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

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# coding: utf-8
# pylint: disable= arguments-differ
"""VGG, implemented in Gluon."""
from __future__ import division
__all__ = ['VGG',
           'vgg11', 'vgg13', 'vgg16', 'vgg19',
           'vgg11_bn', 'vgg13_bn', 'vgg16_bn', 'vgg19_bn',
           'get_vgg']

from ....context import cpu
from ....initializer import Xavier
from ...block import HybridBlock
from ... import nn


[docs]class VGG(HybridBlock): r"""VGG model from the `"Very Deep Convolutional Networks for Large-Scale Image Recognition" `_ paper. Parameters ---------- layers : list of int Numbers of layers in each feature block. filters : list of int Numbers of filters in each feature block. List length should match the layers. classes : int, default 1000 Number of classification classes. batch_norm : bool, default False Use batch normalization. """ def __init__(self, layers, filters, classes=1000, batch_norm=False, **kwargs): super(VGG, self).__init__(**kwargs) assert len(layers) == len(filters) with self.name_scope(): self.features = self._make_features(layers, filters, batch_norm) self.features.add(nn.Dense(4096, activation='relu', weight_initializer='normal', bias_initializer='zeros')) self.features.add(nn.Dropout(rate=0.5)) self.features.add(nn.Dense(4096, activation='relu', weight_initializer='normal', bias_initializer='zeros')) self.features.add(nn.Dropout(rate=0.5)) self.output = nn.Dense(classes, weight_initializer='normal', bias_initializer='zeros') def _make_features(self, layers, filters, batch_norm): featurizer = nn.HybridSequential(prefix='') for i, num in enumerate(layers): for _ in range(num): featurizer.add(nn.Conv2D(filters[i], kernel_size=3, padding=1, weight_initializer=Xavier(rnd_type='gaussian', factor_type='out', magnitude=2), bias_initializer='zeros')) if batch_norm: featurizer.add(nn.BatchNorm()) featurizer.add(nn.Activation('relu')) featurizer.add(nn.MaxPool2D(strides=2)) return featurizer def hybrid_forward(self, F, x): x = self.features(x) x = self.output(x) return x
# Specification vgg_spec = {11: ([1, 1, 2, 2, 2], [64, 128, 256, 512, 512]), 13: ([2, 2, 2, 2, 2], [64, 128, 256, 512, 512]), 16: ([2, 2, 3, 3, 3], [64, 128, 256, 512, 512]), 19: ([2, 2, 4, 4, 4], [64, 128, 256, 512, 512])} # Constructors
[docs]def get_vgg(num_layers, pretrained=False, ctx=cpu(), root='~/.mxnet/models', **kwargs): r"""VGG model from the `"Very Deep Convolutional Networks for Large-Scale Image Recognition" `_ paper. Parameters ---------- num_layers : int Number of layers for the variant of densenet. Options are 11, 13, 16, 19. 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. """ layers, filters = vgg_spec[num_layers] net = VGG(layers, filters, **kwargs) if pretrained: from ..model_store import get_model_file batch_norm_suffix = '_bn' if kwargs.get('batch_norm') else '' net.load_params(get_model_file('vgg%d%s'%(num_layers, batch_norm_suffix), root=root), ctx=ctx) return net
[docs]def vgg11(**kwargs): r"""VGG-11 model from the `"Very Deep Convolutional Networks for Large-Scale Image Recognition" `_ 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_vgg(11, **kwargs)
[docs]def vgg13(**kwargs): r"""VGG-13 model from the `"Very Deep Convolutional Networks for Large-Scale Image Recognition" `_ 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_vgg(13, **kwargs)
[docs]def vgg16(**kwargs): r"""VGG-16 model from the `"Very Deep Convolutional Networks for Large-Scale Image Recognition" `_ 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_vgg(16, **kwargs)
[docs]def vgg19(**kwargs): r"""VGG-19 model from the `"Very Deep Convolutional Networks for Large-Scale Image Recognition" `_ 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_vgg(19, **kwargs)
[docs]def vgg11_bn(**kwargs): r"""VGG-11 model with batch normalization from the `"Very Deep Convolutional Networks for Large-Scale Image Recognition" `_ 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. """ kwargs['batch_norm'] = True return get_vgg(11, **kwargs)
[docs]def vgg13_bn(**kwargs): r"""VGG-13 model with batch normalization from the `"Very Deep Convolutional Networks for Large-Scale Image Recognition" `_ 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. """ kwargs['batch_norm'] = True return get_vgg(13, **kwargs)
[docs]def vgg16_bn(**kwargs): r"""VGG-16 model with batch normalization from the `"Very Deep Convolutional Networks for Large-Scale Image Recognition" `_ 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. """ kwargs['batch_norm'] = True return get_vgg(16, **kwargs)
[docs]def vgg19_bn(**kwargs): r"""VGG-19 model with batch normalization from the `"Very Deep Convolutional Networks for Large-Scale Image Recognition" `_ 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. """ kwargs['batch_norm'] = True return get_vgg(19, **kwargs)