Implementation of DropBlock: A regularization method for convolutional networks in mxnet.
In your symbol,between convolutional operators, you need to previously compute the feature map size and confirm the mask size, this part will be improved in future commit.
self.block_mask = nd.ones((256, 48, 7, 7)) # mask size:(batch_size,channel,mask_size,mask_size)
In my experiment, feature map size is 7, the schedule for drop block probability has been finished, you can set step and prob range in operator.
drop_layer = mx.sym.Custom(conv5,drop_prob=0.0,block_size=3,drop_prob_max=0.3,step=15000,block_factor_prob = 0.04 ,op_type = 'DropBlock')
For different task, maybe you need to try different parameters for many times.
DropBlock for 3D convolution