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

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# coding: utf-8
# pylint: disable= arguments-differ
"""SqueezeNet, implemented in Gluon."""
__all__ = ['SqueezeNet', 'squeezenet1_0', 'squeezenet1_1']

import os

from ....device import cpu
from ...block import HybridBlock
from ... import nn
from .... import base
from ....util import use_np, wrap_ctx_to_device_func

# Helpers
def _make_fire(squeeze_channels, expand1x1_channels, expand3x3_channels):
    out = nn.HybridSequential()
    out.add(_make_fire_conv(squeeze_channels, 1))

    paths = nn.HybridConcatenate(axis=1)
    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()
    out.add(nn.Conv2D(channels, kernel_size, padding=padding))
    out.add(nn.Activation('relu'))
    return out

# Net
[docs]@use_np class SqueezeNet(HybridBlock): r"""SqueezeNet model from the `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size" <https://arxiv.org/abs/1602.07360>`_ paper. SqueezeNet 1.1 model from the `official SqueezeNet repo <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_. 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)) self.features = nn.HybridSequential() 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() 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())
[docs] def forward(self, x): x = self.features(x) x = self.output(x) return x
# Constructor @wrap_ctx_to_device_func def get_squeezenet(version, pretrained=False, device=cpu(), root=os.path.join(base.data_dir(), 'models'), **kwargs): r"""SqueezeNet model from the `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size" <https://arxiv.org/abs/1602.07360>`_ paper. SqueezeNet 1.1 model from the `official SqueezeNet repo <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_. 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. device : Device, default CPU The device in which to load the pretrained weights. root : str, default $MXNET_HOME/models Location for keeping the model parameters. """ net = SqueezeNet(version, **kwargs) if pretrained: from ..model_store import get_model_file net.load_parameters(get_model_file(f'squeezenet{version}', root=root), device=device) return net
[docs]@wrap_ctx_to_device_func def squeezenet1_0(**kwargs): r"""SqueezeNet 1.0 model from the `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size" <https://arxiv.org/abs/1602.07360>`_ paper. Parameters ---------- pretrained : bool, default False Whether to load the pretrained weights for model. device : Device, default CPU The device in which to load the pretrained weights. root : str, default '$MXNET_HOME/models' Location for keeping the model parameters. """ return get_squeezenet('1.0', **kwargs)
[docs]@wrap_ctx_to_device_func def squeezenet1_1(**kwargs): r"""SqueezeNet 1.1 model from the `official SqueezeNet repo <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_. 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. device : Device, default CPU The device in which to load the pretrained weights. root : str, default '$MXNET_HOME/models' Location for keeping the model parameters. """ return get_squeezenet('1.1', **kwargs)