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649 lines
32 KiB
Python
649 lines
32 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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'''
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Adapted from https://github.com/tornadomeet/ResNet/blob/master/symbol_resnet.py
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Original author Wei Wu
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Implemented the following paper:
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Identity Mappings in Deep Residual Networks"
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'''
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import sys
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import os
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import mxnet as mx
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import numpy as np
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import symbol_utils
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import memonger
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import sklearn
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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from config import config
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def Conv(**kwargs):
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#name = kwargs.get('name')
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#_weight = mx.symbol.Variable(name+'_weight')
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#_bias = mx.symbol.Variable(name+'_bias', lr_mult=2.0, wd_mult=0.0)
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#body = mx.sym.Convolution(weight = _weight, bias = _bias, **kwargs)
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body = mx.sym.Convolution(**kwargs)
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return body
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def Act(data, act_type, name):
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if act_type=='prelu':
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body = mx.sym.LeakyReLU(data = data, act_type='prelu', name = name)
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else:
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body = mx.symbol.Activation(data=data, act_type=act_type, name=name)
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return body
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def residual_unit_v1(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs):
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"""Return ResNet Unit symbol for building ResNet
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Parameters
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----------
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data : str
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Input data
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num_filter : int
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Number of output channels
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bnf : int
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Bottle neck channels factor with regard to num_filter
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stride : tuple
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Stride used in convolution
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dim_match : Boolean
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True means channel number between input and output is the same, otherwise means differ
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name : str
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Base name of the operators
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workspace : int
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Workspace used in convolution operator
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"""
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use_se = kwargs.get('version_se', 1)
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bn_mom = kwargs.get('bn_mom', 0.9)
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workspace = kwargs.get('workspace', 256)
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memonger = kwargs.get('memonger', False)
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act_type = kwargs.get('version_act', 'prelu')
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#print('in unit1')
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if bottle_neck:
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conv1 = Conv(data=data, num_filter=int(num_filter*0.25), kernel=(1,1), stride=stride, pad=(0,0),
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no_bias=True, workspace=workspace, name=name + '_conv1')
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bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
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act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1')
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conv2 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1),
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no_bias=True, workspace=workspace, name=name + '_conv2')
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bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')
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act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2')
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conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True,
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workspace=workspace, name=name + '_conv3')
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bn3 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')
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if use_se:
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#se begin
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body = mx.sym.Pooling(data=bn3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')
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body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv1", workspace=workspace)
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body = Act(data=body, act_type=act_type, name=name+'_se_relu1')
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body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv2", workspace=workspace)
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body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid")
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bn3 = mx.symbol.broadcast_mul(bn3, body)
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#se end
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if dim_match:
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shortcut = data
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else:
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conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
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workspace=workspace, name=name+'_conv1sc')
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shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc')
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if memonger:
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shortcut._set_attr(mirror_stage='True')
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return Act(data=bn3 + shortcut, act_type=act_type, name=name + '_relu3')
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else:
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conv1 = Conv(data=data, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),
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no_bias=True, workspace=workspace, name=name + '_conv1')
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bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1')
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act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1')
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conv2 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),
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no_bias=True, workspace=workspace, name=name + '_conv2')
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bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2')
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if use_se:
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#se begin
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body = mx.sym.Pooling(data=bn2, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')
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body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv1", workspace=workspace)
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body = Act(data=body, act_type=act_type, name=name+'_se_relu1')
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body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv2", workspace=workspace)
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body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid")
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bn2 = mx.symbol.broadcast_mul(bn2, body)
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#se end
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if dim_match:
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shortcut = data
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else:
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conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
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workspace=workspace, name=name+'_conv1sc')
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shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc')
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if memonger:
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shortcut._set_attr(mirror_stage='True')
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return Act(data=bn2 + shortcut, act_type=act_type, name=name + '_relu3')
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def residual_unit_v1_L(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs):
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"""Return ResNet Unit symbol for building ResNet
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Parameters
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----------
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data : str
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Input data
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num_filter : int
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Number of output channels
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bnf : int
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Bottle neck channels factor with regard to num_filter
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stride : tuple
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Stride used in convolution
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dim_match : Boolean
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True means channel number between input and output is the same, otherwise means differ
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name : str
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Base name of the operators
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workspace : int
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Workspace used in convolution operator
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"""
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use_se = kwargs.get('version_se', 1)
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bn_mom = kwargs.get('bn_mom', 0.9)
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workspace = kwargs.get('workspace', 256)
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memonger = kwargs.get('memonger', False)
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act_type = kwargs.get('version_act', 'prelu')
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#print('in unit1')
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if bottle_neck:
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conv1 = Conv(data=data, num_filter=int(num_filter*0.25), kernel=(1,1), stride=(1,1), pad=(0,0),
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no_bias=True, workspace=workspace, name=name + '_conv1')
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bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
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act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1')
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conv2 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1),
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no_bias=True, workspace=workspace, name=name + '_conv2')
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bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')
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act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2')
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conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1,1), stride=stride, pad=(0,0), no_bias=True,
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workspace=workspace, name=name + '_conv3')
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bn3 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')
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if use_se:
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#se begin
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body = mx.sym.Pooling(data=bn3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')
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body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv1", workspace=workspace)
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body = Act(data=body, act_type=act_type, name=name+'_se_relu1')
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body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv2", workspace=workspace)
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body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid")
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bn3 = mx.symbol.broadcast_mul(bn3, body)
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#se end
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if dim_match:
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shortcut = data
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else:
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conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
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workspace=workspace, name=name+'_conv1sc')
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shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc')
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if memonger:
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shortcut._set_attr(mirror_stage='True')
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return Act(data=bn3 + shortcut, act_type=act_type, name=name + '_relu3')
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else:
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conv1 = Conv(data=data, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),
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no_bias=True, workspace=workspace, name=name + '_conv1')
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bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1')
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act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1')
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conv2 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),
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no_bias=True, workspace=workspace, name=name + '_conv2')
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bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2')
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if use_se:
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#se begin
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body = mx.sym.Pooling(data=bn2, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')
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body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv1", workspace=workspace)
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body = Act(data=body, act_type=act_type, name=name+'_se_relu1')
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body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv2", workspace=workspace)
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body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid")
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bn2 = mx.symbol.broadcast_mul(bn2, body)
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#se end
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if dim_match:
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shortcut = data
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else:
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conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
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workspace=workspace, name=name+'_conv1sc')
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shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc')
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if memonger:
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shortcut._set_attr(mirror_stage='True')
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return Act(data=bn2 + shortcut, act_type=act_type, name=name + '_relu3')
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def residual_unit_v2(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs):
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"""Return ResNet Unit symbol for building ResNet
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Parameters
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----------
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data : str
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Input data
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num_filter : int
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Number of output channels
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bnf : int
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Bottle neck channels factor with regard to num_filter
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stride : tuple
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Stride used in convolution
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dim_match : Boolean
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True means channel number between input and output is the same, otherwise means differ
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name : str
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Base name of the operators
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workspace : int
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Workspace used in convolution operator
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"""
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use_se = kwargs.get('version_se', 1)
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bn_mom = kwargs.get('bn_mom', 0.9)
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workspace = kwargs.get('workspace', 256)
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memonger = kwargs.get('memonger', False)
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act_type = kwargs.get('version_act', 'prelu')
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#print('in unit2')
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if bottle_neck:
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# the same as https://github.com/facebook/fb.resnet.torch#notes, a bit difference with origin paper
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bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
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act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1')
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conv1 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(1,1), stride=(1,1), pad=(0,0),
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no_bias=True, workspace=workspace, name=name + '_conv1')
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bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')
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act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2')
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conv2 = Conv(data=act2, num_filter=int(num_filter*0.25), kernel=(3,3), stride=stride, pad=(1,1),
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no_bias=True, workspace=workspace, name=name + '_conv2')
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bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')
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act3 = Act(data=bn3, act_type=act_type, name=name + '_relu3')
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conv3 = Conv(data=act3, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True,
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workspace=workspace, name=name + '_conv3')
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if use_se:
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#se begin
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body = mx.sym.Pooling(data=conv3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')
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body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv1", workspace=workspace)
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body = Act(data=body, act_type=act_type, name=name+'_se_relu1')
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body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv2", workspace=workspace)
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body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid")
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conv3 = mx.symbol.broadcast_mul(conv3, body)
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if dim_match:
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shortcut = data
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else:
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shortcut = Conv(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
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workspace=workspace, name=name+'_sc')
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if memonger:
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shortcut._set_attr(mirror_stage='True')
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return conv3 + shortcut
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else:
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bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1')
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act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1')
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conv1 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),
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no_bias=True, workspace=workspace, name=name + '_conv1')
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bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2')
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act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2')
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conv2 = Conv(data=act2, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),
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no_bias=True, workspace=workspace, name=name + '_conv2')
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if use_se:
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#se begin
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body = mx.sym.Pooling(data=conv2, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')
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body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv1", workspace=workspace)
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body = Act(data=body, act_type=act_type, name=name+'_se_relu1')
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body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv2", workspace=workspace)
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body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid")
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conv2 = mx.symbol.broadcast_mul(conv2, body)
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if dim_match:
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shortcut = data
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else:
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shortcut = Conv(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
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workspace=workspace, name=name+'_sc')
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if memonger:
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shortcut._set_attr(mirror_stage='True')
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return conv2 + shortcut
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def residual_unit_v3(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs):
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"""Return ResNet Unit symbol for building ResNet
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Parameters
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----------
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data : str
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Input data
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num_filter : int
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Number of output channels
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bnf : int
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Bottle neck channels factor with regard to num_filter
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stride : tuple
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Stride used in convolution
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dim_match : Boolean
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True means channel number between input and output is the same, otherwise means differ
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name : str
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Base name of the operators
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workspace : int
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Workspace used in convolution operator
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"""
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use_se = kwargs.get('version_se', 1)
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bn_mom = kwargs.get('bn_mom', 0.9)
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workspace = kwargs.get('workspace', 256)
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memonger = kwargs.get('memonger', False)
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act_type = kwargs.get('version_act', 'prelu')
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#print('in unit3')
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if bottle_neck:
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bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
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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
|
|
"""
|
|
version_se = kwargs.get('version_se', 1)
|
|
version_input = kwargs.get('version_input', 1)
|
|
assert version_input>=0
|
|
version_output = kwargs.get('version_output', 'E')
|
|
fc_type = version_output
|
|
version_unit = kwargs.get('version_unit', 3)
|
|
act_type = kwargs.get('version_act', 'prelu')
|
|
memonger = kwargs.get('memonger', False)
|
|
print(version_se, version_input, version_output, version_unit, act_type, memonger)
|
|
num_unit = len(units)
|
|
assert(num_unit == num_stages)
|
|
data = mx.sym.Variable(name='data')
|
|
if version_input==0:
|
|
#data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data')
|
|
data = mx.sym.identity(data=data, name='id')
|
|
data = data-127.5
|
|
data = data*0.0078125
|
|
body = Conv(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 = Act(data=body, act_type=act_type, name='relu0')
|
|
#body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max')
|
|
elif version_input==2:
|
|
data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data')
|
|
body = Conv(data=data, num_filter=filter_list[0], kernel=(3,3), stride=(1,1), pad=(1,1),
|
|
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 = Act(data=body, act_type=act_type, name='relu0')
|
|
else:
|
|
data = mx.sym.identity(data=data, name='id')
|
|
data = data-127.5
|
|
data = data*0.0078125
|
|
body = data
|
|
body = Conv(data=body, num_filter=filter_list[0], kernel=(3,3), stride=(1,1), pad=(1, 1),
|
|
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 = Act(data=body, act_type=act_type, name='relu0')
|
|
|
|
for i in range(num_stages):
|
|
#if version_input==0:
|
|
# 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, **kwargs)
|
|
#else:
|
|
# body = residual_unit(body, filter_list[i+1], (2, 2), False,
|
|
# name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, **kwargs)
|
|
body = residual_unit(body, filter_list[i+1], (2, 2), False,
|
|
name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, **kwargs)
|
|
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, **kwargs)
|
|
|
|
if bottle_neck:
|
|
body = Conv(data=body, num_filter=512, kernel=(1,1), stride=(1,1), pad=(0,0),
|
|
no_bias=True, name="convd", workspace=workspace)
|
|
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bnd')
|
|
body = Act(data=body, act_type=act_type, name='relud')
|
|
|
|
fc1 = symbol_utils.get_fc1(body, num_classes, fc_type)
|
|
return fc1
|
|
|
|
def get_symbol():
|
|
"""
|
|
Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py
|
|
Original author Wei Wu
|
|
"""
|
|
num_classes = config.emb_size
|
|
num_layers = config.num_layers
|
|
if num_layers >= 500:
|
|
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 == 49:
|
|
units = [3, 4, 14, 3]
|
|
elif num_layers == 50:
|
|
units = [3, 4, 14, 3]
|
|
elif num_layers == 74:
|
|
units = [3, 6, 24, 3]
|
|
elif num_layers == 90:
|
|
units = [3, 8, 30, 3]
|
|
elif num_layers == 98:
|
|
units = [3, 4, 38, 3]
|
|
elif num_layers == 99:
|
|
units = [3, 8, 35, 3]
|
|
elif num_layers == 100:
|
|
units = [3, 13, 30, 3]
|
|
elif num_layers == 134:
|
|
units = [3, 10, 50, 3]
|
|
elif num_layers == 136:
|
|
units = [3, 13, 48, 3]
|
|
elif num_layers == 140:
|
|
units = [3, 15, 48, 3]
|
|
elif num_layers == 124:
|
|
units = [3, 13, 40, 5]
|
|
elif num_layers == 160:
|
|
units = [3, 24, 49, 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))
|
|
|
|
net = resnet(units = units,
|
|
num_stages = num_stages,
|
|
filter_list = filter_list,
|
|
num_classes = num_classes,
|
|
bottle_neck = bottle_neck)
|
|
|
|
if config.memonger:
|
|
dshape = (config.per_batch_size, config.image_shape[2], config.image_shape[0], config.image_shape[1])
|
|
net_mem_planned = memonger.search_plan(net, data=dshape)
|
|
old_cost = memonger.get_cost(net, data=dshape)
|
|
new_cost = memonger.get_cost(net_mem_planned, data=dshape)
|
|
|
|
print('Old feature map cost=%d MB' % old_cost)
|
|
print('New feature map cost=%d MB' % new_cost)
|
|
net = net_mem_planned
|
|
return net
|
|
|
|
|
|
|