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"""
Reference:
J. Redmon. Darknet: Open source neural networks in c.
http://pjreddie.com/darknet/, 2013-2016. 5
"""

import mxnet as mx

def conv_act_layer(from_layer, name, num_filter, kernel=(3, 3), pad=(1, 1), \
    stride=(1,1), act_type="relu", use_batchnorm=True):

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"""References:

Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for
large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
"""
import mxnet as mx

def depthwise_conv(data, kernel, pad, num_filter, name, num_group):
    conv = mx.symbol.Convolution(data=data, kernel=kernel, pad=pad,
        num_filter=num_group, name=name+'_depthwise', num_group=num_group)

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"""References:
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang,
Tobias Weyand, Marco Andreetto, Hartwig Adam.
"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications."
arXiv preprint arXiv:1704.04861
"""
import mxnet as mx

def depthwise_conv(data, kernel, pad, num_filter, num_group, stride, name):
    conv = mx.symbol.Convolution(data=data, kernel=kernel, pad=pad, stride=stride,

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import mxnet as mx

def inflated_layer(data, num_in, num_out, name):
    assert(num_out % num_in == 0)
    num_group = num_out / num_in
    outputs = []
    for i in range(num_group):
        bias = mx.sym.Variable(shape=(1, num_in, 1, 1),
            name="{}_{}_bias".format(name, i))
        outputs.append(mx.sym.broadcast_add(lhs=data, rhs=bias))

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import numpy as np

OLD_CLASSES = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
               'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
               'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']

NEW_CLASSES = ['bicycle', 'bus', 'car', 'cat', 'cow', 'dog', 'horse', 'motorbike',
               'person', 'train']

OLD_INDICES = [OLD_CLASSES.index(x) for x in NEW_CLASSES]

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Techniques to accelerate convolutional neural networks

• Depthwise convolution (mobilenet)
• Shufflenet
• Densenet

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import argparse, time
import logging
logging.basicConfig(level=logging.INFO)
fh = logging.FileHandler('training.log')
logger = logging.getLogger()
logger.addHandler(fh)

import mxnet as mx
from mxnet import gluon
from mxnet.gluon import nn

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import mxnet as mx
print(mx.__version__)
import time
import os

print('worker:', os.environ.get('MXNET_CPU_WORKER_NTHREADS', 1))

num_batch = 100
batch_size = 32
data_shape = (3, 224, 224)

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"""Diagnose script for checking OS/hardware/python/pip/mxnet/network.
The output of this script can be a very good hint to issue/problem.
"""
import platform, subprocess, sys, os
import socket, time
try:
    from urllib.request import urlopen
    from urllib.parse import urlparse
except ImportError:
    from urlparse import urlparse

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name: "GoogleNet"
layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param { shape: { dim: 10 dim: 3 dim: 224 dim: 224 } }
}
layer {
  name: "conv1/7x7_s2"
  type: "Convolution"