RT-ODLab/models/detectors/yolovx/yolovx_basic.py

import numpy as np
import torch
import torch.nn as nn


# ---------------------------- 2D CNN ----------------------------
class SiLU(nn.Module):
    """export-friendly version of nn.SiLU()"""

    @staticmethod
    def forward(x):
        return x * torch.sigmoid(x)


def get_conv2d(c1, c2, k, p, s, d, g, bias=False):
    conv = nn.Conv2d(c1, c2, k, stride=s, padding=p, dilation=d, groups=g, bias=bias)

    return conv


def get_activation(act_type=None):
    if act_type == 'relu':
        return nn.ReLU(inplace=True)
    elif act_type == 'lrelu':
        return nn.LeakyReLU(0.1, inplace=True)
    elif act_type == 'mish':
        return nn.Mish(inplace=True)
    elif act_type == 'silu':
        return nn.SiLU(inplace=True)
    elif act_type is None:
        return nn.Identity()


def get_norm(norm_type, dim):
    if norm_type == 'BN':
        return nn.BatchNorm2d(dim)
    elif norm_type == 'GN':
        return nn.GroupNorm(num_groups=32, num_channels=dim)


# Basic conv layer
class Conv(nn.Module):
    def __init__(self, 
                 c1,                   # in channels
                 c2,                   # out channels 
                 k=1,                  # kernel size 
                 p=0,                  # padding
                 s=1,                  # padding
                 d=1,                  # dilation
                 act_type='lrelu',     # activation
                 norm_type='BN',       # normalization
                 depthwise=False):
        super(Conv, self).__init__()
        convs = []
        add_bias = False if norm_type else True
        p = p if d == 1 else d
        if depthwise:
            convs.append(get_conv2d(c1, c1, k=k, p=p, s=s, d=d, g=c1, bias=add_bias))
            # depthwise conv
            if norm_type:
                convs.append(get_norm(norm_type, c1))
            if act_type:
                convs.append(get_activation(act_type))
            # pointwise conv
            convs.append(get_conv2d(c1, c2, k=1, p=0, s=1, d=d, g=1, bias=add_bias))
            if norm_type:
                convs.append(get_norm(norm_type, c2))
            if act_type:
                convs.append(get_activation(act_type))

        else:
            convs.append(get_conv2d(c1, c2, k=k, p=p, s=s, d=d, g=1, bias=add_bias))
            if norm_type:
                convs.append(get_norm(norm_type, c2))
            if act_type:
                convs.append(get_activation(act_type))
            
        self.convs = nn.Sequential(*convs)


    def forward(self, x):
        return self.convs(x)


# ---------------------------- Modified YOLOv7's Modules ----------------------------
## ELANBlock for Backbone
class ELANBlock(nn.Module):
    def __init__(self, in_dim, out_dim, expand_ratio=0.5, depth=1.0, act_type='silu', norm_type='BN', depthwise=False):
        super(ELANBlock, self).__init__()
        if isinstance(expand_ratio, float):
            inter_dim = int(in_dim * expand_ratio)
            inter_dim2 = inter_dim
        elif isinstance(expand_ratio, list):
            assert len(expand_ratio) == 2
            e1, e2 = expand_ratio
            inter_dim = int(in_dim * e1)
            inter_dim2 = int(inter_dim * e2)
        # branch-1
        self.cv1 = Conv(in_dim, inter_dim, k=1, act_type=act_type, norm_type=norm_type)
        # branch-2
        self.cv2 = Conv(in_dim, inter_dim, k=1, act_type=act_type, norm_type=norm_type)
        # branch-3
        for idx in range(round(3*depth)):
            if idx == 0:
                cv3 = [Conv(inter_dim, inter_dim2, k=3, p=1, act_type=act_type, norm_type=norm_type, depthwise=depthwise)]
            else:
                cv3.append(Conv(inter_dim2, inter_dim2, k=3, p=1, act_type=act_type, norm_type=norm_type, depthwise=depthwise))
        self.cv3 = nn.Sequential(*cv3)
        # branch-4
        self.cv4 = nn.Sequential(*[
            Conv(inter_dim2, inter_dim2, k=3, p=1, act_type=act_type, norm_type=norm_type, depthwise=depthwise)
            for _ in range(round(3*depth))
        ])
        # output
        self.out = Conv(inter_dim*2 + inter_dim2*2, out_dim, k=1, act_type=act_type, norm_type=norm_type)


    def forward(self, x):
        """
        Input:
            x: [B, C_in, H, W]
        Output:
            out: [B, C_out, H, W]
        """
        x1 = self.cv1(x)
        x2 = self.cv2(x)
        x3 = self.cv3(x2)
        x4 = self.cv4(x3)

        # [B, C, H, W] -> [B, 2C, H, W]
        out = self.out(torch.cat([x1, x2, x3, x4], dim=1))

        return out

## PaFPN's ELAN-Block proposed by YOLOv7
class ELANBlockFPN(nn.Module):
    def __init__(self, in_dim, out_dim, expand_ratio=0.5, width=1.0, depth=1.0, act_type='silu', norm_type='BN', depthwise=False):
        super(ELANBlockFPN, self).__init__()
        inter_dim = int(in_dim * expand_ratio)
        inter_dim2 = int(inter_dim * expand_ratio) 
        # branch-1
        self.cv1 = Conv(in_dim, inter_dim, k=1, act_type=act_type, norm_type=norm_type)
        # branch-2
        self.cv2 = Conv(in_dim, inter_dim, k=1, act_type=act_type, norm_type=norm_type)
        # more branches
        self.cv3 = nn.ModuleList()
        for idx in range(round(4*width)):
            if idx == 0:
                cvs = [Conv(inter_dim, inter_dim2, k=3, p=1, act_type=act_type, norm_type=norm_type, depthwise=depthwise)]
            else:
                cvs = [Conv(inter_dim2, inter_dim2, k=3, p=1, act_type=act_type, norm_type=norm_type, depthwise=depthwise)]
            # deeper
            for _ in range(1, round(depth)):
                cvs.append(Conv(inter_dim2, inter_dim2, k=3, p=1, act_type=act_type, norm_type=norm_type, depthwise=depthwise))
            self.cv3.append(nn.Sequential(*cvs))

        self.out = Conv(inter_dim*2+inter_dim2*len(self.cv3), out_dim, k=1, act_type=act_type, norm_type=norm_type)


    def forward(self, x):
        x1 = self.cv1(x)
        x2 = self.cv2(x)
        inter_outs = [x1, x2]
        for m in self.cv3:
            y1 = inter_outs[-1]
            y2 = m(y1)
            inter_outs.append(y2)
        out = self.out(torch.cat(inter_outs, dim=1))

        return out

## DownSample
class DownSample(nn.Module):
    def __init__(self, in_dim, out_dim, act_type='silu', norm_type='BN', depthwise=False):
        super().__init__()
        inter_dim = out_dim // 2
        self.mp = nn.MaxPool2d((2, 2), 2)
        self.cv1 = Conv(in_dim, inter_dim, k=1, act_type=act_type, norm_type=norm_type)
        self.cv2 = nn.Sequential(
            Conv(in_dim, inter_dim, k=1, act_type=act_type, norm_type=norm_type),
            Conv(inter_dim, inter_dim, k=3, p=1, s=2, act_type=act_type, norm_type=norm_type, depthwise=depthwise)
        )

    def forward(self, x):
        x1 = self.cv1(self.mp(x))
        x2 = self.cv2(x)
        out = torch.cat([x1, x2], dim=1)

        return out


# ---------------------------- FPN Modules ----------------------------
## build fpn's core block
def build_fpn_block(cfg, in_dim, out_dim):
    if cfg['fpn_core_block'] == 'elanblock':
        layer = ELANBlockFPN(in_dim=in_dim,
                             out_dim=out_dim,
                             expand_ratio=0.5,
                             width=cfg['width'],
                             depth=cfg['depth'],
                             act_type=cfg['fpn_act'],
                             norm_type=cfg['fpn_norm'],
                             depthwise=cfg['fpn_depthwise']
                             )
        
    return layer

## build fpn's reduce layer
def build_reduce_layer(cfg, in_dim, out_dim):
    if cfg['fpn_reduce_layer'] == 'conv':
        layer = Conv(in_dim, out_dim, k=1, act_type=cfg['fpn_act'], norm_type=cfg['fpn_norm'])
        
    return layer

## build fpn's downsample layer
def build_downsample_layer(cfg, in_dim, out_dim):
    if cfg['fpn_downsample_layer'] == 'conv':
        layer = Conv(in_dim, out_dim, k=3, s=2, p=1, act_type=cfg['fpn_act'], norm_type=cfg['fpn_norm'])
    elif cfg['fpn_downsample_layer'] == 'dsblock':
        layer = DownSample(in_dim, out_dim, act_type=cfg['fpn_act'], norm_type=cfg['fpn_norm'])
        
    return layer