| """Convert caffe mean |
| """ |
| import argparse |
| import mxnet as mx |
| import numpy as np |
| import caffe_parser |
| |
| def convert_mean(binaryproto_fname, output=None): |
| """Convert caffe mean |
| |
| Parameters |
| ---------- |
| binaryproto_fname : str |
| Filename of the mean |
| output : str, optional |
| Save the mean into mxnet's format |
| |
| Returns |
| ------- |
| NDArray |
| Mean in ndarray |
| """ |
| mean_blob = caffe_parser.caffe_pb2.BlobProto() |
| with open(binaryproto_fname, 'rb') as f: |
| mean_blob.ParseFromString(f.read()) |
| |
| img_mean_np = np.array(mean_blob.data) |
| img_mean_np = img_mean_np.reshape( |
| mean_blob.channels, mean_blob.height, mean_blob.width |
| ) |
| # swap channels from Caffe BGR to RGB |
| img_mean_np[[0, 2], :, :] = img_mean_np[[2, 0], :, :] |
| nd = mx.nd.array(img_mean_np) |
| if output is not None: |
| mx.nd.save(output, {"mean_image": nd}) |
| return nd |
| |
| def main(): |
| parser = argparse.ArgumentParser(description='Convert caffe mean') |
| parser.add_argument('binaryproto_fname', help='Filename of the mean') |
| parser.add_argument('output', help='The name of the output file') |
| args = parser.parse_args() |
| convert_mean(args.binaryproto_fname, args.output) |
| |
| if __name__ == '__main__': |
| main() |
