class QuizzMachine:
def save_image(self, input, result_dir, filename, logger):
- img = sky.seq2img(input.to("cpu"), self.height, self.width)
+ img = self.sky.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)
logger(f"wrote {image_name}")
):
super().__init__()
+ self.sky = sky.Sky(height=6, width=8, nb_birds=3, nb_iterations=2)
self.batch_size = batch_size
self.device = device
- self.height = 6
- self.width = 8
- self.train_w_quizzes = sky.generate_seq(
- nb_train_samples, height=self.height, width=self.width
- ).to(device)
-
- self.test_w_quizzes = sky.generate_seq(
- nb_test_samples, height=self.height, width=self.width
- ).to(device)
+ self.train_w_quizzes = self.sky.generate_seq(nb_train_samples).to(device)
+ self.test_w_quizzes = self.sky.generate_seq(nb_test_samples).to(device)
self.nb_codes = max(self.train_w_quizzes.max(), self.test_w_quizzes.max()) + 1
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:] = sky.generate_seq(nb, height=self.height, width=self.width).to(
- self.device
- )
+ input[-nb:] = self.sky.generate_seq(nb).to(self.device)
def store_c_quizzes(self, new_c_quizzes, for_train=True):
if for_train:
# Generate quizzes with model
c_quizzes = torch.empty(
- nb, self.height * self.width * 2 + 1, device=self.device, dtype=torch.int64
+ nb, self.train_w_quizzes.size(1), device=self.device, dtype=torch.int64
)
ar_mask = torch.full(c_quizzes.size(), 1, device=self.device)
###############################################################
# Create the reverse quizzes
- l = self.height * self.width
+ l = (c_quizzes.size(1) - 1) // 2
direction = c_quizzes[:, l : l + 1]
- direction = sky.token_forward * (
- direction == sky.token_backward
- ) + sky.token_backward * (direction == sky.token_forward)
+ direction = self.sky.token_forward * (
+ direction == self.sky.token_backward
+ ) + self.sky.token_backward * (direction == self.sky.token_forward)
reverse_c_quizzes = torch.cat(
[c_quizzes[:, l + 1 :], direction, c_quizzes[:, :l]], dim=1
)
######################################################################
-colors = torch.tensor(
- [
- [255, 255, 255],
- [255, 0, 0],
- [0, 192, 0],
- [0, 0, 255],
- [255, 192, 0],
- [0, 255, 255],
- [255, 0, 255],
- [192, 255, 192],
- [255, 192, 192],
- [192, 192, 255],
- [192, 192, 192],
- ]
-)
-
-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)]) + "><"
+class Sky:
+ colors = torch.tensor(
+ [
+ [255, 255, 255],
+ [255, 0, 0],
+ [0, 192, 0],
+ [0, 0, 255],
+ [255, 192, 0],
+ [0, 255, 255],
+ [255, 0, 255],
+ [192, 255, 192],
+ [255, 192, 192],
+ [192, 192, 255],
+ [192, 192, 192],
+ ]
+ )
+ 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
-class Sky:
- def __init__(self, height, width):
- self.heigh = heigh
+ token2char = (
+ "_" + "".join([chr(ord("A") + n) for n in range(len(colors) - 1)]) + "><"
+ )
+
+ def __init__(self, height=6, width=8, nb_birds=3, nb_iterations=2):
+ self.height = height
self.width = width
+ self.nb_birds = nb_birds
+ self.nb_iterations = nb_iterations
- def generate_seq(
- nb, height, width, nb_birds=3, nb_iterations=2, return_iterations=False
- ):
+ def generate_seq(self, nb, return_iterations=False):
pairs = []
kept_iterations = []
while True:
iterations = []
- f_start = torch.zeros(height, width, dtype=torch.int64)
+ f_start = torch.zeros(self.height, self.width, dtype=torch.int64)
i, j, vi, vj = (
- torch.empty(nb_birds, dtype=torch.int64),
- torch.empty(nb_birds, dtype=torch.int64),
- torch.empty(nb_birds, dtype=torch.int64),
- torch.empty(nb_birds, dtype=torch.int64),
+ torch.empty(self.nb_birds, dtype=torch.int64),
+ torch.empty(self.nb_birds, dtype=torch.int64),
+ torch.empty(self.nb_birds, dtype=torch.int64),
+ torch.empty(self.nb_birds, dtype=torch.int64),
)
- col = torch.randperm(colors.size(0) - 1)[:nb_birds].sort().values + 1
+ col = (
+ torch.randperm(self.colors.size(0) - 1)[: self.nb_birds]
+ .sort()
+ .values
+ + 1
+ )
- for n in range(nb_birds):
+ for n in range(self.nb_birds):
c = col[n]
while True:
i[n], j[n] = (
- torch.randint(height, (1,))[0],
- torch.randint(width, (1,))[0],
+ torch.randint(self.height, (1,))[0],
+ torch.randint(self.width, (1,))[0],
)
vm = torch.randint(4, (1,))[0]
vi[n], vj[n] = (vm % 2) * 2 - 1, (vm // 2) * 2 - 1
if (
i[n] - vi[n] >= 0
- and i[n] - vi[n] < height
+ and i[n] - vi[n] < self.height
and j[n] - vj[n] >= 0
- and j[n] - vj[n] < width
+ and j[n] - vj[n] < self.width
and f_start[i[n], j[n]] == 0
and f_start[i[n] - vi[n], j[n]] == 0
and f_start[i[n], j[n] - vj[n]] == 0
f_end = f_start.clone()
- for l in range(nb_iterations):
+ for l in range(self.nb_iterations):
iterations.append(f_end.clone())
f_end[...] = 0
nb_collisions = 0
- for n in range(nb_birds):
+ for n in range(self.nb_birds):
c = col[n]
pi, pj, pvi, pvj = (
)
if (i[n] == 0 and vi[n] == -1) or (
- i[n] == height - 1 and vi[n] == 1
+ i[n] == self.height - 1 and vi[n] == 1
):
vi[n] = -vi[n]
if (j[n] == 0 and vj[n] == -1) or (
- j[n] == width - 1 and vj[n] == 1
+ j[n] == self.width - 1 and vj[n] == 1
):
vj[n] = -vj[n]
if torch.rand(1) < 0.5:
result.append(
torch.cat(
- [p[0].flatten(), torch.tensor([token_forward]), p[1].flatten()],
+ [
+ p[0].flatten(),
+ torch.tensor([self.token_forward]),
+ p[1].flatten(),
+ ],
dim=0,
)[None, :]
)
torch.cat(
[
p[1].flatten(),
- torch.tensor([token_backward]),
+ torch.tensor([self.token_backward]),
p[0].flatten(),
],
dim=0,
######################################################################
def generate_seq_old(
+ self,
nb,
- height,
- width,
- nb_birds=3,
- nb_iterations=2,
):
pairs = []
for n in tqdm.tqdm(range(nb), dynamic_ncols=True, desc="world generation"):
- f_start = torch.zeros(height, width, dtype=torch.int64)
- f_end = torch.zeros(height, width, dtype=torch.int64)
+ f_start = torch.zeros(self.height, self.width, dtype=torch.int64)
+ f_end = torch.zeros(self.height, self.width, dtype=torch.int64)
n = torch.arange(f_start.size(0))
for c in (
- (torch.randperm(nb_bird_tokens) + first_bird_token)[:nb_birds]
+ (torch.randperm(self.nb_bird_tokens) + self.first_bird_token)[
+ : self.nb_birds
+ ]
.sort()
.values
):
i, j = (
- torch.randint(height - 2, (1,))[0] + 1,
- torch.randint(width - 2, (1,))[0] + 1,
+ torch.randint(self.height - 2, (1,))[0] + 1,
+ torch.randint(self.width - 2, (1,))[0] + 1,
)
vm = torch.randint(4, (1,))[0]
vi, vj = (vm // 2) * (2 * (vm % 2) - 1), (1 - vm // 2) * (
f_start[i + vj, j - vi] = c
f_start[i - vj, j + vi] = c
- for l in range(nb_iterations):
+ for l in range(self.nb_iterations):
i += vi
j += vj
- if i < 0 or i >= height or j < 0 or j >= width:
+ if i < 0 or i >= self.height or j < 0 or j >= self.width:
i -= vi
j -= vj
vi, vj = -vi, -vj
if torch.rand(1) < 0.5:
result.append(
torch.cat(
- [p[0].flatten(), torch.tensor([token_forward]), p[1].flatten()],
+ [
+ p[0].flatten(),
+ torch.tensor([self.token_forward]),
+ p[1].flatten(),
+ ],
dim=0,
)[None, :]
)
torch.cat(
[
p[1].flatten(),
- torch.tensor([token_backward]),
+ torch.tensor([self.token_backward]),
p[0].flatten(),
],
dim=0,
return torch.cat(result, dim=0)
- def frame2img(x, height, width, upscale=15):
- x = x.reshape(-1, height, width)
- m = torch.logical_and(x >= 0, x < first_bird_token + nb_bird_tokens).long()
- x = colors[x * m].permute(0, 3, 1, 2)
+ def frame2img(self, x, upscale=15):
+ x = x.reshape(-1, self.height, self.width)
+ m = torch.logical_and(
+ x >= 0, x < self.first_bird_token + self.nb_bird_tokens
+ ).long()
+ x = self.colors[x * m].permute(0, 3, 1, 2)
s = x.shape
x = x[:, :, :, None, :, None].expand(-1, -1, -1, upscale, -1, upscale)
x = x.reshape(s[0], s[1], s[2] * upscale, s[3] * upscale)
return x
- def seq2img(seq, height, width, upscale=15):
- f_first = seq[:, : height * width].reshape(-1, height, width)
- f_second = seq[:, height * width + 1 :].reshape(-1, height, width)
- direction = seq[:, height * width]
+ def seq2img(self, seq, upscale=15):
+ f_first = seq[:, : self.height * self.width].reshape(
+ -1, self.height, self.width
+ )
+ f_second = seq[:, self.height * self.width + 1 :].reshape(
+ -1, self.height, self.width
+ )
+ direction = seq[:, self.height * self.width]
direction_symbol = torch.full(
- (direction.size(0), height * upscale - 1, upscale), 0
+ (direction.size(0), self.height * upscale - 1, upscale), 0
)
- direction_symbol = colors[direction_symbol].permute(0, 3, 1, 2)
- separator = torch.full((direction.size(0), 3, height * upscale - 1, 1), 0)
+ direction_symbol = self.colors[direction_symbol].permute(0, 3, 1, 2)
+ separator = torch.full((direction.size(0), 3, self.height * upscale - 1, 1), 0)
for n in range(direction_symbol.size(0)):
- if direction[n] == token_forward:
+ if direction[n] == self.token_forward:
for k in range(upscale):
direction_symbol[
n,
:,
- (height * upscale) // 2 - upscale // 2 + k,
+ (self.height * upscale) // 2 - upscale // 2 + k,
3 + upscale // 2 - abs(k - upscale // 2),
] = 0
- elif direction[n] == token_backward:
+ elif direction[n] == self.token_backward:
for k in range(upscale):
direction_symbol[
n,
:,
- (height * upscale) // 2 - upscale // 2 + k,
+ (self.height * upscale) // 2 - upscale // 2 + k,
3 + abs(k - upscale // 2),
] = 0
else:
for k in range(2, upscale - 2):
direction_symbol[
- n, :, (height * upscale) // 2 - upscale // 2 + k, k
+ n, :, (self.height * upscale) // 2 - upscale // 2 + k, k
] = 0
direction_symbol[
n,
:,
- (height * upscale) // 2 - upscale // 2 + k,
+ (self.height * upscale) // 2 - upscale // 2 + k,
upscale - 1 - k,
] = 0
return torch.cat(
[
- frame2img(f_first, height, width, upscale),
+ self.frame2img(f_first, upscale),
separator,
direction_symbol,
separator,
- frame2img(f_second, height, width, upscale),
+ self.frame2img(f_second, upscale),
],
dim=3,
)
- def seq2str(seq):
+ def seq2str(self, seq):
result = []
for s in seq:
- result.append("".join([token2char[v] for v in s]))
+ result.append("".join([self.token2char[v] for v in s]))
return result
if __name__ == "__main__":
import time
- height, width = 6, 8
+ sky = Sky(height=6, width=8, nb_iterations=100)
+
start_time = time.perf_counter()
- seq, it = generate_seq(
- nb=64, height=height, width=width, nb_iterations=100, return_iterations=True
- )
+ seq, it = sky.generate_seq(nb=64, return_iterations=True)
delay = time.perf_counter() - start_time
print(f"{seq.size(0)/delay:02f} samples/s")
- print(seq2str(seq[:4]))
+ print(sky.seq2str(seq[:4]))
for t in range(len(it[0])):
- img = torch.cat([frame2img(f[t], height, width) for f in it], dim=0)
+ img = torch.cat([sky.frame2img(f[t]) for f in it], dim=0)
torchvision.utils.save_image(
img.float() / 255.0,
f"/tmp/frame_{t:03d}.png",
# m = (torch.rand(seq.size()) < 0.05).long()
# seq = (1 - m) * seq + m * 23
- img = seq2img(seq, height, width)
+ img = sky.seq2img(seq)
print(img.size())
torchvision.utils.save_image(
projects / culture.git / commitdiff