Python mxnet.Symbols() Examples
The following are 18
code examples of mxnet.Symbols().
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Example #1
| Source File: common.py From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 | 5 votes |
|
def legacy_conv_act_layer(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \
stride=(1,1), act_type="relu", use_batchnorm=False):
"""
wrapper for a small Convolution group
Parameters:
----------
from_layer : mx.symbol
continue on which layer
name : str
base name of the new layers
num_filter : int
how many filters to use in Convolution layer
kernel : tuple (int, int)
kernel size (h, w)
pad : tuple (int, int)
padding size (h, w)
stride : tuple (int, int)
stride size (h, w)
act_type : str
activation type, can be relu...
use_batchnorm : bool
whether to use batch normalization
Returns:
----------
(conv, relu) mx.Symbols
"""
assert not use_batchnorm, "batchnorm not yet supported"
bias = mx.symbol.Variable(name="conv{}_bias".format(name),
init=mx.init.Constant(0.0), attr={'__lr_mult__': '2.0'})
conv = mx.symbol.Convolution(data=from_layer, bias=bias, kernel=kernel, pad=pad, \
stride=stride, num_filter=num_filter, name="conv{}".format(name))
relu = mx.symbol.Activation(data=conv, act_type=act_type, \
name="{}{}".format(act_type, name))
if use_batchnorm:
relu = mx.symbol.BatchNorm(data=relu, name="bn{}".format(name))
return conv, relu
Example #2
| Source File: common.py From SNIPER-mxnet with Apache License 2.0 | 5 votes |
|
def legacy_conv_act_layer(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \
stride=(1,1), act_type="relu", use_batchnorm=False):
"""
wrapper for a small Convolution group
Parameters:
----------
from_layer : mx.symbol
continue on which layer
name : str
base name of the new layers
num_filter : int
how many filters to use in Convolution layer
kernel : tuple (int, int)
kernel size (h, w)
pad : tuple (int, int)
padding size (h, w)
stride : tuple (int, int)
stride size (h, w)
act_type : str
activation type, can be relu...
use_batchnorm : bool
whether to use batch normalization
Returns:
----------
(conv, relu) mx.Symbols
"""
assert not use_batchnorm, "batchnorm not yet supported"
bias = mx.symbol.Variable(name="conv{}_bias".format(name),
init=mx.init.Constant(0.0), attr={'__lr_mult__': '2.0'})
conv = mx.symbol.Convolution(data=from_layer, bias=bias, kernel=kernel, pad=pad, \
stride=stride, num_filter=num_filter, name="conv{}".format(name))
relu = mx.symbol.Activation(data=conv, act_type=act_type, \
name="{}{}".format(act_type, name))
if use_batchnorm:
relu = mx.symbol.BatchNorm(data=relu, name="bn{}".format(name))
return conv, relu
Example #3
| Source File: common.py From SNIPER-mxnet with Apache License 2.0 | 5 votes |
|
def conv_act_layer(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \
stride=(1,1), act_type="relu", use_batchnorm=False):
"""
wrapper for a small Convolution group
Parameters:
----------
from_layer : mx.symbol
continue on which layer
name : str
base name of the new layers
num_filter : int
how many filters to use in Convolution layer
kernel : tuple (int, int)
kernel size (h, w)
pad : tuple (int, int)
padding size (h, w)
stride : tuple (int, int)
stride size (h, w)
act_type : str
activation type, can be relu...
use_batchnorm : bool
whether to use batch normalization
Returns:
----------
(conv, relu) mx.Symbols
"""
conv = mx.symbol.Convolution(data=from_layer, kernel=kernel, pad=pad, \
stride=stride, num_filter=num_filter, name="{}_conv".format(name))
if use_batchnorm:
conv = mx.symbol.BatchNorm(data=conv, name="{}_bn".format(name))
relu = mx.symbol.Activation(data=conv, act_type=act_type, \
name="{}_{}".format(name, act_type))
return relu
Example #4
| Source File: common.py From mxnet-ssd with MIT License | 5 votes |
|
def legacy_conv_act_layer(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \
stride=(1,1), act_type="relu", use_batchnorm=False):
"""
wrapper for a small Convolution group
Parameters:
----------
from_layer : mx.symbol
continue on which layer
name : str
base name of the new layers
num_filter : int
how many filters to use in Convolution layer
kernel : tuple (int, int)
kernel size (h, w)
pad : tuple (int, int)
padding size (h, w)
stride : tuple (int, int)
stride size (h, w)
act_type : str
activation type, can be relu...
use_batchnorm : bool
whether to use batch normalization
Returns:
----------
(conv, relu) mx.Symbols
"""
assert not use_batchnorm, "batchnorm not yet supported"
bias = mx.symbol.Variable(name="conv{}_bias".format(name),
init=mx.init.Constant(0.0), attr={'__lr_mult__': '2.0'})
conv = mx.symbol.Convolution(data=from_layer, bias=bias, kernel=kernel, pad=pad, \
stride=stride, num_filter=num_filter, name="conv{}".format(name))
relu = mx.symbol.Activation(data=conv, act_type=act_type, \
name="{}{}".format(act_type, name))
if use_batchnorm:
relu = mx.symbol.BatchNorm(data=relu, name="bn{}".format(name))
return conv, relu
Example #5
| Source File: common.py From mxnet-ssd with MIT License | 5 votes |
|
def conv_act_layer(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \
stride=(1,1), act_type="relu", use_batchnorm=False):
"""
wrapper for a small Convolution group
Parameters:
----------
from_layer : mx.symbol
continue on which layer
name : str
base name of the new layers
num_filter : int
how many filters to use in Convolution layer
kernel : tuple (int, int)
kernel size (h, w)
pad : tuple (int, int)
padding size (h, w)
stride : tuple (int, int)
stride size (h, w)
act_type : str
activation type, can be relu...
use_batchnorm : bool
whether to use batch normalization
Returns:
----------
(conv, relu) mx.Symbols
"""
bias = mx.symbol.Variable(name="{}_conv_bias".format(name),
init=mx.init.Constant(0.0), attr={'__lr_mult__': '2.0'})
conv = mx.symbol.Convolution(data=from_layer, kernel=kernel, pad=pad, \
stride=stride, num_filter=num_filter, name="{}_conv".format(name), bias=bias)
if use_batchnorm:
conv = mx.symbol.BatchNorm(data=conv, name="{}_bn".format(name))
relu = mx.symbol.Activation(data=conv, act_type=act_type, \
name="{}_{}".format(name, act_type))
return relu
Example #6
| Source File: common.py From training_results_v0.6 with Apache License 2.0 | 5 votes |
|
def legacy_conv_act_layer(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \
stride=(1,1), act_type="relu", use_batchnorm=False):
"""
wrapper for a small Convolution group
Parameters:
----------
from_layer : mx.symbol
continue on which layer
name : str
base name of the new layers
num_filter : int
how many filters to use in Convolution layer
kernel : tuple (int, int)
kernel size (h, w)
pad : tuple (int, int)
padding size (h, w)
stride : tuple (int, int)
stride size (h, w)
act_type : str
activation type, can be relu...
use_batchnorm : bool
whether to use batch normalization
Returns:
----------
(conv, relu) mx.Symbols
"""
assert not use_batchnorm, "batchnorm not yet supported"
bias = mx.symbol.Variable(name="conv{}_bias".format(name),
init=mx.init.Constant(0.0), attr={'__lr_mult__': '2.0'})
conv = mx.symbol.Convolution(data=from_layer, bias=bias, kernel=kernel, pad=pad, \
stride=stride, num_filter=num_filter, name="conv{}".format(name))
relu = mx.symbol.Activation(data=conv, act_type=act_type, \
name="{}{}".format(act_type, name))
if use_batchnorm:
relu = mx.symbol.BatchNorm(data=relu, name="bn{}".format(name))
return conv, relu
Example #7
| Source File: common.py From training_results_v0.6 with Apache License 2.0 | 5 votes |
|
def conv_act_layer(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \
stride=(1,1), act_type="relu", use_batchnorm=False):
"""
wrapper for a small Convolution group
Parameters:
----------
from_layer : mx.symbol
continue on which layer
name : str
base name of the new layers
num_filter : int
how many filters to use in Convolution layer
kernel : tuple (int, int)
kernel size (h, w)
pad : tuple (int, int)
padding size (h, w)
stride : tuple (int, int)
stride size (h, w)
act_type : str
activation type, can be relu...
use_batchnorm : bool
whether to use batch normalization
Returns:
----------
(conv, relu) mx.Symbols
"""
conv = mx.symbol.Convolution(data=from_layer, kernel=kernel, pad=pad, \
stride=stride, num_filter=num_filter, name="{}_conv".format(name))
if use_batchnorm:
conv = mx.symbol.BatchNorm(data=conv, name="{}_bn".format(name))
relu = mx.symbol.Activation(data=conv, act_type=act_type, \
name="{}_{}".format(name, act_type))
return relu
Example #8
| Source File: common.py From mxnet-yolo with MIT License | 5 votes |
|
def conv_act_layer(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \
stride=(1,1), act_type="relu", use_batchnorm=False):
"""
wrapper for a small Convolution group
Parameters:
----------
from_layer : mx.symbol
continue on which layer
name : str
base name of the new layers
num_filter : int
how many filters to use in Convolution layer
kernel : tuple (int, int)
kernel size (h, w)
pad : tuple (int, int)
padding size (h, w)
stride : tuple (int, int)
stride size (h, w)
act_type : str
activation type, can be relu...
use_batchnorm : bool
whether to use batch normalization
Returns:
----------
(conv, relu) mx.Symbols
"""
conv = mx.symbol.Convolution(data=from_layer, kernel=kernel, pad=pad, \
stride=stride, num_filter=num_filter, name="conv{}".format(name))
if use_batchnorm:
conv = mx.symbol.BatchNorm(data=conv, name="bn{}".format(name))
relu = mx.symbol.Activation(data=conv, act_type=act_type, \
name="{}{}".format(act_type, name))
return relu
Example #9
| Source File: common.py From mxnet-dssd with GNU General Public License v3.0 | 5 votes |
|
def legacy_conv_act_layer(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \
stride=(1,1), act_type="relu", use_batchnorm=False):
"""
wrapper for a small Convolution group
Parameters:
----------
from_layer : mx.symbol
continue on which layer
name : str
base name of the new layers
num_filter : int
how many filters to use in Convolution layer
kernel : tuple (int, int)
kernel size (h, w)
pad : tuple (int, int)
padding size (h, w)
stride : tuple (int, int)
stride size (h, w)
act_type : str
activation type, can be relu...
use_batchnorm : bool
whether to use batch normalization
Returns:
----------
(conv, relu) mx.Symbols
"""
assert not use_batchnorm, "batchnorm not yet supported"
bias = mx.symbol.Variable(name="conv{}_bias".format(name),
init=mx.init.Constant(0.0), attr={'__lr_mult__': '2.0'})
conv = mx.symbol.Convolution(data=from_layer, bias=bias, kernel=kernel, pad=pad, \
stride=stride, num_filter=num_filter, name="conv{}".format(name))
relu = mx.symbol.Activation(data=conv, act_type=act_type, \
name="{}{}".format(act_type, name))
if use_batchnorm:
relu = mx.symbol.BatchNorm(data=relu, name="bn{}".format(name))
return conv, relu
Example #10
| Source File: common.py From mxnet-dssd with GNU General Public License v3.0 | 5 votes |
|
def conv_act_layer(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \
stride=(1,1), act_type="relu", use_batchnorm=False):
"""
wrapper for a small Convolution group
Parameters:
----------
from_layer : mx.symbol
continue on which layer
name : str
base name of the new layers
num_filter : int
how many filters to use in Convolution layer
kernel : tuple (int, int)
kernel size (h, w)
pad : tuple (int, int)
padding size (h, w)
stride : tuple (int, int)
stride size (h, w)
act_type : str
activation type, can be relu...
use_batchnorm : bool
whether to use batch normalization
Returns:
----------
(conv, relu) mx.Symbols
"""
conv = mx.symbol.Convolution(data=from_layer, kernel=kernel, pad=pad, \
stride=stride, num_filter=num_filter, name="{}_conv".format(name))
if use_batchnorm:
conv = mx.symbol.BatchNorm(data=conv, name="{}_bn".format(name))
relu = mx.symbol.Activation(data=conv, act_type=act_type, \
name="{}_{}".format(name, act_type))
return relu
Example #11
| Source File: common.py From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 | 5 votes |
|
def conv_act_layer(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \
stride=(1,1), act_type="relu", use_batchnorm=False):
"""
wrapper for a small Convolution group
Parameters:
----------
from_layer : mx.symbol
continue on which layer
name : str
base name of the new layers
num_filter : int
how many filters to use in Convolution layer
kernel : tuple (int, int)
kernel size (h, w)
pad : tuple (int, int)
padding size (h, w)
stride : tuple (int, int)
stride size (h, w)
act_type : str
activation type, can be relu...
use_batchnorm : bool
whether to use batch normalization
Returns:
----------
(conv, relu) mx.Symbols
"""
conv = mx.symbol.Convolution(data=from_layer, kernel=kernel, pad=pad, \
stride=stride, num_filter=num_filter, name="{}_conv".format(name))
if use_batchnorm:
conv = mx.symbol.BatchNorm(data=conv, name="{}_bn".format(name))
relu = mx.symbol.Activation(data=conv, act_type=act_type, \
name="{}_{}".format(name, act_type))
return relu
Example #12
| Source File: symbol_darknet19.py From mxnet-yolo with MIT License | 4 votes |
|
def conv_act_layer(from_layer, name, num_filter, kernel=(3, 3), pad=(1, 1), \
stride=(1,1), act_type="relu", use_batchnorm=True):
"""
wrapper for a small Convolution group
Parameters:
----------
from_layer : mx.symbol
continue on which layer
name : str
base name of the new layers
num_filter : int
how many filters to use in Convolution layer
kernel : tuple (int, int)
kernel size (h, w)
pad : tuple (int, int)
padding size (h, w)
stride : tuple (int, int)
stride size (h, w)
act_type : str
activation type, can be relu...
use_batchnorm : bool
whether to use batch normalization
Returns:
----------
(conv, relu) mx.Symbols
"""
conv = mx.symbol.Convolution(data=from_layer, kernel=kernel, pad=pad, \
stride=stride, num_filter=num_filter, name="{}".format(name))
if use_batchnorm:
conv = mx.symbol.BatchNorm(data=conv, name="bn_{}".format(name))
if act_type in ['elu', 'leaky', 'prelu', 'rrelu']:
relu = mx.symbol.LeakyReLU(data=conv, act_type=act_type,
name="{}_{}".format(act_type, name), slope=0.1)
elif act_type in ['relu', 'sigmoid', 'softrelu', 'tanh']:
relu = mx.symbol.Activation(data=conv, act_type=act_type, \
name="{}_{}".format(act_type, name))
else:
assert isinstance(act_type, str)
raise ValueError("Invalid activation type: " + str(act_type))
return relu
Example #13
| Source File: symbol_resnet50_yolo.py From mxnet-yolo with MIT License | 4 votes |
|
def conv_act_layer(from_layer, name, num_filter, kernel=(3, 3), pad=(1, 1), \
stride=(1,1), act_type="relu", use_batchnorm=True):
"""
wrapper for a small Convolution group
Parameters:
----------
from_layer : mx.symbol
continue on which layer
name : str
base name of the new layers
num_filter : int
how many filters to use in Convolution layer
kernel : tuple (int, int)
kernel size (h, w)
pad : tuple (int, int)
padding size (h, w)
stride : tuple (int, int)
stride size (h, w)
act_type : str
activation type, can be relu...
use_batchnorm : bool
whether to use batch normalization
Returns:
----------
(conv, relu) mx.Symbols
"""
conv = mx.symbol.Convolution(data=from_layer, kernel=kernel, pad=pad, \
stride=stride, num_filter=num_filter, name="{}".format(name))
if use_batchnorm:
conv = mx.symbol.BatchNorm(data=conv, name="bn_{}".format(name))
if act_type in ['elu', 'leaky', 'prelu', 'rrelu']:
relu = mx.symbol.LeakyReLU(data=conv, act_type=act_type,
name="{}_{}".format(act_type, name), slope=0.1)
elif act_type in ['relu', 'sigmoid', 'softrelu', 'tanh']:
relu = mx.symbol.Activation(data=conv, act_type=act_type, \
name="{}_{}".format(act_type, name))
else:
assert isinstance(act_type, str)
raise ValueError("Invalid activation type: " + str(act_type))
return relu
Example #14
| Source File: common.py From mxnet-dssd with GNU General Public License v3.0 | 4 votes |
|
def multi_layer_feature(body, from_layers, num_filters, strides, pads, min_filter=128, use_res_unit=False):
"""Wrapper function to extract features from base network, attaching extra
layers and SSD specific layers
Parameters
----------
from_layers : list of str
feature extraction layers, use '' for add extra layers
For example:
from_layers = ['relu4_3', 'fc7', '', '', '', '']
which means extract feature from relu4_3 and fc7, adding 4 extra layers
on top of fc7
num_filters : list of int
number of filters for extra layers, you can use -1 for extracted features,
however, if normalization and scale is applied, the number of filter for
that layer must be provided.
For example:
num_filters = [512, -1, 512, 256, 256, 256]
strides : list of int
strides for the 3x3 convolution appended, -1 can be used for extracted
feature layers
pads : list of int
paddings for the 3x3 convolution, -1 can be used for extracted layers
min_filter : int
minimum number of filters used in 1x1 convolution
Returns
-------
list of mx.Symbols
"""
# arguments check
assert len(from_layers) > 0
assert isinstance(from_layers[0], str) and len(from_layers[0].strip()) > 0
assert len(from_layers) == len(num_filters) == len(strides) == len(pads)
internals = body.get_internals()
layers = []
for k, params in enumerate(zip(from_layers, num_filters, strides, pads)):
from_layer, num_filter, s, p = params
if from_layer.strip():
# extract from base network
layer = internals[from_layer.strip() + '_output']
layers.append(layer)
else:
# attach from last feature layer
assert len(layers) > 0
assert num_filter > 0
layer = layers[-1]
if use_res_unit:
conv = dt.residual_unit(layer,num_filter,stride=s,name=str(k),bn_mom=0.9,workspace=256)
layers.append(conv)
else:
num_1x1 = max(min_filter, num_filter // 2)
conv_1x1 = conv_act_layer(layer, 'multi_feat_%d_conv_1x1' % (k),
num_1x1, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu')
conv_3x3 = conv_act_layer(conv_1x1, 'multi_feat_%d_conv_3x3' % (k),
num_filter, kernel=(3, 3), pad=(p, p), stride=(s, s), act_type='relu')
layers.append(conv_3x3)
return layers
Example #15
| Source File: common.py From training_results_v0.6 with Apache License 2.0 | 4 votes |
|
def multi_layer_feature(body, from_layers, num_filters, strides, pads, min_filter=128):
"""Wrapper function to extract features from base network, attaching extra
layers and SSD specific layers
Parameters
----------
from_layers : list of str
feature extraction layers, use '' for add extra layers
For example:
from_layers = ['relu4_3', 'fc7', '', '', '', '']
which means extract feature from relu4_3 and fc7, adding 4 extra layers
on top of fc7
num_filters : list of int
number of filters for extra layers, you can use -1 for extracted features,
however, if normalization and scale is applied, the number of filter for
that layer must be provided.
For example:
num_filters = [512, -1, 512, 256, 256, 256]
strides : list of int
strides for the 3x3 convolution appended, -1 can be used for extracted
feature layers
pads : list of int
paddings for the 3x3 convolution, -1 can be used for extracted layers
min_filter : int
minimum number of filters used in 1x1 convolution
Returns
-------
list of mx.Symbols
"""
# arguments check
assert len(from_layers) > 0
assert isinstance(from_layers[0], str) and len(from_layers[0].strip()) > 0
assert len(from_layers) == len(num_filters) == len(strides) == len(pads)
internals = body.get_internals()
layers = []
for k, params in enumerate(zip(from_layers, num_filters, strides, pads)):
from_layer, num_filter, s, p = params
if from_layer.strip():
# extract from base network
layer = internals[from_layer.strip() + '_output']
layers.append(layer)
else:
# attach from last feature layer
assert len(layers) > 0
assert num_filter > 0
layer = layers[-1]
num_1x1 = max(min_filter, num_filter // 2)
conv_1x1 = conv_act_layer(layer, 'multi_feat_%d_conv_1x1' % (k),
num_1x1, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu')
conv_3x3 = conv_act_layer(conv_1x1, 'multi_feat_%d_conv_3x3' % (k),
num_filter, kernel=(3, 3), pad=(p, p), stride=(s, s), act_type='relu')
layers.append(conv_3x3)
return layers
Example #16
| Source File: common.py From mxnet-ssd with MIT License | 4 votes |
|
def multi_layer_feature(body, from_layers, num_filters, strides, pads, min_filter=128):
"""Wrapper function to extract features from base network, attaching extra
layers and SSD specific layers
Parameters
----------
from_layers : list of str
feature extraction layers, use '' for add extra layers
For example:
from_layers = ['relu4_3', 'fc7', '', '', '', '']
which means extract feature from relu4_3 and fc7, adding 4 extra layers
on top of fc7
num_filters : list of int
number of filters for extra layers, you can use -1 for extracted features,
however, if normalization and scale is applied, the number of filter for
that layer must be provided.
For example:
num_filters = [512, -1, 512, 256, 256, 256]
strides : list of int
strides for the 3x3 convolution appended, -1 can be used for extracted
feature layers
pads : list of int
paddings for the 3x3 convolution, -1 can be used for extracted layers
min_filter : int
minimum number of filters used in 1x1 convolution
Returns
-------
list of mx.Symbols
"""
# arguments check
assert len(from_layers) > 0
assert isinstance(from_layers[0], str) and len(from_layers[0].strip()) > 0
assert len(from_layers) == len(num_filters) == len(strides) == len(pads)
internals = body.get_internals()
layers = []
for k, params in enumerate(zip(from_layers, num_filters, strides, pads)):
from_layer, num_filter, s, p = params
if from_layer.strip():
# extract from base network
layer = internals[from_layer.strip() + '_output']
layers.append(layer)
else:
# attach from last feature layer
assert len(layers) > 0
assert num_filter > 0
layer = layers[-1]
num_1x1 = max(min_filter, num_filter // 2)
conv_1x1 = conv_act_layer(layer, 'multi_feat_%d_conv_1x1' % (k),
num_1x1, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu')
conv_3x3 = conv_act_layer(conv_1x1, 'multi_feat_%d_conv_3x3' % (k),
num_filter, kernel=(3, 3), pad=(p, p), stride=(s, s), act_type='relu')
layers.append(conv_3x3)
return layers
Example #17
| Source File: common.py From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 | 4 votes |
|
def multi_layer_feature(body, from_layers, num_filters, strides, pads, min_filter=128):
"""Wrapper function to extract features from base network, attaching extra
layers and SSD specific layers
Parameters
----------
from_layers : list of str
feature extraction layers, use '' for add extra layers
For example:
from_layers = ['relu4_3', 'fc7', '', '', '', '']
which means extract feature from relu4_3 and fc7, adding 4 extra layers
on top of fc7
num_filters : list of int
number of filters for extra layers, you can use -1 for extracted features,
however, if normalization and scale is applied, the number of filter for
that layer must be provided.
For example:
num_filters = [512, -1, 512, 256, 256, 256]
strides : list of int
strides for the 3x3 convolution appended, -1 can be used for extracted
feature layers
pads : list of int
paddings for the 3x3 convolution, -1 can be used for extracted layers
min_filter : int
minimum number of filters used in 1x1 convolution
Returns
-------
list of mx.Symbols
"""
# arguments check
assert len(from_layers) > 0
assert isinstance(from_layers[0], str) and len(from_layers[0].strip()) > 0
assert len(from_layers) == len(num_filters) == len(strides) == len(pads)
internals = body.get_internals()
layers = []
for k, params in enumerate(zip(from_layers, num_filters, strides, pads)):
from_layer, num_filter, s, p = params
if from_layer.strip():
# extract from base network
layer = internals[from_layer.strip() + '_output']
layers.append(layer)
else:
# attach from last feature layer
assert len(layers) > 0
assert num_filter > 0
layer = layers[-1]
num_1x1 = max(min_filter, num_filter // 2)
conv_1x1 = conv_act_layer(layer, 'multi_feat_%d_conv_1x1' % (k),
num_1x1, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu')
conv_3x3 = conv_act_layer(conv_1x1, 'multi_feat_%d_conv_3x3' % (k),
num_filter, kernel=(3, 3), pad=(p, p), stride=(s, s), act_type='relu')
layers.append(conv_3x3)
return layers
Example #18
| Source File: common.py From SNIPER-mxnet with Apache License 2.0 | 4 votes |
|
def multi_layer_feature(body, from_layers, num_filters, strides, pads, min_filter=128):
"""Wrapper function to extract features from base network, attaching extra
layers and SSD specific layers
Parameters
----------
from_layers : list of str
feature extraction layers, use '' for add extra layers
For example:
from_layers = ['relu4_3', 'fc7', '', '', '', '']
which means extract feature from relu4_3 and fc7, adding 4 extra layers
on top of fc7
num_filters : list of int
number of filters for extra layers, you can use -1 for extracted features,
however, if normalization and scale is applied, the number of filter for
that layer must be provided.
For example:
num_filters = [512, -1, 512, 256, 256, 256]
strides : list of int
strides for the 3x3 convolution appended, -1 can be used for extracted
feature layers
pads : list of int
paddings for the 3x3 convolution, -1 can be used for extracted layers
min_filter : int
minimum number of filters used in 1x1 convolution
Returns
-------
list of mx.Symbols
"""
# arguments check
assert len(from_layers) > 0
assert isinstance(from_layers[0], str) and len(from_layers[0].strip()) > 0
assert len(from_layers) == len(num_filters) == len(strides) == len(pads)
internals = body.get_internals()
layers = []
for k, params in enumerate(zip(from_layers, num_filters, strides, pads)):
from_layer, num_filter, s, p = params
if from_layer.strip():
# extract from base network
layer = internals[from_layer.strip() + '_output']
layers.append(layer)
else:
# attach from last feature layer
assert len(layers) > 0
assert num_filter > 0
layer = layers[-1]
num_1x1 = max(min_filter, num_filter // 2)
conv_1x1 = conv_act_layer(layer, 'multi_feat_%d_conv_1x1' % (k),
num_1x1, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu')
conv_3x3 = conv_act_layer(conv_1x1, 'multi_feat_%d_conv_3x3' % (k),
num_filter, kernel=(3, 3), pad=(p, p), stride=(s, s), act_type='relu')
layers.append(conv_3x3)
return layers
