def get_symbol_train(num_classes=20, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs): """ Single-shot multi-box detection with VGG 16 layers ConvNet This is a modified version, with fc6/fc7 layers replaced by conv layers And the network is slightly smaller than original VGG 16 network This is a training network with losses Parameters: ---------- num_classes: int number of object classes not including background nms_thresh : float non-maximum suppression threshold force_suppress : boolean whether suppress different class objects nms_topk : int apply NMS to top K detections Returns: ---------- mx.Symbol """ 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), num_filter=1024, name="conv6") relu6 = mx.symbol.Activation(data=conv6, act_type="relu", name="relu6") # drop6 = mx.symbol.Dropout(data=relu6, p=0.5, name="drop6") # group 7 conv7 = mx.symbol.Convolution( data=relu6, kernel=(1, 1), pad=(0, 0), num_filter=1024, name="conv7") relu7 = mx.symbol.Activation(data=conv7, act_type="relu", name="relu7") # drop7 = mx.symbol.Dropout(data=relu7, p=0.5, name="drop7") ### ssd extra layers ### conv8_1, relu8_1 = legacy_conv_act_layer(relu7, "8_1", 256, kernel=(1,1), pad=(0,0), \ stride=(1,1), act_type="relu", use_batchnorm=False) conv8_2, relu8_2 = legacy_conv_act_layer(relu8_1, "8_2", 512, kernel=(3,3), pad=(1,1), \ stride=(2,2), act_type="relu", use_batchnorm=False) conv9_1, relu9_1 = legacy_conv_act_layer(relu8_2, "9_1", 128, kernel=(1,1), pad=(0,0), \ stride=(1,1), act_type="relu", use_batchnorm=False) conv9_2, relu9_2 = legacy_conv_act_layer(relu9_1, "9_2", 256, kernel=(3,3), pad=(1,1), \ stride=(2,2), act_type="relu", use_batchnorm=False) conv10_1, relu10_1 = legacy_conv_act_layer(relu9_2, "10_1", 128, kernel=(1,1), pad=(0,0), \ stride=(1,1), act_type="relu", use_batchnorm=False) conv10_2, relu10_2 = legacy_conv_act_layer(relu10_1, "10_2", 256, kernel=(3,3), pad=(0,0), \ stride=(1,1), act_type="relu", use_batchnorm=False) conv11_1, relu11_1 = legacy_conv_act_layer(relu10_2, "11_1", 128, kernel=(1,1), pad=(0,0), \ stride=(1,1), act_type="relu", use_batchnorm=False) conv11_2, relu11_2 = legacy_conv_act_layer(relu11_1, "11_2", 256, kernel=(3,3), pad=(0,0), \ stride=(1,1), act_type="relu", use_batchnorm=False) # specific parameters for VGG16 network from_layers = [relu4_3, relu7, relu8_2, relu9_2, relu10_2, relu11_2] sizes = [[.1, .141], [.2,.272], [.37, .447], [.54, .619], [.71, .79], [.88, .961]] ratios = [[1,2,.5], [1,2,.5,3,1./3], [1,2,.5,3,1./3], [1,2,.5,3,1./3], \ [1,2,.5], [1,2,.5]] normalizations = [20, -1, -1, -1, -1, -1] steps = [ x / 300.0 for x in [8, 16, 32, 64, 100, 300]] num_channels = [512] loc_preds, cls_preds, anchor_boxes = multibox_layer(from_layers, \ num_classes, sizes=sizes, ratios=ratios, normalization=normalizations, \ num_channels=num_channels, clip=False, interm_layer=0, steps=steps) tmp = mx.symbol.contrib.MultiBoxTarget( *[anchor_boxes, label, cls_preds], overlap_threshold=.5, \ ignore_label=-1, negative_mining_ratio=3, minimum_negative_samples=0, \ negative_mining_thresh=.5, variances=(0.1, 0.1, 0.2, 0.2), name="multibox_target") loc_target = tmp[0] loc_target_mask = tmp[1] cls_target = tmp[2] cls_prob = mx.symbol.SoftmaxOutput(data=cls_preds, label=cls_target, \ ignore_label=-1, use_ignore=True, grad_scale=1., multi_output=True, \ normalization='valid', name="cls_prob") loc_loss_ = mx.symbol.smooth_l1(name="loc_loss_", \ data=loc_target_mask * (loc_preds - loc_target), scalar=1.0) loc_loss = mx.symbol.MakeLoss(loc_loss_, grad_scale=1., \ normalization='valid', name="loc_loss") # monitoring training status cls_label = mx.symbol.MakeLoss(data=cls_target, grad_scale=0, name="cls_label") det = mx.symbol.contrib.MultiBoxDetection(*[cls_prob, loc_preds, anchor_boxes], \ name="detection", nms_threshold=nms_thresh, force_suppress=force_suppress, variances=(0.1, 0.1, 0.2, 0.2), nms_topk=nms_topk) det = mx.symbol.MakeLoss(data=det, grad_scale=0, name="det_out") # group output out = mx.symbol.Group([cls_prob, loc_loss, cls_label, det]) return out