example/ssd/symbol/legacy_vgg16_ssd_300.py [28:89]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - data = mx.symbol.Variable(name="data") label = mx.symbol.Variable(name="label") # 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") conv1_2 = mx.symbol.Convolution( data=relu1_1, kernel=(3, 3), pad=(1, 1), num_filter=64, name="conv1_2") relu1_2 = mx.symbol.Activation(data=conv1_2, act_type="relu", name="relu1_2") pool1 = mx.symbol.Pooling( data=relu1_2, 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") conv2_2 = mx.symbol.Convolution( data=relu2_1, kernel=(3, 3), pad=(1, 1), num_filter=128, name="conv2_2") relu2_2 = mx.symbol.Activation(data=conv2_2, act_type="relu", name="relu2_2") pool2 = mx.symbol.Pooling( data=relu2_2, 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") conv3_3 = mx.symbol.Convolution( data=relu3_2, kernel=(3, 3), pad=(1, 1), num_filter=256, name="conv3_3") relu3_3 = mx.symbol.Activation(data=conv3_3, act_type="relu", name="relu3_3") pool3 = mx.symbol.Pooling( data=relu3_3, pool_type="max", kernel=(2, 2), stride=(2, 2), \ pooling_convention="full", 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") conv4_3 = mx.symbol.Convolution( data=relu4_2, kernel=(3, 3), pad=(1, 1), num_filter=512, name="conv4_3") relu4_3 = mx.symbol.Activation(data=conv4_3, act_type="relu", name="relu4_3") pool4 = mx.symbol.Pooling( data=relu4_3, 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") conv5_3 = mx.symbol.Convolution( data=relu5_2, kernel=(3, 3), pad=(1, 1), num_filter=512, name="conv5_3") relu5_3 = mx.symbol.Activation(data=conv5_3, act_type="relu", name="relu5_3") pool5 = mx.symbol.Pooling( data=relu5_3, pool_type="max", kernel=(3, 3), stride=(1, 1), pad=(1,1), name="pool5") # group 6 conv6 = mx.symbol.Convolution( data=pool5, kernel=(3, 3), pad=(6, 6), dilate=(6, 6), - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - example/ssd/symbol/vgg16_reduced.py [9:70]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - data = mx.symbol.Variable(name="data") label = mx.symbol.Variable(name="label") # 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") conv1_2 = mx.symbol.Convolution( data=relu1_1, kernel=(3, 3), pad=(1, 1), num_filter=64, name="conv1_2") relu1_2 = mx.symbol.Activation(data=conv1_2, act_type="relu", name="relu1_2") pool1 = mx.symbol.Pooling( data=relu1_2, 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") conv2_2 = mx.symbol.Convolution( data=relu2_1, kernel=(3, 3), pad=(1, 1), num_filter=128, name="conv2_2") relu2_2 = mx.symbol.Activation(data=conv2_2, act_type="relu", name="relu2_2") pool2 = mx.symbol.Pooling( data=relu2_2, 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") conv3_3 = mx.symbol.Convolution( data=relu3_2, kernel=(3, 3), pad=(1, 1), num_filter=256, name="conv3_3") relu3_3 = mx.symbol.Activation(data=conv3_3, act_type="relu", name="relu3_3") pool3 = mx.symbol.Pooling( data=relu3_3, pool_type="max", kernel=(2, 2), stride=(2, 2), \ pooling_convention="full", 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") conv4_3 = mx.symbol.Convolution( data=relu4_2, kernel=(3, 3), pad=(1, 1), num_filter=512, name="conv4_3") relu4_3 = mx.symbol.Activation(data=conv4_3, act_type="relu", name="relu4_3") pool4 = mx.symbol.Pooling( data=relu4_3, 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") conv5_3 = mx.symbol.Convolution( data=relu5_2, kernel=(3, 3), pad=(1, 1), num_filter=512, name="conv5_3") relu5_3 = mx.symbol.Activation(data=conv5_3, act_type="relu", name="relu5_3") pool5 = mx.symbol.Pooling( data=relu5_3, pool_type="max", kernel=(3, 3), stride=(1, 1), pad=(1,1), name="pool5") # group 6 conv6 = mx.symbol.Convolution( data=pool5, kernel=(3, 3), pad=(6, 6), dilate=(6, 6), - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -