Remove data extraction scripts

* Resolved MLCE-886

Change-Id: I3b8fbe662c715b82c08c63fa27892471a572fdd8
Signed-off-by: Pablo Marquez Tello <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/7945
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Benchmark: Gunes Bayir <gunes.bayir@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

2 files changed, 0 insertions, 96 deletions

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)
diff --git a/scripts/tensorflow_data_extractor.py b/scripts/tensorflow_data_extractor.py
deleted file mode 100755
index 1dbf0e127e..0000000000
--- a/scripts/tensorflow_data_extractor.py
+++ /dev/null
@@ -1,51 +0,0 @@
-#!/usr/bin/env python
-"""Extracts trainable parameters from Tensorflow models and stores them in numpy arrays.
-Usage
-    python tensorflow_data_extractor -m path_to_binary_checkpoint_file -n path_to_metagraph_file
-
-Saves each variable to a {variable_name}.npy binary file.
-
-Note that since Tensorflow version 0.11 the binary checkpoint file which contains the values for each parameter has the format of:
-    {model_name}.data-{step}-of-{max_step}
-instead of:
-    {model_name}.ckpt
-When dealing with binary files with version >= 0.11, only pass {model_name} to -m option;
-when dealing with binary files with version < 0.11, pass the whole file name {model_name}.ckpt to -m option.
-
-Also note that this script relies on the parameters to be extracted being in the
-'trainable_variables' tensor collection. By default all variables are automatically added to this collection unless
-specified otherwise by the user. Thus should a user alter this default behavior and/or want to extract parameters from other
-collections, tf.GraphKeys.TRAINABLE_VARIABLES should be replaced accordingly.
-
-Tested with Tensorflow 1.2, 1.3 on Python 2.7.6 and Python 3.4.3.
-"""
-import argparse
-import numpy as np
-import os
-import tensorflow as tf
-
-
-if __name__ == "__main__":
-    # Parse arguments
-    parser = argparse.ArgumentParser('Extract Tensorflow net parameters')
-    parser.add_argument('-m', dest='modelFile', type=str, required=True, help='Path to Tensorflow checkpoint binary\
-            file. For Tensorflow version >= 0.11, only include model name; for Tensorflow version < 0.11, include\
-            model name with ".ckpt" extension')
-    parser.add_argument('-n', dest='netFile', type=str, required=True, help='Path to Tensorflow MetaGraph file')
-    args = parser.parse_args()
-
-    # Load Tensorflow Net
-    saver = tf.train.import_meta_graph(args.netFile)
-    with tf.Session() as sess:
-        # Restore session
-        saver.restore(sess, args.modelFile)
-        print('Model restored.')
-        # Save trainable variables to numpy arrays
-        for t in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):
-            varname = t.name
-            if os.path.sep in t.name:
-                varname = varname.replace(os.path.sep, '_')
-                print("Renaming variable {0} to {1}".format(t.name, varname))
-            print("Saving variable {0} with shape {1} ...".format(varname, t.shape))
-            # Dump as binary
-            np.save(varname, sess.run(t))