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| from chainercv.links.model.vgg.vgg16 import VGG16 # NOQA |
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| from __future__ import division | ||
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| import numpy as np | ||
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| import chainer | ||
| from chainer.functions import dropout | ||
| from chainer.functions import max_pooling_2d | ||
| from chainer.functions import relu | ||
| from chainer.functions import softmax | ||
| from chainer.initializers import constant | ||
| from chainer.initializers import normal | ||
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| from chainer.links import Linear | ||
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| from chainercv.utils import download_model | ||
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| from chainercv.links.connection.conv_2d_activ import Conv2DActiv | ||
| from chainercv.links.model.sequential_feature_extractor import \ | ||
| SequentialFeatureExtractor | ||
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| # RGB order | ||
| _imagenet_mean = np.array( | ||
| [123.68, 116.779, 103.939], dtype=np.float32)[:, np.newaxis, np.newaxis] | ||
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| class VGG16(SequentialFeatureExtractor): | ||
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| """VGG-16 Network for classification and feature extraction. | ||
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| This is a feature extraction model. | ||
| The network can choose output features from set of all | ||
| intermediate features. | ||
| The value of :obj:`VGG16.feature_names` selects the features that are going | ||
| to be collected by :meth:`__call__`. | ||
| :obj:`self.all_feature_names` is the list of the names of features | ||
| that can be collected. | ||
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| Examples: | ||
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| >>> model = VGG16() | ||
| # By default, __call__ returns a probability score (after Softmax). | ||
| >>> prob = model(imgs) | ||
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| >>> model.feature_names = 'conv5_3' | ||
| # This is feature conv5_3 (after ReLU). | ||
| >>> feat5_3 = model(imgs) | ||
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| >>> model.feature_names = ['conv5_3', 'fc6'] | ||
| >>> # These are features conv5_3 (after ReLU) and fc6 (before ReLU). | ||
| >>> feat5_3, feat6 = model(imgs) | ||
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| .. seealso:: | ||
| :class:`chainercv.links.model.SequentialFeatureExtractor` | ||
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| When :obj:`pretrained_model` is the path of a pre-trained chainer model | ||
| serialized as a :obj:`.npz` file in the constructor, this chain model | ||
| automatically initializes all the parameters with it. | ||
| When a string in the prespecified set is provided, a pretrained model is | ||
| loaded from weights distributed on the Internet. | ||
| The list of pretrained models supported are as follows: | ||
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| * :obj:`imagenet`: Loads weights trained with ImageNet and distributed \ | ||
| at `Model Zoo \ | ||
| <https://github.com/BVLC/caffe/wiki/Model-Zoo>`_. | ||
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| Args: | ||
| pretrained_model (str): The destination of the pre-trained | ||
| chainer model serialized as a :obj:`.npz` file. | ||
| If this is one of the strings described | ||
| above, it automatically loads weights stored under a directory | ||
| :obj:`$CHAINER_DATASET_ROOT/pfnet/chainercv/models/`, | ||
| where :obj:`$CHAINER_DATASET_ROOT` is set as | ||
| :obj:`$HOME/.chainer/dataset` unless you specify another value | ||
| by modifying the environment variable. | ||
| n_class (int): The number of classes. If :obj:`None`, | ||
| the default values are used. | ||
| If a supported pretrained model is used, | ||
| the number of classes used to train the pretrained model | ||
| is used. Otherwise, the number of classes in ILSVRC 2012 dataset | ||
| is used. | ||
| mean (numpy.ndarray): A mean value. If :obj:`None`, | ||
| the default values are used. | ||
| If a supported pretrained model is used, | ||
| the mean value used to train the pretrained model is used. | ||
| Otherwise, the mean value calculated from ILSVRC 2012 dataset | ||
| is used. | ||
| initialW (callable): Initializer for the weights. | ||
| initial_bias (callable): Initializer for the biases. | ||
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| """ | ||
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| _models = { | ||
| 'imagenet': { | ||
| 'n_class': 1000, | ||
| 'url': 'https://github.com/yuyu2172/share-weights/releases/' | ||
| 'download/0.0.4/vgg16_imagenet_convert_2017_07_18.npz', | ||
| 'mean': _imagenet_mean | ||
| } | ||
| } | ||
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| def __init__(self, | ||
| pretrained_model=None, n_class=None, mean=None, | ||
| initialW=None, initial_bias=None): | ||
| if n_class is None: | ||
| if pretrained_model in self._models: | ||
| n_class = self._models[pretrained_model]['n_class'] | ||
| else: | ||
| n_class = 1000 | ||
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| if mean is None: | ||
| if pretrained_model in self._models: | ||
| mean = self._models[pretrained_model]['mean'] | ||
| else: | ||
| mean = _imagenet_mean | ||
|
There was a problem hiding this comment. How about adding a note about these behaviours? The default values of |
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| self.mean = mean | ||
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| if initialW is None: | ||
| # Employ default initializers used in the original paper. | ||
| initialW = normal.Normal(0.01) | ||
| if pretrained_model: | ||
| # As a sampling process is time-consuming, | ||
| # we employ a zero initializer for faster computation. | ||
| initialW = constant.Zero() | ||
| kwargs = {'initialW': initialW, 'initial_bias': initial_bias} | ||
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| super(VGG16, self).__init__() | ||
| with self.init_scope(): | ||
| self.conv1_1 = Conv2DActiv(None, 64, 3, 1, 1, **kwargs) | ||
| self.conv1_2 = Conv2DActiv(None, 64, 3, 1, 1, **kwargs) | ||
| self.pool1 = _max_pooling_2d | ||
| self.conv2_1 = Conv2DActiv(None, 128, 3, 1, 1, **kwargs) | ||
| self.conv2_2 = Conv2DActiv(None, 128, 3, 1, 1, **kwargs) | ||
| self.pool2 = _max_pooling_2d | ||
| self.conv3_1 = Conv2DActiv(None, 256, 3, 1, 1, **kwargs) | ||
| self.conv3_2 = Conv2DActiv(None, 256, 3, 1, 1, **kwargs) | ||
| self.conv3_3 = Conv2DActiv(None, 256, 3, 1, 1, **kwargs) | ||
| self.pool3 = _max_pooling_2d | ||
| self.conv4_1 = Conv2DActiv(None, 512, 3, 1, 1, **kwargs) | ||
| self.conv4_2 = Conv2DActiv(None, 512, 3, 1, 1, **kwargs) | ||
| self.conv4_3 = Conv2DActiv(None, 512, 3, 1, 1, **kwargs) | ||
| self.pool4 = _max_pooling_2d | ||
| self.conv5_1 = Conv2DActiv(None, 512, 3, 1, 1, **kwargs) | ||
| self.conv5_2 = Conv2DActiv(None, 512, 3, 1, 1, **kwargs) | ||
| self.conv5_3 = Conv2DActiv(None, 512, 3, 1, 1, **kwargs) | ||
| self.pool5 = _max_pooling_2d | ||
| self.fc6 = Linear(None, 4096, **kwargs) | ||
| self.fc6_relu = relu | ||
| self.fc6_dropout = dropout | ||
| self.fc7 = Linear(None, 4096, **kwargs) | ||
| self.fc7_relu = relu | ||
| self.fc7_dropout = dropout | ||
| self.fc8 = Linear(None, n_class, **kwargs) | ||
| self.prob = softmax | ||
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| if pretrained_model in self._models: | ||
| path = download_model(self._models[pretrained_model]['url']) | ||
| chainer.serializers.load_npz(path, self) | ||
| elif pretrained_model: | ||
| chainer.serializers.load_npz(pretrained_model, self) | ||
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| def _max_pooling_2d(x): | ||
| return max_pooling_2d(x, ksize=2) | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,11 @@ | ||
| VGG | ||
| === | ||
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| .. module:: chainercv.links.model.vgg | ||
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| VGG16 | ||
| ----- | ||
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| .. autoclass:: VGG16 | ||
| :members: |
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# These are features conv5_3 (after ReLU) and fc6 (before ReLU).