def power_iteration(W, u_, update=True, eps=1e-12):
# Lists holding singular vectors and values
Wt = torch.Tensor(W).t()
us, vs, svs = [], [], []
for i, u in enumerate(u_):
# Run one step of the power iteration
with torch.no_grad():
if W.shape[1] == 27:
a = 1
v = torch.matmul(u, W)
# if (W.shape[0]==u.shape[1]) :
# v = torch.matmul(u, W)
# else:
# v = torch.matmul(u, Wt)
# Run Gram-Schmidt to subtract components of all other singular vectors
v = F.normalize(gram_schmidt(v, vs), eps=eps)
# Add to the list
vs += [v]
# Update the other singular vector
u = torch.matmul(v, Wt)
# if (W.shape[0]!=v.shape[1]):
# u = torch.matmul(v, Wt )
# else:
# u = torch.matmul(v, W)
# Run Gram-Schmidt to subtract components of all other singular vectors
u = F.normalize(gram_schmidt(u, us), eps=eps)
# Add to the list
us += [u]
if update:
torch.copy(u, u_[i])
# u_[i][:] = u
# Compute this singular value and add it to the list
svs += [torch.squeeze(torch.matmul(torch.matmul(v, Wt), u.t()))]
# if (W.shape[0]!=v.shape[1]):
# svs += [torch.squeeze(torch.matmul(torch.matmul(v, Wt ), u.t() ))]
# else:
# svs += [torch.squeeze(torch.matmul(torch.matmul(v, W), u.t()))]
#svs += [torch.sum(F.linear(u, W.transpose(0, 1)) * v)]
return svs, us, vs
def W_(self):
self.training = True
if isinstance(self, SNLinear):
W_mat = torch.Tensor(self.weight).t(
) ##linear layer weight is different from pytorch weight, need to transpose
else:
W_mat = torch.Tensor(self.weight).view(self.weight.shape[0], -1)
if self.transpose:
W_mat = W_mat.t()
# Apply num_itrs power iterations
for _ in range(self.num_itrs):
svs, us, vs = power_iteration(W_mat,
self.u,
update=self.training,
eps=self.eps)
# Update the svs
if self.training:
with torch.no_grad(
): # Make sure to do this in a no_grad() context or you'll get memory leaks!
for i, sv in enumerate(svs):
torch.copy(sv, self.sv[i])
# self.sv[i][:] = sv
return self.weight / svs[0]
def copy_(self, src):
torch.copy(src, self)
return self
def __div__(self, other_var):
paddorch.copy(self.values / other_var, self.values)
# self.values.set_value(self.values/other_var)
return self
def __mul__(self, other):
paddorch.copy(self.values * other, self.values)
# self.values.set_value(self.values*other)
return self
def moving_average(model, model_test, beta=0.999):
for param, param_test in zip(model.parameters(), model_test.parameters()):
porch.copy(porch.lerp(param, param_test, beta), param_test)
def preprocess(x):
"""Preprocess 98-dimensional heatmaps."""
N, C, H, W = x.shape
x = truncate(x)
x = normalize(x)
sw = H // 256
operations = Munch(chin=OPPAIR(0, 3),
eyebrows=OPPAIR(-7 * sw, 2),
nostrils=OPPAIR(8 * sw, 4),
lipupper=OPPAIR(-8 * sw, 4),
liplower=OPPAIR(8 * sw, 4),
lipinner=OPPAIR(-2 * sw, 3))
for part, ops in operations.items():
start, end = index_map[part]
torch.copy(resize(shift(x[:, start:end], ops.shift), ops.resize),
x[:, start:end])
# =
zero_out = torch.cat([
torch.arange(0, index_map.chin.start),
torch.arange(index_map.chin.end, 33),
torch.LongTensor([
index_map.eyebrowsedges.start, index_map.eyebrowsedges.end,
index_map.lipedges.start, index_map.lipedges.end
])
])
x.fill_(val=0, dim=1, indices=zero_out.numpy())
# x_numpy=x.numpy()
# x_numpy[:, zero_out.numpy()] = 0
# x.set_value(x_numpy)
# torch.copy(target)
# x[:, zero_out] = 0
start, end = index_map.nose
torch.copy(shift(x[:, start + 1:end], 4 * sw), x[:, start + 1:end])
torch.copy(resize(x[:, start:end], 1), x[:, start:end])
# x[:, start+1:end] = shift(x[:, start+1:end], 4*sw)
# x[:, start:end] = resize(x[:, start:end], 1)
start, end = index_map.eyes
torch.copy(resize(x[:, start:end], 1), x[:, start:end])
torch.copy(resize(shift(x[:, start:end], -8), 3) + \
shift(x[:, start:end], -24), x[:, start:end] )
# x[:, start:end] = resize(x[:, start:end], 1)
# x[:, start:end] = resize(shift(x[:, start:end], -8), 3) + \
# shift(x[:, start:end], -24)
# Second-level mask
x2 = x.clone() #deepcopy(x)
x2.fill_(0, 1, list(range(index_map.chin.start, index_map.chin.end)))
x2.fill_(0, 1, list(range(index_map.lipedges.start,
index_map.lipedges.end)))
x2.fill_(0, 1, list(range(index_map.eyebrows.start,
index_map.eyebrows.end)))
# x2[:, index_map.chin.start:index_map.chin.end] = 0 # start:end was 0:33
# x2[:, index_map.lipedges.start:index_map.lipinner.end] = 0 # start:end was 76:96
# x2[:, index_map.eyebrows.start:index_map.eyebrows.end] = 0 # start:end was 33:51
x = torch.sum(x, dim=1, keepdim=True) # (N, 1, H, W)
x2 = torch.sum(x2, dim=1, keepdim=True) # mask without faceline and mouth
x_numpy = x.numpy
zero_indices = np.where(x_numpy != x_numpy)[0]
x.fill_(0, 0, zero_indices)
x2.fill_(0, 0, zero_indices)
# x[x != x] = 0 # set nan to zero
# x2[x != x] = 0 # set nan to zero
return x.clamp_(0, 1), x2.clamp_(0, 1)