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
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