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symbol_net4.py
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'''
Reproducing paper:
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Identity Mappings in Deep Residual Networks"
'''
import mxnet as mx
def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck=True, bn_mom=0.9, workspace=512):
"""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 : tupe
Stride used in convolution
dim_match : Boolen
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:
# the same as https://github.com/facebook/fb.resnet.torch#notes, a bit difference with origin paper
bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False,
eps=2e-5, momentum=bn_mom, name=name + '_bn1')
act1 = mx.sym.Activation(
data=bn1, act_type='relu', name=name + '_relu1')
conv1 = mx.sym.Convolution(data=act1, num_filter=int(num_filter*0.25),
kernel=(1, 1), stride=(1, 1), pad=(0, 0),
no_bias=True, workspace=workspace, name=name + '_conv1')
bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False,
eps=2e-5, momentum=bn_mom, name=name + '_bn2')
act2 = mx.sym.Activation(
data=bn2, act_type='relu', name=name + '_relu2')
conv2 = mx.sym.Convolution(data=act2, num_filter=int(num_filter*0.25),
kernel=(3, 3), stride=stride, pad=(1, 1),
no_bias=True, workspace=workspace, name=name + '_conv2')
bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False,
eps=2e-5, momentum=bn_mom, name=name + '_bn3')
act3 = mx.sym.Activation(
data=bn3, act_type='relu', name=name + '_relu3')
conv3 = mx.sym.Convolution(data=act3, num_filter=num_filter,
kernel=(1, 1), stride=(1, 1), pad=(0, 0),
no_bias=True, workspace=workspace, name=name + '_conv3')
if dim_match:
shortcut = data
else:
shortcut = mx.sym.Convolution(data=data, num_filter=num_filter,
kernel=(1, 1), stride=stride, no_bias=True,
workspace=workspace, name=name+'_sc')
return conv3 + shortcut
else:
raise ValueError("must have bottleneck structure")
def transition_block(num_stage, data, num_filter, stride, name, bn_mom=0.9, workspace=512):
"""Return transition_block unit sym for building DenseNet
Parameters
----------
num_stage : int
Number of stage
data : str
Input data
num : int
Number of output channels
stride : tuple
Stride used in convolution
name : str
Base name of the operators
workspace : int
Workspace used in convolution operator
"""
bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False,
eps=2e-5, momentum=bn_mom, name=name + '_bn1')
act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
conv1 = mx.sym.Convolution(data=act1, num_filter=int(num_filter),
kernel=(1, 1), stride=stride, pad=(0, 0),
no_bias=True, workspace=workspace, name=name + '_conv1')
return mx.sym.Pooling(conv1, global_pool=False,
kernel=(2, 2), stride=(2, 2),
pool_type='avg', name=name + '_pool%d' % (num_stage + 1))
def conv(data, name, num_filter=12, bn_mom=0.9, workspace=1024): # need beautify
name = name + 'conv'
# bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False,
# eps=2e-5, momentum=bn_mom, name=name + '_bn1')
# act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
conv1 = mx.sym.Convolution(data=data, num_filter=num_filter,
kernel=(1, 1), stride=(1, 1), pad=(0, 0),
no_bias=True, workspace=workspace, name=name + '_conv1')
return conv1
def net4(units, num_stage, filter_list, num_class, bottle_neck=True, bn_mom=0.9, workspace=512):
"""Return ResNet symbol of cifar10 and imagenet
Parameters
----------
units : list
Number of units in each stage
num_stage : int
Number of stage
filter_list : list
Channel size of each stage
num_class : int
Ouput size of symbol
workspace : int
Workspace used in convolution operator
"""
num_unit = len(units)
assert(num_unit == num_stage)
data = mx.sym.Variable(name='data')
data = mx.sym.BatchNorm(data=data, fix_gamma=True,
eps=2e-5, momentum=bn_mom, name='bn_data')
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)
for i in range(num_stage):
if i != 0:
body = transition_block(i, body, filter_list[i + 1], stride=(
1, 1), name='stage%d_trans' % (i + 1), bn_mom=bn_mom, workspace=workspace)
con = conv(body, name='stage%d_trans' % (i + 1))
body = residual_unit(body, filter_list[i + 1], (1, 1), False,
name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, workspace=workspace)
con = mx.sym.Concat(
con, conv(body, name='stage%d_unit%d' % (i + 1, 1)))
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)
con = mx.sym.Concat(
con, conv(body, name='stage%d_unit%d' % (i + 1, j + 2)))
body = con
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=relu1, 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_class, name='fc1')
return mx.sym.SoftmaxOutput(data=fc1, name='softmax')