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symbol_resnext.py
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symbol_resnext.py
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'''
Adapted from https://github.com/tornadomeet/ResNet/blob/master/symbol_resnet.py
Original author Wei Wu
Implemented the following paper:
Saining Xie, Ross Girshick, Piotr Dollar, Zhuowen Tu, Kaiming He. "Aggregated Residual Transformations for Deep Neural Network"
'''
import mxnet as mx
import numpy as np
def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck=True, num_group=32, 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:
# 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*0.5),
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*0.5), num_group=num_group,
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')
if memonger:
shortcut._set_attr(mirror_stage='True')
return conv3 + shortcut
else:
raise ValueError("must have bottleneck structure to differ from resnet")
# 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:
# shortcut_conv = mx.sym.Convolution(data=data, num_filter=num_filter,
# kernel=(1, 1), stride=stride, no_bias=True,
# workspace=workspace, name=name+'_sc')
# shortcut = mx.sym.BatchNorm(data=shortcut_conv, fix_gamma=False,
# eps=2e-5, momentum=bn_mom, name=name + '_sc_bn')
# if memonger:
# shortcut._set_attr(mirror_stage='True')
# eltwise = bn2 + shortcut
# return mx.sym.Activation(data=eltwise, act_type='relu', name=name + '_relu')
def resnext(units, num_stage, filter_list, num_class, num_group, bottle_neck=True, bn_mom=0.9, workspace=256, memonger=False):
"""Return ResNeXt symbol of
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
num_groupes : int
Number of conv groups
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):
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, num_group=num_group,
bn_mom=bn_mom, 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, num_group=num_group, bn_mom=bn_mom, 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')
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')