Bug fix. Change softmax dim. #271
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In line 64, Change the softmax dim from 2 to 1. According to this line, `probs = F.softmax(self.scale * probs, dim=2)# batch x k x hw` In this code, the input dimension is [batch_size, num_class, fh\*fw]. And the softmax dimension is 2, which means that the summation of the dimensions of the feature map (fh\*fw) is one. However, in my opinion, I thinke the softmax dimension should be 1 to make the summation of the dimension of the num_class (num_class) is one. The corrected code is as follows： `probs = F.softmax(self.scale * probs, dim=1)# batch x num_class x hw`
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In line 64, Change the softmax dim from 2 to 1.
According to this line,
probs = F.softmax(self.scale * probs, dim=2)# batch x k x hwIn this code, the input dimension is [batch_size, num_class, fh*fw]. And the softmax dimension is 2, which means that the summation of the dimensions of the feature map (fh*fw) is one.
However, in my opinion, I thinke the softmax dimension should be 1 to make the summation of the dimension of the num_class (num_class) is one.
The corrected code is as follows：
probs = F.softmax(self.scale * probs, dim=1)# batch x num_class x hwBy the way, I had report this to issue, but without answer.
And I have a simple comparative experimental verification, the results show that dim1 can convergence faster, and get a better mIOU.