Created
August 25, 2017 06:50
-
-
Save benwu232/1fbf1cd6b637810f5d57902fa6d4ef1b to your computer and use it in GitHub Desktop.
weight matrix loss for pytorch
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
def one_hot(size, index): | |
""" Creates a matrix of one hot vectors. | |
``` | |
import torch | |
import torch_extras | |
setattr(torch, 'one_hot', torch_extras.one_hot) | |
size = (3, 3) | |
index = torch.LongTensor([2, 0, 1]).view(-1, 1) | |
torch.one_hot(size, index) | |
# [[0, 0, 1], [1, 0, 0], [0, 1, 0]] | |
``` | |
""" | |
y_onehot = torch.LongTensor(*size).fill_(0) | |
y_onehot = Variable(y_onehot, volatile=index.volatile) | |
ones = Variable(torch.LongTensor(index.size()).fill_(1)) | |
y_onehot = y_onehot.scatter_(1, index.view(-1,1), ones.view(-1,1)) | |
return y_onehot | |
#weight_matrix is an N*N matrix which describes the weights between classes | |
class WeightMatrixLoss(torch.nn.Module): | |
def __init__(self, weight_matrix=None): | |
super().__init__() | |
#self.register_buffer('weight_matrix', weight_matrix) | |
self.weight_matrix = weight_matrix | |
def forward(self, p_onehot, target): | |
batch_size = len(target) | |
target = target.cpu() | |
t_onehot = one_hot(p_onehot.size(), target) | |
t = t_onehot.unsqueeze(1).cuda() | |
#p_onehot = p_onehot.cpu() | |
p = p_onehot.unsqueeze(2) | |
ce = -torch.bmm(t.float(), p) | |
#ce = torch.squeeze(ce, 1) | |
ce = ce.view((1, -1)) | |
_, predict_value = torch.max(p_onehot.data, 1) | |
weight_line = np.zeros(batch_size, dtype=np.float32) | |
#weight_matrix = self.weight_matrix.numpy() | |
np_t = target.data.numpy() | |
np_p = predict_value.cpu().view(-1).numpy() | |
for k in range(batch_size): | |
weight_line[k] = self.weight_matrix[np_t[k]][np_p[k]] | |
weight_line = Variable(torch.from_numpy(weight_line).view((-1, 1))).cuda() | |
wce = torch.mm(ce, weight_line).view(-1) | |
return (wce / batch_size) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment