nbnb

nb is nerual network builder for quick network prototyping


Keywords
deep, learning, neural, network, builder
License
Apache-2.0
Install
pip install nbnb==0.0.6

Documentation

NB

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.

Install

nb can be installed from PIP, remember the name is nbnb:

sudo pip3 install nbnb

Usage

Here is an example of using NB to build YoloV5!

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

Updates

  • 2020.09.22: New backbone of Ghostnet and MobilenetV3 included. Both of them can be used to replace any of your application's backbone.

  • 2020.09.14: We release a primary version of 0.04, which you can build a simple YoloV5 with nb easily!

    pip install nbnb
  • 2020.09.12: 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()
  • 2020.09.11: New added blocks:

    resnet.Bottleneck
    resnet.BasicBlock
    
    ConvBase
    

Support Matrix

We list all conv and block support in nb here:

Copyright

@Lucas Jin all rights reserved.