class Life:
def __init__(self, size, n=1):
self.grid = pt.zeros(n, 1, size, size).to(device)
self.mask = pt.tensor([[[[1, 1, 1], [1, 0, 1], [1, 1, 1]]]],
dtype=pt.float).to(device)
self.viewer = ImageViewer()
self.t = 0
def reset(self):
self.grid.fill_(0)
self.t = 0
def rand_init(self, seed=0, p=0.2):
pt.manual_seed(seed)
self.grid = Bernoulli(p).sample(self.grid.size()).to(device)
def render(self, invert=False):
b, c, h, w = self.grid.shape
nrow = int(np.ceil(np.sqrt(b)))
grid = make_grid(1 - self.grid, nrow=nrow)
grid = grid if invert else 1 - grid
grid = 255 * grid.to(pt.uint8).cpu()
grid = grid.numpy().transpose((1, 2, 0))
self.viewer.imshow(grid, caption=f't={self.t}')
def step(self):
padded = F.pad(self.grid, (1, 1, 1, 1), mode='circular')
neighbors = F.conv2d(padded, self.mask)
mask0 = (neighbors < 2).to(pt.float) * self.grid
mask1 = (neighbors > 3).to(pt.float) * self.grid
mask2 = (neighbors == 3).to(pt.float) * (1 - self.grid)
self.grid[mask0.to(pt.bool)] = 0
self.grid[mask1.to(pt.bool)] = 0
self.grid[mask2.to(pt.bool)] = 1
self.t += 1
def close(self):
self.viewer.close()
def forward(self, x):
# shape: (bsize, channels, height, width)
if not self.training or self.drop_prob == 0.:
assert x.dim() == 4, \
"Expected input with 4 dimensions (bsize, channels, height, width)"
return x
else:
y = x[1]
assert x[0].shape == y.shape, "Not equal"
# get gamma value
gamma = self._compute_gamma(x[0])
# sample mask
mask = (torch.rand(x[0].shape[0], *x[0].shape[2:]) < gamma).float()
# place mask on input device
mask = mask.to(x[0].device)
# compute block mask
block_student_mask, block_teacher_mask = self._compute_block_mask(mask)
# apply block mask
student_out = x[0] * block_student_mask[:, None, :, :]
teacher_out = y * block_teacher_mask[:, None, :, :]
student_out_flatten = student_out.view(student_out.size(0), -1)
teacher_out_flatten = teacher_out.view(teacher_out.size(0), -1)
cos_sim = self.cos(student_out_flatten, teacher_out_flatten)
cos_sim = self._compress(cos_sim)
cos_sim_bernou = Bernoulli(cos_sim).sample()
prob_sim = 1 - cos_sim_bernou.view(cos_sim_bernou.size(0), 1, 1, 1)
out = student_out + teacher_out * prob_sim.expand_as(teacher_out)
return out
import torch from torch.distributions import Bernoulli mask_sizes = [7, 7] bbb = Bernoulli(torch.tensor(0.9)).sample((3, *mask_sizes)) print(bbb) print(bbb.size())