def coldfilt(X, ha, hb, highpass=False, mode='symmetric'):
if X is None or X.shape == torch.Size([]):
return torch.zeros(1,1,1,1, device=X.device)
batch, ch, r, c = X.shape
r2 = r // 2
if r % 4 != 0:
raise ValueError('No. of rows in X must be a multiple of 4\n' +
'X was {}'.format(X.shape))
if mode == 'symmetric':
m = ha.shape[2]
xe = symm_pad(r, m)
X1 = X[:,:,xe[2::2]]
X2 = X[:,:,xe[3::2]]
h = torch.cat((ha.repeat(ch, 1, 1, 1), hb.repeat(ch, 1, 1, 1)), dim=0)
Y = F.conv2d(torch.cat((X1, X2), dim=1), h, stride=(2,1), groups=ch*2)
Y1 = Y[:, :ch]
Y2 = Y[:, ch:]
del Y
else:
raise NotImplementedError()
# Reshape result to be shape [Batch, ch, r/2, c]. This reshaping
# interleaves the columns
if highpass:
Y = torch.stack((Y2, Y1), dim=-2).view(batch, ch, r2, c)
else:
Y = torch.stack((Y1, Y2), dim=-2).view(batch, ch, r2, c)
return Y
def colfilter(X, h):
if X is None or X.shape == torch.Size([0]):
return torch.zeros(1,1,1,1, device=X.device)
ch, r = X.shape[1:3]
m = h.shape[2] // 2
xe = symm_pad(r, m)
return F.conv2d(X[:,:,xe], h.repeat(ch,1,1,1), groups=ch)
def rowdfilt(X, ha, hb, highpass=False, mode='symmetric'):
if X is None or X.shape == torch.Size([]):
return torch.zeros(1, 1, 1, 1, device=X.device)
batch, ch, r, c = X.shape
c2 = c // 2
if c % 4 != 0:
raise ValueError('No. of cols in X must be a multiple of 4\n' +
'X was {}'.format(X.shape))
if mode == 'symmetric':
m = ha.shape[2]
xe = symm_pad(c, m)
X = torch.cat((X[:, :, :, xe[2::2]], X[:, :, :, xe[3::2]]), dim=1)
h = torch.cat(
(ha.reshape(1, 1, 1, m).repeat(ch, 1, 1, 1), hb.reshape(
1, 1, 1, m).repeat(ch, 1, 1, 1)),
dim=0)
X = F.conv2d(X, h, stride=(1, 2), groups=ch * 2)
else:
raise NotImplementedError()
# Reshape result to be shape [Batch, ch, r/2, c]. This reshaping
# interleaves the columns
if highpass:
Y = torch.stack((X[:, ch:], X[:, :ch]), dim=-1).view(batch, ch, r, c2)
else:
Y = torch.stack((X[:, :ch], X[:, ch:]), dim=-1).view(batch, ch, r, c2)
return Y
def rowfilter(X, h):
if X is None or X.shape == torch.Size([0]):
return torch.zeros(1,1,1,1, device=X.device)
ch, _, c = X.shape[1:]
m = h.shape[2] // 2
xe = symm_pad(c, m)
h = h.transpose(2,3).contiguous()
return F.conv2d(X[:,:,:,xe], h.repeat(ch,1,1,1), groups=ch)
def colfilter(X, h, mode='symmetric'):
if X is None or X.shape == torch.Size([]):
return torch.zeros(1,1,1,1, device=X.device)
ch, r = X.shape[1:3]
m = h.shape[2] // 2
if mode == 'symmetric':
xe = symm_pad(r, m)
y = F.conv2d(X[:,:,xe], h.repeat(ch,1,1,1), groups=ch)
else:
y = F.conv2d(X, h.repeat(ch, 1, 1, 1), groups=ch, padding=(m, 0))
return y
def colifilt(X, ha, hb, highpass=False, mode='symmetric'):
if X is None or X.shape == torch.Size([]):
return torch.zeros(1, 1, 1, 1, device=X.device)
m = ha.shape[2]
m2 = m // 2
hao = ha[:, :, 1::2]
hae = ha[:, :, ::2]
hbo = hb[:, :, 1::2]
hbe = hb[:, :, ::2]
batch, ch, r, c = X.shape
if r % 2 != 0:
raise ValueError('No. of rows in X must be a multiple of 2.\n' +
'X was {}'.format(X.shape))
xe = symm_pad(r, m2)
if m2 % 2 == 0:
h1 = hae
h2 = hbe
h3 = hao
h4 = hbo
if highpass:
X = torch.cat((X[:, :, xe[1:-2:2]], X[:, :, xe[:-2:2]],
X[:, :, xe[3::2]], X[:, :, xe[2::2]]),
dim=1)
else:
X = torch.cat((X[:, :, xe[:-2:2]], X[:, :, xe[1:-2:2]],
X[:, :, xe[2::2]], X[:, :, xe[3::2]]),
dim=1)
else:
h1 = hao
h2 = hbo
h3 = hae
h4 = hbe
if highpass:
X = torch.cat((X[:, :, xe[2:-1:2]], X[:, :, xe[1:-1:2]],
X[:, :, xe[2:-1:2]], X[:, :, xe[1:-1:2]]),
dim=1)
else:
X = torch.cat((X[:, :, xe[1:-1:2]], X[:, :, xe[2:-1:2]],
X[:, :, xe[1:-1:2]], X[:, :, xe[2:-1:2]]),
dim=1)
h = torch.cat(
(h1.repeat(ch, 1, 1, 1), h2.repeat(ch, 1, 1, 1), h3.repeat(
ch, 1, 1, 1), h4.repeat(ch, 1, 1, 1)),
dim=0)
X = F.conv2d(X, h, groups=4 * ch)
# Stack 4 tensors of shape [batch, ch, r2, c] into one tensor
# [batch, ch, r2, 4, c]
X = torch.stack(
[X[:, :ch], X[:, ch:2 * ch], X[:, 2 * ch:3 * ch], X[:, 3 * ch:]],
dim=3).view(batch, ch, r * 2, c)
return X
def rowfilter(X, h, mode='symmetric'):
if X is None or X.shape == torch.Size([]):
return torch.zeros(1,1,1,1, device=X.device)
ch, _, c = X.shape[1:]
m = h.shape[2] // 2
h = h.transpose(2,3).contiguous()
if mode == 'symmetric':
xe = symm_pad(c, m)
y = F.conv2d(X[:,:,:,xe], h.repeat(ch,1,1,1), groups=ch)
else:
y = F.conv2d(X, h.repeat(ch,1,1,1), groups=ch, padding=(0, m))
return y
def rowifilt(X, ha, hb, highpass=False, mode='symmetric'):
if X is None or X.shape == torch.Size([]):
return torch.zeros(1, 1, 1, 1, device=X.device)
m = ha.shape[2]
m2 = m // 2
hao = ha[:, :, 1::2]
hae = ha[:, :, ::2]
hbo = hb[:, :, 1::2]
hbe = hb[:, :, ::2]
batch, ch, r, c = X.shape
if c % 2 != 0:
raise ValueError('No. of cols in X must be a multiple of 2.\n' +
'X was {}'.format(X.shape))
xe = symm_pad(c, m2)
if m2 % 2 == 0:
h1 = hae
h2 = hbe
h3 = hao
h4 = hbo
X1 = X[:, :, :, xe[:-2:2]]
X2 = X[:, :, :, xe[1:-2:2]]
X3 = X[:, :, :, xe[2::2]]
X4 = X[:, :, :, xe[3::2]]
else:
h1 = hao
h2 = hbo
h3 = hae
h4 = hbe
X1 = X[:, :, :, xe[1:-1:2]]
X2 = X[:, :, :, xe[2:-1:2]]
X3 = X[:, :, :, xe[1:-1:2]]
X4 = X[:, :, :, xe[2:-1:2]]
if highpass:
X2, X1 = X1, X2
X4, X3 = X3, X4
h = torch.cat(
(h1.repeat(ch, 1, 1, 1), h2.repeat(ch, 1, 1, 1), h3.repeat(
ch, 1, 1, 1), h4.repeat(ch, 1, 1, 1)),
dim=0).reshape(4 * ch, 1, 1, m2)
X = torch.cat((X1, X2, X3, X4), dim=1)
Y = F.conv2d(X, h, groups=4 * ch)
# Stack 4 tensors of shape [batch, ch, r2, c] into one tensor
# [batch, ch, r2, 4, c]
Y = torch.stack(
[Y[:, :ch], Y[:, ch:2 * ch], Y[:, 2 * ch:3 * ch], Y[:, 3 * ch:]],
dim=4).view(batch, ch, r, c * 2)
return Y
