From 3964f17fd46a8b1ee39ea10408d3825c9a67af0b Mon Sep 17 00:00:00 2001 From: Pablo Marquez Tello Date: Wed, 20 Jul 2022 09:16:20 +0100 Subject: Remove data extraction scripts * Resolved MLCE-886 Change-Id: I3b8fbe662c715b82c08c63fa27892471a572fdd8 Signed-off-by: Pablo Marquez Tello Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/7945 Tested-by: Arm Jenkins Reviewed-by: Gunes Bayir Benchmark: Gunes Bayir Comments-Addressed: Arm Jenkins --- scripts/caffe_data_extractor.py | 45 ----------------------------------------- 1 file changed, 45 deletions(-) delete mode 100755 scripts/caffe_data_extractor.py (limited to 'scripts/caffe_data_extractor.py') diff --git a/scripts/caffe_data_extractor.py b/scripts/caffe_data_extractor.py deleted file mode 100755 index 47d24b265f..0000000000 --- a/scripts/caffe_data_extractor.py +++ /dev/null @@ -1,45 +0,0 @@ -#!/usr/bin/env python -"""Extracts trainable parameters from Caffe models and stores them in numpy arrays. -Usage - python caffe_data_extractor -m path_to_caffe_model_file -n path_to_caffe_netlist - -Saves each variable to a {variable_name}.npy binary file. - -Tested with Caffe 1.0 on Python 2.7 -""" -import argparse -import caffe -import os -import numpy as np - - -if __name__ == "__main__": - # Parse arguments - parser = argparse.ArgumentParser('Extract Caffe net parameters') - parser.add_argument('-m', dest='modelFile', type=str, required=True, help='Path to Caffe model file') - parser.add_argument('-n', dest='netFile', type=str, required=True, help='Path to Caffe netlist') - args = parser.parse_args() - - # Create Caffe Net - net = caffe.Net(args.netFile, 1, weights=args.modelFile) - - # Read and dump blobs - for name, blobs in net.params.iteritems(): - print('Name: {0}, Blobs: {1}'.format(name, len(blobs))) - for i in range(len(blobs)): - # Weights - if i == 0: - outname = name + "_w" - # Bias - elif i == 1: - outname = name + "_b" - else: - continue - - varname = outname - if os.path.sep in varname: - varname = varname.replace(os.path.sep, '_') - print("Renaming variable {0} to {1}".format(outname, varname)) - print("Saving variable {0} with shape {1} ...".format(varname, blobs[i].data.shape)) - # Dump as binary - np.save(varname, blobs[i].data) -- cgit v1.2.1