("cyan", [0, 255, 255]),
("violet", [255, 0, 255]),
("lightgreen", [192, 255, 192]),
- ("pink", [255, 192, 192]),
+ ("brown", [165, 42, 42]),
("lightblue", [192, 192, 255]),
- ("gray", [192, 192, 192]),
+ ("gray", [128, 128, 128]),
]
- def __init__(
- self,
- ):
+ def __init__(self, device=torch.device("cpu")):
self.colors = torch.tensor([c for _, c in self.named_colors])
self.name2color = dict([(p[0], i) for i, p in enumerate(self.named_colors)])
self.height = 10
self.width = 10
+ self.device = device
######################################################################
def frame2img(self, x, scale=15):
+ x = x.reshape(x.size(0), self.height, -1)
+ m = torch.logical_and(x >= 0, x < self.nb_token_values()).long()
+ x = self.colors[x * m].permute(0, 3, 1, 2)
+ s = x.shape
+ x = x[:, :, :, None, :, None].expand(-1, -1, -1, scale, -1, scale)
+ x = x.reshape(s[0], s[1], s[2] * scale, s[3] * scale)
+
+ x[:, :, :, torch.arange(0, x.size(3), scale)] = 0
+ x[:, :, torch.arange(0, x.size(2), scale), :] = 0
+ x = x[:, :, 1:, 1:]
+
+ for n in range(m.size(0)):
+ for i in range(m.size(1)):
+ for j in range(m.size(2)):
+ if m[n, i, j] == 0:
+ for k in range(2, scale - 2):
+ for l in [0, 1]:
+ x[n, :, i * scale + k, j * scale + k - l] = 0
+ x[
+ n, :, i * scale + scale - 1 - k, j * scale + k - l
+ ] = 0
+
+ return x
+
+ def frame2img_(self, x, scale=15):
x = x.reshape(x.size(0), self.height, -1)
x = self.colors[x].permute(0, 3, 1, 2)
s = x.shape
def nb_token_values(self):
return len(self.colors)
- def rec_coo(self, x, n, min_height=3, min_width=3):
+ # 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.
+ def rec_coo(self, nb_rec, min_height=3, min_width=3):
+ nb_trials = 200
+
+ 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()
+
+ h = (
+ (
+ torch.rand(nb_trials * nb_rec, self.width + 1, device=self.device)
+ .sort(dim=-1)
+ .indices
+ < 2
+ )
+ .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)
+
+ r = v[:, :, :, None] * h[:, :, None, :]
+
+ valid = r.sum(dim=1).flatten(1).max(dim=-1).values == 1
+
+ v = v[valid]
+ h = h[valid]
+
+ if v.size(0) > 0:
+ 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,
+ )
+ ]
+
+ def rec_coo_(self, x, n, min_height=3, min_width=3):
collision = x.new(x.size())
while True:
collision[...] = 0
######################################################################
def task_replace_color(self, A, f_A, B, f_B):
- N = 3
- c = torch.randperm(len(self.colors) - 1)[: N + 1] + 1
+ 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(X, N)
- for n in range(N):
+ r = self.rec_coo(nb_rec)
+ 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 > 0 else -1]
def task_move(self, A, f_A, B, f_B):
- di, dj = torch.randint(2, (2,)) * 2 - 1
- N = 3
- c = torch.randperm(len(self.colors) - 1)[:N] + 1
+ di, dj = torch.randint(3, (2,)) - 1
+ nb_rec = 3
+ 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(X, N)
- i1, j1, i2, j2 = r[N - 1]
+ r = self.rec_coo(nb_rec)
+ i1, j1, i2, j2 = r[nb_rec - 1]
if (
i1 + di >= 0
and i2 + di < X.size(0)
):
break
- for n in range(N):
+ for n in range(nb_rec):
i1, j1, i2, j2 = r[n]
X[i1:i2, j1:j2] = c[n]
- if n == N - 1:
+ if n == nb_rec - 1:
f_X[i1 + di : i2 + di, j1 + dj : j2 + dj] = c[n]
else:
f_X[i1:i2, j1:j2] = c[n]
def task_grow(self, A, f_A, B, f_B):
di, dj = torch.randint(2, (2,)) * 2 - 1
- N = 3
- c = torch.randperm(len(self.colors) - 1)[:N] + 1
+ nb_rec = 3
+ c = torch.randperm(len(self.colors) - 1)[:nb_rec] + 1
direction = torch.randint(2, (1,))
for X, f_X in [(A, f_A), (B, f_B)]:
while True:
- r = self.rec_coo(X, N)
- i1, j1, i2, j2 = r[N - 1]
+ r = self.rec_coo(nb_rec)
+ i1, j1, i2, j2 = r[nb_rec - 1]
if i1 + 3 < i2 and j1 + 3 < j2:
break
- for n in range(N):
+ for n in range(nb_rec):
i1, j1, i2, j2 = r[n]
- if n == N - 1:
+ if n == nb_rec - 1:
if direction == 0:
X[i1 + 1 : i2 - 1, j1 + 1 : j2 - 1] = c[n]
f_X[i1:i2, j1:j2] = c[n]
def task_color_grow(self, A, f_A, B, f_B):
di, dj = torch.randint(2, (2,)) * 2 - 1
- N = 3
- c = torch.randperm(len(self.colors) - 1)[: 2 * N] + 1
- direction = torch.randint(2, (1,))
+ nb_rec = 3
+ 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(X, N)
- for n in range(N):
+ r = self.rec_coo(nb_rec)
+ for n in range(nb_rec):
i1, j1, i2, j2 = r[n]
- i = (i1 + i2) // 2
X[i1:i2, j1:j2] = c[2 * n]
- X[i : i + 1, j1:j2] = c[2 * n + 1]
f_X[i1:i2, j1:j2] = c[2 * n]
- if n == N - 1:
- f_X[i:i2, j1:j2] = c[2 * n + 1]
- else:
- f_X[i : i + 1, j1:j2] = c[2 * n + 1]
+ # Not my proudest moment
+ if direction == 0:
+ i = (i1 + i2) // 2
+ X[i : i + 1, j1:j2] = c[2 * n + 1]
+ if n == nb_rec - 1:
+ f_X[i:i2, j1:j2] = c[2 * n + 1]
+ else:
+ f_X[i : i + 1, j1:j2] = c[2 * n + 1]
+ elif direction == 1:
+ i = (i1 + i2 - 1) // 2
+ X[i : i + 1, j1:j2] = c[2 * n + 1]
+ if n == nb_rec - 1:
+ f_X[i1 : i + 1, j1:j2] = c[2 * n + 1]
+ else:
+ f_X[i : i + 1, j1:j2] = c[2 * n + 1]
+ elif direction == 2:
+ j = (j1 + j2) // 2
+ X[i1:i2, j : j + 1] = c[2 * n + 1]
+ if n == nb_rec - 1:
+ f_X[i1:i2, j:j2] = c[2 * n + 1]
+ else:
+ f_X[i1:i2, j : j + 1] = c[2 * n + 1]
+ elif direction == 3:
+ j = (j1 + j2 - 1) // 2
+ X[i1:i2, j : j + 1] = c[2 * n + 1]
+ if n == nb_rec - 1:
+ f_X[i1:i2, j1 : j + 1] = c[2 * n + 1]
+ else:
+ f_X[i1:i2, j : j + 1] = c[2 * n + 1]
def task_frame(self, A, f_A, B, f_B):
- N = 3
- c = torch.randperm(len(self.colors) - 1)[: N + 1] + 1
+ 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(X, N)
- for n in range(N):
+ r = self.rec_coo(nb_rec)
+ 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 == N - 1:
+ if n == nb_rec - 1:
f_X[i1 + 1 : i2 - 1, j1 + 1 : j2 - 1] = 0
def task_detect(self, A, f_A, B, f_B):
- N = 3
- c = torch.randperm(len(self.colors) - 1)[: N + 1] + 1
+ 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(X, N)
- for n in range(N):
+ r = self.rec_coo(nb_rec)
+ for n in range(nb_rec):
i1, j1, i2, j2 = r[n]
X[i1:i2, j1:j2] = c[n]
- f_X[i1, j1] = c[-1]
+ if n < nb_rec - 1:
+ f_X[i1, j1] = c[-1]
######################################################################
- def generate_prompts_and_answers(self, nb):
+ def generate_prompts_and_answers(self, nb, device="cpu"):
tasks = [
self.task_replace_color,
self.task_move,
f_B = answer
task = tasks[torch.randint(len(tasks), (1,))]
task(A, f_A, B, f_B)
+
return prompts.flatten(1), answers.flatten(1)
def save_quizzes(
delay = time.perf_counter() - start_time
print(f"{prompts.size(0)/delay:02f} seq/s")
- # predicted_prompts = torch.rand(prompts.size(0)) < 0.5
- # predicted_answers = torch.logical_not(predicted_prompts)
+ predicted_prompts = torch.rand(prompts.size(0)) < 0.5
+ predicted_answers = torch.logical_not(predicted_prompts)
reasoning.save_quizzes(
"/tmp",
"test",
- prompts[:36],
- answers[:36],
+ prompts[:64],
+ answers[:64],
# You can add a bool to put a frame around the predicted parts
- # predicted_prompts, predicted_answers
+ predicted_prompts[:64],
+ predicted_answers[:64],
)
projects / culture.git / blobdiff