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In calculating accuracy of test dataset:
https://github.com/floodsung/LearningToCompare_FSL/blob/master/omniglot/omniglot_train_one_shot.py#L237
sample_images,sample_labels = sample_dataloader.__iter__().next()
test_images,test_labels = test_dataloader.__iter__().next()
sample_features = feature_encoder(Variable(sample_images).cuda(GPU)) # 5x64
test_features = feature_encoder(Variable(test_images).cuda(GPU)) # 20x64
sample_features_ext = sample_features.unsqueeze(0).repeat(SAMPLE_NUM_PER_CLASS*CLASS_NUM,1,1,1,1) test_features_ext = test_features.unsqueeze(0).repeat(SAMPLE_NUM_PER_CLASS*CLASS_NUM,1,1,1,1) test_features_ext = torch.transpose(test_features_ext,0,1)
relation_pairs = torch.cat((sample_features_ext,test_features_ext),2).view(-1,FEATURE_DIM*2,5,5) relations = relation_network(relation_pairs).view(-1,CLASS_NUM)
_,predict_labels = torch.max(relations.data,1)
rewards = [1 if predict_labels[j]==test_labels[j] else 0 for j in range(CLASS_NUM)]
I think the reward must be summed over all images in the batch size, so the j in the last line should be in range(len(test_labels))
Why was it sum over j in CLASS_NUM?
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