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symbol_resnext_ibn_a_w_d_v2.py
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symbol_resnext_ibn_a_w_d_v2.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"
This modification version is based on ResNet v1
Modified by Lin Xiong Feb-11, 2017
We modified resnext architecture as illustrated in following paper
Jie Hu, Li Shen, Gang Sun. "Squeeze-and-Excitation Networks" https://arxiv.org/pdf/1709.01507v1.pdf
(a) The first 7x7 convoluational layer was replaced with three consecutive 3x3 convolutional layers.
(b) The down-sampling projection 1x1 with stride-2 convolution was replaced with a 3x3 stride-2 convolution to preserve information.
(c) A dropout layer (with a drop ratio of 0.2) was inserted before the classifier layer to prevent overfitting.
(d) Label-smoothing regularization (as introduced in Christian Szegedy et. al. "Rethinking the Inception Architecture for Computer Vision") was used during training.
So we called it as resnext_w_d_v2
Modified by Lin Xiong Sep-13, 2017
Implemented the following paper:
Xingang Pan, Ping Luo, Jianping Shi, and Xiaoou Tang. "Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net",
https://arxiv.org/pdf/1807.09441.pdf
Added IBN_a block by Lin Xiong Jul-28, 2018
'''
import mxnet as mx
def ibn_block(data, num_filter, name, eps=2e-5, bn_mom=0.9):
split = mx.symbol.split(data=data, axis=1, num_outputs=2)
# import pdb
# pdb.set_trace()
out1 = mx.symbol.InstanceNorm(data=split[0], eps=eps, name=name + '_in1')
out2 = mx.sym.BatchNorm(data=split[1],fix_gamma=False, eps=eps, momentum=bn_mom, name=name + '_bn1')
out = mx.symbol.Concat(out1, out2, dim=1, name=name + '_ibn1')
return out
def residual_unit(data, num_filter, stride, dim_match, name, ibn=True, bottle_neck=True, num_group=48, bn_mom=0.9, workspace=256, memonger=False):
"""Return ResNext Unit symbol for building ResNext
Parameters
----------
data : str
Input data
num_filter : int
Number of output channels
stride : tupe
Stride used in convolution
dim_match : Boolen
True means channel number between input and output is the same, otherwise means differ
ibn : Boolen
True means ibn block integrated, otherwise means no ibn block
name : str
Base name of the operators
bottle_neck : Boolen
Whether or not to adopt bottle_neck trick as did in ResNet
num_group : int
Number of convolution groupes
bn_mom : float
Momentum of batch normalization
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
if num_filter == 2048:
ibn = False
conv1 = mx.sym.Convolution(data=data, num_filter=int(num_filter*0.5), kernel=(1,1), stride=(1,1), pad=(0,0),
no_bias=True, workspace=workspace, name=name + '_conv1')
if ibn:
bn1 = ibn_block(data=data, num_filter=int(num_filter*0.5), name=name)
else:
bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
# 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*0.5), num_group=num_group, 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, 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:
if stride == (2, 2):
shortcut_conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1, 1), 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')
else:
shortcut_conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, pad=(0, 0), 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 = bn3 + shortcut
return mx.sym.Activation(data=eltwise, act_type='relu', name=name + '_relu')
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:
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, data_type, drop_out, ibn, 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_stages : int
Number of stage
filter_list : list
Channel size of each stage
num_class : int
Ouput size of symbol
num_groupes: int
Number of convolution groups
drop_out : float
Probability of an element to be zeroed. Default = 0.0
ibn : Boolen
True means ibn block integrated, otherwise means no ibn block
data_type : str
Dataset type, only cifar10, imagenet and vggface supports
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')
if data_type == '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)
elif data_type == '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.Convolution(data=data, num_filter=filter_list[0], kernel=(3, 3), stride=(1, 1), pad=(1, 1),
no_bias=True, name="conv01", workspace=workspace)
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn01')
body = mx.sym.Activation(data=body, act_type='relu', name='relu01')
body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(3, 3), stride=(1, 1), pad=(1, 1),
no_bias=True, name="conv02", workspace=workspace)
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn02')
body = mx.sym.Activation(data=body, act_type='relu', name='relu02')
body = mx.sym.Convolution(data=body, num_filter=filter_list[0], kernel=(3, 3), stride=(2, 2), pad=(1, 1),
no_bias=True, name="conv03", workspace=workspace)
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn03')
body = mx.sym.Activation(data=body, act_type='relu', name='relu03')
body = mx.symbol.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max')
elif data_type == 'vggface':
# 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.Convolution(data=data, num_filter=filter_list[0], kernel=(3, 3), stride=(1, 1), pad=(1, 1),
no_bias=True, name="conv01", workspace=workspace)
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn01')
body = mx.sym.Activation(data=body, act_type='relu', name='relu01')
body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(3, 3), stride=(1, 1), pad=(1, 1),
no_bias=True, name="conv02", workspace=workspace)
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn02')
body = mx.sym.Activation(data=body, act_type='relu', name='relu02')
body = mx.sym.Convolution(data=body, num_filter=filter_list[0], kernel=(3, 3), stride=(2, 2), pad=(1, 1),
no_bias=True, name="conv03", workspace=workspace)
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn03')
body = mx.sym.Activation(data=body, act_type='relu', name='relu03')
body = mx.symbol.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max')
elif data_type == 'msface':
# 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.Convolution(data=data, num_filter=filter_list[0], kernel=(3, 3), stride=(1, 1), pad=(1, 1),
no_bias=True, name="conv01", workspace=workspace)
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn01')
body = mx.sym.Activation(data=body, act_type='relu', name='relu01')
body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(3, 3), stride=(1, 1), pad=(1, 1),
no_bias=True, name="conv02", workspace=workspace)
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn02')
body = mx.sym.Activation(data=body, act_type='relu', name='relu02')
body = mx.sym.Convolution(data=body, num_filter=filter_list[0], kernel=(3, 3), stride=(2, 2), pad=(1, 1),
no_bias=True, name="conv03", workspace=workspace)
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn03')
body = mx.sym.Activation(data=body, act_type='relu', name='relu03')
body = mx.symbol.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max')
else:
raise ValueError("do not support {} yet".format(data_type))
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), ibn=ibn, 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), ibn=ibn,
bottle_neck=bottle_neck, num_group=num_group, bn_mom=bn_mom, workspace=workspace, memonger=memonger)
pool1 = mx.symbol.Pooling(data=body, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
flat = mx.symbol.Flatten(data=pool1)
drop1= mx.symbol.Dropout(data=flat, p=drop_out, name='dp1')
fc1 = mx.symbol.FullyConnected(data=drop1, num_hidden=num_class, name='fc1')
#fc1 = mx.symbol.FullyConnected(data=flat, num_hidden=num_class, name='fc1')
# return mx.symbol.SoftmaxOutput(data=fc1, name='softmax')
return mx.symbol.SoftmaxOutput(data=fc1, smooth_alpha=0.1 ,name='softmax')