Add more models to benchmark_score

benchmark/python/gluon/benchmark_gluon.py

@@ -0,0 +1,164 @@
+# 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://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.
+
+import mxnet as mx
+import mxnet.gluon.model_zoo.vision as models
+import time
+import logging
+import argparse
+import subprocess
+import os
+import errno
+
+logging.basicConfig(level=logging.INFO)
+parser = argparse.ArgumentParser(description='Gluon modelzoo-based CNN performance benchmark')
+
+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'])
+
+opt = parser.parse_args()
+
+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)]
+
+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])]
+
+    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
+
+
+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])]
+
+    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
+
+if __name__ == '__main__':
+    runtype = opt.type
+    bs = opt.batch_size
+
+    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]
+
+    devs = [mx.gpu(int(i)) for i in opt.gpus.split(',')] if opt.gpus.strip() else [mx.cpu()]
+    num_gpus = len(devs)
+
+    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)

example/image-classification/benchmark_score.py

@@ -21,26 +21,49 @@
 from common import find_mxnet
 from common.util import get_gpus
 import mxnet as mx
+import mxnet.gluon.model_zoo.vision as models
 from importlib import import_module
 import logging
+import argparse
 import time
 import numpy as np
 logging.basicConfig(level=logging.DEBUG)
 
+parser = argparse.ArgumentParser(description='SymbolAPI-based CNN inference performance benchmark')
+parser.add_argument('--network', type=str, default='all', 
+                                 choices=['all', 'alexnet', 'vgg-16', 'resnetv1-50', 'resnet-50',
+                                          'resnet-152', 'inception-bn', 'inception-v3', 
+                                          'inception-v4', 'inception-resnet-v2', 'mobilenet',
+                                          'densenet121', 'squeezenet1.1'])
+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')
+
+opt = parser.parse_args()
+
 def get_symbol(network, batch_size, dtype):
-    image_shape = (3,299,299) if network == 'inception-v3' else (3,224,224)
+    image_shape = (3,299,299) if network in ['inception-v3', 'inception-v4'] else (3,224,224)
     num_layers = 0
-    if 'resnet' in network:
+    if network == 'inception-resnet-v2':
+        network = network
+    elif 'resnet' in network:
         num_layers = int(network.split('-')[1])
         network = network.split('-')[0]
     if 'vgg' in network:
         num_layers = int(network.split('-')[1])
         network = 'vgg'
-    net = import_module('symbols.'+network)
-    sym = net.get_symbol(num_classes=1000,
-                         image_shape=','.join([str(i) for i in image_shape]),
-                         num_layers=num_layers,
-                         dtype=dtype)
+    if network in ['densenet121', 'squeezenet1.1']:
+        sym = models.get_model(network)
+        sym.hybridize()
+        data = mx.sym.var('data')
+        sym = sym(data)
+        sym = mx.sym.SoftmaxOutput(sym, name='softmax')
+    else:
+        net = import_module('symbols.'+network)
+        sym = net.get_symbol(num_classes=1000,
+                             image_shape=','.join([str(i) for i in image_shape]),
+                             num_layers=num_layers,
+                             dtype=dtype)
     return (sym, [('data', (batch_size,)+image_shape)])
 
 def score(network, dev, batch_size, num_batches, dtype):
@@ -69,14 +92,31 @@ def score(network, dev, batch_size, num_batches, dtype):
     return num_batches*batch_size/(time.time() - tic)
 
 if __name__ == '__main__':
-    networks = ['alexnet', 'vgg-16', 'inception-bn', 'inception-v3', 'resnetv1-50', 'resnet-50', 'resnet-152']
+    if opt.network == 'all':
+        networks = ['alexnet', 'vgg-16', 'resnetv1-50', 'resnet-50',
+                    'resnet-152', 'inception-bn', 'inception-v3', 
+                    'inception-v4', 'inception-resnet-v2', 
+                    'mobilenet', 'densenet121', 'squeezenet1.1']
+        logging.info('It may take some time to run all models, '
+                     'set --network to run a specific one')
+    else:
+        networks = [opt.network]
     devs = [mx.gpu(0)] if len(get_gpus()) > 0 else []
     # Enable USE_MKLDNN for better CPU performance
     devs.append(mx.cpu())
 
-    batch_sizes = [1, 2, 4, 8, 16, 32]
+    if opt.batch_size == 0:
+        batch_sizes = [1, 32, 64, 128, 256]
+        logging.info('run batchsize [1, 32, 64, 128, 256] by default, '
+                     'set --batch-size to run a specific one')
+    else:
+        batch_sizes = [opt.batch_size]
+
     for net in networks:
         logging.info('network: %s', net)
+        if net in ['densenet121', 'squeezenet1.1']:
+            logging.info('network: %s is converted from gluon modelzoo', net)
+            logging.info('you can run benchmark/python/gluon/benchmark_gluon.py for more models')
         for d in devs:
             logging.info('device: %s', d)
             logged_fp16_warning = False