X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=inline;f=grids.py;h=85d640dda9b3c6550cf7891037be1759a17a734e;hb=428fd9169ecc3d03c9e8282d319682ddab0f098d;hp=47e586119fc052ebd6404ff8046942e4bafee196;hpb=9ec709a2a08eb82dfc17ef1e24aa9a84751d63e0;p=culture.git diff --git a/grids.py b/grids.py index 47e5861..85d640d 100755 --- a/grids.py +++ b/grids.py @@ -32,11 +32,17 @@ class Grids(problem.Problem): ("gray", [128, 128, 128]), ] - def __init__(self, device=torch.device("cpu")): + def __init__( + self, + max_nb_cached_chunks=None, + chunk_size=None, + nb_threads=-1, + ): self.colors = torch.tensor([c for _, c in self.named_colors]) self.height = 10 self.width = 10 - self.device = device + self.cache_rec_coo = {} + super().__init__(max_nb_cached_chunks, chunk_size, nb_threads) ###################################################################### @@ -110,10 +116,10 @@ class Grids(problem.Problem): c = c.long()[:, None] c = ( (1 - ((c == 1).long() + (c == 0).long() + (c == -1).long())) - * torch.tensor([64, 64, 64], device=c.device) - + (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) + * torch.tensor([64, 64, 64]) + + (c == 1).long() * torch.tensor([0, 255, 0]) + + (c == 0).long() * torch.tensor([255, 255, 255]) + + (c == -1).long() * torch.tensor([255, 0, 0]) ) y[...] = c[:, :, None, None] @@ -195,121 +201,133 @@ class Grids(problem.Problem): return len(self.colors) # @torch.compile - def rec_coo_(self, nb_rec, min_height=3, min_width=3): - # @torch.compile - def overlap(ia, ja, ib, jb): - return ( - ia[1] >= ib[0] and ia[0] <= ib[1] and ja[1] >= jb[0] and ja[0] <= jb[1] - ) + def rec_coo( + self, + nb_rec, + min_height=3, + min_width=3, + surface_max=None, + prevent_overlap=False, + ): + if surface_max is None: + surface_max = self.height * self.width // 2 - if nb_rec == 3: - while True: - i = torch.randint(self.height + 1, (nb_rec, 2)).sort(dim=1).values - j = torch.randint(self.width + 1, (nb_rec, 2)).sort(dim=1).values - if ( - not ( - overlap(i[0], j[0], i[1], j[1]) - or overlap(i[0], j[0], i[2], j[2]) - or overlap(i[1], j[1], i[2], j[2]) - ) - and (i[:, 1] - i[:, 0]).min() >= min_height - and (j[:, 1] - j[:, 0]).min() >= min_width - ): - break - return ( - (i[0, 0], j[0, 0], i[0, 1], j[0, 1]), - (i[1, 0], j[1, 0], i[1, 1], j[1, 1]), - (i[2, 0], j[2, 0], i[2, 1], j[2, 1]), - ) + signature = (nb_rec, min_height, min_width, surface_max) - # That's quite a tensorial spaghetti mess to sample - # non-overlapping rectangles quickly, but made the generation of - # 100k samples go from 1h50 with a lame pure python code to 3min30s - # with this one. - # @torch.compile - def rec_coo(self, nb_rec, min_height=3, min_width=3): - nb_trials = 200 + try: + return self.cache_rec_coo[signature].pop() + except IndexError: + pass + except KeyError: + pass + N = 10000 while True: - v = ( - ( - torch.rand(nb_trials * nb_rec, self.height + 1, device=self.device) - .sort(dim=-1) - .indices - < 2 - ) - .long() - .cumsum(dim=1) - == 1 - ).long() + while True: + i = torch.randint(self.height, (N * nb_rec, 2)).sort(dim=-1).values + j = torch.randint(self.width, (N * nb_rec, 2)).sort(dim=-1).values - h = ( - ( - torch.rand(nb_trials * nb_rec, self.width + 1, device=self.device) - .sort(dim=-1) - .indices - < 2 + big_enough = ( + (i[:, 1] >= i[:, 0] + min_height) + & (j[:, 1] >= j[:, 0] + min_height) + & ((i[:, 1] - i[:, 0]) * (j[:, 1] - j[:, 0]) <= surface_max) ) - .long() - .cumsum(dim=1) - == 1 - ).long() - - i = torch.logical_and( - v.sum(dim=-1) >= min_height, h.sum(dim=-1) >= min_width - ) - v, h = v[i], h[i] - v = v[: v.size(0) - v.size(0) % nb_rec] - h = h[: h.size(0) - h.size(0) % nb_rec] - v = v.reshape(v.size(0) // nb_rec, nb_rec, -1) - h = h.reshape(h.size(0) // nb_rec, nb_rec, -1) + i, j = i[big_enough], j[big_enough] - r = v[:, :, :, None] * h[:, :, None, :] + n = i.size(0) - i.size(0) % nb_rec - valid = r.sum(dim=1).flatten(1).max(dim=-1).values == 1 + if n > 0: + break - v = v[valid] - h = h[valid] + i = i[:n].reshape(n // nb_rec, nb_rec, -1) + j = j[:n].reshape(n // nb_rec, nb_rec, -1) + + if prevent_overlap: + can_fit = ((i[:, :, 1] - i[:, :, 0]) * (j[:, :, 1] - j[:, :, 0])).sum( + dim=-1 + ) <= self.height * self.width + i, j = i[can_fit], j[can_fit] + if nb_rec == 2: + A_i1, A_i2, A_j1, A_j2 = ( + i[:, 0, 0], + i[:, 0, 1], + j[:, 0, 0], + j[:, 0, 1], + ) + B_i1, B_i2, B_j1, B_j2 = ( + i[:, 1, 0], + i[:, 1, 1], + j[:, 1, 0], + j[:, 1, 1], + ) + no_overlap = torch.logical_not( + (A_i1 >= B_i2) + & (A_i2 <= B_i1) + & (A_j1 >= B_j1) + & (A_j2 <= B_j1) + ) + i, j = i[no_overlap], j[no_overlap] + elif nb_rec == 3: + A_i1, A_i2, A_j1, A_j2 = ( + i[:, 0, 0], + i[:, 0, 1], + j[:, 0, 0], + j[:, 0, 1], + ) + B_i1, B_i2, B_j1, B_j2 = ( + i[:, 1, 0], + i[:, 1, 1], + j[:, 1, 0], + j[:, 1, 1], + ) + C_i1, C_i2, C_j1, C_j2 = ( + i[:, 2, 0], + i[:, 2, 1], + j[:, 2, 0], + j[:, 2, 1], + ) + no_overlap = ( + ( + (A_i1 >= B_i2) + | (A_i2 <= B_i1) + | (A_j1 >= B_j2) + | (A_j2 <= B_j1) + ) + & ( + (A_i1 >= C_i2) + | (A_i2 <= C_i1) + | (A_j1 >= C_j2) + | (A_j2 <= C_j1) + ) + & ( + (B_i1 >= C_i2) + | (B_i2 <= C_i1) + | (B_j1 >= C_j2) + | (B_j2 <= C_j1) + ) + ) + i, j = (i[no_overlap], j[no_overlap]) + else: + assert nb_rec == 1 - if v.size(0) > 0: + if i.size(0) > 1: break - av = torch.arange(v.size(2), device=self.device)[None, :] - ah = torch.arange(h.size(2), device=self.device)[None, :] - - return [ - (i1.item(), j1.item(), i2.item() + 1, j2.item() + 1) - for i1, j1, i2, j2 in zip( - v.size(2) - (v[0] * (v.size(2) - av)).max(dim=-1).values, - h.size(2) - (h[0] * (h.size(2) - ah)).max(dim=-1).values, - (v[0] * av).max(dim=-1).values, - (h[0] * ah).max(dim=-1).values, - ) + self.cache_rec_coo[signature] = [ + [ + ( + i[n, k, 0].item(), + j[n, k, 0].item(), + i[n, k, 1].item(), + j[n, k, 1].item(), + ) + for k in range(nb_rec) + ] + for n in range(i.size(0)) ] - # @torch.compile - def rec_coo_(self, x, n, min_height=3, min_width=3): - collision = x.new(x.size()) - while True: - collision[...] = 0 - result = [] - for _ in range(n): - while True: - i1, i2 = torch.randint(x.size(0), (2,)) - if i1 + min_height <= i2: - break - while True: - j1, j2 = torch.randint(x.size(1), (2,)) - if j1 + min_width <= j2: - break - collision[i1:i2, j1:j2] += 1 - if collision.max() > 1: - break - result.append((i1, j1, i2, j2)) - if collision.max() == 1: - break - return result + return self.cache_rec_coo[signature].pop() ###################################################################### @@ -318,7 +336,7 @@ class Grids(problem.Problem): nb_rec = 3 c = torch.randperm(len(self.colors) - 1)[: nb_rec + 1] + 1 for X, f_X in [(A, f_A), (B, f_B)]: - r = self.rec_coo(nb_rec) + r = self.rec_coo(nb_rec, prevent_overlap=True) for n in range(nb_rec): i1, j1, i2, j2 = r[n] X[i1:i2, j1:j2] = c[n] @@ -331,7 +349,7 @@ class Grids(problem.Problem): c = torch.randperm(len(self.colors) - 1)[:nb_rec] + 1 for X, f_X in [(A, f_A), (B, f_B)]: while True: - r = self.rec_coo(nb_rec) + r = self.rec_coo(nb_rec, prevent_overlap=True) i1, j1, i2, j2 = r[nb_rec - 1] if ( i1 + di >= 0 @@ -357,7 +375,7 @@ class Grids(problem.Problem): direction = torch.randint(2, (1,)) for X, f_X in [(A, f_A), (B, f_B)]: while True: - r = self.rec_coo(nb_rec) + r = self.rec_coo(nb_rec, prevent_overlap=True) i1, j1, i2, j2 = r[nb_rec - 1] if i1 + 3 < i2 and j1 + 3 < j2: break @@ -382,7 +400,7 @@ class Grids(problem.Problem): c = torch.randperm(len(self.colors) - 1)[: 2 * nb_rec] + 1 direction = torch.randint(4, (1,)) for X, f_X in [(A, f_A), (B, f_B)]: - r = self.rec_coo(nb_rec) + r = self.rec_coo(nb_rec, prevent_overlap=True) for n in range(nb_rec): i1, j1, i2, j2 = r[n] X[i1:i2, j1:j2] = c[2 * n] @@ -422,20 +440,24 @@ class Grids(problem.Problem): nb_rec = 3 c = torch.randperm(len(self.colors) - 1)[: nb_rec + 1] + 1 for X, f_X in [(A, f_A), (B, f_B)]: - r = self.rec_coo(nb_rec) + r = self.rec_coo(nb_rec, prevent_overlap=True) for n in range(nb_rec): i1, j1, i2, j2 = r[n] X[i1:i2, j1:j2] = c[n] - f_X[i1:i2, j1:j2] = c[n] if n == nb_rec - 1: - f_X[i1 + 1 : i2 - 1, j1 + 1 : j2 - 1] = 0 + f_X[i1:i2, j1] = c[n] + f_X[i1:i2, j2 - 1] = c[n] + f_X[i1, j1:j2] = c[n] + f_X[i2 - 1, j1:j2] = c[n] + else: + f_X[i1:i2, j1:j2] = c[n] # @torch.compile def task_detect(self, A, f_A, B, f_B): nb_rec = 3 c = torch.randperm(len(self.colors) - 1)[: nb_rec + 1] + 1 for X, f_X in [(A, f_A), (B, f_B)]: - r = self.rec_coo(nb_rec) + r = self.rec_coo(nb_rec, prevent_overlap=True) for n in range(nb_rec): i1, j1, i2, j2 = r[n] X[i1:i2, j1:j2] = c[n] @@ -478,29 +500,62 @@ class Grids(problem.Problem): return no, nq, nq_diag - # @torch.compile def task_count(self, A, f_A, B, f_B): N = (torch.randint(4, (1,)) + 2).item() c = torch.randperm(len(self.colors) - 1)[:N] + 1 for X, f_X in [(A, f_A), (B, f_B)]: + l_q = torch.randperm(self.height * self.width)[ + : self.height * self.width // 20 + ] + l_d = torch.randint(N, l_q.size()) nb = torch.zeros(N, dtype=torch.int64) - q = torch.randint(N, (self.height * self.width,)) - k = torch.randperm(self.height * self.width) - for p in range(self.height * self.width): - i, j = k[p] % self.height, k[p] // self.height - no, nq, nq_diag = self.contact(X, i, j, c[q[p]]) - if no == 0 and nq_diag == 0: - if nq == 0: - if nb[q[p]] < self.width: - X[i, j] = c[q[p]] - nb[q[p]] += 1 - if nq == 1: - X[i, j] = c[q[p]] - - for n in range(N): - for j in range(nb[n]): - f_X[n, j] = c[n] + + for q, e in zip(l_q, l_d): + d = c[e] + i, j = q % self.height, q // self.height + if ( + nb[e] < self.width + and X[max(0, i - 1) : i + 2, max(0, j - 1) : j + 2] == 0 + ).all(): + X[i, j] = d + nb[e] += 1 + + l_q = torch.randperm((self.height - 2) * (self.width - 2))[ + : self.height * self.width // 2 + ] + l_d = torch.randint(N, l_q.size()) + for q, e in zip(l_q, l_d): + d = c[e] + i, j = q % (self.height - 2) + 1, q // (self.height - 2) + 1 + a1, a2, a3 = X[i - 1, j - 1 : j + 2] + a8, a4 = X[i, j - 1], X[i, j + 1] + a7, a6, a5 = X[i + 1, j - 1 : j + 2] + if ( + X[i, j] == 0 + and nb[e] < self.width + and (a2 == 0 or a2 == d) + and (a4 == 0 or a4 == d) + and (a6 == 0 or a6 == d) + and (a8 == 0 or a8 == d) + and (a1 == 0 or a2 == d or a8 == d) + and (a3 == 0 or a4 == d or a2 == d) + and (a5 == 0 or a6 == d or a4 == d) + and (a7 == 0 or a8 == d or a6 == d) + ): + o = ( + (a2 != 0).long() + + (a4 != 0).long() + + (a6 != 0).long() + + (a8 != 0).long() + ) + if o <= 1: + X[i, j] = d + nb[e] += 1 - o + + for e in range(N): + for j in range(nb[e]): + f_X[e, j] = c[e] # @torch.compile def task_trajectory(self, A, f_A, B, f_B): @@ -747,9 +802,7 @@ class Grids(problem.Problem): f_Bs = answers return (Bs == f_Bs).long().min(dim=-1).values > 0 - def generate_prompts_and_answers( - self, nb, tasks=None, progress_bar=False, device="cpu" - ): + def generate_prompts_and_answers_(self, nb, tasks=None, progress_bar=False): if tasks is None: tasks = self.all_tasks() @@ -803,6 +856,7 @@ class Grids(problem.Problem): if __name__ == "__main__": import time + # grids = Grids(max_nb_cached_chunks=5, chunk_size=100, nb_threads=4) grids = Grids() # nb = 1000 @@ -816,22 +870,21 @@ if __name__ == "__main__": # print(f"{prompts.size(0)/delay:02f} seq/s") # exit(0) - if True: - nb = 72 - - for t in grids.all_tasks(): - # for t in [grids.task_ortho]: - print(t.__name__) - prompts, answers = grids.generate_prompts_and_answers(nb, tasks=[t]) - grids.save_quizzes("/tmp", t.__name__, prompts[:nb], answers[:nb], nrow=4) + # if True: + nb = 72 - exit(0) + for t in grids.all_tasks(): + # for t in [grids.task_replace_color]: + print(t.__name__) + prompts, answers = grids.generate_prompts_and_answers_(nb, tasks=[t]) + grids.save_quizzes("/tmp", t.__name__, prompts[:nb], answers[:nb], nrow=4) - nb = 500 + nb = 1000 for t in grids.all_tasks(): + # for t in [ grids.task_replace_color ]: #grids.all_tasks(): start_time = time.perf_counter() - prompts, answers = grids.generate_prompts_and_answers(nb, tasks=[t]) + prompts, answers = grids.generate_prompts_and_answers_(nb, tasks=[t]) delay = time.perf_counter() - start_time print(f"{t.__name__} {prompts.size(0)/delay:02f} seq/s")