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new 3c3506f Add resnet50-v1 to benchmark_score (#12595)
3c3506f is described below
commit 3c3506fc681471eafb65ee45885ec4352cfe1da0
Author: Xinyu Chen <[email protected]>
AuthorDate: Tue Oct 9 12:07:29 2018 +0800
Add resnet50-v1 to benchmark_score (#12595)
* add resnet50-v1 to benchmark_score
* rename back and duplicated
* rename v2 back to resnet.py
---
example/image-classification/benchmark_score.py | 4 +-
example/image-classification/symbols/resnetv1.py | 200 +++++++++++++++++++++++
2 files changed, 202 insertions(+), 2 deletions(-)
diff --git a/example/image-classification/benchmark_score.py
b/example/image-classification/benchmark_score.py
index 05e4b48..a4118eb 100644
--- a/example/image-classification/benchmark_score.py
+++ b/example/image-classification/benchmark_score.py
@@ -32,7 +32,7 @@ def get_symbol(network, batch_size, dtype):
num_layers = 0
if 'resnet' in network:
num_layers = int(network.split('-')[1])
- network = 'resnet'
+ network = network.split('-')[0]
if 'vgg' in network:
num_layers = int(network.split('-')[1])
network = 'vgg'
@@ -69,7 +69,7 @@ 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',
'resnet-50', 'resnet-152']
+ networks = ['alexnet', 'vgg-16', 'inception-bn', 'inception-v3',
'resnetv1-50', 'resnet-50', 'resnet-152']
devs = [mx.gpu(0)] if len(get_gpus()) > 0 else []
# Enable USE_MKLDNN for better CPU performance
devs.append(mx.cpu())
diff --git a/example/image-classification/symbols/resnetv1.py
b/example/image-classification/symbols/resnetv1.py
new file mode 100755
index 0000000..e5752f7
--- /dev/null
+++ b/example/image-classification/symbols/resnetv1.py
@@ -0,0 +1,200 @@
+# 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.
+
+'''
+Adapted from https://github.com/tornadomeet/ResNet/blob/master/symbol_resnet.py
+(Original author Wei Wu) by Antti-Pekka Hynninen
+
+Implementing the original resnet ILSVRC 2015 winning network from:
+
+Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Deep Residual Learning for
Image Recognition"
+'''
+import mxnet as mx
+import numpy as np
+
+def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck=True,
bn_mom=0.9, workspace=256, memonger=False):
+ """Return ResNet Unit symbol for building ResNet
+ Parameters
+ ----------
+ data : str
+ Input data
+ num_filter : int
+ Number of output channels
+ bnf : int
+ Bottle neck channels factor with regard to num_filter
+ stride : tuple
+ Stride used in convolution
+ dim_match : Boolean
+ True means channel number between input and output is the same,
otherwise means differ
+ name : str
+ Base name of the operators
+ workspace : int
+ Workspace used in convolution operator
+ """
+ if bottle_neck:
+ conv1 = mx.sym.Convolution(data=data, num_filter=int(num_filter*0.25),
kernel=(1,1), stride=stride, pad=(0,0),
+ no_bias=True, workspace=workspace,
name=name + '_conv1')
+ bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5,
momentum=bn_mom, name=name + '_bn1')
+ act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name +
'_relu1')
+ conv2 = mx.sym.Convolution(data=act1, num_filter=int(num_filter*0.25),
kernel=(3,3), stride=(1,1), pad=(1,1),
+ no_bias=True, workspace=workspace,
name=name + '_conv2')
+ bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5,
momentum=bn_mom, name=name + '_bn2')
+ act2 = mx.sym.Activation(data=bn2, act_type='relu', name=name +
'_relu2')
+ conv3 = mx.sym.Convolution(data=act2, num_filter=num_filter,
kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True,
+ workspace=workspace, name=name + '_conv3')
+ bn3 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5,
momentum=bn_mom, name=name + '_bn3')
+
+ if dim_match:
+ shortcut = data
+ else:
+ conv1sc = mx.sym.Convolution(data=data, num_filter=num_filter,
kernel=(1,1), stride=stride, no_bias=True,
+ workspace=workspace,
name=name+'_conv1sc')
+ shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False,
eps=2e-5, momentum=bn_mom, name=name + '_sc')
+ if memonger:
+ shortcut._set_attr(mirror_stage='True')
+ return mx.sym.Activation(data=bn3 + shortcut, act_type='relu',
name=name + '_relu3')
+ else:
+ conv1 = mx.sym.Convolution(data=data, num_filter=num_filter,
kernel=(3,3), stride=stride, pad=(1,1),
+ no_bias=True, workspace=workspace,
name=name + '_conv1')
+ bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom,
eps=2e-5, name=name + '_bn1')
+ act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name +
'_relu1')
+ conv2 = mx.sym.Convolution(data=act1, num_filter=num_filter,
kernel=(3,3), stride=(1,1), pad=(1,1),
+ no_bias=True, workspace=workspace,
name=name + '_conv2')
+ bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, momentum=bn_mom,
eps=2e-5, name=name + '_bn2')
+
+ if dim_match:
+ shortcut = data
+ else:
+ conv1sc = mx.sym.Convolution(data=data, num_filter=num_filter,
kernel=(1,1), stride=stride, no_bias=True,
+ workspace=workspace,
name=name+'_conv1sc')
+ shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False,
momentum=bn_mom, eps=2e-5, name=name + '_sc')
+ if memonger:
+ shortcut._set_attr(mirror_stage='True')
+ return mx.sym.Activation(data=bn2 + shortcut, act_type='relu',
name=name + '_relu3')
+
+def resnet(units, num_stages, filter_list, num_classes, image_shape,
bottle_neck=True, bn_mom=0.9, workspace=256, dtype='float32', memonger=False):
+ """Return ResNet symbol of
+ Parameters
+ ----------
+ units : list
+ Number of units in each stage
+ num_stages : int
+ Number of stage
+ filter_list : list
+ Channel size of each stage
+ num_classes : int
+ Ouput size of symbol
+ dataset : str
+ Dataset type, only cifar10 and imagenet supports
+ workspace : int
+ Workspace used in convolution operator
+ dtype : str
+ Precision (float32 or float16)
+ """
+ num_unit = len(units)
+ assert(num_unit == num_stages)
+ data = mx.sym.Variable(name='data')
+ if dtype == 'float32':
+ data = mx.sym.identity(data=data, name='id')
+ else:
+ if dtype == 'float16':
+ data = mx.sym.Cast(data=data, dtype=np.float16)
+ (nchannel, height, width) = image_shape
+ if height <= 32: # such as cifar10
+ body = mx.sym.Convolution(data=data, num_filter=filter_list[0],
kernel=(3, 3), stride=(1,1), pad=(1, 1),
+ no_bias=True, name="conv0",
workspace=workspace)
+ # Is this BatchNorm supposed to be here?
+ body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5,
momentum=bn_mom, name='bn0')
+ else: # often expected to be 224 such as imagenet
+ body = mx.sym.Convolution(data=data, num_filter=filter_list[0],
kernel=(7, 7), stride=(2,2), pad=(3, 3),
+ no_bias=True, name="conv0",
workspace=workspace)
+ body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5,
momentum=bn_mom, name='bn0')
+ body = mx.sym.Activation(data=body, act_type='relu', name='relu0')
+ body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2),
pad=(1,1), pool_type='max')
+
+ for i in range(num_stages):
+ body = residual_unit(body, filter_list[i+1], (1 if i==0 else 2, 1 if
i==0 else 2), False,
+ name='stage%d_unit%d' % (i + 1, 1),
bottle_neck=bottle_neck, workspace=workspace,
+ memonger=memonger)
+ for j in range(units[i]-1):
+ body = residual_unit(body, filter_list[i+1], (1,1), True,
name='stage%d_unit%d' % (i + 1, j + 2),
+ bottle_neck=bottle_neck, workspace=workspace,
memonger=memonger)
+ # bn1 = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5,
momentum=bn_mom, name='bn1')
+ # relu1 = mx.sym.Activation(data=bn1, act_type='relu', name='relu1')
+ # Although kernel is not used here when global_pool=True, we should put one
+ pool1 = mx.sym.Pooling(data=body, global_pool=True, kernel=(7, 7),
pool_type='avg', name='pool1')
+ flat = mx.sym.Flatten(data=pool1)
+ fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='fc1')
+ if dtype == 'float16':
+ fc1 = mx.sym.Cast(data=fc1, dtype=np.float32)
+ return mx.sym.SoftmaxOutput(data=fc1, name='softmax')
+
+def get_symbol(num_classes, num_layers, image_shape, conv_workspace=256,
dtype='float32', **kwargs):
+ """
+ Adapted from
https://github.com/tornadomeet/ResNet/blob/master/symbol_resnet.py
+ (Original author Wei Wu) by Antti-Pekka Hynninen
+ Implementing the original resnet ILSVRC 2015 winning network from:
+ Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Deep Residual Learning
for Image Recognition"
+ """
+ image_shape = [int(l) for l in image_shape.split(',')]
+ (nchannel, height, width) = image_shape
+ if height <= 28:
+ num_stages = 3
+ if (num_layers-2) % 9 == 0 and num_layers >= 164:
+ per_unit = [(num_layers-2)//9]
+ filter_list = [16, 64, 128, 256]
+ bottle_neck = True
+ elif (num_layers-2) % 6 == 0 and num_layers < 164:
+ per_unit = [(num_layers-2)//6]
+ filter_list = [16, 16, 32, 64]
+ bottle_neck = False
+ else:
+ raise ValueError("no experiments done on num_layers {}, you can do
it yourself".format(num_layers))
+ units = per_unit * num_stages
+ else:
+ if num_layers >= 50:
+ filter_list = [64, 256, 512, 1024, 2048]
+ bottle_neck = True
+ else:
+ filter_list = [64, 64, 128, 256, 512]
+ bottle_neck = False
+ num_stages = 4
+ if num_layers == 18:
+ units = [2, 2, 2, 2]
+ elif num_layers == 34:
+ units = [3, 4, 6, 3]
+ elif num_layers == 50:
+ units = [3, 4, 6, 3]
+ elif num_layers == 101:
+ units = [3, 4, 23, 3]
+ elif num_layers == 152:
+ units = [3, 8, 36, 3]
+ elif num_layers == 200:
+ units = [3, 24, 36, 3]
+ elif num_layers == 269:
+ units = [3, 30, 48, 8]
+ else:
+ raise ValueError("no experiments done on num_layers {}, you can do
it yourself".format(num_layers))
+
+ return resnet(units = units,
+ num_stages = num_stages,
+ filter_list = filter_list,
+ num_classes = num_classes,
+ image_shape = image_shape,
+ bottle_neck = bottle_neck,
+ workspace = conv_workspace,
+ dtype = dtype)
