def __call__(self, x: TensorList):
"""
Compute residuals
:param x: [filters, projection_matrices]
:return: [data_terms, filter_regularizations, proj_mat_regularizations]
"""
filter = x[:len(x) // 2] # w2 in paper
P = x[len(x) // 2:] # w1 in paper
# Do first convolution
compressed_samples = operation.conv1x1(self.training_samples, P).apply(
self.projection_activation)
# Do second convolution
residuals = operation.conv2d(compressed_samples, filter,
mode='same').apply(
self.response_activation)
# Compute data residuals
residuals = residuals - self.y
residuals = (self.sample_weights.sqrt().view(-1, 1, 1,
1)).variable() * residuals
# Add regularization for projection matrix
residuals.extend(self.filter_reg.apply(math.sqrt) * filter)
# Add regularization for projection matrix
residuals.extend(self.projection_reg.apply(math.sqrt) * P)
return residuals
def ip_input(self, a: TensorList, b: TensorList):
num = len(a) // 2 # Number of filters
a_filter = a[:num]
b_filter = b[:num]
a_P = a[num:]
b_P = b[num:]
# Filter inner product
# ip_out = a_filter.reshape(-1) @ b_filter.reshape(-1)
ip_out = operation.conv2d(a_filter, b_filter).view(-1)
# Add projection matrix part
# ip_out += a_P.reshape(-1) @ b_P.reshape(-1)
ip_out += operation.conv2d(a_P.view(1, -1, 1, 1), b_P.view(1, -1, 1, 1)).view(-1)
# Have independent inner products for each filter
return ip_out.concat(ip_out.clone())
def __call__(self, x: TensorList):
"""
Compute residuals
:param x: [filters]
:return: [data_terms, filter_regularizations]
"""
# Do convolution and compute residuals
residuals = operation.conv2d(self.training_samples, x, mode='same').apply(self.response_activation)
residuals = residuals - self.y
residuals = self.sample_weights.sqrt().view(-1, 1, 1, 1).variable() * residuals
# Add regularization for projection matrix
residuals.extend(self.filter_reg.apply(math.sqrt) * x)
return residuals
def ip_input(self, a: TensorList, b: TensorList):
# return a.reshape(-1) @ b.reshape(-1)
# return (a * b).sum()
return operation.conv2d(a, b).view(-1)