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Add more models to benchmark_score (apache#12780)
* add models to cnn benchmark * improve benchmark score * add benchmark_gluon * improve lint * improve lint * add licsence for script * improve script lint * mv benchmark_gluon to new location * support multi-gpus * Add a new parameter 'global batchsize' for the batch size multiplication for multi-gpu case * add batch size argument help * improve help and change default batchsize * simplify benchmark_gluon
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# Licensed to the Apache Software Foundation (ASF) under one | ||
# or more contributor license agreements. See the NOTICE file | ||
# distributed with this work for additional information | ||
# regarding copyright ownership. The ASF licenses this file | ||
# to you under the Apache License, Version 2.0 (the | ||
# "License"); you may not use this file except in compliance | ||
# with the License. You may obtain a copy of the License at | ||
# | ||
# http:https://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, | ||
# software distributed under the License is distributed on an | ||
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
# KIND, either express or implied. See the License for the | ||
# specific language governing permissions and limitations | ||
# under the License. | ||
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import mxnet as mx | ||
import mxnet.gluon.model_zoo.vision as models | ||
import time | ||
import logging | ||
import argparse | ||
import subprocess | ||
import os | ||
import errno | ||
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logging.basicConfig(level=logging.INFO) | ||
parser = argparse.ArgumentParser(description='Gluon modelzoo-based CNN performance benchmark') | ||
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parser.add_argument('--model', type=str, default='all', | ||
choices=['all', 'alexnet', 'densenet121', 'densenet161', | ||
'densenet169', 'densenet201', 'inceptionv3', 'mobilenet0.25', | ||
'mobilenet0.5', 'mobilenet0.75', 'mobilenet1.0', 'mobilenetv2_0.25', | ||
'mobilenetv2_0.5', 'mobilenetv2_0.75', 'mobilenetv2_1.0', 'resnet101_v1', | ||
'resnet101_v2', 'resnet152_v1', 'resnet152_v2', 'resnet18_v1', | ||
'resnet18_v2', 'resnet34_v1', 'resnet34_v2', 'resnet50_v1', | ||
'resnet50_v2', 'squeezenet1.0', 'squeezenet1.1', 'vgg11', | ||
'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', | ||
'vgg19', 'vgg19_bn']) | ||
parser.add_argument('--batch-size', type=int, default=0, | ||
help='Batch size to use for benchmarking. Example: 32, 64, 128.' | ||
'By default, runs benchmark for batch sizes - 1, 32, 64, 128, 256') | ||
parser.add_argument('--num-batches', type=int, default=10) | ||
parser.add_argument('--gpus', type=str, default='', | ||
help='GPU IDs to use for this benchmark task. Example: --gpus=0,1,2,3 to use 4 GPUs.' | ||
'By default, use CPU only.') | ||
parser.add_argument('--type', type=str, default='inference', choices=['all', 'training', 'inference']) | ||
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opt = parser.parse_args() | ||
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num_batches = opt.num_batches | ||
dry_run = 10 # use 10 iterations to warm up | ||
batch_inf = [1, 32, 64, 128, 256] | ||
batch_train = [1, 32, 64, 128, 256] | ||
image_shapes = [(3, 224, 224), (3, 299, 299)] | ||
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def score(network, batch_size, ctx): | ||
assert (batch_size >= len(ctx)), "ERROR: batch size should not be smaller than num of GPUs." | ||
net = models.get_model(network) | ||
if 'inceptionv3' == network: | ||
data_shape = [('data', (batch_size,) + image_shapes[1])] | ||
else: | ||
data_shape = [('data', (batch_size,) + image_shapes[0])] | ||
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data = mx.sym.var('data') | ||
out = net(data) | ||
softmax = mx.sym.SoftmaxOutput(out, name='softmax') | ||
mod = mx.mod.Module(softmax, context=ctx) | ||
mod.bind(for_training = False, | ||
inputs_need_grad = False, | ||
data_shapes = data_shape) | ||
mod.init_params(initializer=mx.init.Xavier(magnitude=2.)) | ||
data = [mx.random.uniform(-1.0, 1.0, shape=shape, ctx=ctx[0]) for _, shape in mod.data_shapes] | ||
batch = mx.io.DataBatch(data, []) | ||
for i in range(dry_run + num_batches): | ||
if i == dry_run: | ||
tic = time.time() | ||
mod.forward(batch, is_train=False) | ||
for output in mod.get_outputs(): | ||
output.wait_to_read() | ||
fwd = time.time() - tic | ||
return fwd | ||
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def train(network, batch_size, ctx): | ||
assert (batch_size >= len(ctx)), "ERROR: batch size should not be smaller than num of GPUs." | ||
net = models.get_model(network) | ||
if 'inceptionv3' == network: | ||
data_shape = [('data', (batch_size,) + image_shapes[1])] | ||
else: | ||
data_shape = [('data', (batch_size,) + image_shapes[0])] | ||
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data = mx.sym.var('data') | ||
out = net(data) | ||
softmax = mx.sym.SoftmaxOutput(out, name='softmax') | ||
mod = mx.mod.Module(softmax, context=ctx) | ||
mod.bind(for_training = True, | ||
inputs_need_grad = False, | ||
data_shapes = data_shape) | ||
mod.init_params(initializer=mx.init.Xavier(magnitude=2.)) | ||
if len(ctx) > 1: | ||
mod.init_optimizer(kvstore='device', optimizer='sgd') | ||
else: | ||
mod.init_optimizer(kvstore='local', optimizer='sgd') | ||
data = [mx.random.uniform(-1.0, 1.0, shape=shape, ctx=ctx[0]) for _, shape in mod.data_shapes] | ||
batch = mx.io.DataBatch(data, []) | ||
for i in range(dry_run + num_batches): | ||
if i == dry_run: | ||
tic = time.time() | ||
mod.forward(batch, is_train=True) | ||
for output in mod.get_outputs(): | ||
output.wait_to_read() | ||
mod.backward() | ||
mod.update() | ||
bwd = time.time() - tic | ||
return bwd | ||
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if __name__ == '__main__': | ||
runtype = opt.type | ||
bs = opt.batch_size | ||
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if opt.model == 'all': | ||
networks = ['alexnet', 'densenet121', 'densenet161', 'densenet169', 'densenet201', | ||
'inceptionv3', 'mobilenet0.25', 'mobilenet0.5', 'mobilenet0.75', | ||
'mobilenet1.0', 'mobilenetv2_0.25', 'mobilenetv2_0.5', 'mobilenetv2_0.75', | ||
'mobilenetv2_1.0', 'resnet101_v1', 'resnet101_v2', 'resnet152_v1', 'resnet152_v2', | ||
'resnet18_v1', 'resnet18_v2', 'resnet34_v1', 'resnet34_v2', 'resnet50_v1', | ||
'resnet50_v2', 'squeezenet1.0', 'squeezenet1.1', 'vgg11', 'vgg11_bn', 'vgg13', | ||
'vgg13_bn', 'vgg16', 'vgg16_bn', 'vgg19', 'vgg19_bn'] | ||
logging.info('It may take some time to run all models, ' | ||
'set --network to run a specific one') | ||
else: | ||
networks = [opt.model] | ||
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devs = [mx.gpu(int(i)) for i in opt.gpus.split(',')] if opt.gpus.strip() else [mx.cpu()] | ||
num_gpus = len(devs) | ||
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for network in networks: | ||
logging.info('network: %s', network) | ||
logging.info('device: %s', devs) | ||
if runtype == 'inference' or runtype == 'all': | ||
if bs != 0: | ||
fwd_time = score(network, bs, devs) | ||
fps = (bs * num_batches)/fwd_time | ||
logging.info(network + ' inference perf for BS %d is %f img/s', bs, fps) | ||
else: | ||
logging.info('run batchsize [1, 2, 4, 8, 16, 32] by default, ' | ||
'set --batch-size to run a specific one') | ||
for batch_size in batch_inf: | ||
fwd_time = score(network, batch_size, devs) | ||
fps = (batch_size * num_batches) / fwd_time | ||
logging.info(network + ' inference perf for BS %d is %f img/s', batch_size, fps) | ||
if runtype == 'training' or runtype == 'all': | ||
if bs != 0: | ||
bwd_time = train(network, bs, devs) | ||
fps = (bs * num_batches) / bwd_time | ||
logging.info(network + ' training perf for BS %d is %f img/s', bs, fps) | ||
else: | ||
logging.info('run batchsize [1, 2, 4, 8, 16, 32] by default, ' | ||
'set --batch-size to run a specific one') | ||
for batch_size in batch_train: | ||
bwd_time = train(network, batch_size, devs) | ||
fps = (batch_size * num_batches) / bwd_time | ||
logging.info(network + ' training perf for BS %d is %f img/s', batch_size, fps) |
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