Scale layer in Caffe can be used along with batch normalization to implement scale and shift operation, or it can be used to multiply 2 bottom blobs together. For example, in this paper it is used simply to multiple bottom blobs together and this layer doesn't have any weights and bias. I assume this possible utilization of Scale layer was missed during writing CaffeParser.py.
Overall the bug is the following. If you take a squeeze-and-excitation caffe model (or any other model with Scale layer as multiplier of bottom layers) and run mvNCProfile deploy.prototxt -is 224 224 the code will fail at line 237 return blobs[layer.name][0].data.astype(dtype=data_type), None in CaffeParser.py since this layer neither has weights nor biases.
