def __init__(self,
height=64,
width=64,
with_r=False,
with_boundary=False):
super(AddCoordsTh, self).__init__()
self.with_r = with_r
self.with_boundary = with_boundary
device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
with torch.no_grad():
x_coords = torch.arange(height).unsqueeze(1).expand(
height, width).float()
y_coords = torch.arange(width).unsqueeze(0).expand(
height, width).float()
x_coords = (x_coords / (height - 1)) * 2 - 1
y_coords = (y_coords / (width - 1)) * 2 - 1
coords = torch.stack([x_coords, y_coords],
dim=0) # (2, height, width)
if self.with_r:
rr = torch.sqrt(
torch.pow(x_coords, 2) +
torch.pow(y_coords, 2)) # (height, width)
rr = (rr / torch.max(rr)).unsqueeze(0)
coords = torch.cat([coords, rr], dim=0)
self.coords = coords.unsqueeze(0).to(
device) # (1, 2 or 3, height, width)
self.x_coords = x_coords.to(device)
self.y_coords = y_coords.to(device)
def preprocess(x):
"""Preprocess 98-dimensional heatmaps."""
N, C, H, W = x.size()
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]
x[:, start:end] = resize(shift(x[:, start:end], ops.shift), ops.resize)
zero_out = porch.cat([
porch.arange(0, index_map.chin.start),
porch.arange(index_map.chin.end, 33),
porch.LongTensor([
index_map.eyebrowsedges.start, index_map.eyebrowsedges.end,
index_map.lipedges.start, index_map.lipedges.end
])
])
x[:, zero_out] = 0
start, end = index_map.nose
x[:, start + 1:end] = shift(x[:, start + 1:end], 4 * sw)
x[:, start:end] = resize(x[:, start:end], 1)
start, end = index_map.eyes
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 = deepcopy(x)
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 = porch.sum(x, dim=1, keepdim=True) # (N, 1, H, W)
x2 = porch.sum(x2, dim=1, keepdim=True) # mask without faceline and mouth
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)
def forward(self, x):
bsz = x.shape[0]
x = x.squeeze()
# positives on the diagonal
label = torch.arange(bsz).cuda().long()
loss = self.criterion(x, label)
return loss
def shift(x, N):
"""Shift N pixels up or down."""
up = N >= 0
N = abs(N)
_, _, H, W = x.shape
if N == 0:
return x
if up:
head = torch.arange(H - N) + N
tail = torch.arange(N)
else:
head = torch.arange(N) + (H - N)
tail = torch.arange(H - N)
# permutation indices
perm = torch.cat([head, tail])
out = torch.stack([x[:, :, int(a)] for a in perm.numpy()], dim=2)
return out
def shift(x, N):
"""Shift N pixels up or down."""
up = N >= 0
N = abs(N)
_, _, H, W = x.size()
head = porch.arange(N)
tail = porch.arange(H - N)
if up:
head = porch.arange(H - N) + N
tail = porch.arange(N)
else:
head = porch.arange(N) + (H - N)
tail = porch.arange(H - N)
# permutation indices
perm = porch.cat([head, tail]).to(x.device)
out = x[:, :, perm, :]
return out
import paddorch
arr = paddorch.convertTensor(1.0 / paddorch.arange(1, 30))
print(arr)
arr[arr > 0.1] = 0.0
print(arr)
arr[paddorch.isfinite(arr)] = 1.0
print(arr)
arr[paddorch.isinf(arr)] = 2.0
print(arr)
arr = paddorch.convertTensor(1.0 / paddorch.arange(1, 31)).view(-1, 5, 2)
print("before", arr)
arr[:, 2, :] = 999
print("after", arr)
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)
import paddorch
from paddorch import index_copy_inplace_nograd
memory = paddorch.zeros((4, 3))
k = paddorch.arange(0, 6).view(2, 3)
out_ids = paddorch.LongTensor([1, 3])
index_copy_inplace_nograd(memory, 0, out_ids, k)
print("paddorch", memory)
import torch
memory = torch.zeros((4, 3))
k = torch.arange(0, 6).view(2, 3).float()
out_ids = torch.LongTensor([1, 3])
memory.index_copy_(0, out_ids, k)
print("pytorch", memory)