Comments (3)
Hi @foreverYoungGitHub
I borrowed that piece of code from https://github.com/tensorflow/benchmarks/blob/master/scripts/tf_cnn_benchmarks/convnet_builder.py#L112-L118
Looks like this came from tensorflow/benchmarks#63
You may want to ask the TensorFlow community on that.
from deep-learning-benchmark.
Oh, Thanks for that!
Actually, I just did the time benchmark for that. And the filter/kernel is actually computed as float16, which is much faster.
But when I run your code and the my own time benchmark code. I found that the speed of your code is much faster than my. Except the warm-up part in your code, what causes this different?
I attach my code as following.
import tensorflow as tf
import time
from datetime import datetime
import math
import argparse
import sys
import numpy as np
slim = tf.contrib.slim
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"]="0"
def time_tensorflow_run_placeholder(session, target, feed_dict, info_string):
num_steps_burn_in = 10
total_duration = 0.0
total_duration_squared = 0.0
for i in range(FLAGS.num_batches + num_steps_burn_in):
start_time = time.time()
_ = session.run(target,feed_dict=feed_dict)
duration = time.time() - start_time
if i >= num_steps_burn_in:
if not i % 10:
print('%s: step %d, duration = %.3f' % (datetime.now(), i - num_steps_burn_in, duration))
total_duration += duration
total_duration_squared += duration * duration
mn = total_duration / FLAGS.num_batches
vr = total_duration_squared / FLAGS.num_batches - mn * mn
sd = math.sqrt(vr)
print('%s: %s across %d steps, %.3f +/- %.3f sec / batch' % (datetime.now(), info_string, FLAGS.num_batches, mn, sd))
def run_benchmark():
graph_filename = FLAGS.graph_dir + "-{DATA_FORMAT}-{PRECISION}/frozen_graph.pb".format(DATA_FORMAT=FLAGS.data_format, PRECISION=FLAGS.precision)
# Create a graph def object to read the graph
with tf.gfile.GFile(graph_filename, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with tf.Graph().as_default() as graph:
with tf.device('/'+FLAGS.mode+':0'):
if FLAGS.data_format == 'NCHW':
inputs = np.random.random((FLAGS.batch_size, 3, FLAGS.input_width, FLAGS.input_height))
elif FLAGS.data_format == 'NHWC':
inputs = np.random.random((FLAGS.batch_size, FLAGS.input_width, FLAGS.input_height, 3))
if precision == 'fp16':
inputs = inputs.astype(np.float16)
tf.import_graph_def(graph_def)
config = tf.ConfigProto()
config.gpu_options.allocator_type = 'BFC'
sess = tf.Session(config=config)
# We define the input and output node we will feed in
input_node = graph.get_tensor_by_name('import/input:0')
output_node = graph.get_tensor_by_name('import/predictions/Reshape_1:0')
time_tensorflow_run_placeholder(sess, output_node, {input_node: inputs}, "Forward")
def main(_):
run_benchmark()
from deep-learning-benchmark.
@foreverYoungGitHub Off the top of my head, things that can definitely affect performance is the data format. NCHW is much faster than NHWC. Also, I noticed with TensorFlow is that it takes many warmup runs to get to the optimal speed. That's why in my code the number of warmup runs is set to 20... This was to specifically accommodate TensorFlow (PyTorch, for example, would "warmup" in just one run.)
from deep-learning-benchmark.
Related Issues (10)
- Minibatch size when going to mixed precision
- Tensorflow 1.5 HOT 6
- V100 for TensorFlow 1.5 and Pytorch 3 (update) HOT 4
- What frameworks / models / GPUs, etc., do you want to see for comparison? HOT 9
- Nvidia claims 6x performance improvement with cudnn 7.2 HOT 6
- How is possible to work with FP16 in GTX 1080 Ti? HOT 1
- Which dataset you use in images per second? HOT 1
- Running the benchmark with rtx 2080 Ti HOT 1
- Cite the benchmark results
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from deep-learning-benchmark.