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symbol_densenet.py
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symbol_densenet.py
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"""
Adapted from https://github.com/tornadomeet/ResNet/blob/master/symbol_resnet.py
Original author Wei Wu
Referenced https://github.com/bamos/densenet.pytorch/blob/master/densenet.py
Original author bamos
Referenced https://github.com/andreasveit/densenet-pytorch/blob/master/densenet.py
Original author andreasveit
Referenced https://github.com/Nicatio/Densenet/blob/master/mxnet/symbol_densenet.py
Original author Nicatio
Implemented the following paper: DenseNet-BC
Gao Huang, Zhuang Liu, Kilian Q. Weinberger, Laurens van der Maaten. "Densely Connected Convolutional Networks"
Coded by Lin Xiong Mar-1, 2017
"""
import mxnet as mx
import math
def BasicBlock(data, growth_rate, stride, name, bottle_neck=True, drop_out=0.0, bn_mom=0.9, workspace=512):
"""Return BaiscBlock Unit symbol for building DenseBlock
Parameters
----------
data : str
Input data
growth_rate : int
Number of output channels
stride : tupe
Stride used in convolution
drop_out : float
Probability of an element to be zeroed. Default = 0.2
name : str
Base name of the operators
workspace : int
Workspace used in convolution operator
"""
# import pdb
# pdb.set_trace()
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(growth_rate*4), kernel=(1,1), stride=(1,1), pad=(0,0),
no_bias=True, workspace=workspace, name=name + '_conv1')
if drop_out > 0:
conv1 = mx.symbol.Dropout(data=conv1, p=drop_out, name=name + '_dp1')
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(growth_rate), kernel=(3,3), stride=stride, pad=(1,1),
no_bias=True, workspace=workspace, name=name + '_conv2')
if drop_out > 0:
conv2 = mx.symbol.Dropout(data=conv2, p=drop_out, name=name + '_dp2')
#return mx.symbol.Concat(data, conv2, name=name + '_concat0')
return conv2
else:
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(growth_rate), kernel=(3,3), stride=(1,1), pad=(1,1),
no_bias=True, workspace=workspace, name=name + '_conv1')
if drop_out > 0:
conv1 = mx.symbol.Dropout(data=conv1, p=drop_out, name=name + '_dp1')
#return mx.symbol.Concat(data, conv1, name=name + '_concat0')
return conv1
def DenseBlock(units_num, data, growth_rate, name, bottle_neck=True, drop_out=0.0, bn_mom=0.9, workspace=512):
"""Return DenseBlock Unit symbol for building DenseNet
Parameters
----------
units_num : int
the number of BasicBlock in each DenseBlock
data : str
Input data
growth_rate : int
Number of output channels
drop_out : float
Probability of an element to be zeroed. Default = 0.2
workspace : int
Workspace used in convolution operator
"""
# import pdb
# pdb.set_trace()
for i in range(units_num):
Block = BasicBlock(data, growth_rate=growth_rate, stride=(1,1), name=name + '_unit%d' % (i+1),
bottle_neck=bottle_neck, drop_out=drop_out,
bn_mom=bn_mom, workspace=workspace)
data = mx.symbol.Concat(data, Block, name=name + '_concat%d' % (i+1))
return data
def TransitionBlock(num_stage, data, num_filter, stride, name, drop_out=0.0, bn_mom=0.9, workspace=512):
"""Return TransitionBlock Unit symbol for building DenseNet
Parameters
----------
num_stage : int
Number of stage
data : str
Input data
num : int
Number of output channels
stride : tupe
Stride used in convolution
name : str
Base name of the operators
drop_out : float
Probability of an element to be zeroed. Default = 0.2
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=num_filter,
kernel=(1,1), stride=stride, pad=(0,0), no_bias=True,
workspace=workspace, name=name + '_conv1')
if drop_out > 0:
conv1 = mx.symbol.Dropout(data=conv1, p=drop_out, name=name + '_dp1')
return mx.symbol.Pooling(conv1, global_pool=False, kernel=(2,2), stride=(2,2), pool_type='avg', name=name + '_pool%d' % (num_stage+1))
def DenseNet(units, num_stage, growth_rate, num_class, data_type, reduction=0.5, drop_out=0., bottle_neck=True, bn_mom=0.9, workspace=512):
"""Return DenseNet symbol of imagenet
Parameters
----------
units : list
Number of units in each stage
num_stage : int
Number of stage
growth_rate : int
Number of output channels
num_class : int
Ouput size of symbol
data_type : str
the type of dataset
reduction : float
Compression ratio. Default = 0.5
drop_out : float
Probability of an element to be zeroed. Default = 0.2
workspace : int
Workspace used in convolution operator
"""
num_unit = len(units)
assert(num_unit == num_stage)
init_channels = 2 * growth_rate
n_channels = init_channels
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 == 'imagenet':
body = mx.sym.Convolution(data=data, num_filter=growth_rate*2, 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 = mx.sym.Activation(data=body, act_type='relu', name='relu0')
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=growth_rate*2, 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 = mx.sym.Activation(data=body, act_type='relu', name='relu0')
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=growth_rate*2, 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 = mx.sym.Activation(data=body, act_type='relu', name='relu0')
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-1):
body = DenseBlock(units[i], body, growth_rate=growth_rate, name='DBstage%d' % (i + 1), bottle_neck=bottle_neck, drop_out=drop_out, bn_mom=bn_mom, workspace=workspace)
n_channels += units[i]*growth_rate
n_channels = int(math.floor(n_channels*reduction))
body = TransitionBlock(i, body, n_channels, stride=(1,1), name='TBstage%d' % (i + 1), drop_out=drop_out, bn_mom=bn_mom, workspace=workspace)
body = DenseBlock(units[num_stage-1], body, growth_rate=growth_rate, name='DBstage%d' % (num_stage), bottle_neck=bottle_neck, drop_out=drop_out, bn_mom=bn_mom, workspace=workspace)
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.symbol.Pooling(data=relu1, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
flat = mx.symbol.Flatten(data=pool1)
fc1 = mx.symbol.FullyConnected(data=flat, num_hidden=num_class, name='fc1')
return mx.symbol.SoftmaxOutput(data=fc1, name='softmax')