modeling/backbone/vit.py

import logging
import math
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn as nn
from torch.nn import functional as F
from detectron2.layers import CNNBlockBase, Conv2d, get_norm
from detectron2.modeling.backbone.fpn import _assert_strides_are_log2_contiguous
from fairscale.nn.checkpoint import checkpoint_wrapper
from timm.models.layers import DropPath, Mlp, trunc_normal_
from .backbone import Backbone
from .utils import (
    PatchEmbed,
    add_decomposed_rel_pos,
    get_abs_pos,
    window_partition,
    window_unpartition,
)

logger = logging.getLogger(__name__)


__all__ = ["ViT"]


class Attention(nn.Module):
    """Multi-head Attention block with relative position embeddings."""

    def __init__(
        self,
        dim,
        num_heads=8,
        qkv_bias=True,
        use_rel_pos=False,
        rel_pos_zero_init=True,
        input_size=None,
    ):
        """
        Args:
            dim (int): Number of input channels.
            num_heads (int): Number of attention heads.
            qkv_bias (bool:  If True, add a learnable bias to query, key, value.
            rel_pos (bool): If True, add relative positional embeddings to the attention map.
            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
            input_size (int or None): Input resolution for calculating the relative positional
                parameter size.
        """
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = head_dim**-0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.proj = nn.Linear(dim, dim)

        self.use_rel_pos = use_rel_pos
        if self.use_rel_pos:
            # initialize relative positional embeddings
            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))

            if not rel_pos_zero_init:
                trunc_normal_(self.rel_pos_h, std=0.02)
                trunc_normal_(self.rel_pos_w, std=0.02)

    def forward(self, x):
        B, H, W, _ = x.shape
        # qkv with shape (3, B, nHead, H * W, C)
        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
        # q, k, v with shape (B * nHead, H * W, C)
        q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)

        attn = (q * self.scale) @ k.transpose(-2, -1)

        if self.use_rel_pos:
            attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))

        attn = attn.softmax(dim=-1)
        x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
        x = self.proj(x)

        return x

class LayerNorm(nn.Module):
    r""" LayerNorm that supports two data formats: channels_last (default) or channels_first. 
    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with 
    shape (batch_size, height, width, channels) while channels_first corresponds to inputs 
    with shape (batch_size, channels, height, width).
    """
    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.eps = eps
        self.data_format = data_format
        if self.data_format not in ["channels_last", "channels_first"]:
            raise NotImplementedError 
        self.normalized_shape = (normalized_shape, )
    
    def forward(self, x):
        if self.data_format == "channels_last":
            return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
        elif self.data_format == "channels_first":
            u = x.mean(1, keepdim=True)
            s = (x - u).pow(2).mean(1, keepdim=True)
            x = (x - u) / torch.sqrt(s + self.eps)
            x = self.weight[:, None, None] * x + self.bias[:, None, None]
            return x

class ResBottleneckBlock(CNNBlockBase):
    """
    The standard bottleneck residual block without the last activation layer.
    It contains 3 conv layers with kernels 1x1, 3x3, 1x1.
    """

    def __init__(
        self,
        in_channels,
        out_channels,
        bottleneck_channels,
        norm="LN",
        act_layer=nn.GELU,
        conv_kernels=3,
        conv_paddings=1,
    ):
        """
        Args:
            in_channels (int): Number of input channels.
            out_channels (int): Number of output channels.
            bottleneck_channels (int): number of output channels for the 3x3
                "bottleneck" conv layers.
            norm (str or callable): normalization for all conv layers.
                See :func:`layers.get_norm` for supported format.
            act_layer (callable): activation for all conv layers.
        """
        super().__init__(in_channels, out_channels, 1)

        self.conv1 = Conv2d(in_channels, bottleneck_channels, 1, bias=False)
        self.norm1 = get_norm(norm, bottleneck_channels)
        self.act1 = act_layer()

        self.conv2 = Conv2d(
            bottleneck_channels,
            bottleneck_channels,
            conv_kernels,
            padding=conv_paddings,
            bias=False,
        )
        self.norm2 = get_norm(norm, bottleneck_channels)
        self.act2 = act_layer()

        self.conv3 = Conv2d(bottleneck_channels, out_channels, 1, bias=False)
        self.norm3 = get_norm(norm, out_channels)

        for layer in [self.conv1, self.conv2, self.conv3]:
            weight_init.c2_msra_fill(layer)
        for layer in [self.norm1, self.norm2]:
            layer.weight.data.fill_(1.0)
            layer.bias.data.zero_()
        # zero init last norm layer.
        self.norm3.weight.data.zero_()
        self.norm3.bias.data.zero_()

    def forward(self, x):
        out = x
        for layer in self.children():
            out = layer(out)

        out = x + out
        return out


class Block(nn.Module):
    """Transformer blocks with support of window attention and residual propagation blocks"""

    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=True,
        drop_path=0.0,
        norm_layer=nn.LayerNorm,
        act_layer=nn.GELU,
        use_rel_pos=False,
        rel_pos_zero_init=True,
        window_size=0,
        use_cc_attn = False,
        use_residual_block=False,
        use_convnext_block=False,
        input_size=None,
        res_conv_kernel_size=3,
        res_conv_padding=1,
    ):
        """
        Args:
            dim (int): Number of input channels.
            num_heads (int): Number of attention heads in each ViT block.
            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
            qkv_bias (bool): If True, add a learnable bias to query, key, value.
            drop_path (float): Stochastic depth rate.
            norm_layer (nn.Module): Normalization layer.
            act_layer (nn.Module): Activation layer.
            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
            window_size (int): Window size for window attention blocks. If it equals 0, then not
                use window attention.
            use_residual_block (bool): If True, use a residual block after the MLP block.
            input_size (int or None): Input resolution for calculating the relative positional
                parameter size.
        """
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            use_rel_pos=use_rel_pos,
            rel_pos_zero_init=rel_pos_zero_init,
            input_size=input_size if window_size == 0 else (window_size, window_size),
        )

        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer)

        self.window_size = window_size

        self.use_residual_block = use_residual_block
        if use_residual_block:
            # Use a residual block with bottleneck channel as dim // 2
            self.residual = ResBottleneckBlock(
                in_channels=dim,
                out_channels=dim,
                bottleneck_channels=dim // 2,
                norm="LN",
                act_layer=act_layer,
                conv_kernels=res_conv_kernel_size,
                conv_paddings=res_conv_padding,
            )
        self.use_convnext_block = use_convnext_block
        if use_convnext_block:
            self.convnext = ConvNextBlock(dim = dim)

        if use_cc_attn:
            self.attn = CrissCrossAttention(dim)


    def forward(self, x):
        shortcut = x
        x = self.norm1(x)

        if self.training==False:
            if self.window_size > 0:
                # Window partition
                if self.window_size > 0:
                    H, W = x.shape[1], x.shape[2]
                    x, pad_hw = window_partition(x, self.window_size)
                
                x = self.attn(x)
                # Reverse window partition
                if self.window_size > 0:
                    x = window_unpartition(x, self.window_size, pad_hw, (H, W))
            
            else:
                x_ori = x
                B, H, W, C = x.shape
                fea = torch.zeros_like(x)
                xs = []
                stride_h, stride_w = 2, 2
                for sh in range(stride_h):
                    for sw in range(stride_w):
                        xs.append(x[:, sh::stride_h, sw::stride_w])
                x = torch.cat(xs, dim=0)

                fea_list = []
                torch.cuda.empty_cache()
                for i in range(x.shape[0]):
                    fea_list.append(self.attn(x[i:i+1]))
                    torch.cuda.empty_cache()
                x = torch.cat(fea_list, dim=0)

                i = 0
                for sh in range(stride_h):
                    for sw in range(stride_w):
                        fea[:, sh::stride_h, sw::stride_w] = x[i:i+1]
                        i = i+1
                x = fea
        else:
            # Window partition
            if self.window_size > 0:
                H, W = x.shape[1], x.shape[2]
                x, pad_hw = window_partition(x, self.window_size)

            x = self.attn(x)

            # Reverse window partition
            if self.window_size > 0:
                x = window_unpartition(x, self.window_size, pad_hw, (H, W))

        x = shortcut + self.drop_path(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        if self.use_residual_block:
            x = self.residual(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
        if self.use_convnext_block:
            x = self.convnext(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)

        return x


class ViT(Backbone):
    """
    This module implements Vision Transformer (ViT) backbone in :paper:`vitdet`.
    "Exploring Plain Vision Transformer Backbones for Object Detection",
    https://arxiv.org/abs/2203.16527
    """

    def __init__(
        self,
        img_size=1024,
        patch_size=16,
        in_chans=3,
        embed_dim=768,
        depth=12,
        num_heads=12,
        mlp_ratio=4.0,
        qkv_bias=True,
        drop_path_rate=0.0,
        norm_layer=nn.LayerNorm,
        act_layer=nn.GELU,
        use_abs_pos=True,
        use_rel_pos=False,
        rel_pos_zero_init=True,
        window_size=0,
        window_block_indexes=(),
        residual_block_indexes=(),
        use_act_checkpoint=False,
        pretrain_img_size=224,
        pretrain_use_cls_token=True,
        out_feature="last_feat",
        res_conv_kernel_size=3, 
        res_conv_padding=1,
    ):
        """
        Args:
            img_size (int): Input image size.
            patch_size (int): Patch size.
            in_chans (int): Number of input image channels.
            embed_dim (int): Patch embedding dimension.
            depth (int): Depth of ViT.
            num_heads (int): Number of attention heads in each ViT block.
            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
            qkv_bias (bool): If True, add a learnable bias to query, key, value.
            drop_path_rate (float): Stochastic depth rate.
            norm_layer (nn.Module): Normalization layer.
            act_layer (nn.Module): Activation layer.
            use_abs_pos (bool): If True, use absolute positional embeddings.
            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
            window_size (int): Window size for window attention blocks.
            window_block_indexes (list): Indexes for blocks using window attention.
            residual_block_indexes (list): Indexes for blocks using conv propagation.
            use_act_checkpoint (bool): If True, use activation checkpointing.
            pretrain_img_size (int): input image size for pretraining models.
            pretrain_use_cls_token (bool): If True, pretrainig models use class token.
            out_feature (str): name of the feature from the last block.
        """
        super().__init__()
        self.pretrain_use_cls_token = pretrain_use_cls_token

        self.patch_embed = PatchEmbed(
            kernel_size=(patch_size, patch_size),
            stride=(patch_size, patch_size),
            in_chans=in_chans,
            embed_dim=embed_dim,
        )

        if use_abs_pos:
            # Initialize absolute positional embedding with pretrain image size.
            num_patches = (pretrain_img_size // patch_size) * (pretrain_img_size // patch_size)
            num_positions = (num_patches + 1) if pretrain_use_cls_token else num_patches
            self.pos_embed = nn.Parameter(torch.zeros(1, num_positions, embed_dim))
        else:
            self.pos_embed = None

        # stochastic depth decay rule
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]

        self.blocks = nn.ModuleList()
        for i in range(depth):
            block = Block(
                dim=embed_dim,
                num_heads=num_heads,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                drop_path=dpr[i],
                norm_layer=norm_layer,
                act_layer=act_layer,
                use_rel_pos=use_rel_pos,
                rel_pos_zero_init=rel_pos_zero_init,
                window_size=window_size if i in window_block_indexes else 0,
                use_residual_block=i in residual_block_indexes,
                input_size=(img_size // patch_size, img_size // patch_size),
                res_conv_kernel_size=res_conv_kernel_size,
                res_conv_padding=res_conv_padding,
            )
            if use_act_checkpoint:
                block = checkpoint_wrapper(block)
            self.blocks.append(block)

        self._out_feature_channels = {out_feature: embed_dim}
        self._out_feature_strides = {out_feature: patch_size}
        self._out_features = [out_feature]

        if self.pos_embed is not None:
            trunc_normal_(self.pos_embed, std=0.02)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    def forward(self, x):
        x = self.patch_embed(x)
        if self.pos_embed is not None:
            x = x + get_abs_pos(
                self.pos_embed, self.pretrain_use_cls_token, (x.shape[1], x.shape[2])
            )

        for blk in self.blocks:
            x = blk(x)

        outputs = {self._out_features[0]: x.permute(0, 3, 1, 2)}

        return outputs['last_feat']