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Vernadsky Daniil
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release S-2021.09
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‎caffe_models/evgen_util.bash‎

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# Utilities to be sourced in another script
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if [ -z "$name" ] ; then
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name="test"
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fi
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if [[ -z "$EV_CNNSDK_HOME" || ! -d $EV_CNNSDK_HOME ]] ; then
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echo "EV_CNNSDK_HOME not correctly set in the environment"
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exit 1
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fi
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evgen=${EV_CNNSDK_HOME}/evgencnn/scripts/evgencnn
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blobr=${EV_CNNSDK_HOME}/install/bin/blobreader
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if [ ! -f $evgen ] ; then
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echo "evgencnn executable not found : $evgen"
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exit 1
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fi
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# Secret evgencnn options : wof & vof
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export __SAVER=1
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# More verbose evgencnn output
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export show_classify=1
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if [ -f "${name}.opts" ] ; then
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base_opts=$(<${name}.opts)
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base_opts=$(echo $base_opts)
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echo "Using ${name}.opts"
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fi
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function runcmd()
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{
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echo "$@"
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eval "$@"
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if [ $? -ne 0 ] ; then
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echo "Failed to run command"
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exit 1
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fi
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}
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# Verification info loaded from bin files
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function wr_bld_vof()
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{
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local out=$1
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local dir=$(dirname ${out})
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if [ ! -d $dir ] ; then
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echo "Dir nto found : $dir"
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exit 2
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fi
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cat <<- EOF > $out
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#!/bin/bash -ex
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g++ -O2 -DWEIGHTS_IN_FILE -DVERIFY_IN_FILE \\
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-I ./include \\
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code/${name}_impl.cc \\
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verify/${name}_verify.cc \\
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-o ${name}.exe
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EOF
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chmod u+x $out
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echo "Created $out"
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pushd $dir
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runcmd ./$(basename ${out})
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popd
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}
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# Verification info loaded from bin files + dump intermediate values
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function wr_bld_vof_dump()
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{
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local out=$1
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cat <<- EOF > $out
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#!/bin/bash -ex
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g++ -O2 -DWEIGHTS_IN_FILE -DVERIFY_IN_FILE -DCNN_NATIVE_VERIFY \\
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-I ./include -I \${EV_CNNSDK_HOME}/include \\
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code/${name}_impl.cc \\
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verify/${name}_verify.c \\
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-o ${name}_dump.exe
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EOF
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chmod u+x $out
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echo "Created $out"
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}
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# Statically link all the input data (found in .c files).
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# This leads to slow compilation and slow startup. Unfortunatelly it is
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# the default mode of operation for the CNN tools.
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function wr_bld_static()
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{
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local out=$1
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cat <<- EOF > $out
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#!/bin/bash -ex
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# Argv 1 : the name of the C file containing the input image
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input_img=\${1:-no_input_specified}
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g++ -O2 -DCNN_NATIVE_VERIFY \\
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-I ./include -I \${EV_CNNSDK_HOME}/include \\
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code/${name}_impl.cc \\
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weights/${name}_filters.c \\
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verify/${name}_verify.c \\
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\${input_img} \\
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-o ${name}_static.exe
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EOF
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chmod u+x $out
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echo "Created $out"
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}
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‎caffe_models/mtcnn_v1/caffe_model/convert-mtcnn-relu.py‎

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#!/usr/bin/env python
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import os
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#os.environ['GLOG_minloglevel'] = '2'
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import numpy as np
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import caffe
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netPatched = caffe.Net('mtcnn-relu.prototxt', caffe.TEST)
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def convertDet1():
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net = caffe.Net('det1.prototxt', 'det1.caffemodel', caffe.TEST)
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for layer in ['conv1', 'conv2', 'conv3', 'conv4-1', 'conv4-2']:
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netPatched.params['pnet-'+layer][0].data[...] = net.params[layer][0].data
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netPatched.params['pnet-'+layer][1].data[...] = net.params[layer][1].data
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def convertDet2():
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net = caffe.Net('det2.prototxt', 'det2.caffemodel', caffe.TEST)
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for layer in ['conv1', 'conv2', 'conv3', 'conv4', 'conv5-1', 'conv5-2']:
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netPatched.params['rnet-'+layer][0].data[...] = net.params[layer][0].data
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netPatched.params['rnet-'+layer][1].data[...] = net.params[layer][1].data
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def convertDet3():
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net = caffe.Net('det3.prototxt', 'det3.caffemodel', caffe.TEST)
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for layer in ['conv1', 'conv2', 'conv3', 'conv4', 'conv5', 'conv6-1', 'conv6-2', 'conv6-3']:
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netPatched.params['onet-'+layer][0].data[...] = net.params[layer][0].data
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netPatched.params['onet-'+layer][1].data[...] = net.params[layer][1].data
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convertDet1()
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convertDet2()
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convertDet3()
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netPatched.save('mtcnn-relu.caffemodel')

‎caffe_models/mtcnn_v1/caffe_model/convert-mtcnn.py‎

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#!/usr/bin/env python
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import os
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os.environ['GLOG_minloglevel'] = '2'
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import numpy as np
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import caffe
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import argparse
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parser = argparse.ArgumentParser(description='Conversion options')
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parser.add_argument('-relu', action='store_true', help='Use ReLU instead of PReLU')
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parser.add_argument('-transp', action='store_true', help='Transpose convolution coefficients')
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args = parser.parse_args()
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baseName = "mtcnn"
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if args.relu:
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baseName += "-relu"
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if args.transp:
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baseName += "-transp"
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netPatched = caffe.Net("{}.prototxt".format(baseName), caffe.TEST)
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def convertDet1():
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net = caffe.Net('det1.prototxt', 'det1.caffemodel', caffe.TEST)
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for pnet in ['pnet1', 'pnet2', 'pnet3', 'pnet4', 'pnet5', 'pnet6', 'pnet7', 'pnet8', 'pnet9']:
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for layer in ['conv1', 'conv2', 'conv3']:
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if args.transp:
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netPatched.params[pnet+'-'+layer][0].data[...] = np.transpose(net.params[layer][0].data, (0,1,3,2))
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else:
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netPatched.params[pnet+'-'+layer][0].data[...] = net.params[layer][0].data
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netPatched.params[pnet+'-'+layer][1].data[...] = net.params[layer][1].data
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netPatched.params[pnet+'-conv4'][0].data[...] = \
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np.concatenate((net.params['conv4-1'][0].data, net.params['conv4-2'][0].data), axis=0)
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netPatched.params[pnet+'-conv4'][1].data[...] = \
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np.concatenate((net.params['conv4-1'][1].data, net.params['conv4-2'][1].data), axis=0)
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if not args.relu:
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for layer in ['PReLU1', 'PReLU2', 'PReLU3']:
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netPatched.params[pnet+'-'+layer][0].data[...] = net.params[layer][0].data
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def convertDet2():
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net = caffe.Net('det2.prototxt', 'det2.caffemodel', caffe.TEST)
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for layer in ['conv1', 'conv2', 'conv3']:
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if args.transp:
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netPatched.params['rnet-'+layer][0].data[...] = np.transpose(net.params[layer][0].data, (0,1,3,2))
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else:
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netPatched.params['rnet-'+layer][0].data[...] = net.params[layer][0].data
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netPatched.params['rnet-'+layer][1].data[...] = net.params[layer][1].data
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for layer in ['conv4']:
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if args.transp:
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netPatched.params['rnet-'+layer][0].data[...] = np.reshape(np.transpose(np.reshape(net.params[layer][0].data, (128,64,3,3)), (0,1,3,2)), (128,576))
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else:
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netPatched.params['rnet-'+layer][0].data[...] = net.params[layer][0].data
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netPatched.params['rnet-'+layer][1].data[...] = net.params[layer][1].data
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netPatched.params['rnet-conv5'][0].data[...] = \
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np.concatenate((net.params['conv5-1'][0].data, net.params['conv5-2'][0].data), axis=0)
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netPatched.params['rnet-conv5'][1].data[...] = \
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np.concatenate((net.params['conv5-1'][1].data, net.params['conv5-2'][1].data), axis=0)
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if not args.relu:
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for layer in ['prelu1', 'prelu2', 'prelu3', 'prelu4']:
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netPatched.params['rnet-'+layer][0].data[...] = net.params[layer][0].data
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def convertDet3():
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net = caffe.Net('det3.prototxt', 'det3.caffemodel', caffe.TEST)
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for layer in ['conv1', 'conv2', 'conv3', 'conv4']:
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if args.transp:
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netPatched.params['onet-'+layer][0].data[...] = np.transpose(net.params[layer][0].data, (0,1,3,2))
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else:
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netPatched.params['onet-'+layer][0].data[...] = net.params[layer][0].data
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netPatched.params['onet-'+layer][1].data[...] = net.params[layer][1].data
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for layer in ['conv5']:
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if args.transp:
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netPatched.params['onet-'+layer][0].data[...] = np.reshape(np.transpose(np.reshape(net.params[layer][0].data, (256,128,3,3)), (0,1,3,2)), (256,1152))
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else:
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netPatched.params['onet-'+layer][0].data[...] = net.params[layer][0].data
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netPatched.params['onet-'+layer][1].data[...] = net.params[layer][1].data
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netPatched.params['onet-conv6'][0].data[...] = \
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np.concatenate((net.params['conv6-1'][0].data,
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net.params['conv6-2'][0].data,
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net.params['conv6-3'][0].data), axis=0)
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netPatched.params['onet-conv6'][1].data[...] = \
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np.concatenate((net.params['conv6-1'][1].data,
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net.params['conv6-2'][1].data,
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net.params['conv6-3'][1].data), axis=0)
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if not args.relu:
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for layer in ['prelu1', 'prelu2', 'prelu3', 'prelu4', 'prelu5']:
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netPatched.params['onet-'+layer][0].data[...] = net.params[layer][0].data
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convertDet1()
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convertDet2()
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convertDet3()
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netPatched.save('{}.caffemodel'.format(baseName))

‎caffe_models/mtcnn_v1/caffe_model/convert-relu.py‎

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#!/usr/bin/env python
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import os
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os.environ['GLOG_minloglevel'] = '2'
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import numpy as np
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import caffe
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def convertDet1():
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net = caffe.Net('det1.prototxt', 'det1.caffemodel', caffe.TEST)
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netPatched = caffe.Net('det1-relu.prototxt', caffe.TEST)
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for layer in ['conv1', 'conv2', 'conv3', 'conv4-1', 'conv4-2']:
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netPatched.params[layer][0].data[...] = net.params[layer][0].data
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netPatched.params[layer][1].data[...] = net.params[layer][1].data
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netPatched.save('det1-relu.caffemodel')
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def convertDet2():
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net = caffe.Net('det2.prototxt', 'det2.caffemodel', caffe.TEST)
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netPatched = caffe.Net('det2-relu.prototxt', caffe.TEST)
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for layer in ['conv1', 'conv2', 'conv3', 'conv4', 'conv5-1', 'conv5-2']:
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netPatched.params[layer][0].data[...] = net.params[layer][0].data
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netPatched.params[layer][1].data[...] = net.params[layer][1].data
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netPatched.save('det2-relu.caffemodel')
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def convertDet3():
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net = caffe.Net('det3.prototxt', 'det3.caffemodel', caffe.TEST)
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netPatched = caffe.Net('det3-relu.prototxt', caffe.TEST)
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for layer in ['conv1', 'conv2', 'conv3', 'conv4', 'conv5', 'conv6-1', 'conv6-2', 'conv6-3']:
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netPatched.params[layer][0].data[...] = net.params[layer][0].data
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netPatched.params[layer][1].data[...] = net.params[layer][1].data
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netPatched.save('det3-relu.caffemodel')
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convertDet1()
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convertDet2()
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convertDet3()
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‎caffe_models/mtcnn_v1/caffe_model/convert-transp.py‎

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#!/usr/bin/env python
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import os
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os.environ['GLOG_minloglevel'] = '2'
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import numpy as np
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import caffe
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def convertDet1():
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net = caffe.Net('det1.prototxt', 'det1.caffemodel', caffe.TEST)
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netPatched = caffe.Net('det1-patched.prototxt', caffe.TEST)
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for layer in ['conv1', 'conv2', 'conv3']:
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netPatched.params[layer][0].data[...] = np.transpose(net.params[layer][0].data, (0,1,3,2))
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netPatched.params[layer][1].data[...] = net.params[layer][1].data
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netPatched.params['conv4'][0].data[...] = \
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np.concatenate((net.params['conv4-1'][0].data, net.params['conv4-2'][0].data), axis=0)
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netPatched.params['conv4'][1].data[...] = \
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np.concatenate((net.params['conv4-1'][1].data, net.params['conv4-2'][1].data), axis=0)
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netPatched.save('det1-patched-transp.caffemodel')
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def convertDet2():
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net = caffe.Net('det2.prototxt', 'det2.caffemodel', caffe.TEST)
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netPatched = caffe.Net('det2-patched.prototxt', caffe.TEST)
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for layer in ['conv1', 'conv2', 'conv3']:
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netPatched.params[layer][0].data[...] = np.transpose(net.params[layer][0].data, (0,1,3,2))
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netPatched.params[layer][1].data[...] = net.params[layer][1].data
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for layer in ['conv4']:
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netPatched.params[layer][0].data[...] = np.reshape(np.transpose(np.reshape(net.params[layer][0].data, (128,64,3,3)), (0,1,3,2)), (128,576))
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netPatched.params[layer][1].data[...] = net.params[layer][1].data
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netPatched.params['conv5'][0].data[...] = \
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np.concatenate((net.params['conv5-1'][0].data, net.params['conv5-2'][0].data), axis=0)
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netPatched.params['conv5'][1].data[...] = \
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np.concatenate((net.params['conv5-1'][1].data, net.params['conv5-2'][1].data), axis=0)
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netPatched.save('det2-patched-transp.caffemodel')
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def convertDet3():
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net = caffe.Net('det3.prototxt', 'det3.caffemodel', caffe.TEST)
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netPatched = caffe.Net('det3-patched.prototxt', caffe.TEST)
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for layer in ['conv1', 'conv2', 'conv3', 'conv4']:
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netPatched.params[layer][0].data[...] = np.transpose(net.params[layer][0].data, (0,1,3,2))
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netPatched.params[layer][1].data[...] = net.params[layer][1].data
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for layer in ['conv5']:
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netPatched.params[layer][0].data[...] = np.reshape(np.transpose(np.reshape(net.params[layer][0].data, (256,128,3,3)), (0,1,3,2)), (256,1152))
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netPatched.params[layer][1].data[...] = net.params[layer][1].data
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netPatched.params['conv6'][0].data[...] = \
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np.concatenate((net.params['conv6-1'][0].data,
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net.params['conv6-2'][0].data,
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net.params['conv6-3'][0].data), axis=0)
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netPatched.params['conv6'][1].data[...] = \
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np.concatenate((net.params['conv6-1'][1].data,
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net.params['conv6-2'][1].data,
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net.params['conv6-3'][1].data), axis=0)
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netPatched.save('det3-patched-transp.caffemodel')
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convertDet1()
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convertDet2()
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convertDet3()
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