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[ICCV 2019] Harmonious Bottleneck on Two Orthogonal Dimensions, surpassing MobileNetV2

Home Page: https://arxiv.org/abs/1908.03888

License: Apache License 2.0

Python 100.00%
efficient-model iccv2019 imagenet mobilenetv2 pretrained-models pytorch

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hbonet's Issues

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1

HarmoniousBottleneck_2x

开源代码中HarmoniousBottleneck_2x模块forward函数的elif self.stride == 2: return torch.cat((self.avgpool(x[:, -(self.oup - self.oup // 2):, :, :]), self.conv(x)), dim=1),仅仅使用了cat,其他模块中包含了add之后再cat操作,文章结构示意图也是先add再cat,应该以哪个为准呢。

about feature selection

thanks for your contribution a lot,here if I want to combine hbonet with FPN(feature pyramid network) ,a combined feature extraction network is just like resnet101-FPN and so on.so which feature maps extracted from hbonet would be suitable for FPN?

could you please give me some suggestion taking the following as an example?

self.cfgs = [
            # t, c, n, s, block
            [1, 20, 1, 1, InvertedResidual],

            # alternative blocks for 8x varaint model
            # [2,  36, 1, 1, HarmoniousBottleneck_8x],
            # [2,  72, 3, 2, HarmoniousBottleneck_8x],
            # [2,  96, 4, 2, HarmoniousBottleneck_4x],

            # alternative blocks for 4x varaint model
            # [2,  36, 1, 1, HarmoniousBottleneck_4x],
            # [2,  72, 3, 2, HarmoniousBottleneck_4x],
            # [2,  96, 4, 2, HarmoniousBottleneck_4x],

            # alternative blocks for 2x main model
            [2, 36, 1, 1, HarmoniousBottleneck_2x],
            [2, 72, 3, 2, HarmoniousBottleneck_2x],
            [2, 96, 4, 2, HarmoniousBottleneck_2x],

            # fixed blocks
            [2, 192, 4, 2, HarmoniousBottleneck_2x],
            [2, 288, 1, 1, HarmoniousBottleneck_2x],
            [0, 144, 1, 1, conv_1x1_bn_hbo],
            [6, 200, 2, 2, InvertedResidual],
            [6, 400, 1, 1, InvertedResidual],
        ]

thanks in advance.

About FLOPs

Hi,

I'm calculating the FLOPs of the default settings of HBOnet where width_mult = 1.0 and input size is 3x224x224 with thop (https://github.com/Lyken17/pytorch-OpCounter). Below please find my script with Python3.5.

import hbonet
from thop import profile, clever_format
import torch

model = hbonet.hbonet()
flops, params = profile(model, inputs=(torch.randn(1,3,224,224), ))
flops, params = clever_format([flops, params], "%.3f")
print(flops, params) #984.760M 4.562M

It turns out to me that FLOPs and params are 984.760M & 4.562M, respectively. As you can see, the number of flops is far more than 300M claimed in your paper.

May I know if you counted nn.Upsample and nn.BatchNorm2d in your paper? To make it clear, could you please release the code you used to count FLOPs in your paper? Many thanks in advance.

Some question about other network

Thanks for your great work!
I want to know if you tried the channel Shuffle at the end of each block like ShufflenetV2? Can this be improved? Look forward to your reply!

training epoch

Hi,

First of all, congratulation for your acception in ICCV. This work looks nice.

I have two questions,

  1. In your paper, these models were trained for 150 epochs while this repo indicates 300epoch.

Which one is correct?

  1. This repo notices the dependency of NVIDIA DALI which is not recommended by you. Without the DALI, I can train the model?

Is the paper available now

Thanks for sharing the awesome work! Could you please provide a link to your paper, the link in the README is invalid somehow.

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