Table Of Contents
Table Of Contents

Source code for gluoncv.model_zoo.googlenet

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# coding: utf-8
# pylint: disable=missing-docstring,arguments-differ,unused-argument
"""GoogleNet, implemented in Gluon."""

__all__ = ['GoogLeNet', 'googlenet']

from mxnet.context import cpu
from mxnet.gluon.block import HybridBlock
from mxnet.gluon import nn
from mxnet.gluon.nn import BatchNorm
from mxnet.gluon.contrib.nn import HybridConcurrent

def _make_basic_conv(in_channels, channels, norm_layer=BatchNorm, norm_kwargs=None, **kwargs):
    out = nn.HybridSequential(prefix='')
    out.add(nn.Conv2D(in_channels=in_channels, channels=channels, use_bias=False, **kwargs))
    out.add(norm_layer(in_channels=channels, epsilon=0.001,
                       **({} if norm_kwargs is None else norm_kwargs)))
    out.add(nn.Activation('relu'))
    return out

def _make_branch(use_pool, norm_layer, norm_kwargs, *conv_settings):
    out = nn.HybridSequential(prefix='')
    if use_pool == 'avg':
        out.add(nn.AvgPool2D(pool_size=3, strides=1, padding=1))
    elif use_pool == 'max':
        out.add(nn.MaxPool2D(pool_size=3, strides=1, padding=1))
    setting_names = ['in_channels', 'channels', 'kernel_size', 'strides', 'padding']
    for setting in conv_settings:
        kwargs = {}
        for i, value in enumerate(setting):
            if value is not None:
                if setting_names[i] == 'in_channels':
                    in_channels = value
                elif setting_names[i] == 'channels':
                    channels = value
                else:
                    kwargs[setting_names[i]] = value
        out.add(_make_basic_conv(in_channels, channels, norm_layer, norm_kwargs, **kwargs))
    return out

def _make_Mixed_3a(in_channels, pool_features, prefix, norm_layer, norm_kwargs):
    out = HybridConcurrent(axis=1, prefix=prefix)
    with out.name_scope():
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 64, 1, None, None)))
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 96, 1, None, None),
                             (96, 128, 3, None, 1)))
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 16, 1, None, None),
                             (16, 32, 3, None, 1)))
        out.add(_make_branch('max', norm_layer, norm_kwargs,
                             (in_channels, pool_features, 1, None, None)))
    return out

def _make_Mixed_3b(in_channels, pool_features, prefix, norm_layer, norm_kwargs):
    out = HybridConcurrent(axis=1, prefix=prefix)
    with out.name_scope():
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 128, 1, None, None)))
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 128, 1, None, None),
                             (128, 192, 3, None, 1)))
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 32, 1, None, None),
                             (32, 96, 3, None, 1)))
        out.add(_make_branch('max', norm_layer, norm_kwargs,
                             (in_channels, pool_features, 1, None, None)))
    return out

def _make_Mixed_4a(in_channels, pool_features, prefix, norm_layer, norm_kwargs):
    out = HybridConcurrent(axis=1, prefix=prefix)
    with out.name_scope():
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 192, 1, None, None)))
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 96, 1, None, None),
                             (96, 208, 3, None, 1)))
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 16, 1, None, None),
                             (16, 48, 3, None, 1)))
        out.add(_make_branch('max', norm_layer, norm_kwargs,
                             (in_channels, pool_features, 1, None, None)))
    return out

def _make_Mixed_4b(in_channels, pool_features, prefix, norm_layer, norm_kwargs):
    out = HybridConcurrent(axis=1, prefix=prefix)
    with out.name_scope():
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 160, 1, None, None)))
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 112, 1, None, None),
                             (112, 224, 3, None, 1)))
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 24, 1, None, None),
                             (24, 64, 3, None, 1)))
        out.add(_make_branch('max', norm_layer, norm_kwargs,
                             (in_channels, pool_features, 1, None, None)))
    return out

def _make_Mixed_4c(in_channels, pool_features, prefix, norm_layer, norm_kwargs):
    out = HybridConcurrent(axis=1, prefix=prefix)
    with out.name_scope():
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 128, 1, None, None)))
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 128, 1, None, None),
                             (128, 256, 3, None, 1)))
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 24, 1, None, None),
                             (24, 64, 3, None, 1)))
        out.add(_make_branch('max', norm_layer, norm_kwargs,
                             (in_channels, pool_features, 1, None, None)))
    return out

def _make_Mixed_4d(in_channels, pool_features, prefix, norm_layer, norm_kwargs):
    out = HybridConcurrent(axis=1, prefix=prefix)
    with out.name_scope():
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 112, 1, None, None)))
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 144, 1, None, None),
                             (144, 288, 3, None, 1)))
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 32, 1, None, None),
                             (32, 64, 3, None, 1)))
        out.add(_make_branch('max', norm_layer, norm_kwargs,
                             (in_channels, pool_features, 1, None, None)))
    return out

def _make_Mixed_4e(in_channels, pool_features, prefix, norm_layer, norm_kwargs):
    out = HybridConcurrent(axis=1, prefix=prefix)
    with out.name_scope():
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 256, 1, None, None)))
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 160, 1, None, None),
                             (160, 320, 3, None, 1)))
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 32, 1, None, None),
                             (32, 128, 3, None, 1)))
        out.add(_make_branch('max', norm_layer, norm_kwargs,
                             (in_channels, pool_features, 1, None, None)))
    return out

def _make_Mixed_5a(in_channels, pool_features, prefix, norm_layer, norm_kwargs):
    out = HybridConcurrent(axis=1, prefix=prefix)
    with out.name_scope():
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 256, 1, None, None)))
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 160, 1, None, None),
                             (160, 320, 3, None, 1)))
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 32, 1, None, None),
                             (32, 128, 3, None, 1)))
        out.add(_make_branch('max', norm_layer, norm_kwargs,
                             (in_channels, pool_features, 1, None, None)))
    return out

def _make_Mixed_5b(in_channels, pool_features, prefix, norm_layer, norm_kwargs):
    out = HybridConcurrent(axis=1, prefix=prefix)
    with out.name_scope():
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 384, 1, None, None)))
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 192, 1, None, None),
                             (192, 384, 3, None, 1)))
        out.add(_make_branch(None, norm_layer, norm_kwargs,
                             (in_channels, 48, 1, None, None),
                             (48, 128, 3, None, 1)))
        out.add(_make_branch('max', norm_layer, norm_kwargs,
                             (in_channels, pool_features, 1, None, None)))
    return out

def _make_aux(in_channels, classes, norm_layer, norm_kwargs):
    out = nn.HybridSequential(prefix='')
    out.add(nn.AvgPool2D(pool_size=5, strides=3))
    out.add(_make_basic_conv(in_channels=in_channels, channels=128, kernel_size=1,
                             norm_layer=norm_layer, norm_kwargs=norm_kwargs))

    out.add(nn.Flatten())
    out.add(nn.Dense(units=1024, in_units=2048))
    out.add(nn.Activation('relu'))
    out.add(nn.Dropout(0.7))
    out.add(nn.Dense(units=classes, in_units=1024))
    return out

[docs]class GoogLeNet(HybridBlock): r"""GoogleNet model from `"Going Deeper with Convolutions" <https://arxiv.org/abs/1409.4842>`_ paper. `"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" <https://arxiv.org/abs/1502.03167>`_ paper. Parameters ---------- classes : int, default 1000 Number of classification classes. norm_layer : object Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`) Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`. norm_kwargs : dict Additional `norm_layer` arguments, for example `num_devices=4` for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`. partial_bn : bool, default False Freeze all batch normalization layers during training except the first layer. """ def __init__(self, classes=1000, norm_layer=BatchNorm, dropout_ratio=0.4, aux_logits=False, norm_kwargs=None, partial_bn=False, pretrained_base=True, ctx=None, **kwargs): super(GoogLeNet, self).__init__(**kwargs) self.dropout_ratio = dropout_ratio self.aux_logits = aux_logits with self.name_scope(): self.conv1 = _make_basic_conv(in_channels=3, channels=64, kernel_size=7, strides=2, padding=3, norm_layer=norm_layer, norm_kwargs=norm_kwargs) self.maxpool1 = nn.MaxPool2D(pool_size=3, strides=2, ceil_mode=True) if partial_bn: if norm_kwargs is not None: norm_kwargs['use_global_stats'] = True else: norm_kwargs = {} norm_kwargs['use_global_stats'] = True self.conv2 = _make_basic_conv(in_channels=64, channels=64, kernel_size=1, norm_layer=norm_layer, norm_kwargs=norm_kwargs) self.conv3 = _make_basic_conv(in_channels=64, channels=192, kernel_size=3, padding=1, norm_layer=norm_layer, norm_kwargs=norm_kwargs) self.maxpool2 = nn.MaxPool2D(pool_size=3, strides=2, ceil_mode=True) self.inception3a = _make_Mixed_3a(192, 32, 'Mixed_3a_', norm_layer, norm_kwargs) self.inception3b = _make_Mixed_3b(256, 64, 'Mixed_3b_', norm_layer, norm_kwargs) self.maxpool3 = nn.MaxPool2D(pool_size=3, strides=2, ceil_mode=True) self.inception4a = _make_Mixed_4a(480, 64, 'Mixed_4a_', norm_layer, norm_kwargs) self.inception4b = _make_Mixed_4b(512, 64, 'Mixed_4b_', norm_layer, norm_kwargs) self.inception4c = _make_Mixed_4c(512, 64, 'Mixed_4c_', norm_layer, norm_kwargs) self.inception4d = _make_Mixed_4d(512, 64, 'Mixed_4d_', norm_layer, norm_kwargs) self.inception4e = _make_Mixed_4e(528, 128, 'Mixed_4e_', norm_layer, norm_kwargs) self.maxpool4 = nn.MaxPool2D(pool_size=2, strides=2) self.inception5a = _make_Mixed_5a(832, 128, 'Mixed_5a_', norm_layer, norm_kwargs) self.inception5b = _make_Mixed_5b(832, 128, 'Mixed_5b_', norm_layer, norm_kwargs) if self.aux_logits: self.aux1 = _make_aux(512, classes, norm_layer, norm_kwargs) self.aux2 = _make_aux(528, classes, norm_layer, norm_kwargs) self.head = nn.HybridSequential(prefix='') self.avgpool = nn.AvgPool2D(pool_size=7) self.dropout = nn.Dropout(self.dropout_ratio) self.output = nn.Dense(units=classes, in_units=1024) self.head.add(self.avgpool) self.head.add(self.dropout) self.head.add(self.output)
[docs] def hybrid_forward(self, F, x): x = self.conv1(x) x = self.maxpool1(x) x = self.conv2(x) x = self.conv3(x) x = self.maxpool2(x) x = self.inception3a(x) x = self.inception3b(x) x = self.maxpool3(x) x = self.inception4a(x) if self.aux_logits: aux1 = self.aux1(x) x = self.inception4b(x) x = self.inception4c(x) x = self.inception4d(x) if self.aux_logits: aux2 = self.aux2(x) x = self.inception4e(x) x = self.maxpool4(x) x = self.inception5a(x) x = self.inception5b(x) x = self.head(x) if self.aux_logits: return (x, aux2, aux1) return x
[docs]def googlenet(classes=1000, pretrained=False, pretrained_base=True, ctx=cpu(), dropout_ratio=0.4, aux_logits=False, root='~/.mxnet/models', partial_bn=False, **kwargs): r"""GoogleNet model from `"Going Deeper with Convolutions" <https://arxiv.org/abs/1409.4842>`_ paper. `"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" <https://arxiv.org/abs/1502.03167>`_ paper. Parameters ---------- pretrained : bool or str Boolean value controls whether to load the default pretrained weights for model. String value represents the hashtag for a certain version of pretrained weights. 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. partial_bn : bool, default False Freeze all batch normalization layers during training except the first layer. norm_layer : object Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`) Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`. norm_kwargs : dict Additional `norm_layer` arguments, for example `num_devices=4` for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`. """ net = GoogLeNet(classes=classes, partial_bn=partial_bn, pretrained_base=pretrained_base, dropout_ratio=dropout_ratio, aux_logits=aux_logits, ctx=ctx, **kwargs) if pretrained: from .model_store import get_model_file net.load_parameters(get_model_file('googlenet', tag=pretrained, root=root), ctx=ctx, cast_dtype=True) from ..data import ImageNet1kAttr attrib = ImageNet1kAttr() net.synset = attrib.synset net.classes = attrib.classes net.classes_long = attrib.classes_long return net