| """References: |
| |
| Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for |
| large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014). |
| """ |
| import mxnet as mx |
| |
| def get_symbol(num_classes, **kwargs): |
| ## define VGG11 |
| data = mx.symbol.Variable(name="data") |
| # group 1 |
| conv1_1 = mx.symbol.Convolution(data=data, kernel=(3, 3), pad=(1, 1), num_filter=64, name="conv1_1") |
| relu1_1 = mx.symbol.Activation(data=conv1_1, act_type="relu", name="relu1_1") |
| pool1 = mx.symbol.Pooling( |
| data=relu1_1, pool_type="max", kernel=(2, 2), stride=(2,2), name="pool1") |
| # group 2 |
| conv2_1 = mx.symbol.Convolution( |
| data=pool1, kernel=(3, 3), pad=(1, 1), num_filter=128, name="conv2_1") |
| relu2_1 = mx.symbol.Activation(data=conv2_1, act_type="relu", name="relu2_1") |
| pool2 = mx.symbol.Pooling( |
| data=relu2_1, pool_type="max", kernel=(2, 2), stride=(2,2), name="pool2") |
| # group 3 |
| conv3_1 = mx.symbol.Convolution( |
| data=pool2, kernel=(3, 3), pad=(1, 1), num_filter=256, name="conv3_1") |
| relu3_1 = mx.symbol.Activation(data=conv3_1, act_type="relu", name="relu3_1") |
| conv3_2 = mx.symbol.Convolution( |
| data=relu3_1, kernel=(3, 3), pad=(1, 1), num_filter=256, name="conv3_2") |
| relu3_2 = mx.symbol.Activation(data=conv3_2, act_type="relu", name="relu3_2") |
| pool3 = mx.symbol.Pooling( |
| data=relu3_2, pool_type="max", kernel=(2, 2), stride=(2,2), name="pool3") |
| # group 4 |
| conv4_1 = mx.symbol.Convolution( |
| data=pool3, kernel=(3, 3), pad=(1, 1), num_filter=512, name="conv4_1") |
| relu4_1 = mx.symbol.Activation(data=conv4_1, act_type="relu", name="relu4_1") |
| conv4_2 = mx.symbol.Convolution( |
| data=relu4_1, kernel=(3, 3), pad=(1, 1), num_filter=512, name="conv4_2") |
| relu4_2 = mx.symbol.Activation(data=conv4_2, act_type="relu", name="relu4_2") |
| pool4 = mx.symbol.Pooling( |
| data=relu4_2, pool_type="max", kernel=(2, 2), stride=(2,2), name="pool4") |
| # group 5 |
| conv5_1 = mx.symbol.Convolution( |
| data=pool4, kernel=(3, 3), pad=(1, 1), num_filter=512, name="conv5_1") |
| relu5_1 = mx.symbol.Activation(data=conv5_1, act_type="relu", name="relu5_1") |
| conv5_2 = mx.symbol.Convolution( |
| data=relu5_1, kernel=(3, 3), pad=(1, 1), num_filter=512, name="conv5_2") |
| relu5_2 = mx.symbol.Activation(data=conv5_2, act_type="relu", name="relu5_2") |
| pool5 = mx.symbol.Pooling( |
| data=relu5_2, pool_type="max", kernel=(2, 2), stride=(2,2), name="pool5") |
| # group 6 |
| flatten = mx.symbol.Flatten(data=pool5, name="flatten") |
| fc6 = mx.symbol.FullyConnected(data=flatten, num_hidden=4096, name="fc6") |
| relu6 = mx.symbol.Activation(data=fc6, act_type="relu", name="relu6") |
| drop6 = mx.symbol.Dropout(data=relu6, p=0.5, name="drop6") |
| # group 7 |
| fc7 = mx.symbol.FullyConnected(data=drop6, num_hidden=4096, name="fc7") |
| relu7 = mx.symbol.Activation(data=fc7, act_type="relu", name="relu7") |
| drop7 = mx.symbol.Dropout(data=relu7, p=0.5, name="drop7") |
| # output |
| fc8 = mx.symbol.FullyConnected(data=drop7, num_hidden=num_classes, name="fc8") |
| softmax = mx.symbol.SoftmaxOutput(data=fc8, name='softmax') |
| return softmax |
