RT-ODLab/models/detectors/rtpdetr/basic_modules/basic.py

import math
import torch
import torch.nn as nn


# ----------------- Customed NormLayer Ops -----------------
class FrozenBatchNorm2d(torch.nn.Module):
    def __init__(self, n):
        super(FrozenBatchNorm2d, self).__init__()
        self.register_buffer("weight", torch.ones(n))
        self.register_buffer("bias", torch.zeros(n))
        self.register_buffer("running_mean", torch.zeros(n))
        self.register_buffer("running_var", torch.ones(n))

    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
                              missing_keys, unexpected_keys, error_msgs):
        num_batches_tracked_key = prefix + 'num_batches_tracked'
        if num_batches_tracked_key in state_dict:
            del state_dict[num_batches_tracked_key]

        super(FrozenBatchNorm2d, self)._load_from_state_dict(
            state_dict, prefix, local_metadata, strict,
            missing_keys, unexpected_keys, error_msgs)

    def forward(self, x):
        # move reshapes to the beginning
        # to make it fuser-friendly
        w = self.weight.reshape(1, -1, 1, 1)
        b = self.bias.reshape(1, -1, 1, 1)
        rv = self.running_var.reshape(1, -1, 1, 1)
        rm = self.running_mean.reshape(1, -1, 1, 1)
        eps = 1e-5
        scale = w * (rv + eps).rsqrt()
        bias = b - rm * scale
        return x * scale + bias

class LayerNorm2D(nn.Module):
    def __init__(self, normalized_shape, norm_layer=nn.LayerNorm):
        super().__init__()
        self.ln = norm_layer(normalized_shape) if norm_layer is not None else nn.Identity()

    def forward(self, x):
        """
        x: N C H W
        """
        x = x.permute(0, 2, 3, 1)
        x = self.ln(x)
        x = x.permute(0, 3, 1, 2)
        return x


# ----------------- Basic CNN Ops -----------------
def get_conv2d(c1, c2, k, p, s, g, bias=False):
    conv = nn.Conv2d(c1, c2, k, stride=s, padding=p, 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 == 'gelu':
        return nn.GELU()
    elif act_type is None:
        return nn.Identity()
    else:
        raise NotImplementedError
        
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


class BasicConv(nn.Module):
    def __init__(self, 
                 in_dim,                   # in channels
                 out_dim,                  # out channels 
                 kernel_size=1,            # kernel size 
                 padding=0,                # padding
                 stride=1,                 # padding
                 act_type  :str = 'lrelu', # activation
                 norm_type :str = 'BN',    # normalization
                ):
        super(BasicConv, self).__init__()
        add_bias = False if norm_type else True
        self.conv = get_conv2d(in_dim, out_dim, k=kernel_size, p=padding, s=stride, g=1, bias=add_bias)
        self.norm = get_norm(norm_type, out_dim)
        self.act  = get_activation(act_type)

    def forward(self, x):
        return self.act(self.norm(self.conv(x)))

class UpSampleWrapper(nn.Module):
    """Upsample last feat map to specific stride."""
    def __init__(self, in_dim, upsample_factor):
        super(UpSampleWrapper, self).__init__()
        # ---------- Basic parameters ----------
        self.upsample_factor = upsample_factor

        # ---------- Network parameters ----------
        if upsample_factor == 1:
            self.upsample = nn.Identity()
        else:
            scale = int(math.log2(upsample_factor))
            dim = in_dim
            layers = []
            for _ in range(scale-1):
                layers += [
                    nn.ConvTranspose2d(dim, dim // 2, kernel_size=2, stride=2),
                    LayerNorm2D(dim // 2),
                    nn.GELU()
                ]
                dim = dim // 2
            layers += [nn.ConvTranspose2d(dim, dim // 2, kernel_size=2, stride=2)]
            dim = dim // 2
            self.upsample = nn.Sequential(*layers)
            self.out_dim = dim

    def forward(self, x):
        x = self.upsample(x)

        return x


# ----------------- MLP modules -----------------
class MLP(nn.Module):
    def __init__(self, in_dim, hidden_dim, out_dim, num_layers):
        super().__init__()
        self.num_layers = num_layers
        h = [hidden_dim] * (num_layers - 1)
        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([in_dim] + h, h + [out_dim]))

    def forward(self, x):
        for i, layer in enumerate(self.layers):
            x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
        return x

class FFN(nn.Module):
    def __init__(self, d_model=256, mlp_ratio=4.0, dropout=0., act_type='relu'):
        super().__init__()
        self.fpn_dim = round(d_model * mlp_ratio)
        self.linear1 = nn.Linear(d_model, self.fpn_dim)
        self.activation = get_activation(act_type)
        self.dropout2 = nn.Dropout(dropout)
        self.linear2 = nn.Linear(self.fpn_dim, d_model)
        self.dropout3 = nn.Dropout(dropout)
        self.norm = nn.LayerNorm(d_model)

    def forward(self, src):
        src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
        src = src + self.dropout3(src2)
        src = self.norm(src)
        
        return src
    

# ----------------- Basic CNN Ops -----------------
class FrozenBatchNorm2d(torch.nn.Module):
    def __init__(self, n):
        super(FrozenBatchNorm2d, self).__init__()
        self.register_buffer("weight", torch.ones(n))
        self.register_buffer("bias", torch.zeros(n))
        self.register_buffer("running_mean", torch.zeros(n))
        self.register_buffer("running_var", torch.ones(n))

    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
                              missing_keys, unexpected_keys, error_msgs):
        num_batches_tracked_key = prefix + 'num_batches_tracked'
        if num_batches_tracked_key in state_dict:
            del state_dict[num_batches_tracked_key]

        super(FrozenBatchNorm2d, self)._load_from_state_dict(
            state_dict, prefix, local_metadata, strict,
            missing_keys, unexpected_keys, error_msgs)

    def forward(self, x):
        # move reshapes to the beginning
        # to make it fuser-friendly
        w = self.weight.reshape(1, -1, 1, 1)
        b = self.bias.reshape(1, -1, 1, 1)
        rv = self.running_var.reshape(1, -1, 1, 1)
        rm = self.running_mean.reshape(1, -1, 1, 1)
        eps = 1e-5
        scale = w * (rv + eps).rsqrt()
        bias = b - rm * scale
        return x * scale + bias