Source code for mxnet.gluon.model_zoo.vision.densenet

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# coding: utf-8
# pylint: disable= arguments-differ
"""DenseNet, implemented in Gluon."""
__all__ = ['DenseNet', 'densenet121', 'densenet161', 'densenet169', 'densenet201']

from ....context import cpu
from ...block import HybridBlock
from ... import nn
from ..custom_layers import HybridConcurrent, Identity

# 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(concat_dim=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='~/.mxnet/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/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_params(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/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/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/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/models' Location for keeping the model parameters. """ return get_densenet(201, **kwargs)