[root@localhost custom-resnet-v2]# cat runme.sh
#python demo_slim.py -h
#python demo_slim.py --cpu_num 8 --inter_op_threads 1 --intra_op_threads 8 --dump_timeline True
# export KMP_AFFINITY=verbose,granularity=fine,proclist=[0,1,2,3],explicit
# numactl -C 0-3 python demo_slim.py --cpu_num 4 --inter_op_threads 1 --intra_op_threads 4 >& run1.log &
export OMP_NUM_THREADS=8
python demo_slim.py --cpu_num 8 --inter_op_threads 1 --intra_op_threads 8
[root@localhost custom-resnet-v2]# cat demo_slim.py
# coding: utf8
import os
import sys
import numpy as np
import tensorflow as tf
from tensorflow.python.client import timeline
import argparse
import time
def make_fake_input(batch_size, input_height, input_width, input_channel):
im = np.zeros((input_height,input_width,input_channel), np.uint8)
im[:,:,:] = 1
images = np.zeros((batch_size, input_height, input_width, input_channel), dtype=np.float32)
for i in xrange(batch_size):
images[i, 0:im.shape[0], 0:im.shape[1], :] = im
#channel_swap = (0, 3, 1, 2) # caffe
#images = np.transpose(images, channel_swap)
#cv2.imwrite("test.jpg", im)
return images
def get_parser():
"""
create a parser to parse argument "--cpu_num --inter_op_threads --intra_op_threads"
"""
parser = argparse.ArgumentParser(description="Specify tensorflow parallelism")
parser.add_argument("--cpu_num", dest="cpu_num", default=1, help="specify how many cpus to use.(default: 1)")
parser.add_argument("--inter_op_threads", dest="inter_op_threads", default=1, help="specify max inter op parallelism.(default: 1)")
parser.add_argument("--intra_op_threads", dest="intra_op_threads", default=1, help="specify max intra op parallelism.(default: 1)")
parser.add_argument("--dump_timeline", dest="dump_timeline", default=False, help="specify to dump timeline.(default: False)")
return parser
def main():
parser = get_parser()
args = parser.parse_args()
#parser.print_help()
cpu_num = int(args.cpu_num)
inter_op_threads = int(args.inter_op_threads)
intra_op_threads = int(args.intra_op_threads)
dump_timeline = bool(args.dump_timeline)
print("cpu_num: ", cpu_num)
print("inter_op_threads: ", inter_op_threads)
print("intra_op_threads: ", intra_op_threads)
print("dump_timeline: ", dump_timeline)
config = tf.ConfigProto(device_count={"CPU": cpu_num}, # limit to num_cpu_core CPU usage
inter_op_parallelism_threads = inter_op_threads,
intra_op_parallelism_threads = intra_op_threads,
log_device_placement=False)
with tf.Session(config = config) as sess:
imgs = make_fake_input(1, 224, 224, 3)
#init_start = time.time()
saver = tf.train.import_meta_graph("slim_model/slim_model.ckpt.meta")
saver.restore(sess, tf.train.latest_checkpoint("slim_model/"))
## Operations
#for op in tf.get_default_graph().get_operations():
# print(op.name)
# print(op.values())
graph = tf.get_default_graph()
input_data = graph.get_tensor_by_name("Placeholder:0")
fc6 = graph.get_tensor_by_name("resnet_v2/avg_fc_fc6_Conv2D/BiasAdd:0")
#init_end = time.time()
#print("initialization time: ", init_end-init_start, "s")
time_start = time.time()
for step in range(200):
if dump_timeline:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
result = sess.run(fc6, feed_dict={input_data:imgs}, options=run_options, run_metadata=run_metadata)
tm = timeline.Timeline(run_metadata.step_stats)
ctf = tm.generate_chrome_trace_format()
with open('timeline.json', 'w') as f:
f.write(ctf)
else:
result = sess.run(fc6, feed_dict={input_data:imgs})
print(result[0][0][0])
time_end = time.time()
avg_time = (time_end-time_start) * 1000 / 200;
print("AVG Time: ", avg_time, " ms")
return 0
if __name__ == "__main__":
sys.exit(main())