MXNet shufflenet from https://github.com/ZiyueHuang/MXShuffleNet

params.sh

python mxnet/example/image-classification/train_imagenet.py --network shufflenet --data-train ~/efs/users/joshuazz/data/imagenet/record/train_480_q95.rec --data-val ~/efs/users/joshuazz/data/imagenet/record/val_256_q90.rec --batch-size 512 --gpus 0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15 --num-epochs 150 --lr-step-epochs 30,60,90 --min-random-scale 0.533 --lr 0.01 --disp-batches 100 --top-k 5 --data-nthreads 32 --random-mirror 1 --max-random-shear-ratio 0 --max-random-rotate-angle 0 --max-random-h 0 --max-random-l 0 --max-random-s 0 --model-prefix model/shufflenet  | tee ~/efs/users/joshuazz/temp/train_imagenet_logs/shufflenet.log

shufflenet.py

import mxnet as mx

def combine(residual, data, combine):
	if combine == 'add':
		return residual + data
	elif combine == 'concat':
		return mx.sym.concat(residual, data, dim=1)
	return None

def channel_shuffle(data, groups):
	data = mx.sym.reshape(data, shape=(0, -4, groups, -1, -2))
	data = mx.sym.swapaxes(data, 1, 2)
	data = mx.sym.reshape(data, shape=(0, -3, -2))
	return data

def shuffleUnit(residual, in_channels, out_channels, combine_type, groups=3, grouped_conv=True):

    if combine_type == 'add':
        DWConv_stride = 1
    elif combine_type == 'concat':
        DWConv_stride = 2
        out_channels -= in_channels

    first_groups = groups if grouped_conv else 1

    bottleneck_channels = out_channels // 4

    data = mx.sym.Convolution(data=residual, num_filter=bottleneck_channels, 
    	              kernel=(1, 1), stride=(1, 1), num_group=first_groups)
    data = mx.sym.BatchNorm(data=data)
    data = mx.sym.Activation(data=data, act_type='relu')

    data = channel_shuffle(data, groups)

    data = mx.sym.Convolution(data=data, num_filter=bottleneck_channels, kernel=(3, 3), 
    	               pad=(1, 1), stride=(DWConv_stride, DWConv_stride), num_group=groups)
    data = mx.sym.BatchNorm(data=data)

    data = mx.sym.Convolution(data=data, num_filter=out_channels, 
    	               kernel=(1, 1), stride=(1, 1), num_group=groups)
    data = mx.sym.BatchNorm(data=data)

    if combine_type == 'concat':
        residual = mx.sym.Pooling(data=residual, kernel=(3, 3), pool_type='avg', 
        	                  stride=(2, 2), pad=(1, 1))

    data = combine(residual, data, combine_type)

    return data

def make_stage(data, stage, groups=3):
    stage_repeats = [3, 7, 3]

    grouped_conv = stage > 2

    if groups == 1:
        out_channels = [-1, 24, 144, 288, 567]
    elif groups == 2:
        out_channels = [-1, 24, 200, 400, 800]
    elif groups == 3:
        out_channels = [-1, 24, 240, 480, 960]
    elif groups == 4:
        out_channels = [-1, 24, 272, 544, 1088]
    elif groups == 8:
        out_channels = [-1, 24, 384, 768, 1536]
       
    data = shuffleUnit(data, out_channels[stage - 1], out_channels[stage], 
    	               'concat', groups, grouped_conv)

    for i in range(stage_repeats[stage - 2]):
        data = shuffleUnit(data, out_channels[stage], out_channels[stage], 
        	               'add', groups, True)

    return data

def get_shufflenet(num_classes=10):
    data = mx.sym.var('data')
    data = mx.sym.Convolution(data=data, num_filter=24, 
        	                  kernel=(3, 3), stride=(2, 2), pad=(1, 1))
    data = mx.sym.Pooling(data=data, kernel=(3, 3), pool_type='max', 
    	                  stride=(2, 2), pad=(1, 1))
    
    data = make_stage(data, 2)
    
    data = make_stage(data, 3)
    
    data = make_stage(data, 4)
     
    data = mx.sym.Pooling(data=data, kernel=(1, 1), global_pool=True, pool_type='avg')
    
    data = mx.sym.flatten(data=data)
    
    data = mx.sym.FullyConnected(data=data, num_hidden=num_classes)
    
    out = mx.sym.SoftmaxOutput(data=data, name='softmax')

    return out

"bottleneck_channels = out_channels // 4" should be at the place before "out_channels -= in_channels"