| import find_mxnet |
| import mxnet as mx |
| |
| def get_symbol(num_classes = 121): |
| net = mx.sym.Variable("data") |
| net = mx.sym.Convolution(data=net, kernel=(5, 5), num_filter=32, pad=(2, 2)) |
| net = mx.sym.Activation(data=net, act_type="relu") |
| net = mx.sym.Convolution(data=net, kernel=(5, 5), num_filter=64, pad=(2, 2)) |
| net = mx.sym.Activation(data=net, act_type="relu") |
| net = mx.sym.Pooling(data=net, pool_type="max", kernel=(3, 3), stride=(2, 2)) |
| # stage 2 |
| net = mx.sym.Convolution(data=net, kernel=(3, 3), num_filter=64, pad=(1, 1)) |
| net = mx.sym.Activation(data=net, act_type="relu") |
| net = mx.sym.Convolution(data=net, kernel=(3, 3), num_filter=64, pad=(1, 1)) |
| net = mx.sym.Activation(data=net, act_type="relu") |
| net = mx.sym.Convolution(data=net, kernel=(3, 3), num_filter=128, pad=(1, 1)) |
| net = mx.sym.Activation(data=net, act_type="relu") |
| net = mx.sym.Pooling(data=net, pool_type="max", kernel=(3, 3), stride=(2, 2)) |
| # stage 3 |
| net = mx.sym.Convolution(data=net, kernel=(3, 3), num_filter=256, pad=(1, 1)) |
| net = mx.sym.Activation(data=net, act_type="relu") |
| net = mx.sym.Convolution(data=net, kernel=(3, 3), num_filter=256, pad=(1, 1)) |
| net = mx.sym.Activation(data=net, act_type="relu") |
| net = mx.sym.Pooling(data=net, pool_type="avg", kernel=(9, 9), stride=(1, 1)) |
| # stage 4 |
| net = mx.sym.Flatten(data=net) |
| net = mx.sym.Dropout(data=net, p=0.25) |
| net = mx.sym.FullyConnected(data=net, num_hidden=121) |
| net = mx.symbol.SoftmaxOutput(data=net, name='softmax') |
| |
| return net |
| |