symbol_resnet50_yolo.py 文件源码

def get_symbol(num_classes=20, nms_thresh=0.5, force_nms=False, **kwargs):
    body = resnet.get_symbol(num_classes, 50, '3,224,224')
    conv1 = body.get_internals()['_plus12_output']
    conv2 = body.get_internals()['_plus15_output']
    # anchors
    anchors = [1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071]
    num_anchor = len(anchors) // 2

    # extra layers
    conv7_1 = conv_act_layer(conv2, 'conv7_1', 1024, kernel=(3, 3), pad=(1, 1),
        act_type='leaky')
    conv7_2 = conv_act_layer(conv7_1, 'conv7_2', 1024, kernel=(3, 3), pad=(1, 1),
        act_type='leaky')

    # re-organize
    conv5_6 = mx.sym.stack_neighbor(data=conv1, kernel=(2, 2), name='stack_downsample')
    concat = mx.sym.Concat(*[conv5_6, conv7_2], dim=1)
    # concat = conv7_2
    conv8_1 = conv_act_layer(concat, 'conv8_1', 1024, kernel=(3, 3), pad=(1, 1),
        act_type='leaky')
    pred = mx.symbol.Convolution(data=conv8_1, name='conv_pred', kernel=(1, 1),
        num_filter=num_anchor * (num_classes + 4 + 1))

    out = mx.contrib.symbol.YoloOutput(data=pred, num_class=num_classes,
        num_anchor=num_anchor, object_grad_scale=5.0, background_grad_scale=1.0,
        coord_grad_scale=1.0, class_grad_scale=1.0, anchors=anchors,
        nms_topk=400, warmup_samples=12800, name='yolo_output')
    return out