I have a question about crf
Closed this issue · 7 comments
Does it improve the model performance?
Is there any difference in this version ?
Hi, I'm not sure what your question means. I assume you're asking if using a CRF layer improves performance compared to per-token softmax? It some cases it does. I'd try both and compare.
I used to use crf but not this version, and I found you deleted the older version, I found there is a huge change between two version code. and I want to know why you change it.
@kmkurn
class CRF(nn.Module):
"""
Class for learning and inference in conditional random field model using mean field approximation
and convolutional approximation in pairwise potentials term.
Parameters
----------
n_spatial_dims : int
Number of spatial dimensions of input tensors.
filter_size : int or sequence of ints
Size of the gaussian filters in message passing.
If it is a sequence its length must be equal to n_spatial_dims.
n_iter : int
Number of iterations in mean field approximation.
requires_grad : bool
Whether or not to train CRF's parameters.
returns : str
Can be 'logits', 'proba', 'log-proba'.
smoothness_weight : float
Initial weight of smoothness kernel.
smoothness_theta : float or sequence of floats
Initial bandwidths for each spatial feature in the gaussian smoothness kernel.
If it is a sequence its length must be equal to n_spatial_dims.
"""
def __init__(self, n_spatial_dims, filter_size=11, n_iter=5, requires_grad=True,
returns='logits', smoothness_weight=1, smoothness_theta=1):
super().__init__()
self.n_spatial_dims = n_spatial_dims
self.n_iter = n_iter
self.filter_size = np.broadcast_to(filter_size, n_spatial_dims)
self.returns = returns
self.requires_grad = requires_grad
self._set_param('smoothness_weight', smoothness_weight)
self._set_param('inv_smoothness_theta', 1 / np.broadcast_to(smoothness_theta, n_spatial_dims))
def _set_param(self, name, init_value):
setattr(self, name, nn.Parameter(torch.tensor(init_value, dtype=torch.float, requires_grad=self.requires_grad)))
def forward(self, x, spatial_spacings=None, verbose=False):
"""
Parameters
----------
x : torch.tensor
Tensor of shape ``(batch_size, n_classes, *spatial)`` with negative unary potentials, e.g. the CNN's output.
spatial_spacings : array of floats or None
Array of shape ``(batch_size, len(spatial))`` with spatial spacings of tensors in batch ``x``.
None is equivalent to all ones. Used to adapt spatial gaussian filters to different inputs' resolutions.
verbose : bool
Whether to display the iterations using tqdm-bar.
Returns
-------
output : torch.tensor
Tensor of shape ``(batch_size, n_classes, *spatial)``
with logits or (log-)probabilities of assignment to each class.
"""
batch_size, n_classes, *spatial = x.shape
assert len(spatial) == self.n_spatial_dims
# binary segmentation case
if n_classes == 1:
x = torch.cat([x, torch.zeros(x.shape).to(x)], dim=1)
if spatial_spacings is None:
spatial_spacings = np.ones((batch_size, self.n_spatial_dims))
negative_unary = x.clone()
for i in tqdm(range(self.n_iter), disable=not verbose):
# normalizing
x = F.softmax(x, dim=1)
# message passing
x = self.smoothness_weight * self._smoothing_filter(x, spatial_spacings)
# compatibility transform
x = self._compatibility_transform(x)
# adding unary potentials
x = negative_unary - x
if self.returns == 'logits':
output = x
elif self.returns == 'proba':
output = F.softmax(x, dim=1)
elif self.returns == 'log-proba':
output = F.log_softmax(x, dim=1)
else:
raise ValueError("Attribute ``returns`` must be 'logits', 'proba' or 'log-proba'.")
if n_classes == 1:
output = output[:, 0] - output[:, 1] if self.returns == 'logits' else output[:, 0]
output.unsqueeze_(1)
return output
def _smoothing_filter(self, x, spatial_spacings):
"""
Parameters
----------
x : torch.tensor
Tensor of shape ``(batch_size, n_classes, *spatial)`` with negative unary potentials, e.g. logits.
spatial_spacings : torch.tensor or None
Tensor of shape ``(batch_size, len(spatial))`` with spatial spacings of tensors in batch ``x``.
Returns
-------
output : torch.tensor
Tensor of shape ``(batch_size, n_classes, *spatial)``.
"""
return torch.stack([self._single_smoothing_filter(x[i], spatial_spacings[i]) for i in range(x.shape[0])])
@staticmethod
def _pad(x, filter_size):
padding = []
for fs in filter_size:
padding += 2 * [fs // 2]
return F.pad(x, list(reversed(padding))) # F.pad pads from the end
def _single_smoothing_filter(self, x, spatial_spacing):
"""
Parameters
----------
x : torch.tensor
Tensor of shape ``(n, *spatial)``.
spatial_spacing : sequence of len(spatial) floats
Returns
-------
output : torch.tensor
Tensor of shape ``(n, *spatial)``.
"""
x = self._pad(x, self.filter_size)
for i, dim in enumerate(range(1, x.ndim)):
# reshape to (-1, 1, x.shape[dim])
x = x.transpose(dim, -1)
shape_before_flatten = x.shape[:-1]
x = x.flatten(0, -2).unsqueeze(1)
# 1d gaussian filtering
kernel = self._create_gaussian_kernel1d(self.inv_smoothness_theta[i], spatial_spacing[i],
self.filter_size[i]).view(1, 1, -1).to(x)
x = F.conv1d(x, kernel)
# reshape back to (n, *spatial)
x = x.squeeze(1).view(*shape_before_flatten, x.shape[-1]).transpose(-1, dim)
return x
@staticmethod
def _create_gaussian_kernel1d(inverse_theta, spacing, filter_size):
"""
Parameters
----------
inverse_theta : torch.tensor
Tensor of shape ``(,)``
spacing : float
filter_size : int
Returns
-------
kernel : torch.tensor
Tensor of shape ``(filter_size,)``.
"""
distances = spacing * torch.arange(-(filter_size // 2), filter_size // 2 + 1).to(inverse_theta)
kernel = torch.exp(-(distances * inverse_theta) ** 2 / 2)
zero_center = torch.ones(filter_size).to(kernel)
zero_center[filter_size // 2] = 0
return kernel * zero_center
def _compatibility_transform(self, x):
"""
Parameters
----------
x : torch.Tensor of shape ``(batch_size, n_classes, *spatial)``.
Returns
-------
output : torch.tensor of shape ``(batch_size, n_classes, *spatial)``.
"""
labels = torch.arange(x.shape[1])
compatibility_matrix = self._compatibility_function(labels, labels.unsqueeze(1)).to(x)
return torch.einsum('ij..., jk -> ik...', x, compatibility_matrix)
@staticmethod
def _compatibility_function(label1, label2):
"""
Input tensors must be broadcastable.
Parameters
----------
label1 : torch.Tensor
label2 : torch.Tensor
Returns
-------
compatibility : torch.Tensor
"""
return -(label1 == label2).float()
this is the older version
I see. From the code snippet, I don't think it's from this package. Perhaps there's another CRF package that you used in the past? I don't recognise the code as mine.
Thank you! I used to pip install crf, and crf document led me to this url.
the pip document perhaps change the code.