YOLO-Tutorial-v2/odlab/models/basic/conv.py

import torch.nn as nn


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

    return conv

def get_activation(act_type=None):
    if act_type is None:
        return nn.Identity()
    elif 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 == 'gelu':
        return nn.GELU()
    else:
        raise NotImplementedError(act_type)

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)
    elif norm_type is None:
        return nn.Identity()
    else:
        raise NotImplementedError(norm_type)


# ----------------- CNN ops -----------------
class ConvModule(nn.Module):
    def __init__(self,
                 c1,
                 c2,
                 k=1,
                 p=0,
                 s=1,
                 d=1,
                 act_type='relu',
                 norm_type='BN', 
                 depthwise=False):
        super(ConvModule, self).__init__()
        convs = []
        if depthwise:
            convs.append(get_conv2d(c1, c1, k=k, p=p, s=s, d=d, g=c1))
            # 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))
            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))
            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)