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convnet benchmark minor change

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Yangqing Jia 2015-12-18 22:42:14 -08:00
parent 8c1bbaa2ab
commit 809d54ee50
1 changed files with 38 additions and 15 deletions
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53
pycaffe2/convnet_benchmarks.py
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@ -2,14 +2,23 @@
Benchmark for common convnets.
Speed on Titan X, with 10 warmup steps and 10 main steps and with different
versions of cudnn, are as follows:
versions of cudnn, are as follows (time reported below is per-batch time,
forward / forward+backward):
V3 v4
CuDNN V3 CuDNN v4
AlexNet 32.5 / 108.0 27.4 / 90.1
OverFeat 113.0 / 342.3 91.7 / 276.5
Inception 134.5 / 485.8 125.7 / 450.6
VGG (batch 64) 200.8 / 650.0 164.1 / 551.7
Speed on Inception with varied batch sizes and CuDNN v4 is as follows:
Batch Size Speed per batch Speed per image
16 22.8 / 72.7 1.43 / 4.54
32 38.0 / 127.5 1.19 / 3.98
64 67.2 / 233.6 1.05 / 3.65
128 125.7 / 450.6 0.98 / 3.52
(Note that these numbers involve a "full" backprop, i.e. the gradient
with respect to the input image is also computed.)
@ -28,6 +37,13 @@ PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \
PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \
--batch_size 64 --model VGGA
for BS in 16 32 64 128; do
PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \
--batch_size $BS --model Inception --forward_only True
PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \
--batch_size $BS --model Inception
done
Note that VGG needs to be run at batch 64 due to memory limit on the backward
pass.
"""
@ -236,7 +252,19 @@ def Inception(order):
def Benchmark(model_gen, arg):
model, input_size = model_gen(arg.order)
for op in model.net._net.op:
# In order to be able to run everything without feeding more stuff, let's
# add the data and label blobs to the parameter initialization net as well.
if arg.order == "NCHW":
input_shape = [arg.batch_size, 3, input_size, input_size]
else:
input_shape = [arg.batch_size, input_size, input_size, 3]
model.param_init_net.GaussianFill([], "data", shape=input_shape,
mean=0.0, std=1.0)
model.param_init_net.UniformIntFill([], "label", shape=[arg.batch_size,],
min=0, max=999)
for op in model.net.Proto().op:
if op.type == 'Conv':
op.engine = 'CUDNN'
#op.arg.add().CopyFrom(utils.MakeArgument('ws_nbytes_limit', arg.cudnn_limit))
@ -256,17 +284,6 @@ def Benchmark(model_gen, arg):
model.param_init_net.RunAllOnGPU()
model.net.RunAllOnGPU()
workspace.ResetWorkspace()
if arg.order == 'NCHW':
data_shape = (arg.batch_size, 3, input_size, input_size)
else:
data_shape = (arg.batch_size, input_size, input_size, 3)
device_option = model.net.Proto().device_option
workspace.FeedBlob("data", np.random.randn(*data_shape).astype(np.float32),
device_option)
workspace.FeedBlob("label", np.asarray(range(arg.batch_size)).astype(np.int32),
device_option)
workspace.RunNetOnce(model.param_init_net)
workspace.CreateNet(model.net)
for i in range(arg.warmup_iterations):
@ -280,7 +297,13 @@ def Benchmark(model_gen, arg):
print 'Layer-wise benchmark.'
workspace.BenchmarkNet(
model.net.Proto().name, 1, arg.iterations, True)
# Writes out the pbtxt for benchmarks on e.g. Android
with open("{0}_init_batch_{1}.pbtxt".format(arg.model, arg.batch_size),
"w") as fid:
fid.write(str(model.param_init_net.Proto()))
with open("{0}.pbtxt".format(arg.model, arg.batch_size),
"w") as fid:
fid.write(str(model.net.Proto()))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Caffe2 benchmark.")