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# coding: utf-8
# pylint: disable= arguments-differ
"""SqueezeNet, implemented in Gluon."""
__all__ = ['SqueezeNet', 'squeezenet1_0', 'squeezenet1_1']
import os
from ....context import cpu
from ...block import HybridBlock
from ... import nn
from ..custom_layers import HybridConcurrent
# Helpers
def _make_fire(squeeze_channels, expand1x1_channels, expand3x3_channels):
out = nn.HybridSequential(prefix='')
out.add(_make_fire_conv(squeeze_channels, 1))
paths = HybridConcurrent(concat_dim=1, prefix='')
paths.add(_make_fire_conv(expand1x1_channels, 1))
paths.add(_make_fire_conv(expand3x3_channels, 3, 1))
out.add(paths)
return out
def _make_fire_conv(channels, kernel_size, padding=0):
out = nn.HybridSequential(prefix='')
out.add(nn.Conv2D(channels, kernel_size, padding=padding))
out.add(nn.Activation('relu'))
return out
# Net
[docs]class SqueezeNet(HybridBlock):
r"""SqueezeNet model from the `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
and <0.5MB model size" `_ paper.
SqueezeNet 1.1 model from the `official SqueezeNet repo
`_.
SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
than SqueezeNet 1.0, without sacrificing accuracy.
Parameters
----------
version : str
Version of squeezenet. Options are '1.0', '1.1'.
classes : int, default 1000
Number of classification classes.
"""
def __init__(self, version, classes=1000, **kwargs):
super(SqueezeNet, self).__init__(**kwargs)
assert version in ['1.0', '1.1'], ("Unsupported SqueezeNet version {version}:"
"1.0 or 1.1 expected".format(version=version))
with self.name_scope():
self.features = nn.HybridSequential(prefix='')
if version == '1.0':
self.features.add(nn.Conv2D(96, kernel_size=7, strides=2))
self.features.add(nn.Activation('relu'))
self.features.add(nn.MaxPool2D(pool_size=3, strides=2, ceil_mode=True))
self.features.add(_make_fire(16, 64, 64))
self.features.add(_make_fire(16, 64, 64))
self.features.add(_make_fire(32, 128, 128))
self.features.add(nn.MaxPool2D(pool_size=3, strides=2, ceil_mode=True))
self.features.add(_make_fire(32, 128, 128))
self.features.add(_make_fire(48, 192, 192))
self.features.add(_make_fire(48, 192, 192))
self.features.add(_make_fire(64, 256, 256))
self.features.add(nn.MaxPool2D(pool_size=3, strides=2, ceil_mode=True))
self.features.add(_make_fire(64, 256, 256))
else:
self.features.add(nn.Conv2D(64, kernel_size=3, strides=2))
self.features.add(nn.Activation('relu'))
self.features.add(nn.MaxPool2D(pool_size=3, strides=2, ceil_mode=True))
self.features.add(_make_fire(16, 64, 64))
self.features.add(_make_fire(16, 64, 64))
self.features.add(nn.MaxPool2D(pool_size=3, strides=2, ceil_mode=True))
self.features.add(_make_fire(32, 128, 128))
self.features.add(_make_fire(32, 128, 128))
self.features.add(nn.MaxPool2D(pool_size=3, strides=2, ceil_mode=True))
self.features.add(_make_fire(48, 192, 192))
self.features.add(_make_fire(48, 192, 192))
self.features.add(_make_fire(64, 256, 256))
self.features.add(_make_fire(64, 256, 256))
self.features.add(nn.Dropout(0.5))
self.output = nn.HybridSequential(prefix='')
self.output.add(nn.Conv2D(classes, kernel_size=1))
self.output.add(nn.Activation('relu'))
self.output.add(nn.AvgPool2D(13))
self.output.add(nn.Flatten())
def hybrid_forward(self, F, x):
x = self.features(x)
x = self.output(x)
return x
# Constructor
def get_squeezenet(version, pretrained=False, ctx=cpu(),
root=os.path.join('~', '.mxnet', 'models'), **kwargs):
r"""SqueezeNet model from the `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
and <0.5MB model size" `_ paper.
SqueezeNet 1.1 model from the `official SqueezeNet repo
`_.
SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
than SqueezeNet 1.0, without sacrificing accuracy.
Parameters
----------
version : str
Version of squeezenet. Options are '1.0', '1.1'.
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.
"""
net = SqueezeNet(version, **kwargs)
if pretrained:
from ..model_store import get_model_file
net.load_params(get_model_file('squeezenet%s'%version, root=root), ctx=ctx)
return net
[docs]def squeezenet1_0(**kwargs):
r"""SqueezeNet 1.0 model from the `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
and <0.5MB model size" `_ 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_squeezenet('1.0', **kwargs)
[docs]def squeezenet1_1(**kwargs):
r"""SqueezeNet 1.1 model from the `official SqueezeNet repo
`_.
SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
than SqueezeNet 1.0, without sacrificing accuracy.
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_squeezenet('1.1', **kwargs)
