adeagle
commented
7 years ago 一、数据集CASIA-webface
二、数据清洗方法: https://github.com/happynear/FaceVerification Good News: @潘泳苹果皮 and his colleagues have washed the CASIA-webface database manually. After washing, 27703 wrong images are deleted. The washed list can be downloaded from http://pan.baidu.com/s/1hrKpbm8
三、制作数据集 raw_data_path=/opt/mxnet/mxnet-face-master/verification/FullData align_data_path=/opt/mxnet/mxnet-face-master/verification/FullData-align/ makelist_path=/opt/mxnet/mxnet/tools/make_list.py
the number threads used for align data, you shold change this depend on your environment
num_process=4 landmarks=innerEyesAndBottomLip face_size=144 ts=0.1 list_name=casia rec_name=casia im2rec_path=/opt/mxnet/mxnet/bin/im2rec
step1:align the face iamge
if ! [ -e $align_data_path ];then mkdir -p $align_data_path for N in $(seq $num_process);do echo "the sub-process is : $N" python ../util/align_face.py $raw_data_path align $landmarks $align_data_path --ts $ts --size $face_size & done else echo "$align_data_path already exist." fi wait echo "Align face image done"
step2: generate .lst for im2rec
if ! [ -e ${list_name}image_train.lst ];then python -u $makelist_path $align_data_path $list_name --train_ratio 0.95 --recursive True else echo ".lst file for training already exist." fi echo "generated .lst file done"
step3: use img2rec to generate .rec file for training
if ! [ -e ${rec_name}_train.rec ]; then $im2rec_path ${list_name}image_train.lst $align_data_path ${rec_name}_train.rec color=0 encoding='.png' & $im2rec_path ${list_name}image_val.lst $align_data_path ${rec_name}_val.rec color=0 encoding='.png' & else echo "$rec_name already exist." fi wait echo "generate .rec done" echo "trining done!"
四、数据集类数和训练样本数 ls FullData-align/ | wc 10575 cat casiaimage_train.lst | wc 413914
五、训练源码 lightened_cnn.py:
import argparse,logging import mxnet as mx import matplotlib.pyplot as plt import numpy as np import time import threading import Queue
logger = logging.getLogger() logger.setLevel(logging.INFO) ctx = mx.gpu(0) msg=Queue.Queue(maxsize = 1000)
def group(data, num_r, num, kernel, stride, pad, layer): if num_r > 0: conv_r = mx.symbol.Convolution(data=data, num_filter=num_r, kernel=(1,1), name=('conv%s_r' % layer)) slice_r = mx.symbol.SliceChannel(data=conv_r, num_outputs=2, name=('slice%s_r' % layer)) mfm_r = mx.symbol.maximum(slice_r[0], slice_r[1]) conv = mx.symbol.Convolution(data=mfm_r, kernel=kernel, stride=stride, pad=pad, num_filter=num, name=('conv%s' % layer)) else: conv = mx.symbol.Convolution(data=data, kernel=kernel, stride=stride, pad=pad, num_filter=num, name=('conv%s' % layer)) slice = mx.symbol.SliceChannel(data=conv, num_outputs=2, name=('slice%s' % layer)) mfm = mx.symbol.maximum(slice[0], slice[1]) pool = mx.symbol.Pooling(data=mfm, pool_type="max", kernel=(2, 2), stride=(2,2), name=('pool%s' % layer)) return pool
def lightened_cnn_a_feature(): data = mx.symbol.Variable(name="data") pool1 = group(data, 0, 96, (9,9), (1,1), (0,0), str(1)) pool2 = group(pool1, 0, 192, (5,5), (1,1), (0,0), str(2)) pool3 = group(pool2, 0, 256, (5,5), (1,1), (0,0), str(3)) pool4 = group(pool3, 0, 384, (4,4), (1,1), (0,0), str(4)) flatten = mx.symbol.Flatten(data=pool4) fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=512, name="fc1") slice_fc1 = mx.symbol.SliceChannel(data=fc1, num_outputs=2, name="slice_fc1") mfm_fc1 = mx.symbol.maximum(slice_fc1[0], slice_fc1[1]) drop1 = mx.symbol.Dropout(data=mfm_fc1, p=0.7, name="drop1") return drop1
def lightened_cnn_a(num_classes=10575): drop1 = lightened_cnn_a_feature() fc2 = mx.symbol.FullyConnected(data=drop1, num_hidden=num_classes, name="fc2") softmax = mx.symbol.SoftmaxOutput(data=fc2, name='softmax') return softmax
def lightened_cnn_b_feature(): data = mx.symbol.Variable(name="data") pool1 = group(data, 0, 96, (5,5), (1,1), (2,2), str(1)) pool2 = group(pool1, 96, 192, (3,3), (1,1), (1,1), str(2)) pool3 = group(pool2, 192, 384, (3,3), (1,1), (1,1), str(3)) pool4 = group(pool3, 384, 256, (3,3), (1,1), (1,1), str(4)) pool5 = group(pool4, 256, 256, (3,3), (1,1), (1,1), str(5)) flatten = mx.symbol.Flatten(data=pool5) fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=512, name="fc1") slice_fc1 = mx.symbol.SliceChannel(data=fc1, num_outputs=2, name="slice_fc1") mfm_fc1 = mx.symbol.maximum(slice_fc1[0], slice_fc1[1]) drop1 = mx.symbol.Dropout(data=mfm_fc1, p=0.7, name="drop1") return drop1
def lightened_cnn_b(num_classes=10575): drop1 = lightened_cnn_b_feature() fc2 = mx.symbol.FullyConnected(data=drop1, num_hidden=num_classes, name="fc2") softmax = mx.symbol.SoftmaxOutput(data=fc2, name='softmax') return softmax
class MySpeedometer(object):
def __init__(self, batch_size, frequent=50):
self.batch_size = batch_size
self.frequent = frequent
self.init = False
self.tic = 0
self.last_count = 0
self.data=[]
self.epoch=0
def __call__(self, param):
"""Callback to Show speed."""
count = param.nbatch
if self.last_count > count:
self.init = False
self.last_count = count
if self.init:
if count % self.frequent == 0:
speed = self.frequent * self.batch_size / (time.time() - self.tic)
if param.eval_metric is not None:
name_value = param.eval_metric.get_name_value()
param.eval_metric.reset()
for name, value in name_value:
#print name,"+",value
if name.find("accuracy")>=0:
if self.epoch!=param.epoch:
#msg.put(value)
self.epoch=param.epoch
logging.info('Epoch[%d] Batch [%d]\tSpeed: %.2f samples/sec\tTrain-%s=%f',
param.epoch, count, speed, name, value)
else:
logging.info("Iter[%d] Batch [%d]\tSpeed: %.2f samples/sec",
param.epoch, count, speed)
self.tic = time.time()
else:
self.init = True
self.tic = time.time()def main():
lightened_cnn = lightened_cnn_a()
lightened_cnn = lightened_cnn_b()
devs = mx.cpu() if args.gpus is None else [mx.gpu(int(i)) for i in args.gpus.split(',')]
epoch_size = args.num_examples / args.batch_size
checkpoint = mx.callback.do_checkpoint(args.model_save_prefix)
kv = mx.kvstore.create(args.kv_store)
arg_params = None
aux_params = None
if args.retrain:
_, arg_params, aux_params = mx.model.load_checkpoint(args.model_load_prefix, args.model_load_epoch)
train = mx.io.ImageRecordIter(
path_imgrec = args.data_dir + "casia_train.rec",
data_shape = (1, 128, 128),
scale = 1./255,
batch_size = args.batch_size,
rand_crop = True,
rand_mirror = True,
num_parts = kv.num_workers,
part_index = kv.rank)
if not args.retrain:
val = mx.io.ImageRecordIter(
path_imgrec = args.data_dir + "casia_val.rec",
batch_size = args.batch_size,
data_shape = (1, 128, 128),
scale = 1./255,
rand_crop = True,
rand_mirror = False,
num_parts = kv.num_workers,
part_index = kv.rank)
else:
val = None
model = mx.model.FeedForward(
ctx = devs,
symbol = lightened_cnn,
arg_params = arg_params,
aux_params = aux_params,
num_epoch = 200,
learning_rate = args.lr,
momentum = 0.9,
wd = 0.0005,
lr_scheduler = mx.lr_scheduler.FactorScheduler(step=5*max(int(epoch_size * 1), 1), factor=0.8, stop_factor_lr=5e-5),
initializer = mx.init.Xavier(factor_type="in", magnitude=2.34))
model.fit(
X = train,
eval_data = val,
kvstore = kv,
batch_end_callback = MySpeedometer(args.batch_size, 100),
epoch_end_callback = checkpoint)if name == "main": parser = argparse.ArgumentParser(description="command for training lightened-cnn") parser.add_argument('--gpus', type=str, help='the gpus will be used, e.g "0,1,2,3"') parser.add_argument('--data-dir', type=str, default='./', help='the input data directory') parser.add_argument('--model-save-prefix', type=str, default='../model/lightened_cnn/lightened_cnn', help='the prefix of the model to save') parser.add_argument('--lr', type=float, default=0.05, help='initialization learning reate') parser.add_argument('--batch-size', type=int, default=100, help='the batch size') parser.add_argument('--num-examples', type=int, default=413914, help='the number of training examples') parser.add_argument('--kv-store', type=str, default='local', help='the kvstore type') parser.add_argument('--model-load-prefix', type=str, default='../model/lightened_cnn', help='the prefix of the model to load') parser.add_argument('--model-load-epoch', type=int, default=1, help='load the model on an epoch using the model-load-prefix') parser.add_argument('--retrain', action='store_true', default=False, help='true means continue training') args = parser.parse_args() logging.info(args) t1 = threading.Thread(target=main) t1.start() t1.join()
plt.figure(figsize=(8, 6))
data=[]
while True:
if msg.empty()==True:
time.sleep(1)
else:
data.append(msg.get())
#print data
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.plot(range(len(data)), data, 'o', linestyle='-', color="r",label="Train accuracy")
plt.ylim([0,1])
plt.draw()
plt.waitforbuttonpress(timeout=0.001)六、现象及问题: 1、我试过改lr 从0.008 - 0.1 如果是0.1 Train-Accuracy 会收敛在0.0001左右 如果在0.008-0.05 之间，会收敛在0.75左右 2、我的问题是，这个数据集用 lightened cnn 能否收敛到90%以上呢？
i don't know, because no detail about your experiment.