import os
import sys
import torch
from torch.autograd import Variable
import torch.nn as nn
from qpth.qp import QPFunction
def computeGramMatrix(A, B):
"""
Constructs a linear kernel matrix between A and B.
We assume that each row in A and B represents a d-dimensional feature vector.
Parameters:
A: a (n_batch, n, d) Tensor.
B: a (n_batch, m, d) Tensor.
Returns: a (n_batch, n, m) Tensor.
"""
assert(A.dim() == 3)
assert(B.dim() == 3)
assert(A.size(0) == B.size(0) and A.size(2) == B.size(2))
return torch.bmm(A, B.transpose(1,2))
def binv(b_mat):
"""
Computes an inverse of each matrix in the batch.
Pytorch 0.4.1 does not support batched matrix inverse.
Hence, we are solving AX=I.
Parameters:
b_mat: a (n_batch, n, n) Tensor.
Returns: a (n_batch, n, n) Tensor.
"""
id_matrix = b_mat.new_ones(b_mat.size(-1)).diag().expand_as(b_mat).cuda()
b_inv, _ = torch.gesv(id_matrix, b_mat)
return b_inv
def one_hot(indices, depth):
"""
Returns a one-hot tensor.
This is a PyTorch equivalent of Tensorflow's tf.one_hot.
Parameters:
indices: a (n_batch, m) Tensor or (m) Tensor.
depth: a scalar. Represents the depth of the one hot dimension.
Returns: a (n_batch, m, depth) Tensor or (m, depth) Tensor.
"""
encoded_indicies = torch.zeros(indices.size() + torch.Size([depth])).cuda()
index = indices.view(indices.size()+torch.Size([1]))
encoded_indicies = encoded_indicies.scatter_(1,index,1)
return encoded_indicies
def batched_kronecker(matrix1, matrix2):
matrix1_flatten = matrix1.reshape(matrix1.size()[0], -1)
matrix2_flatten = matrix2.reshape(matrix2.size()[0], -1)
return torch.bmm(matrix1_flatten.unsqueeze(2), matrix2_flatten.unsqueeze(1)).reshape([matrix1.size()[0]] + list(matrix1.size()[1:]) + list(matrix2.size()[1:])).permute([0, 1, 3, 2, 4]).reshape(matrix1.size(0), matrix1.size(1) * matrix2.size(1), matrix1.size(2) * matrix2.size(2))
def MetaOptNetHead_Ridge(query, support, support_labels, n_way, n_shot, lambda_reg=50.0, double_precision=False):
"""
Fits the support set with ridge regression and
returns the classification score on the query set.
Parameters:
query: a (tasks_per_batch, n_query, d) Tensor.
support: a (tasks_per_batch, n_support, d) Tensor.
support_labels: a (tasks_per_batch, n_support) Tensor.
n_way: a scalar. Represents the number of classes in a few-shot classification task.
n_shot: a scalar. Represents the number of support examples given per class.
lambda_reg: a scalar. Represents the strength of L2 regularization.
Returns: a (tasks_per_batch, n_query, n_way) Tensor.
"""
tasks_per_batch = query.size(0)
n_support = support.size(1)
n_query = query.size(1)
assert(query.dim() == 3)
assert(support.dim() == 3)
assert(query.size(0) == support.size(0) and query.size(2) == support.size(2))
assert(n_support == n_way * n_shot) # n_support must equal to n_way * n_shot
#Here we solve the dual problem:
#Note that the classes are indexed by m & samples are indexed by i.
#min_{\alpha} 0.5 \sum_m ||w_m(\alpha)||^2 + \sum_i \sum_m e^m_i alpha^m_i
#where w_m(\alpha) = \sum_i \alpha^m_i x_i,
#\alpha is an (n_support, n_way) matrix
kernel_matrix = computeGramMatrix(support, support)
kernel_matrix += lambda_reg * torch.eye(n_support).expand(tasks_per_batch, n_support, n_support).cuda()
block_kernel_matrix = kernel_matrix.repeat(n_way, 1, 1) #(n_way * tasks_per_batch, n_support, n_support)
support_labels_one_hot = one_hot(support_labels.view(tasks_per_batch * n_support), n_way) # (tasks_per_batch * n_support, n_way)
support_labels_one_hot = support_labels_one_hot.transpose(0, 1) # (n_way, tasks_per_batch * n_support)
support_labels_one_hot = support_labels_one_hot.reshape(n_way * tasks_per_batch, n_support) # (n_way*tasks_per_batch, n_support)
G = block_kernel_matrix
e = -2.0 * support_labels_one_hot
#This is a fake inequlity constraint as qpth does not support QP without an inequality constraint.
id_matrix_1 = torch.zeros(tasks_per_batch*n_way, n_support, n_support)
C = Variable(id_matrix_1)
h = Variable(torch.zeros((tasks_per_batch*n_way, n_support)))
dummy = Variable(torch.Tensor()).cuda() # We want to ignore the equality constraint.
if double_precision:
G, e, C, h = [x.double().cuda() for x in [G, e, C, h]]
else:
G, e, C, h = [x.float().cuda() for x in [G, e, C, h]]
# Solve the following QP to fit SVM:
# \hat z = argmin_z 1/2 z^T G z + e^T z
# subject to Cz <= h
# We use detach() to prevent backpropagation to fixed variables.
qp_sol = QPFunction(verbose=False)(G, e.detach(), C.detach(), h.detach(), dummy.detach(), dummy.detach())
#qp_sol = QPFunction(verbose=False)(G, e.detach(), dummy.detach(), dummy.detach(), dummy.detach(), dummy.detach())
#qp_sol (n_way*tasks_per_batch, n_support)
qp_sol = qp_sol.reshape(n_way, tasks_per_batch, n_support)
#qp_sol (n_way, tasks_per_batch, n_support)
qp_sol = qp_sol.permute(1, 2, 0)
#qp_sol (tasks_per_batch, n_support, n_way)
# Compute the classification score.
compatibility = computeGramMatrix(support, query)
compatibility = compatibility.float()
compatibility = compatibility.unsqueeze(3).expand(tasks_per_batch, n_support, n_query, n_way)
qp_sol = qp_sol.reshape(tasks_per_batch, n_support, n_way)
logits = qp_sol.float().unsqueeze(2).expand(tasks_per_batch, n_support, n_query, n_way)
logits = logits * compatibility
logits = torch.sum(logits, 1)
return logits
def R2D2Head(query, support, support_labels, n_way, n_shot, l2_regularizer_lambda=50.0):
"""
Fits the support set with ridge regression and
returns the classification score on the query set.
This model is the classification head described in:
Meta-learning with differentiable closed-form solvers
(Bertinetto et al., in submission to NIPS 2018).
Parameters:
query: a (tasks_per_batch, n_query, d) Tensor.
support: a (tasks_per_batch, n_support, d) Tensor.
support_labels: a (tasks_per_batch, n_support) Tensor.
n_way: a scalar. Represents the number of classes in a few-shot classification task.
n_shot: a scalar. Represents the number of support examples given per class.
l2_regularizer_lambda: a scalar. Represents the strength of L2 regularization.
Returns: a (tasks_per_batch, n_query, n_way) Tensor.
"""
tasks_per_batch = query.size(0)
n_support = support.size(1)
assert(query.dim() == 3)
assert(support.dim() == 3)
assert(query.size(0) == support.size(0) and query.size(2) == support.size(2))
assert(n_support == n_way * n_shot) # n_support must equal to n_way * n_shot
support_labels_one_hot = one_hot(support_labels.view(tasks_per_batch * n_support), n_way)
support_labels_one_hot = support_labels_one_hot.view(tasks_per_batch, n_support, n_way)
id_matrix = torch.eye(n_support).expand(tasks_per_batch, n_support, n_support).cuda()
# Compute the dual form solution of the ridge regression.
# W = X^T(X X^T - lambda * I)^(-1) Y
ridge_sol = computeGramMatrix(support, support) + l2_regularizer_lambda * id_matrix
ridge_sol = binv(ridge_sol)
ridge_sol = torch.bmm(support.transpose(1,2), ridge_sol)
ridge_sol = torch.bmm(ridge_sol, support_labels_one_hot)
# Compute the classification score.
# score = W X
logits = torch.bmm(query, ridge_sol)
return logits
def MetaOptNetHead_SVM_He(query, support, support_labels, n_way, n_shot, C_reg=0.01, double_precision=False):
"""
Fits the support set with multi-class SVM and
returns the classification score on the query set.
This is the multi-class SVM presented in:
A simplified multi-class support vector machine with reduced dual optimization
(He et al., Pattern Recognition Letter 2012).
This SVM is desirable because the dual variable of size is n_support
(as opposed to n_way*n_support in the Weston&Watkins or Crammer&Singer multi-class SVM).
This model is the classification head that we have initially used for our project.
This was dropped since it turned out that it performs suboptimally on the meta-learning scenarios.
Parameters:
query: a (tasks_per_batch, n_query, d) Tensor.
support: a (tasks_per_batch, n_support, d) Tensor.
support_labels: a (tasks_per_batch, n_support) Tensor.
n_way: a scalar. Represents the number of classes in a few-shot classification task.
n_shot: a scalar. Represents the number of support examples given per class.
C_reg: a scalar. Represents the cost parameter C in SVM.
Returns: a (tasks_per_batch, n_query, n_way) Tensor.
"""
tasks_per_batch = query.size(0)
n_support = support.size(1)
n_query = query.size(1)
assert(query.dim() == 3)
assert(support.dim() == 3)
assert(query.size(0) == support.size(0) and query.size(2) == support.size(2))
assert(n_support == n_way * n_shot) # n_support must equal to n_way * n_shot
kernel_matrix = computeGramMatrix(support, support)
V = (support_labels * n_way - torch.ones(tasks_per_batch, n_support, n_way).cuda()) / (n_way - 1)
G = computeGramMatrix(V, V).detach()
G = kernel_matrix * G
e = Variable(-1.0 * torch.ones(tasks_per_batch, n_support))
id_matrix = torch.eye(n_support).expand(tasks_per_batch, n_support, n_support)
C = Variable(torch.cat((id_matrix, -id_matrix), 1))
h = Variable(torch.cat((C_reg * torch.ones(tasks_per_batch, n_support), torch.zeros(tasks_per_batch, n_support)), 1))
dummy = Variable(torch.Tensor()).cuda() # We want to ignore the equality constraint.
if double_precision:
G, e, C, h = [x.double().cuda() for x in [G, e, C, h]]
else:
G, e, C, h = [x.cuda() for x in [G, e, C, h]]
# Solve the following QP to fit SVM:
# \hat z = argmin_z 1/2 z^T G z + e^T z
# subject to Cz <= h
# We use detach() to prevent backpropagation to fixed variables.
qp_sol = QPFunction(verbose=False)(G, e.detach(), C.detach(), h.detach(), dummy.detach(), dummy.detach())
# Compute the classification score.
compatibility = computeGramMatrix(query, support)
compatibility = compatibility.float()
logits = qp_sol.float().unsqueeze(1).expand(tasks_per_batch, n_query, n_support)
logits = logits * compatibility
logits = logits.view(tasks_per_batch, n_query, n_shot, n_way)
logits = torch.sum(logits, 2)
return logits
def ProtoNetHead(query, support, support_labels, n_way, n_shot, normalize=True):
"""
Constructs the prototype representation of each class(=mean of support vectors of each class) and
returns the classification score (=L2 distance to each class prototype) on the query set.
This model is the classification head described in:
Prototypical Networks for Few-shot Learning
(Snell et al., NIPS 2017).
Parameters:
query: a (tasks_per_batch, n_query, d) Tensor.
support: a (tasks_per_batch, n_support, d) Tensor.
support_labels: a (tasks_per_batch, n_support) Tensor.
n_way: a scalar. Represents the number of classes in a few-shot classification task.
n_shot: a scalar. Represents the number of support examples given per class.
normalize: a boolean. Represents whether if we want to normalize the distances by the embedding dimension.
Returns: a (tasks_per_batch, n_query, n_way) Tensor.
"""
tasks_per_batch = query.size(0)
n_support = support.size(1)
n_query = query.size(1)
d = query.size(2)
assert(query.dim() == 3)
assert(support.dim() == 3)
assert(query.size(0) == support.size(0) and query.size(2) == support.size(2))
assert(n_support == n_way * n_shot) # n_support must equal to n_way * n_shot
support_labels_one_hot = one_hot(support_labels.view(tasks_per_batch * n_support), n_way)
support_labels_one_hot = support_labels_one_hot.view(tasks_per_batch, n_support, n_way)
# From:
# https://github.com/gidariss/FewShotWithoutForgetting/blob/master/architectures/PrototypicalNetworksHead.py
#************************* Compute Prototypes **************************
labels_train_transposed = support_labels_one_hot.transpose(1,2)
# Batch matrix multiplication:
# prototypes = labels_train_transposed * features_train ==>
# [batch_size x nKnovel x num_channels] =
# [batch_size x nKnovel x num_train_examples] * [batch_size * num_train_examples * num_channels]
prototypes = torch.bmm(labels_train_transposed, support)
# Divide with the number of examples per novel category.
prototypes = prototypes.div(
labels_train_transposed.sum(dim=2, keepdim=True).expand_as(prototypes)
)
# Distance Matrix Vectorization Trick
AB = computeGramMatrix(query, prototypes)
AA = (query * query).sum(dim=2, keepdim=True)
BB = (prototypes * prototypes).sum(dim=2, keepdim=True).reshape(tasks_per_batch, 1, n_way)
logits = AA.expand_as(AB) - 2 * AB + BB.expand_as(AB)
logits = -logits
if normalize:
logits = logits / d
return logits
def MetaOptNetHead_SVM_CS(query, support, support_labels, n_way, n_shot, C_reg=0.1, double_precision=False, maxIter=15):
"""
Fits the support set with multi-class SVM and
returns the classification score on the query set.
This is the multi-class SVM presented in:
On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines
(Crammer and Singer, Journal of Machine Learning Research 2001).
This model is the classification head that we use for the final version.
Parameters:
query: a (tasks_per_batch, n_query, d) Tensor.
support: a (tasks_per_batch, n_support, d) Tensor.
support_labels: a (tasks_per_batch, n_support) Tensor.
n_way: a scalar. Represents the number of classes in a few-shot classification task.
n_shot: a scalar. Represents the number of support examples given per class.
C_reg: a scalar. Represents the cost parameter C in SVM.
Returns: a (tasks_per_batch, n_query, n_way) Tensor.
"""
tasks_per_batch = query.size(0)
n_support = support.size(1)
n_query = query.size(1)
assert(query.dim() == 3)
assert(support.dim() == 3)
assert(query.size(0) == support.size(0) and query.size(2) == support.size(2))
assert(n_support == n_way * n_shot) # n_support must equal to n_way * n_shot
#Here we solve the dual problem:
#Note that the classes are indexed by m & samples are indexed by i.
#min_{\alpha} 0.5 \sum_m ||w_m(\alpha)||^2 + \sum_i \sum_m e^m_i alpha^m_i
#s.t. \alpha^m_i <= C^m_i \forall m,i , \sum_m \alpha^m_i=0 \forall i
#where w_m(\alpha) = \sum_i \alpha^m_i x_i,
#and C^m_i = C if m = y_i,
#C^m_i = 0 if m != y_i.
#This borrows the notation of liblinear.
#\alpha is an (n_support, n_way) matrix
kernel_matrix = computeGramMatrix(support, support)
id_matrix_0 = torch.eye(n_way).expand(tasks_per_batch, n_way, n_way).cuda()
block_kernel_matrix = batched_kronecker(kernel_matrix, id_matrix_0)
#This seems to help avoid PSD error from the QP solver.
block_kernel_matrix += 1.0 * torch.eye(n_way*n_support).expand(tasks_per_batch, n_way*n_support, n_way*n_support).cuda()
support_labels_one_hot = one_hot(support_labels.view(tasks_per_batch * n_support), n_way) # (tasks_per_batch * n_support, n_support)
support_labels_one_hot = support_labels_one_hot.view(tasks_per_batch, n_support, n_way)
support_labels_one_hot = support_labels_one_hot.reshape(tasks_per_batch, n_support * n_way)
G = block_kernel_matrix
e = -1.0 * support_labels_one_hot
#print (G.size())
#This part is for the inequality constraints:
#\alpha^m_i <= C^m_i \forall m,i
#where C^m_i = C if m = y_i,
#C^m_i = 0 if m != y_i.
id_matrix_1 = torch.eye(n_way * n_support).expand(tasks_per_batch, n_way * n_support, n_way * n_support)
C = Variable(id_matrix_1)
h = Variable(C_reg * support_labels_one_hot)
#print (C.size(), h.size())
#This part is for the equality constraints:
#\sum_m \alpha^m_i=0 \forall i
id_matrix_2 = torch.eye(n_support).expand(tasks_per_batch, n_support, n_support).cuda()
A = Variable(batched_kronecker(id_matrix_2, torch.ones(tasks_per_batch, 1, n_way).cuda()))
b = Variable(torch.zeros(tasks_per_batch, n_support))
#print (A.size(), b.size())
if double_precision:
G, e, C, h, A, b = [x.double().cuda() for x in [G, e, C, h, A, b]]
else:
G, e, C, h, A, b = [x.float().cuda() for x in [G, e, C, h, A, b]]
# Solve the following QP to fit SVM:
# \hat z = argmin_z 1/2 z^T G z + e^T z
# subject to Cz <= h
# We use detach() to prevent backpropagation to fixed variables.
qp_sol = QPFunction(verbose=False, maxIter=maxIter)(G, e.detach(), C.detach(), h.detach(), A.detach(), b.detach())
# Compute the classification score.
compatibility = computeGramMatrix(support, query)
compatibility = compatibility.float()
compatibility = compatibility.unsqueeze(3).expand(tasks_per_batch, n_support, n_query, n_way)
qp_sol = qp_sol.reshape(tasks_per_batch, n_support, n_way)
logits = qp_sol.float().unsqueeze(2).expand(tasks_per_batch, n_support, n_query, n_way)
logits = logits * compatibility
logits = torch.sum(logits, 1)
return logits
def MetaOptNetHead_SVM_WW(query, support, support_labels, n_way, n_shot, C_reg=0.00001, double_precision=False):
"""
Fits the support set with multi-class SVM and
returns the classification score on the query set.
This is the multi-class SVM presented in:
Support Vector Machines for Multi Class Pattern Recognition
(Weston and Watkins, ESANN 1999).
Parameters:
query: a (tasks_per_batch, n_query, d) Tensor.
support: a (tasks_per_batch, n_support, d) Tensor.
support_labels: a (tasks_per_batch, n_support) Tensor.
n_way: a scalar. Represents the number of classes in a few-shot classification task.
n_shot: a scalar. Represents the number of support examples given per class.
C_reg: a scalar. Represents the cost parameter C in SVM.
Returns: a (tasks_per_batch, n_query, n_way) Tensor.
"""
"""
Fits the support set with multi-class SVM and
returns the classification score on the query set.
This is the multi-class SVM presented in:
Support Vector Machines for Multi Class Pattern Recognition
(Weston and Watkins, ESANN 1999).
Parameters:
query: a (tasks_per_batch, n_query, d) Tensor.
support: a (tasks_per_batch, n_support, d) Tensor.
support_labels: a (tasks_per_batch, n_support) Tensor.
n_way: a scalar. Represents the number of classes in a few-shot classification task.
n_shot: a scalar. Represents the number of support examples given per class.
C_reg: a scalar. Represents the cost parameter C in SVM.
Returns: a (tasks_per_batch, n_query, n_way) Tensor.
"""
tasks_per_batch = query.size(0)
n_support = support.size(1)
n_query = query.size(1)
assert(query.dim() == 3)
assert(support.dim() == 3)
assert(query.size(0) == support.size(0) and query.size(2) == support.size(2))
assert(n_support == n_way * n_shot) # n_support must equal to n_way * n_shot
#In theory, \alpha is an (n_support, n_way) matrix
#NOTE: In this implementation, we solve for a flattened vector of size (n_way*n_support)
#In order to turn it into a matrix, you must first reshape it into an (n_way, n_support) matrix
#then transpose it, resulting in (n_support, n_way) matrix
kernel_matrix = computeGramMatrix(support, support) + torch.ones(tasks_per_batch, n_support, n_support).cuda()
id_matrix_0 = torch.eye(n_way).expand(tasks_per_batch, n_way, n_way).cuda()
block_kernel_matrix = batched_kronecker(id_matrix_0, kernel_matrix)
kernel_matrix_mask_x = support_labels.reshape(tasks_per_batch, n_support, 1).expand(tasks_per_batch, n_support, n_support)
kernel_matrix_mask_y = support_labels.reshape(tasks_per_batch, 1, n_support).expand(tasks_per_batch, n_support, n_support)
kernel_matrix_mask = (kernel_matrix_mask_x == kernel_matrix_mask_y).float()
block_kernel_matrix_inter = kernel_matrix_mask * kernel_matrix
block_kernel_matrix += block_kernel_matrix_inter.repeat(1, n_way, n_way)
kernel_matrix_mask_second_term = support_labels.reshape(tasks_per_batch, n_support, 1).expand(tasks_per_batch, n_support, n_support * n_way)
kernel_matrix_mask_second_term = kernel_matrix_mask_second_term == torch.arange(n_way).long().repeat(n_support).reshape(n_support, n_way).transpose(1, 0).reshape(1, -1).repeat(n_support, 1).cuda()
kernel_matrix_mask_second_term = kernel_matrix_mask_second_term.float()
block_kernel_matrix -= (2.0 - 1e-4) * (kernel_matrix_mask_second_term * kernel_matrix.repeat(1, 1, n_way)).repeat(1, n_way, 1)
Y_support = one_hot(support_labels.view(tasks_per_batch * n_support), n_way)
Y_support = Y_support.view(tasks_per_batch, n_support, n_way)
Y_support = Y_support.transpose(1, 2) # (tasks_per_batch, n_way, n_support)
Y_support = Y_support.reshape(tasks_per_batch, n_way * n_support)
G = block_kernel_matrix
e = -2.0 * torch.ones(tasks_per_batch, n_way * n_support)
id_matrix = torch.eye(n_way * n_support).expand(tasks_per_batch, n_way * n_support, n_way * n_support)
C_mat = C_reg * torch.ones(tasks_per_batch, n_way * n_support).cuda() - C_reg * Y_support
C = Variable(torch.cat((id_matrix, -id_matrix), 1))
#C = Variable(torch.cat((id_matrix_masked, -id_matrix_masked), 1))
zer = torch.zeros(tasks_per_batch, n_way * n_support).cuda()
h = Variable(torch.cat((C_mat, zer), 1))
dummy = Variable(torch.Tensor()).cuda() # We want to ignore the equality constraint.
if double_precision:
G, e, C, h = [x.double().cuda() for x in [G, e, C, h]]
else:
G, e, C, h = [x.cuda() for x in [G, e, C, h]]
# Solve the following QP to fit SVM:
# \hat z = argmin_z 1/2 z^T G z + e^T z
# subject to Cz <= h
# We use detach() to prevent backpropagation to fixed variables.
#qp_sol = QPFunction(verbose=False)(G, e.detach(), C.detach(), h.detach(), dummy.detach(), dummy.detach())
qp_sol = QPFunction(verbose=False)(G, e, C, h, dummy.detach(), dummy.detach())
# Compute the classification score.
compatibility = computeGramMatrix(support, query) + torch.ones(tasks_per_batch, n_support, n_query).cuda()
compatibility = compatibility.float()
compatibility = compatibility.unsqueeze(1).expand(tasks_per_batch, n_way, n_support, n_query)
qp_sol = qp_sol.float()
qp_sol = qp_sol.reshape(tasks_per_batch, n_way, n_support)
A_i = torch.sum(qp_sol, 1) # (tasks_per_batch, n_support)
A_i = A_i.unsqueeze(1).expand(tasks_per_batch, n_way, n_support)
qp_sol = qp_sol.float().unsqueeze(3).expand(tasks_per_batch, n_way, n_support, n_query)
Y_support_reshaped = Y_support.reshape(tasks_per_batch, n_way, n_support)
Y_support_reshaped = A_i * Y_support_reshaped
Y_support_reshaped = Y_support_reshaped.unsqueeze(3).expand(tasks_per_batch, n_way, n_support, n_query)
logits = (Y_support_reshaped - qp_sol) * compatibility
logits = torch.sum(logits, 2)
return logits.transpose(1, 2)
class ClassificationHead(nn.Module):
def __init__(self, base_learner='MetaOptNet', enable_scale=True):
super(ClassificationHead, self).__init__()
if ('SVM-CS' in base_learner):
self.head = MetaOptNetHead_SVM_CS
elif ('Ridge' in base_learner):
self.head = MetaOptNetHead_Ridge
elif ('R2D2' in base_learner):
self.head = R2D2Head
elif ('Proto' in base_learner):
self.head = ProtoNetHead
elif ('SVM-He' in base_learner):
self.head = MetaOptNetHead_SVM_He
elif ('SVM-WW' in base_learner):
self.head = MetaOptNetHead_SVM_WW
else:
print ("Cannot recognize the base learner type")
assert(False)
# Add a learnable scale
self.enable_scale = enable_scale
self.scale = nn.Parameter(torch.FloatTensor([1.0]))
def forward(self, query, support, support_labels, n_way, n_shot, **kwargs):
if self.enable_scale:
return self.scale * self.head(query, support, support_labels, n_way, n_shot, **kwargs)
else:
return self.head(query, support, support_labels, n_way, n_shot, **kwargs)
