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- """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 depthwise_conv(data, kernel, pad, num_filter, name, num_group):
- conv = mx.symbol.Convolution(data=data, kernel=kernel, pad=pad,
- num_filter=num_group, name=name+'_depthwise', num_group=num_group)
- # bn = mx.symbol.BatchNorm(data=conv)
- bn = conv # for benchmark
- relu = mx.symbol.Activation(data=bn, act_type='relu')
- conv2 = mx.symbol.Convolution(data=relu, kernel=(1, 1), num_filter=num_filter,
- name=name+'_pointwise')
- # bn2 = mx.symbol.BatchNorm(data=conv2)
- bn2 = conv2
- return bn2
- def get_symbol(num_classes, **kwargs):
- ## define alexnet
- data = mx.symbol.Variable(name="data")
- # group 1
- conv1_1 = depthwise_conv(data=data, kernel=(3, 3), pad=(1, 1), num_filter=64, name="conv1_1", num_group=3)
- 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 = depthwise_conv(
- data=pool1, kernel=(3, 3), pad=(1, 1), num_filter=128, name="conv2_1", num_group=64)
- 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 = depthwise_conv(
- data=pool2, kernel=(3, 3), pad=(1, 1), num_filter=256, name="conv3_1", num_group=128)
- relu3_1 = mx.symbol.Activation(data=conv3_1, act_type="relu", name="relu3_1")
- conv3_2 = depthwise_conv(
- data=relu3_1, kernel=(3, 3), pad=(1, 1), num_filter=256, name="conv3_2", num_group=256)
- 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 = depthwise_conv(
- data=pool3, kernel=(3, 3), pad=(1, 1), num_filter=512, name="conv4_1", num_group=256)
- relu4_1 = mx.symbol.Activation(data=conv4_1, act_type="relu", name="relu4_1")
- conv4_2 = depthwise_conv(
- data=relu4_1, kernel=(3, 3), pad=(1, 1), num_filter=512, name="conv4_2", num_group=512)
- 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 = depthwise_conv(
- data=pool4, kernel=(3, 3), pad=(1, 1), num_filter=512, name="conv5_1", num_group=512)
- relu5_1 = mx.symbol.Activation(data=conv5_1, act_type="relu", name="relu5_1")
- conv5_2 = depthwise_conv(
- data=relu5_1, kernel=(3, 3), pad=(1, 1), num_filter=512, name="conv5_2", num_group=512)
- relu5_2 = mx.symbol.Activation(data=conv5_2, act_type="relu", name="conv1_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
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