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"""DenseNet, implemented in Gluon."""
import sys
import os
import mxnet as mx
import mxnet.ndarray as nd
import mxnet.gluon as gluon
import mxnet.gluon.nn as nn
import mxnet.autograd as ag
import symbol_utils
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from config import config
def Act():
if config.net_act == 'prelu':
return nn.PReLU()
else:
return nn.Activation(config.net_act)
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(Act())
new_features.add(nn.Conv2D(bn_size * growth_rate, kernel_size=1, use_bias=False))
new_features.add(nn.BatchNorm())
new_features.add(Act())
new_features.add(nn.Conv2D(growth_rate, kernel_size=3, padding=1, use_bias=False))
if dropout:
new_features.add(nn.Dropout(dropout))
out = gluon.contrib.nn.HybridConcurrent(axis=1, prefix='')
out.add(gluon.contrib.nn.Identity())
out.add(new_features)
return out
def _make_transition(num_output_features):
out = nn.HybridSequential(prefix='')
out.add(nn.BatchNorm())
out.add(Act())
out.add(nn.Conv2D(num_output_features, kernel_size=1, use_bias=False))
out.add(nn.AvgPool2D(pool_size=2, strides=2))
return out
class DenseNet(nn.HybridBlock):
r"""Densenet-BC model from the
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ 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=3,
strides=1, padding=1, 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))
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'))
def hybrid_forward(self, F, x):
x = self.features(x)
return x
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])}
def get_symbol():
num_layers = 121
num_init_features, growth_rate, block_config = densenet_spec[num_layers]
net = DenseNet(num_init_features, growth_rate, block_config, dropout=False)
data = mx.sym.Variable(name='data')
data = data - 127.5
data = data * 0.0078125
body = net(data)
fc1 = symbol_utils.get_fc1(body, config.emb_size, config.net_output)
return fc1
if __name__=="__main__":
fc = get_symbol()
digraph = mx.viz.plot_network(fc, title='densenet121', shape={'data': (1,3,112, 112)},node_attrs={"fixedsize": "false"})
digraph.view()
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