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fresnet.py
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fresnet.py
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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://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
Implemented the following paper:
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Identity Mappings in Deep Residual Networks"
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import os
import mxnet as mx
import numpy as np
import symbol_utils
import memonger
import sklearn
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from config import config
def Conv(**kwargs):
#name = kwargs.get('name')
#_weight = mx.symbol.Variable(name+'_weight')
#_bias = mx.symbol.Variable(name+'_bias', lr_mult=2.0, wd_mult=0.0)
#body = mx.sym.Convolution(weight = _weight, bias = _bias, **kwargs)
body = mx.sym.Convolution(**kwargs)
return body
def Act(data, act_type, name):
if act_type == 'prelu':
body = mx.sym.LeakyReLU(data=data, act_type='prelu', name=name)
else:
body = mx.symbol.Activation(data=data, act_type=act_type, name=name)
return body
def residual_unit_v1(data, num_filter, stride, dim_match, name, bottle_neck,
**kwargs):
"""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
"""
use_se = kwargs.get('version_se', 1)
bn_mom = kwargs.get('bn_mom', 0.9)
workspace = kwargs.get('workspace', 256)
memonger = kwargs.get('memonger', False)
act_type = kwargs.get('version_act', 'prelu')
#print('in unit1')
if bottle_neck:
conv1 = Conv(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 = Act(data=bn1, act_type=act_type, name=name + '_relu1')
conv2 = Conv(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 = Act(data=bn2, act_type=act_type, name=name + '_relu2')
conv3 = Conv(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 use_se:
#se begin
body = mx.sym.Pooling(data=bn3,
global_pool=True,
kernel=(7, 7),
pool_type='avg',
name=name + '_se_pool1')
body = Conv(data=body,
num_filter=num_filter // 16,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv1",
workspace=workspace)
body = Act(data=body, act_type=act_type, name=name + '_se_relu1')
body = Conv(data=body,
num_filter=num_filter,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv2",
workspace=workspace)
body = mx.symbol.Activation(data=body,
act_type='sigmoid',
name=name + "_se_sigmoid")
bn3 = mx.symbol.broadcast_mul(bn3, body)
#se end
if dim_match:
shortcut = data
else:
conv1sc = Conv(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 Act(data=bn3 + shortcut,
act_type=act_type,
name=name + '_relu3')
else:
conv1 = Conv(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 = Act(data=bn1, act_type=act_type, name=name + '_relu1')
conv2 = Conv(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 use_se:
#se begin
body = mx.sym.Pooling(data=bn2,
global_pool=True,
kernel=(7, 7),
pool_type='avg',
name=name + '_se_pool1')
body = Conv(data=body,
num_filter=num_filter // 16,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv1",
workspace=workspace)
body = Act(data=body, act_type=act_type, name=name + '_se_relu1')
body = Conv(data=body,
num_filter=num_filter,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv2",
workspace=workspace)
body = mx.symbol.Activation(data=body,
act_type='sigmoid',
name=name + "_se_sigmoid")
bn2 = mx.symbol.broadcast_mul(bn2, body)
#se end
if dim_match:
shortcut = data
else:
conv1sc = Conv(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 Act(data=bn2 + shortcut,
act_type=act_type,
name=name + '_relu3')
def residual_unit_v1_L(data, num_filter, stride, dim_match, name, bottle_neck,
**kwargs):
"""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
"""
use_se = kwargs.get('version_se', 1)
bn_mom = kwargs.get('bn_mom', 0.9)
workspace = kwargs.get('workspace', 256)
memonger = kwargs.get('memonger', False)
act_type = kwargs.get('version_act', 'prelu')
#print('in unit1')
if bottle_neck:
conv1 = Conv(data=data,
num_filter=int(num_filter * 0.25),
kernel=(1, 1),
stride=(1, 1),
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 = Act(data=bn1, act_type=act_type, name=name + '_relu1')
conv2 = Conv(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 = Act(data=bn2, act_type=act_type, name=name + '_relu2')
conv3 = Conv(data=act2,
num_filter=num_filter,
kernel=(1, 1),
stride=stride,
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 use_se:
#se begin
body = mx.sym.Pooling(data=bn3,
global_pool=True,
kernel=(7, 7),
pool_type='avg',
name=name + '_se_pool1')
body = Conv(data=body,
num_filter=num_filter // 16,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv1",
workspace=workspace)
body = Act(data=body, act_type=act_type, name=name + '_se_relu1')
body = Conv(data=body,
num_filter=num_filter,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv2",
workspace=workspace)
body = mx.symbol.Activation(data=body,
act_type='sigmoid',
name=name + "_se_sigmoid")
bn3 = mx.symbol.broadcast_mul(bn3, body)
#se end
if dim_match:
shortcut = data
else:
conv1sc = Conv(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 Act(data=bn3 + shortcut,
act_type=act_type,
name=name + '_relu3')
else:
conv1 = Conv(data=data,
num_filter=num_filter,
kernel=(3, 3),
stride=(1, 1),
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 = Act(data=bn1, act_type=act_type, name=name + '_relu1')
conv2 = Conv(data=act1,
num_filter=num_filter,
kernel=(3, 3),
stride=stride,
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 use_se:
#se begin
body = mx.sym.Pooling(data=bn2,
global_pool=True,
kernel=(7, 7),
pool_type='avg',
name=name + '_se_pool1')
body = Conv(data=body,
num_filter=num_filter // 16,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv1",
workspace=workspace)
body = Act(data=body, act_type=act_type, name=name + '_se_relu1')
body = Conv(data=body,
num_filter=num_filter,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv2",
workspace=workspace)
body = mx.symbol.Activation(data=body,
act_type='sigmoid',
name=name + "_se_sigmoid")
bn2 = mx.symbol.broadcast_mul(bn2, body)
#se end
if dim_match:
shortcut = data
else:
conv1sc = Conv(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 Act(data=bn2 + shortcut,
act_type=act_type,
name=name + '_relu3')
def residual_unit_v2(data, num_filter, stride, dim_match, name, bottle_neck,
**kwargs):
"""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
"""
use_se = kwargs.get('version_se', 1)
bn_mom = kwargs.get('bn_mom', 0.9)
workspace = kwargs.get('workspace', 256)
memonger = kwargs.get('memonger', False)
act_type = kwargs.get('version_act', 'prelu')
#print('in unit2')
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 = Act(data=bn1, act_type=act_type, name=name + '_relu1')
conv1 = Conv(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 = Act(data=bn2, act_type=act_type, name=name + '_relu2')
conv2 = Conv(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 = Act(data=bn3, act_type=act_type, name=name + '_relu3')
conv3 = Conv(data=act3,
num_filter=num_filter,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
no_bias=True,
workspace=workspace,
name=name + '_conv3')
if use_se:
#se begin
body = mx.sym.Pooling(data=conv3,
global_pool=True,
kernel=(7, 7),
pool_type='avg',
name=name + '_se_pool1')
body = Conv(data=body,
num_filter=num_filter // 16,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv1",
workspace=workspace)
body = Act(data=body, act_type=act_type, name=name + '_se_relu1')
body = Conv(data=body,
num_filter=num_filter,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv2",
workspace=workspace)
body = mx.symbol.Activation(data=body,
act_type='sigmoid',
name=name + "_se_sigmoid")
conv3 = mx.symbol.broadcast_mul(conv3, body)
if dim_match:
shortcut = data
else:
shortcut = Conv(data=act1,
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:
bn1 = mx.sym.BatchNorm(data=data,
fix_gamma=False,
momentum=bn_mom,
eps=2e-5,
name=name + '_bn1')
act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1')
conv1 = Conv(data=act1,
num_filter=num_filter,
kernel=(3, 3),
stride=stride,
pad=(1, 1),
no_bias=True,
workspace=workspace,
name=name + '_conv1')
bn2 = mx.sym.BatchNorm(data=conv1,
fix_gamma=False,
momentum=bn_mom,
eps=2e-5,
name=name + '_bn2')
act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2')
conv2 = Conv(data=act2,
num_filter=num_filter,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
no_bias=True,
workspace=workspace,
name=name + '_conv2')
if use_se:
#se begin
body = mx.sym.Pooling(data=conv2,
global_pool=True,
kernel=(7, 7),
pool_type='avg',
name=name + '_se_pool1')
body = Conv(data=body,
num_filter=num_filter // 16,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv1",
workspace=workspace)
body = Act(data=body, act_type=act_type, name=name + '_se_relu1')
body = Conv(data=body,
num_filter=num_filter,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv2",
workspace=workspace)
body = mx.symbol.Activation(data=body,
act_type='sigmoid',
name=name + "_se_sigmoid")
conv2 = mx.symbol.broadcast_mul(conv2, body)
if dim_match:
shortcut = data
else:
shortcut = Conv(data=act1,
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 conv2 + shortcut
def residual_unit_v3(data, num_filter, stride, dim_match, name, bottle_neck,
**kwargs):
"""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
"""
use_se = kwargs.get('version_se', 1)
bn_mom = kwargs.get('bn_mom', 0.9)
workspace = kwargs.get('workspace', 256)
memonger = kwargs.get('memonger', False)
act_type = kwargs.get('version_act', 'prelu')
#print('in unit3')
if bottle_neck:
bn1 = mx.sym.BatchNorm(data=data,
fix_gamma=False,
eps=2e-5,
momentum=bn_mom,
name=name + '_bn1')
conv1 = Conv(data=bn1,
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')
act1 = Act(data=bn2, act_type=act_type, name=name + '_relu1')
conv2 = Conv(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')
bn3 = mx.sym.BatchNorm(data=conv2,
fix_gamma=False,
eps=2e-5,
momentum=bn_mom,
name=name + '_bn3')
act2 = Act(data=bn3, act_type=act_type, name=name + '_relu2')
conv3 = Conv(data=act2,
num_filter=num_filter,
kernel=(1, 1),
stride=stride,
pad=(0, 0),
no_bias=True,
workspace=workspace,
name=name + '_conv3')
bn4 = mx.sym.BatchNorm(data=conv3,
fix_gamma=False,
eps=2e-5,
momentum=bn_mom,
name=name + '_bn4')
if use_se:
#se begin
body = mx.sym.Pooling(data=bn4,
global_pool=True,
kernel=(7, 7),
pool_type='avg',
name=name + '_se_pool1')
body = Conv(data=body,
num_filter=num_filter // 16,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv1",
workspace=workspace)
body = Act(data=body, act_type=act_type, name=name + '_se_relu1')
body = Conv(data=body,
num_filter=num_filter,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv2",
workspace=workspace)
body = mx.symbol.Activation(data=body,
act_type='sigmoid',
name=name + "_se_sigmoid")
bn4 = mx.symbol.broadcast_mul(bn4, body)
#se end
if dim_match:
shortcut = data
else:
conv1sc = Conv(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 bn4 + shortcut
else:
bn1 = mx.sym.BatchNorm(data=data,
fix_gamma=False,
eps=2e-5,
momentum=bn_mom,
name=name + '_bn1')
conv1 = Conv(data=bn1,
num_filter=num_filter,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
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')
act1 = Act(data=bn2, act_type=act_type, name=name + '_relu1')
conv2 = Conv(data=act1,
num_filter=num_filter,
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')
if use_se:
#se begin
body = mx.sym.Pooling(data=bn3,
global_pool=True,
kernel=(7, 7),
pool_type='avg',
name=name + '_se_pool1')
body = Conv(data=body,
num_filter=num_filter // 16,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv1",
workspace=workspace)
body = Act(data=body, act_type=act_type, name=name + '_se_relu1')
body = Conv(data=body,
num_filter=num_filter,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv2",
workspace=workspace)
body = mx.symbol.Activation(data=body,
act_type='sigmoid',
name=name + "_se_sigmoid")
bn3 = mx.symbol.broadcast_mul(bn3, body)
#se end
if dim_match:
shortcut = data
else:
conv1sc = Conv(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 bn3 + shortcut
def residual_unit_v3_x(data, num_filter, stride, dim_match, name, bottle_neck,
**kwargs):
"""Return ResNeXt Unit symbol for building ResNeXt
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
"""
assert (bottle_neck)
use_se = kwargs.get('version_se', 1)
bn_mom = kwargs.get('bn_mom', 0.9)
workspace = kwargs.get('workspace', 256)
memonger = kwargs.get('memonger', False)
act_type = kwargs.get('version_act', 'prelu')
num_group = 32
#print('in unit3')
bn1 = mx.sym.BatchNorm(data=data,
fix_gamma=False,
eps=2e-5,
momentum=bn_mom,
name=name + '_bn1')
conv1 = Conv(data=bn1,
num_group=num_group,
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')
act1 = Act(data=bn2, act_type=act_type, name=name + '_relu1')
conv2 = Conv(data=act1,
num_group=num_group,
num_filter=int(num_filter * 0.5),
kernel=(3, 3),
stride=(1, 1),
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')
act2 = Act(data=bn3, act_type=act_type, name=name + '_relu2')
conv3 = Conv(data=act2,
num_filter=num_filter,
kernel=(1, 1),
stride=stride,
pad=(0, 0),
no_bias=True,
workspace=workspace,
name=name + '_conv3')
bn4 = mx.sym.BatchNorm(data=conv3,
fix_gamma=False,
eps=2e-5,
momentum=bn_mom,
name=name + '_bn4')
if use_se:
#se begin
body = mx.sym.Pooling(data=bn4,
global_pool=True,
kernel=(7, 7),
pool_type='avg',
name=name + '_se_pool1')
body = Conv(data=body,
num_filter=num_filter // 16,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv1",
workspace=workspace)
body = Act(data=body, act_type=act_type, name=name + '_se_relu1')
body = Conv(data=body,
num_filter=num_filter,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv2",
workspace=workspace)
body = mx.symbol.Activation(data=body,
act_type='sigmoid',
name=name + "_se_sigmoid")
bn4 = mx.symbol.broadcast_mul(bn4, body)
#se end
if dim_match:
shortcut = data
else:
conv1sc = Conv(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 bn4 + shortcut
def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck,
**kwargs):
uv = kwargs.get('version_unit', 3)
version_input = kwargs.get('version_input', 1)
if uv == 1:
if version_input == 0:
return residual_unit_v1(data, num_filter, stride, dim_match, name,
bottle_neck, **kwargs)
else:
return residual_unit_v1_L(data, num_filter, stride, dim_match,
name, bottle_neck, **kwargs)
elif uv == 2:
return residual_unit_v2(data, num_filter, stride, dim_match, name,
bottle_neck, **kwargs)
elif uv == 4:
return residual_unit_v4(data, num_filter, stride, dim_match, name,
bottle_neck, **kwargs)
else:
return residual_unit_v3(data, num_filter, stride, dim_match, name,
bottle_neck, **kwargs)
def resnet(units, num_stages, filter_list, num_classes, bottle_neck):
bn_mom = config.bn_mom
workspace = config.workspace
kwargs = {
'version_se': config.net_se,
'version_input': config.net_input,
'version_output': config.net_output,
'version_unit': config.net_unit,
'version_act': config.net_act,
'bn_mom': bn_mom,
'workspace': workspace,
'memonger': config.memonger,
}
"""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