name.py
, such as inceptionv3.py
.symbol_factory.py
, an example would be:if network == 'vgg16_reduced': if data_shape >= 448: from_layers = ['relu4_3', 'relu7', '', '', '', '', ''] num_filters = [512, -1, 512, 256, 256, 256, 256] strides = [-1, -1, 2, 2, 2, 2, 1] pads = [-1, -1, 1, 1, 1, 1, 1] sizes = [[.07, .1025], [.15,.2121], [.3, .3674], [.45, .5196], [.6, .6708], \ [.75, .8216], [.9, .9721]] ratios = [[1,2,.5], [1,2,.5,3,1./3], [1,2,.5,3,1./3], [1,2,.5,3,1./3], \ [1,2,.5,3,1./3], [1,2,.5], [1,2,.5]] normalizations = [20, -1, -1, -1, -1, -1, -1] steps = [] if data_shape != 512 else [x / 512.0 for x in [8, 16, 32, 64, 128, 256, 512]] else: from_layers = ['relu4_3', 'relu7', '', '', '', ''] num_filters = [512, -1, 512, 256, 256, 256] strides = [-1, -1, 2, 2, 1, 1] pads = [-1, -1, 1, 1, 0, 0] sizes = [[.1, .141], [.2,.272], [.37, .447], [.54, .619], [.71, .79], [.88, .961]] ratios = [[1,2,.5], [1,2,.5,3,1./3], [1,2,.5,3,1./3], [1,2,.5,3,1./3], \ [1,2,.5], [1,2,.5]] normalizations = [20, -1, -1, -1, -1, -1] steps = [] if data_shape != 300 else [x / 300.0 for x in [8, 16, 32, 64, 100, 300]] return locals() elif network == 'inceptionv3': from_layers = ['ch_concat_mixed_7_chconcat', 'ch_concat_mixed_10_chconcat', '', '', '', ''] num_filters = [-1, -1, 512, 256, 256, 128] strides = [-1, -1, 2, 2, 2, 2] pads = [-1, -1, 1, 1, 1, 1] sizes = [[.1, .141], [.2,.272], [.37, .447], [.54, .619], [.71, .79], [.88, .961]] ratios = [[1,2,.5], [1,2,.5,3,1./3], [1,2,.5,3,1./3], [1,2,.5,3,1./3], \ [1,2,.5], [1,2,.5]] normalizations = -1 steps = [] return locals()
Here from_layers
indicate the feature layer you would like to extract from the base network. ''
indicate that we want add extra new layers on top of the last feature layer, and the number of filters must be specified in num_filters
. Similarly, strides
and pads
are required to compose these new layers. sizes
and ratios
are the parameters controlling the anchor generation algorithm. normalizations
is used to normalize and rescale feature if not -1
. steps
: optional, used to calculate the anchor sliding steps.
--network name --data-shape xxx --pretrained pretrained_model