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November 3, 2017 06:10
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DenseNet with BC
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import math | |
from mxnet import init | |
from mxnet.gluon import nn | |
from mxnet.gluon.model_zoo.custom_layers import HybridConcurrent, Identity | |
def sigma(kernel_size, channels): | |
return math.sqrt(2. / (kernel_size * kernel_size * channels)) | |
class DenseNet(nn.HybridSequential): | |
""" https://github.com/liuzhuang13/DenseNet | |
""" | |
def __init__(self, depth, growth, dropout=0.2, reduction=0.5, bottleneck=True, bn_factor=4): | |
super(DenseNet, self).__init__(prefix='') | |
self.depth = depth | |
self.growth = growth | |
self.dropout = dropout | |
self.reduction = reduction | |
self.bottleneck = bottleneck | |
self.bn_factor = bn_factor | |
self.build_cifar10() | |
def build_cifar10(self): | |
layers = (self.depth - 4) // 3 | |
if self.bottleneck: | |
layers //= 2 | |
features = init_channels = 2 * self.growth | |
with self.name_scope(): | |
self.add(nn.Conv2D(init_channels, kernel_size=3, padding=1, use_bias=False, | |
weight_initializer=init.Normal(sigma(3, init_channels)))) | |
for i in range(3): | |
self.add(self.make_dense_block(i, layers)) | |
features = int((features + layers * self.growth) * self.reduction) | |
self.add(self.make_transition(features, 8 if i == 2 else 0)) | |
self.add(nn.Dense(10)) | |
def make_dense_block(self, stage, layers): | |
out = nn.HybridSequential(prefix='stage%d_' % stage) | |
with out.name_scope(): | |
for _ in range(layers): | |
out.add(self.make_dense_layer()) | |
return out | |
def make_dense_layer(self): | |
net = nn.HybridSequential(prefix='') | |
net.add(nn.BatchNorm()) | |
net.add(nn.Activation('relu')) | |
if self.bottleneck: | |
net.add(nn.Conv2D(self.bn_factor * self.growth, kernel_size=1, use_bias=False, | |
weight_initializer=init.Normal(sigma(1, self.bn_factor * self.growth)))) | |
if self.dropout > 0: | |
net.add(nn.Dropout(self.dropout)) | |
net.add(nn.BatchNorm()) | |
net.add(nn.Activation('relu')) | |
net.add(nn.Conv2D(self.growth, kernel_size=3, padding=1, use_bias=False, | |
weight_initializer=init.Normal(sigma(3, self.growth)))) | |
if self.dropout > 0: | |
net.add(nn.Dropout(self.dropout)) | |
out = HybridConcurrent(concat_dim=1, prefix='') | |
out.add(Identity()) | |
out.add(net) | |
return out | |
def make_transition(self, channels, last_pool_size=0): | |
out = nn.HybridSequential(prefix='') | |
out.add(nn.BatchNorm()) | |
out.add(nn.Activation('relu')) | |
if last_pool_size: | |
out.add(nn.AvgPool2D(pool_size=last_pool_size)) | |
else: | |
out.add(nn.Conv2D(channels, kernel_size=1, use_bias=False, | |
weight_initializer=init.Normal(sigma(1, channels)))) | |
if self.dropout > 0: | |
out.add(nn.Dropout(self.dropout)) | |
out.add(nn.AvgPool2D(pool_size=2, strides=2)) | |
return out |
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