1, j0 + 1) - X[i0, j0] = c[2] - # f_X[i0, j0] = c[1] + dist0 = dist0[1:-1, 1:-1] + dist1 = dist1[1:-1, 1:-1] + + D = dist1[i0, j0] + for d in range(1, D): + M = (dist0 == d) & (dist1 == D - d) + f_X[...] = (1 - M) * f_X + M * c[1] - X[i1, j1] = c[1] - f_X[i1, j1] = c[1] + X[i0, j0] = c[2] + f_X[i0, j0] = c[2] + X[i1, j1] = c[2] + f_X[i1, j1] = c[2] # for X, f_X in [(A, f_A), (B, f_B)]: # n = torch.arange(self.height * self.width).reshape(self.height, self.width) @@ -1166,7 +1155,7 @@ if __name__ == "__main__": # nb, nrow = 8, 2 # for t in grids.all_tasks: - for t in [grids.task_islands]: + for t in [grids.task_distance]: print(t.__name__) prompts, answers = grids.generate_prompts_and_answers_(nb, tasks=[t]) grids.save_quiz_illustrations( @@ -1178,7 +1167,7 @@ if __name__ == "__main__": nb = 1000 # for t in grids.all_tasks: - for t in [grids.task_islands]: + for t in [grids.task_distance]: start_time = time.perf_counter() prompts, answers = grids.generate_prompts_and_answers_(nb, tasks=[t]) delay = time.perf_counter() - start_time diff --git a/main.py b/main.py index 7ba5193..6b00bbf 100755 --- a/main.py +++ b/main.py @@ -84,11 +84,11 @@ parser.add_argument("--nb_gpts", type=int, default=5) parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.9) -parser.add_argument("--proba_understands", type=float, default=0.99) +parser.add_argument("--proba_understands", type=float, default=0.9) parser.add_argument("--proba_not_understands", type=float, default=0.5) -parser.add_argument("--generation_temperature", type=float, default=2.0) +parser.add_argument("--generation_temperature", type=float, default=1.0) parser.add_argument("--dirty_debug", action="store_true", default=False) @@ -373,13 +373,16 @@ def one_epoch(model, quiz_machine, local_device=main_device): # This is the key routine that decides what generated quizzes to keep -def compute_valid_quizzes(token_logprobas): +# token_logprobas are NxMxT where M is the number of models + + +def compute_valid_quizzes_(token_logprobas): warnings.warn("validation with uniform constraints", RuntimeWarning) l = token_logprobas.min(dim=-1).values.sort(dim=-1).values return (l[:, 0] < math.log(0.1)) & (l[:, 1] > math.log(0.5)) -def compute_valid_quizzes_(token_logprobas): +def compute_valid_quizzes(token_logprobas): l = token_logprobas.sum(dim=-1).sort(dim=-1).values return (l[:, 0] < math.log(args.proba_not_understands)) & ( l[:, 1] > math.log(args.proba_understands) @@ -617,6 +620,10 @@ for n_epoch in range(args.nb_epochs): quiz_machine.save_c_quizzes(os.path.join(args.result_dir, filename)) log_string(f"wrote {filename}") + # Force one epoch of training + for model in models: + model.main_test_accuracy = 0.0 + ################################################## # Select, improve, and eval the worst model -- 2.39.5