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tf2caffe.py
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tf2caffe.py
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import os
os.environ['GLOG_minloglevel'] = '2'
import caffe
import tensorflow as tf
import numpy as np
import sys
import cv2
from caffe.proto import caffe_pb2
import coremltools
import sys
sys.path.append("mtcnn")
import mtcnn
from time import time
_tstart_stack = []
def tic():
_tstart_stack.append(time())
def toc(fmt="Elapsed: %s s"):
print(fmt % (time()-_tstart_stack.pop()))
FACE_FEED_SIZE = 160 # default 160x160
EMBEDDING_SIZE = 128 # face embedding vector output, default 512
def load_model(model, input_map=None): # load tf model from dir
model_exp = os.path.expanduser(model)
print('Model directory: %s' % model_exp)
meta_file = 'model-20170512-110547.meta'
ckpt_file = 'model-20170512-110547.ckpt-250000'
saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file), input_map=input_map)
saver.restore(tf.get_default_session(), os.path.join(model_exp, ckpt_file))
def Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net):
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/weights:0'))
net.params[caffeLayerName][0].data[...] = var1.transpose((3, 2, 0, 1))
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/BatchNorm/moving_mean:0'))
net.params[caffeLayerName+'/BatchNorm'][0].data[...] = var1
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/BatchNorm/moving_variance:0'))
net.params[caffeLayerName+'/BatchNorm'][1].data[...] = var1 + 0.001
net.params[caffeLayerName+'/BatchNorm'][2].data[...] = 1
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/BatchNorm/beta:0'))
net.params[caffeLayerName+'/Scale'][0].data[...] = 1
net.params[caffeLayerName+'/Scale'][1].data[...] = var1
return net
########## Origin Style
# var1 = sess.run(tf.get_default_graph().get_tensor_by_name('InceptionResnetV1/Conv2d_4b_3x3/weights:0'))
# net.params['Conv2d_4b_3x3'][0].data[...] = var1.transpose((3, 2, 0, 1))
# var1 = sess.run(tf.get_default_graph().get_tensor_by_name('InceptionResnetV1/Conv2d_4b_3x3/BatchNorm/moving_mean:0'))
# net.params['Conv2d_4b_3x3/BatchNorm'][0].data[...] = var1
# var1 = sess.run(tf.get_default_graph().get_tensor_by_name('InceptionResnetV1/Conv2d_4b_3x3/BatchNorm/moving_variance:0'))
# net.params['Conv2d_4b_3x3/BatchNorm'][1].data[...] = var1 + 0.001
# net.params['Conv2d_4b_3x3/BatchNorm'][2].data[...] = 1
# var1 = sess.run(tf.get_default_graph().get_tensor_by_name('InceptionResnetV1/Conv2d_4b_3x3/BatchNorm/beta:0'))
# net.params['Conv2d_4b_3x3/Scale'][0].data[...] = 1
# net.params['Conv2d_4b_3x3/Scale'][1].data[...] = var1
def Block35_Repeat(sess, net):
for i in range(1,6):
caffeLayerName = 'block35_'+str(i)+'/Branch_0/Conv2d_1x1'
tfLayerName = 'InceptionResnetV1/Repeat/block35_'+str(i)+'/Branch_0/Conv2d_1x1'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'block35_'+str(i)+'/Branch_1/Conv2d_0a_1x1'
tfLayerName = 'InceptionResnetV1/Repeat/block35_'+str(i)+'/Branch_1/Conv2d_0a_1x1'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'block35_'+str(i)+'/Branch_1/Conv2d_0b_3x3'
tfLayerName = 'InceptionResnetV1/Repeat/block35_'+str(i)+'/Branch_1/Conv2d_0b_3x3'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'block35_'+str(i)+'/Branch_2/Conv2d_0a_1x1'
tfLayerName = 'InceptionResnetV1/Repeat/block35_'+str(i)+'/Branch_2/Conv2d_0a_1x1'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'block35_'+str(i)+'/Branch_2/Conv2d_0b_3x3'
tfLayerName = 'InceptionResnetV1/Repeat/block35_'+str(i)+'/Branch_2/Conv2d_0b_3x3'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'block35_'+str(i)+'/Branch_2/Conv2d_0c_3x3'
tfLayerName = 'InceptionResnetV1/Repeat/block35_'+str(i)+'/Branch_2/Conv2d_0c_3x3'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'block35_'+str(i)+'/Conv2d_1x1'
tfLayerName = 'InceptionResnetV1/Repeat/block35_'+str(i)+'/Conv2d_1x1'
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/weights:0'))
net.params[caffeLayerName][0].data[...] = var1.transpose((3, 2, 0, 1))
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/biases:0'))
net.params[caffeLayerName][1].data[...] = var1 # Conv Bias
########## Origin Style
# caffeLayerName = 'block35_1/Branch_0/Conv2d_1x1'
# tfLayerName = 'InceptionResnetV1/Repeat/block35_1/Branch_0/Conv2d_1x1'
# net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
#
# caffeLayerName = 'block35_1/Branch_1/Conv2d_0a_1x1'
# tfLayerName = 'InceptionResnetV1/Repeat/block35_1/Branch_1/Conv2d_0a_1x1'
# net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
# caffeLayerName = 'block35_1/Branch_1/Conv2d_0b_3x3'
# tfLayerName = 'InceptionResnetV1/Repeat/block35_1/Branch_1/Conv2d_0b_3x3'
# net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
#
# caffeLayerName = 'block35_1/Branch_2/Conv2d_0a_1x1'
# tfLayerName = 'InceptionResnetV1/Repeat/block35_1/Branch_2/Conv2d_0a_1x1'
# net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
# caffeLayerName = 'block35_1/Branch_2/Conv2d_0b_3x3'
# tfLayerName = 'InceptionResnetV1/Repeat/block35_1/Branch_2/Conv2d_0b_3x3'
# net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
# caffeLayerName = 'block35_1/Branch_2/Conv2d_0c_3x3'
# tfLayerName = 'InceptionResnetV1/Repeat/block35_1/Branch_2/Conv2d_0c_3x3'
# net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
#
# caffeLayerName = 'block35_1/Conv2d_1x1'
# tfLayerName = 'InceptionResnetV1/Repeat/block35_1/Conv2d_1x1'
# var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/weights:0'))
# net.params[caffeLayerName][0].data[...] = var1.transpose((3, 2, 0, 1))
# var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/biases:0'))
# net.params[caffeLayerName][1].data[...] = var1 # Conv Bias
return net
def Mixed_6a(sess, net):
caffeLayerName = 'Mixed_6a/Branch_0/Conv2d_1a_3x3'
tfLayerName = 'InceptionResnetV1/Mixed_6a/Branch_0/Conv2d_1a_3x3'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'Mixed_6a/Branch_1/Conv2d_0a_1x1'
tfLayerName = 'InceptionResnetV1/Mixed_6a/Branch_1/Conv2d_0a_1x1'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'Mixed_6a/Branch_1/Conv2d_0b_3x3'
tfLayerName = 'InceptionResnetV1/Mixed_6a/Branch_1/Conv2d_0b_3x3'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'Mixed_6a/Branch_1/Conv2d_1a_3x3'
tfLayerName = 'InceptionResnetV1/Mixed_6a/Branch_1/Conv2d_1a_3x3'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
return net
def Block17_Repeat(sess, net):
for i in range(1,11):
caffeLayerName = 'block17_'+str(i)+'/Branch_0/Conv2d_1x1'
tfLayerName = 'InceptionResnetV1/Repeat_1/block17_'+str(i)+'/Branch_0/Conv2d_1x1'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'block17_'+str(i)+'/Branch_1/Conv2d_0a_1x1'
tfLayerName = 'InceptionResnetV1/Repeat_1/block17_'+str(i)+'/Branch_1/Conv2d_0a_1x1'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'block17_'+str(i)+'/Branch_1/Conv2d_0b_1x7'
tfLayerName = 'InceptionResnetV1/Repeat_1/block17_'+str(i)+'/Branch_1/Conv2d_0b_1x7'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'block17_'+str(i)+'/Branch_1/Conv2d_0c_7x1'
tfLayerName = 'InceptionResnetV1/Repeat_1/block17_'+str(i)+'/Branch_1/Conv2d_0c_7x1'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'block17_'+str(i)+'/Conv2d_1x1'
tfLayerName = 'InceptionResnetV1/Repeat_1/block17_'+str(i)+'/Conv2d_1x1'
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/weights:0'))
net.params[caffeLayerName][0].data[...] = var1.transpose((3, 2, 0, 1))
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/biases:0'))
net.params[caffeLayerName][1].data[...] = var1 # Conv Bias
return net
def Mixed_7a(sess, net):
caffeLayerName = 'Mixed_7a/Branch_0/Conv2d_0a_1x1'
tfLayerName = 'InceptionResnetV1/Mixed_7a/Branch_0/Conv2d_0a_1x1'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'Mixed_7a/Branch_0/Conv2d_1a_3x3'
tfLayerName = 'InceptionResnetV1/Mixed_7a/Branch_0/Conv2d_1a_3x3'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'Mixed_7a/Branch_1/Conv2d_0a_1x1'
tfLayerName = 'InceptionResnetV1/Mixed_7a/Branch_1/Conv2d_0a_1x1'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'Mixed_7a/Branch_1/Conv2d_1a_3x3'
tfLayerName = 'InceptionResnetV1/Mixed_7a/Branch_1/Conv2d_1a_3x3'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'Mixed_7a/Branch_2/Conv2d_0a_1x1'
tfLayerName = 'InceptionResnetV1/Mixed_7a/Branch_2/Conv2d_0a_1x1'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'Mixed_7a/Branch_2/Conv2d_0b_3x3'
tfLayerName = 'InceptionResnetV1/Mixed_7a/Branch_2/Conv2d_0b_3x3'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'Mixed_7a/Branch_2/Conv2d_1a_3x3'
tfLayerName = 'InceptionResnetV1/Mixed_7a/Branch_2/Conv2d_1a_3x3'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'Conv_const'
convData = np.zeros([896,896,2,2])
for i in range(896):
convData[i][i][0][0] = 1
net.params[caffeLayerName][0].data[...] = convData
return net
def Block8_Repeat(sess, net):
for i in range(1,6):
caffeLayerName = 'block8_'+str(i)+'/Branch_0/Conv2d_1x1'
tfLayerName = 'InceptionResnetV1/Repeat_2/block8_'+str(i)+'/Branch_0/Conv2d_1x1'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'block8_'+str(i)+'/Branch_1/Conv2d_0a_1x1'
tfLayerName = 'InceptionResnetV1/Repeat_2/block8_'+str(i)+'/Branch_1/Conv2d_0a_1x1'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'block8_'+str(i)+'/Branch_1/Conv2d_0b_1x3'
tfLayerName = 'InceptionResnetV1/Repeat_2/block8_'+str(i)+'/Branch_1/Conv2d_0b_1x3'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'block8_'+str(i)+'/Branch_1/Conv2d_0c_3x1'
tfLayerName = 'InceptionResnetV1/Repeat_2/block8_'+str(i)+'/Branch_1/Conv2d_0c_3x1'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'block8_'+str(i)+'/Conv2d_1x1'
tfLayerName = 'InceptionResnetV1/Repeat_2/block8_'+str(i)+'/Conv2d_1x1'
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/weights:0'))
net.params[caffeLayerName][0].data[...] = var1.transpose((3, 2, 0, 1))
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/biases:0'))
net.params[caffeLayerName][1].data[...] = var1 # Conv Bias
return net
def Block8(sess, net):
caffeLayerName = 'Block8/Branch_0/Conv2d_1x1'
tfLayerName = 'InceptionResnetV1/Block8/Branch_0/Conv2d_1x1'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'Block8/Branch_1/Conv2d_0a_1x1'
tfLayerName = 'InceptionResnetV1/Block8/Branch_1/Conv2d_0a_1x1'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'Block8/Branch_1/Conv2d_0b_1x3'
tfLayerName = 'InceptionResnetV1/Block8/Branch_1/Conv2d_0b_1x3'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'Block8/Branch_1/Conv2d_0c_3x1'
tfLayerName = 'InceptionResnetV1/Block8/Branch_1/Conv2d_0c_3x1'
net = Conv_BN_Scale_Relu(caffeLayerName, tfLayerName, sess, net)
caffeLayerName = 'Block8/Conv2d_1x1'
tfLayerName = 'InceptionResnetV1/Block8/Conv2d_1x1'
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/weights:0'))
net.params[caffeLayerName][0].data[...] = var1.transpose((3, 2, 0, 1))
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/biases:0'))
net.params[caffeLayerName][1].data[...] = var1 # Conv Bias
return net
def Bottleneck(sess, net):
caffeLayerName = 'Bottleneck'
tfLayerName = 'InceptionResnetV1/Bottleneck'
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/weights:0'))
net.params[caffeLayerName][0].data[...] = var1.transpose((1,0))
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/BatchNorm/moving_mean:0'))
net.params[caffeLayerName+'/BatchNorm'][0].data[...] = var1
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/BatchNorm/moving_variance:0'))
net.params[caffeLayerName+'/BatchNorm'][1].data[...] = var1 + 0.001
net.params[caffeLayerName+'/BatchNorm'][2].data[...] = 1
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/BatchNorm/beta:0'))
net.params[caffeLayerName+'/Scale'][0].data[...] = 1
net.params[caffeLayerName+'/Scale'][1].data[...] = var1
return net
def Bottleneck_conv1x1(sess, net, embedding_size):
caffeLayerName = 'Conv1x1_512'
if(embedding_size==128):
caffeLayerName = 'Conv1x1_128'
tfLayerName = 'InceptionResnetV1/Bottleneck'
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/weights:0'))
print(var1.transpose((1,0)).shape)
print(var1.transpose((1,0))[np.newaxis][np.newaxis].transpose(2,3,0,1).shape)
print(var1.transpose((1,0))[:,np.newaxis][:,np.newaxis].shape)
print(var1.transpose((1,0))[np.newaxis,:][:,np.newaxis,:].shape)
print(net.params[caffeLayerName][0].data.shape)
net.params[caffeLayerName][0].data[...] = var1.transpose((1,0))[np.newaxis][np.newaxis].transpose(2,3,0,1)
# print(net.params[caffeLayerName][0].data.shape)
caffeLayerName = 'Bottleneck'
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/BatchNorm/moving_mean:0'))
net.params[caffeLayerName+'/BatchNorm'][0].data[...] = var1
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/BatchNorm/moving_variance:0'))
net.params[caffeLayerName+'/BatchNorm'][1].data[...] = var1 + 0.001
net.params[caffeLayerName+'/BatchNorm'][2].data[...] = 1
var1 = sess.run(tf.get_default_graph().get_tensor_by_name(tfLayerName+'/BatchNorm/beta:0'))
net.params[caffeLayerName+'/Scale'][0].data[...] = 1
net.params[caffeLayerName+'/Scale'][1].data[...] = var1
return net
def prewhiten(x):
mean = np.mean(x)
std = np.std(x)
print(mean, std)
std_adj = np.maximum(std, 1.0/np.sqrt(x.size))
y = np.multiply(np.subtract(x, mean), 1/std_adj)
return y
def normL2Vector(bottleNeck):
sum = 0
for v in bottleNeck:
sum += np.power(v, 2)
sqrt = np.max([np.sqrt(sum), 0.0000000001])
vector = np.zeros((bottleNeck.shape))
for (i, v) in enumerate(bottleNeck):
vector[i] = v/sqrt
return vector.astype(np.float32)
def convertTf2Caffe(model_dir, saveDir, embedding_size=512):
with tf.Session() as sess:
load_model(model_dir)
srandData = np.random.random((1, FACE_FEED_SIZE, FACE_FEED_SIZE, 3))
inputTF = srandData #[1,160,160,3]
inputCaffe = srandData.transpose((0,3,1,2)) #[1,3,160,160]
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
feed_dict = { images_placeholder: inputTF, phase_train_placeholder:False }
embedding = sess.run(embeddings, feed_dict=feed_dict)[0]
caffePrototxt = os.path.join(saveDir, 'resnetInception-512.prototxt')
if(embedding_size==128):
caffePrototxt = os.path.join(saveDir, 'resnetInception-128.prototxt')
net = caffe.Net(caffePrototxt, caffe.TEST)
# Conv + BatchNorm + Scale + Relu
net = Conv_BN_Scale_Relu('Conv2d_1a_3x3', 'InceptionResnetV1/Conv2d_1a_3x3', sess, net)
net = Conv_BN_Scale_Relu('Conv2d_2a_3x3', 'InceptionResnetV1/Conv2d_2a_3x3', sess, net)
net = Conv_BN_Scale_Relu('Conv2d_2b_3x3', 'InceptionResnetV1/Conv2d_2b_3x3', sess, net)
# MaxPool
net = Conv_BN_Scale_Relu('Conv2d_3b_1x1', 'InceptionResnetV1/Conv2d_3b_1x1', sess, net)
net = Conv_BN_Scale_Relu('Conv2d_4a_3x3', 'InceptionResnetV1/Conv2d_4a_3x3', sess, net)
net = Conv_BN_Scale_Relu('Conv2d_4b_3x3', 'InceptionResnetV1/Conv2d_4b_3x3', sess, net)
net = Block35_Repeat(sess, net)
net = Mixed_6a(sess, net)
net = Block17_Repeat(sess, net)
net = Mixed_7a(sess, net)
net = Block8_Repeat(sess, net)
net = Block8(sess, net)
# Bottleneck(sess, net)
net = Bottleneck_conv1x1(sess, net, embedding_size)
# var1 = sess.run(tf.get_default_graph().get_tensor_by_name('InceptionResnetV1/Conv2d_1a_3x3/BatchNorm/beta:0'))
# net.params['Conv2d_1a_3x3/BatchNorm'][0].data[...] = var1
net.blobs['data'].data[...] = inputCaffe
net.forward()
vector = normL2Vector(net.blobs['flatten'].data.squeeze())
print('The embedding diff between caffemodel and tfmodel: ')
print(vector-embedding)
net.save(os.path.join(saveDir, 'inception_resnet_v1_conv1x1.caffemodel'))
# print(net.blobs['Conv2d_1a_3x3'].data.transpose((0, 2, 3, 1)))
# print(net.params['Conv2d_1a_3x3'][0].data[...])
def calcCaffeVector(img, model_dir, embedding_size=512):
img = cv2.resize(img, (FACE_FEED_SIZE, FACE_FEED_SIZE))
prewhitened = prewhiten(img)[np.newaxis]
inputCaffe = prewhitened.transpose((0,3,1,2)) #[1,3,160,160]
print(inputCaffe.shape)
caffePrototxt = os.path.join(model_dir, 'resnetInception-512.prototxt')
if(embedding_size==128):
caffePrototxt = os.path.join(model_dir, 'resnetInception-128.prototxt')
caffemodel = os.path.join(model_dir, 'inception_resnet_v1_conv1x1.caffemodel')
net = caffe.Net(caffePrototxt, caffemodel, caffe.TEST)
net.blobs['data'].data[...] = inputCaffe
tic()
net.forward()
vector = normL2Vector(net.blobs['flatten'].data.squeeze())
print('Caffe Vector = {}'.format(vector))
toc()
def calcTFVector(img, model_dir):
with tf.Session() as sess:
load_model(model_dir)
img = cv2.resize(img, (FACE_FEED_SIZE, FACE_FEED_SIZE))
inputTF = prewhiten(img)[np.newaxis]
print(inputTF.shape)
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
feed_dict = { images_placeholder: inputTF, phase_train_placeholder:False }
tic()
print('TFVector: {}'.format(sess.run(embeddings, feed_dict=feed_dict)))
toc()
def caffemodel2Prototxt(modelName, savePath):
with open(modelName, 'rb') as f:
caffemodel = caffe_pb2.NetParameter()
caffemodel.ParseFromString(f.read())
for item in caffemodel.layers:
item.ClearField('blobs')
for item in caffemodel.layer:
item.ClearField('blobs')
print(str(caffemodel))
f = open(savePath, 'w')
f.write(str(caffemodel))
f.close()
def mtcnnDetect(img):
minsize = 40
caffe_model_path = "./mtcnn"
threshold = [0.8, 0.8, 0.6]
factor = 0.709
caffe.set_mode_cpu()
PNet = caffe.Net(caffe_model_path+"/det1.prototxt", caffe_model_path+"/det1.caffemodel", caffe.TEST)
RNet = caffe.Net(caffe_model_path+"/det2.prototxt", caffe_model_path+"/det2.caffemodel", caffe.TEST)
ONet = caffe.Net(caffe_model_path+"/det3.prototxt", caffe_model_path+"/det3.caffemodel", caffe.TEST)
img_matlab = img.copy()
tmp = img_matlab[:,:,2].copy()
img_matlab[:,:,2] = img_matlab[:,:,0]
img_matlab[:,:,0] = tmp
boundingboxes, points = mtcnn.detect_face(img_matlab, minsize, PNet, RNet, ONet, threshold, False, factor)
warped = img_matlab[int(boundingboxes[0][1]):int(boundingboxes[0][3]),
int(boundingboxes[0][0]):int(boundingboxes[0][2])]
print(int(boundingboxes[0][1]), int(boundingboxes[0][3]), int(boundingboxes[0][0]), int(boundingboxes[0][2]))
return warped
### Step 1: tensorflow to caffemodel
tf_model_dir = '/home/logview/workspace/projects/FaceAll/20170512-110547'
convertTf2Caffe(tf_model_dir, 'InceptionResnet_Model', EMBEDDING_SIZE)
### Step 2: caffemodel to CoreML
### use parameter (image_input_names='data') ==> input CVPixelBufferRef in iOS
### (without image_input_names='data') ==> input MLMultiArray in iOS
if(EMBEDDING_SIZE==512):
coreml_model = coremltools.converters.caffe.convert(('InceptionResnet_Model/inception_resnet_v1_conv1x1.caffemodel', 'InceptionResnet_Model/resnetInception-512.prototxt'), is_bgr=False)
coreml_model.save('InceptionResnet_Model/InceptionResnet.mlmodel')
else:
coreml_model = coremltools.converters.caffe.convert(('InceptionResnet_Model/inception_resnet_v1_conv1x1.caffemodel', 'InceptionResnet_Model/resnetInception-128.prototxt'), is_bgr=False)
coreml_model.save('InceptionResnet_Model/InceptionResnet.mlmodel')
### Step 3: calculate embedding from tensorflow model
imgPath = '4550.jpg'
img = cv2.imread(imgPath) # BGR
crop = mtcnnDetect(img) # RGB
calcTFVector(crop, tf_model_dir)
### Step 4: calculate embedding from caffe model
imgPath = '4550.jpg'
img = cv2.imread(imgPath)
crop = mtcnnDetect(img)
caffe_model_dir = 'InceptionResnet_Model'
calcCaffeVector(crop, caffe_model_dir, EMBEDDING_SIZE)