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View Code? Open in Web Editor NEW[ICCV 2019] Harmonious Bottleneck on Two Orthogonal Dimensions, surpassing MobileNetV2
Home Page: https://arxiv.org/abs/1908.03888
License: Apache License 2.0
[ICCV 2019] Harmonious Bottleneck on Two Orthogonal Dimensions, surpassing MobileNetV2
Home Page: https://arxiv.org/abs/1908.03888
License: Apache License 2.0
1
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。
Thanks for your great work! I have a question, the two orthogonal dimensions in the title refer to?
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.
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.
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!
Hi,
First of all, congratulation for your acception in ICCV. This work looks nice.
I have two questions,
Which one is correct?
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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