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# coding: utf-8
# pylint: disable= arguments-differ
"""DenseNet, implemented in Gluon."""
__all__ = ['DenseNet', 'densenet121', 'densenet161', 'densenet169', 'densenet201']
import os
from ....context import cpu
from ...block import HybridBlock
from ... import nn
from ...contrib.nn import HybridConcurrent, Identity
from .... import base
# Helpers
def _make_dense_block(num_layers, bn_size, growth_rate, dropout, stage_index):
out = nn.HybridSequential(prefix='stage%d_'%stage_index)
with out.name_scope():
for _ in range(num_layers):
out.add(_make_dense_layer(growth_rate, bn_size, dropout))
return out
def _make_dense_layer(growth_rate, bn_size, dropout):
new_features = nn.HybridSequential(prefix='')
new_features.add(nn.BatchNorm())
new_features.add(nn.Activation('relu'))
new_features.add(nn.Conv2D(bn_size * growth_rate, kernel_size=1, use_bias=False))
new_features.add(nn.BatchNorm())
new_features.add(nn.Activation('relu'))
new_features.add(nn.Conv2D(growth_rate, kernel_size=3, padding=1, use_bias=False))
if dropout:
new_features.add(nn.Dropout(dropout))
out = HybridConcurrent(axis=1, prefix='')
out.add(Identity())
out.add(new_features)
return out
def _make_transition(num_output_features):
out = nn.HybridSequential(prefix='')
out.add(nn.BatchNorm())
out.add(nn.Activation('relu'))
out.add(nn.Conv2D(num_output_features, kernel_size=1, use_bias=False))
out.add(nn.AvgPool2D(pool_size=2, strides=2))
return out
# Net
[docs]class DenseNet(HybridBlock):
r"""Densenet-BC model from the
`"Densely Connected Convolutional Networks" `_ paper.
Parameters
----------
num_init_features : int
Number of filters to learn in the first convolution layer.
growth_rate : int
Number of filters to add each layer (`k` in the paper).
block_config : list of int
List of integers for numbers of layers in each pooling block.
bn_size : int, default 4
Multiplicative factor for number of bottle neck layers.
(i.e. bn_size * k features in the bottleneck layer)
dropout : float, default 0
Rate of dropout after each dense layer.
classes : int, default 1000
Number of classification classes.
"""
def __init__(self, num_init_features, growth_rate, block_config,
bn_size=4, dropout=0, classes=1000, **kwargs):
super(DenseNet, self).__init__(**kwargs)
with self.name_scope():
self.features = nn.HybridSequential(prefix='')
self.features.add(nn.Conv2D(num_init_features, kernel_size=7,
strides=2, padding=3, use_bias=False))
self.features.add(nn.BatchNorm())
self.features.add(nn.Activation('relu'))
self.features.add(nn.MaxPool2D(pool_size=3, strides=2, padding=1))
# Add dense blocks
num_features = num_init_features
for i, num_layers in enumerate(block_config):
self.features.add(_make_dense_block(num_layers, bn_size, growth_rate, dropout, i+1))
num_features = num_features + num_layers * growth_rate
if i != len(block_config) - 1:
self.features.add(_make_transition(num_features // 2))
num_features = num_features // 2
self.features.add(nn.BatchNorm())
self.features.add(nn.Activation('relu'))
self.features.add(nn.AvgPool2D(pool_size=7))
self.features.add(nn.Flatten())
self.output = nn.Dense(classes)
def hybrid_forward(self, F, x):
x = self.features(x)
x = self.output(x)
return x
# Specification
densenet_spec = {121: (64, 32, [6, 12, 24, 16]),
161: (96, 48, [6, 12, 36, 24]),
169: (64, 32, [6, 12, 32, 32]),
201: (64, 32, [6, 12, 48, 32])}
# Constructor
def get_densenet(num_layers, pretrained=False, ctx=cpu(),
root=os.path.join(base.data_dir(), 'models'), **kwargs):
r"""Densenet-BC model from the
`"Densely Connected Convolutional Networks" `_ paper.
Parameters
----------
num_layers : int
Number of layers for the variant of densenet. Options are 121, 161, 169, 201.
pretrained : bool, default False
Whether to load the pretrained weights for model.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default $MXNET_HOME/models
Location for keeping the model parameters.
"""
num_init_features, growth_rate, block_config = densenet_spec[num_layers]
net = DenseNet(num_init_features, growth_rate, block_config, **kwargs)
if pretrained:
from ..model_store import get_model_file
net.load_parameters(get_model_file('densenet%d'%(num_layers), root=root), ctx=ctx)
return net
[docs]def densenet121(**kwargs):
r"""Densenet-BC 121-layer model from the
`"Densely Connected Convolutional Networks" `_ paper.
Parameters
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
"""
return get_densenet(121, **kwargs)
[docs]def densenet161(**kwargs):
r"""Densenet-BC 161-layer model from the
`"Densely Connected Convolutional Networks" `_ paper.
Parameters
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
"""
return get_densenet(161, **kwargs)
[docs]def densenet169(**kwargs):
r"""Densenet-BC 169-layer model from the
`"Densely Connected Convolutional Networks" `_ paper.
Parameters
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
"""
return get_densenet(169, **kwargs)
[docs]def densenet201(**kwargs):
r"""Densenet-BC 201-layer model from the
`"Densely Connected Convolutional Networks" `_ paper.
Parameters
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
"""
return get_densenet(201, **kwargs)
