Nenural network Blocks (aka: NB, or neural network builder). This library provides massive fancy blocks for you for quick import to build your powerful. Some SOTA tricks and connections such as CSP, ASFF, Attention, BaseConv, Hardswish, all included for quick prototype your model.
nb is an idea comes from engineering, we build model with some common blocks, we exploring new ideas with SOTA tricks, but all those thing can be gathered into one single place, and for model quick design and prototyping.
this project is under construct for now, I will update it quickly once I found some new blocks that really works in model. Also, every single updated block will be recorded in updates.
nb can be installed from PIP, remember the name is nbnb:
sudo pip3 install nbnb
Here is an example of using NB to build YoloV5!
updates: We have another YoloV5-ASFF version added in example!
import torch from torch import nn from nb.torch.blocks.bottleneck_blocks import SimBottleneckCSP from nb.torch.blocks.trans_blocks import Focus from nb.torch.blocks.head_blocks import SPP from nb.torch.blocks.conv_blocks import ConvBase from nb.torch.utils import device class YoloV5(nn.Module): def __init__(self, num_cls=80, ch=3, anchors=None): super(YoloV5, self).__init__() assert anchors != None, 'anchor must be provided' # divid by cd = 2 wd = 3 self.focus = Focus(ch, 64//cd) self.conv1 = ConvBase(64//cd, 128//cd, 3, 2) self.csp1 = SimBottleneckCSP(128//cd, 128//cd, n=3//wd) self.conv2 = ConvBase(128//cd, 256//cd, 3, 2) self.csp2 = SimBottleneckCSP(256//cd, 256//cd, n=9//wd) self.conv3 = ConvBase(256//cd, 512//cd, 3, 2) self.csp3 = SimBottleneckCSP(512//cd, 512//cd, n=9//wd) self.conv4 = ConvBase(512//cd, 1024//cd, 3, 2) self.spp = SPP(1024//cd, 1024//cd) self.csp4 = SimBottleneckCSP(1024//cd, 1024//cd, n=3//wd, shortcut=False) # PANet self.conv5 = ConvBase(1024//cd, 512//cd) self.up1 = nn.Upsample(scale_factor=2) self.csp5 = SimBottleneckCSP(1024//cd, 512//cd, n=3//wd, shortcut=False) self.conv6 = ConvBase(512//cd, 256//cd) self.up2 = nn.Upsample(scale_factor=2) self.csp6 = SimBottleneckCSP(512//cd, 256//cd, n=3//wd, shortcut=False) self.conv7 = ConvBase(256//cd, 256//cd, 3, 2) self.csp7 = SimBottleneckCSP(512//cd, 512//cd, n=3//wd, shortcut=False) self.conv8 = ConvBase(512//cd, 512//cd, 3, 2) self.csp8 = SimBottleneckCSP(512//cd, 1024//cd, n=3//wd, shortcut=False) def _build_backbone(self, x): x = self.focus(x) x = self.conv1(x) x = self.csp1(x) x_p3 = self.conv2(x) # P3 x = self.csp2(x_p3) x_p4 = self.conv3(x) # P4 x = self.csp3(x_p4) x_p5 = self.conv4(x) # P5 x = self.spp(x_p5) x = self.csp4(x) return x_p3, x_p4, x_p5, x def _build_head(self, p3, p4, p5, feas): h_p5 = self.conv5(feas) # head P5 x = self.up1(h_p5) x_concat = torch.cat([x, p4], dim=1) x = self.csp5(x_concat) h_p4 = self.conv6(x) # head P4 x = self.up2(h_p4) x_concat = torch.cat([x, p3], dim=1) x_small = self.csp6(x_concat) x = self.conv7(x_small) x_concat = torch.cat([x, h_p4], dim=1) x_medium = self.csp7(x_concat) x = self.conv8(x_medium) x_concat = torch.cat([x, h_p5], dim=1) x_large = self.csp8(x) return x_small, x_medium, x_large def forward(self, x): p3, p4, p5, feas = self._build_backbone(x) xs, xm, xl = self._build_head(p3, p4, p5, feas) return xs, xm, xl
A simple example to build a layer of conv:
from nb.torch.base.conv_block import ConvBase a = ConvBase(128, 256, 3, 1, 2, norm_cfg=dict(type="BN"), act_cfg=dict(type="Hardswish"))
Be note that, the reason for us using cfg to specific norm and activation is for users dynamically switch their configuration of model in yaml format rather than hard code it.
A simple example of using GhostNet:
from nb.torch.backbones.ghostnet import GhostNet m = GhostNet(num_classes=8) # if you want FPN output m = GhostNet(fpn_levels=[4, 5, 6])
A simple example of using MobilenetV3:
from nb.torch.backbones.mobilenetv3_new import MobilenetV3_Small
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2020年09月28日: ASFF module added inside nb. We have a ASFF design version of YoloV5 now! Some experiment will add here once we confirm ASFF module enhance the model performance.
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2020年09月22日: New backbone of
GhostnetandMobilenetV3included. Both of them can be used to replace any of your application's backbone. -
2020年09月14日: We release a primary version of 0.04, which you can build a simple YoloV5 with nb easily!
pip install nbnb
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2020年09月12日: New backbone SpineNet added:
SpineNet is a backbone model specific for detection, it's a backbone but can do FPN's thing!! More info pls reference google's paper link.
from nb.torch.bakbones.spinenet import SpineNet model = SpineNet()
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2020年09月11日: New added blocks:
resnet.Bottleneck resnet.BasicBlock ConvBase
We list all conv and block support in nb here:
conv:- Conv
- ConvWS: https://arxiv.org/pdf/1903.10520.pdf
- ...
Blocks:- CSPBlock:
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