vgg16_batch_normalization.py

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# Mainly copied from https://github.com/jsk-ros-pkg/jsk_recognition/blob/master/jsk_recognition_utils/python/jsk_recognition_utils/chainermodels/vgg16_batch_normalization.py
 
import chainer
import chainer.functions as F
import chainer.links as L
 
 
class VGG16BatchNormalization(chainer.Chain):
 
    def __init__(self, n_class=1000):
        super(self.__class__, self).__init__(
            conv1_1=L.Convolution2D(3, 64, 3, stride=1, pad=1),
            bn1_1=L.BatchNormalization(64),
            conv1_2=L.Convolution2D(64, 64, 3, stride=1, pad=1),
            bn1_2=L.BatchNormalization(64),
 
            conv2_1=L.Convolution2D(64, 128, 3, stride=1, pad=1),
            bn2_1=L.BatchNormalization(128),
            conv2_2=L.Convolution2D(128, 128, 3, stride=1, pad=1),
            bn2_2=L.BatchNormalization(128),
 
            conv3_1=L.Convolution2D(128, 256, 3, stride=1, pad=1),
            bn3_1=L.BatchNormalization(256),
            conv3_2=L.Convolution2D(256, 256, 3, stride=1, pad=1),
            bn3_2=L.BatchNormalization(256),
            conv3_3=L.Convolution2D(256, 256, 3, stride=1, pad=1),
            bn3_3=L.BatchNormalization(256),
 
            conv4_1=L.Convolution2D(256, 512, 3, stride=1, pad=1),
            bn4_1=L.BatchNormalization(512),
            conv4_2=L.Convolution2D(512, 512, 3, stride=1, pad=1),
            bn4_2=L.BatchNormalization(512),
            conv4_3=L.Convolution2D(512, 512, 3, stride=1, pad=1),
            bn4_3=L.BatchNormalization(512),
 
            conv5_1=L.Convolution2D(512, 512, 3, stride=1, pad=1),
            bn5_1=L.BatchNormalization(512),
            conv5_2=L.Convolution2D(512, 512, 3, stride=1, pad=1),
            bn5_2=L.BatchNormalization(512),
            conv5_3=L.Convolution2D(512, 512, 3, stride=1, pad=1),
            bn5_3=L.BatchNormalization(512),
 
            fc6=L.Linear(25088, 4096),
            fc7=L.Linear(4096, 4096),
            fc8=L.Linear(4096, n_class)
        )
 
    def __call__(self, x, t=None):
        h = F.relu(self.bn1_1(self.conv1_1(x)))
        h = F.relu(self.bn1_2(self.conv1_2(h)))
        h = F.max_pooling_2d(h, 2, stride=2)
 
        h = F.relu(self.bn2_1(self.conv2_1(h)))
        h = F.relu(self.bn2_2(self.conv2_2(h)))
        h = F.max_pooling_2d(h, 2, stride=2)
 
        h = F.relu(self.bn3_1(self.conv3_1(h)))
        h = F.relu(self.bn3_2(self.conv3_2(h)))
        h = F.relu(self.bn3_3(self.conv3_3(h)))
        h = F.max_pooling_2d(h, 2, stride=2)
 
        h = F.relu(self.bn4_1(self.conv4_1(h)))
        h = F.relu(self.bn4_2(self.conv4_2(h)))
        h = F.relu(self.bn4_3(self.conv4_3(h)))
        h = F.max_pooling_2d(h, 2, stride=2)
 
        h = F.relu(self.bn5_1(self.conv5_1(h)))
        h = F.relu(self.bn5_2(self.conv5_2(h)))
        h = F.relu(self.bn5_3(self.conv5_3(h)))
        h = F.max_pooling_2d(h, 2, stride=2)
 
        h = F.dropout(F.relu(self.fc6(h)), ratio=0.5)
        h = F.dropout(F.relu(self.fc7(h)), ratio=0.5)
        h = self.fc8(h)
        fc8 = h
 
        self.pred = F.softmax(h)
 
        if t is None:
            assert not chainer.config.train
            return
 
        self.loss = F.softmax_cross_entropy(fc8, t)
        self.acc = F.accuracy(self.pred, t)
 
        chainer.report({'loss': self.loss, 'accuracy': self.acc}, self)
 
        return self.loss