def small_vgg(input_image, num_channels, num_classes):
def __vgg__(ipt, num_filter, times, dropouts, num_channels_=None):
return img_conv_group(input=ipt,
num_channels=num_channels_,
pool_size=2,
pool_stride=2,
conv_num_filter=[num_filter] * times,
conv_filter_size=3,
conv_act=ReluActivation(),
conv_with_batchnorm=True,
conv_batchnorm_drop_rate=dropouts,
pool_type=MaxPooling())
tmp = __vgg__(input_image, 64, 2, [0.3, 0], num_channels)
tmp = __vgg__(tmp, 128, 2, [0.4, 0])
tmp = __vgg__(tmp, 256, 3, [0.4, 0.4, 0])
tmp = __vgg__(tmp, 512, 3, [0.4, 0.4, 0])
tmp = img_pool_layer(input=tmp,
stride=2,
pool_size=2,
pool_type=MaxPooling())
tmp = dropout_layer(input=tmp, dropout_rate=0.5)
tmp = fc_layer(input=tmp,
size=512,
layer_attr=ExtraAttr(drop_rate=0.5),
act=LinearActivation())
tmp = batch_norm_layer(input=tmp, act=ReluActivation())
return fc_layer(input=tmp, size=num_classes, act=SoftmaxActivation())
def vgg_16_network(input_image, num_channels, num_classes=1000):
"""
Same model from https://gist.github.com/ksimonyan/211839e770f7b538e2d8
:param num_classes:
:param input_image:
:type input_image: LayerOutput
:param num_channels:
:type num_channels: int
:return:
"""
tmp = img_conv_group(input=input_image, num_channels=num_channels,
conv_padding=1, conv_num_filter=[64, 64],
conv_filter_size=3,
conv_act=ReluActivation(), pool_size=2,
pool_stride=2,
pool_type=MaxPooling())
tmp = img_conv_group(input=tmp, conv_num_filter=[128, 128], conv_padding=1,
conv_filter_size=3, conv_act=ReluActivation(),
pool_stride=2, pool_type=MaxPooling(),
pool_size=2)
tmp = img_conv_group(input=tmp, conv_num_filter=[256, 256, 256],
conv_padding=1,
conv_filter_size=3, conv_act=ReluActivation(),
pool_stride=2, pool_type=MaxPooling(), pool_size=2)
tmp = img_conv_group(input=tmp, conv_num_filter=[512, 512, 512],
conv_padding=1,
conv_filter_size=3, conv_act=ReluActivation(),
pool_stride=2, pool_type=MaxPooling(), pool_size=2)
tmp = img_conv_group(input=tmp, conv_num_filter=[512, 512, 512],
conv_padding=1,
conv_filter_size=3, conv_act=ReluActivation(),
pool_stride=2, pool_type=MaxPooling(), pool_size=2)
tmp = fc_layer(input=tmp, size=4096, act=ReluActivation(),
layer_attr=ExtraAttr(drop_rate=0.5))
tmp = fc_layer(input=tmp, size=4096, act=ReluActivation(),
layer_attr=ExtraAttr(drop_rate=0.5))
return fc_layer(input=tmp, size=num_classes, act=SoftmaxActivation())
def __init__(self, inputs, outputs):
super().__init__(inputs, outputs, SoftmaxActivation())
