nbnb 0.1.4

nb is nerual network builder for quick network prototyping

NB

Please install nb with pip install nbnb, the nb name was taken already, so we using nbnb instead!

Neural Network Blocks (aka: NB, or neural network builder). This library provides massive fancy blocks for you to quick build your powerful networks! Some SOTA tricks and connections such as CSP, ASFF, Attention, BaseConv, Hardswish, Mish all included for quick prototype your model. This is an Arsenal for deeplearning forge.

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. And you are very welcome to send me a pull request and add more features!

Updates

• 2022.06.29: Now we add EfficientFormer (unofficial) support! Also, inside YOLOv7, will have a detection model trained with EfficientFormer-L1;
• 2022.06.27: Now we add MobileOne backbone support!
• 2021.03.16: Added some blocks used inside Scaled-YoloV4 (P5,P6,P7). List are:
  • HarDBlock;
  • SPPCSP;
  • VoVCSP;

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!

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.

RoadMap

• More Transformers backbone;
• Favor of more light-weighted but powerful models;
• Pull request are very welcomed!

Copyright

Lucas Jin @2022 all rights reserved.