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mmpretrain.models.backbones.hivit 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import math

import torch
import torch.nn as nn
from mmcv.cnn.bricks import DropPath
from mmengine.model.weight_init import trunc_normal_

from mmpretrain.registry import MODELS
from ..utils import build_norm_layer, to_2tuple
from .base_backbone import BaseBackbone


class Mlp(nn.Module):
    """MLP block.

    Args:
        in_features (int): Number of input dims.
        hidden_features (int): Number of hidden dims.
        out_feature (int): Number of out dims.
        act_layer: MLP activation layer.
        drop (float): MLP dropout rate.
    """

    def __init__(self,
                 in_features,
                 hidden_features=None,
                 out_features=None,
                 act_layer=nn.GELU,
                 drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Attention(nn.Module):
    """Attention.

    Args:
        input size (int): Input size.
        dim (int): Number of input dims.
        num_heads (int): Number of attention heads.
        qkv_bias (bool): Enable bias for qkv projections if True.
        qk_scale (float): The number of divider after q@k. Default to None.
        attn_drop (float): The drop out rate for attention output weights.
            Defaults to 0.
        proj_drop (float): Probability of an element to be zeroed
            after the feed forward layer. Defaults to 0.
        rpe (bool): If True, add relative position embedding to
            the patch embedding.
    """

    def __init__(self,
                 input_size,
                 dim,
                 num_heads,
                 qkv_bias=True,
                 qk_scale=None,
                 attn_drop=0.,
                 proj_drop=0.,
                 rpe=True):
        super().__init__()
        self.input_size = input_size
        self.dim = dim
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5

        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * input_size - 1) *
                        (2 * input_size - 1), num_heads)) if rpe else None
        if rpe:
            coords_h = torch.arange(input_size)
            coords_w = torch.arange(input_size)
            coords = torch.stack(torch.meshgrid([coords_h, coords_w]))
            coords_flatten = torch.flatten(coords, 1)
            relative_coords = coords_flatten[:, :,
                                             None] - coords_flatten[:, None, :]
            relative_coords = relative_coords.permute(1, 2, 0).contiguous()
            relative_coords[:, :, 0] += input_size - 1
            relative_coords[:, :, 1] += input_size - 1
            relative_coords[:, :, 0] *= 2 * input_size - 1
            relative_position_index = relative_coords.sum(-1)
            self.register_buffer('relative_position_index',
                                 relative_position_index)

            trunc_normal_(self.relative_position_bias_table, std=.02)

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, rpe_index=None, mask=None):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads,
                                  C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[
            2]  # make torchscript happy (cannot use tensor as tuple)

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

        if rpe_index is not None:
            rpe_index = self.relative_position_index.view(-1)
            S = int(math.sqrt(rpe_index.size(-1)))
            relative_position_bias = self.relative_position_bias_table[
                rpe_index].view(-1, S, S, self.num_heads)
            relative_position_bias = relative_position_bias.permute(
                0, 3, 1, 2).contiguous()
            attn = attn + relative_position_bias
        if mask is not None:
            mask = mask.bool()
            attn = attn.masked_fill(~mask[:, None, None, :], float('-inf'))
        attn = self.softmax(attn)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class BlockWithRPE(nn.Module):
    """HiViT block.

    Args:
        input_size (int): Input size.
        dim (int): Number of input dims.
        num_heads (int): Number of attention heads.
        mlp_ratio (int): Ratio of MLP hidden dim to embedding dim.
        qkv_bias (bool): Enable bias for qkv projections if True.
        qk_scale (float): The number of divider after q@k. Default to None.
        drop (float): Probability of an element to be zeroed
            after the feed forward layer. Defaults to 0.
        attn_drop (float): The drop out rate for attention output weights.
            Defaults to 0.
        drop_path (float): Stochastic depth rate. Defaults to 0.
        rpe (bool): If True, add relative position embedding to
            the patch embedding.
        layer_scale_init_value (float): Layer-scale init values. Defaults to 0.
        act_layer: MLP activation layer.
        norm_cfg (dict): Config dict for normalization layer.
            Defaults to ``dict(type='LN')``.
    """

    def __init__(self,
                 input_size,
                 dim,
                 num_heads=0.,
                 mlp_ratio=4.,
                 qkv_bias=True,
                 qk_scale=None,
                 drop=0.,
                 attn_drop=0.,
                 drop_path=0.,
                 rpe=True,
                 layer_scale_init_value=0.0,
                 act_layer=nn.GELU,
                 norm_cfg=dict(type='LN')):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.mlp_ratio = mlp_ratio

        with_attn = num_heads > 0.

        self.norm1 = build_norm_layer(norm_cfg, dim) if with_attn else None
        self.attn = Attention(
            input_size,
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop,
            rpe=rpe,
        ) if with_attn else None

        self.drop_path = DropPath(
            drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = build_norm_layer(norm_cfg, dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop)

        if layer_scale_init_value > 0:
            self.gamma_1 = nn.Parameter(
                layer_scale_init_value * torch.ones(
                    (dim)), requires_grad=True) if with_attn else None
            self.gamma_2 = nn.Parameter(
                layer_scale_init_value * torch.ones((dim)), requires_grad=True)
        else:
            self.gamma_1, self.gamma_2 = None, None

    def forward(self, x, rpe_index=None, mask=None):
        if self.attn is not None:
            if self.gamma_1 is not None:
                x = x + self.drop_path(
                    self.gamma_1 * self.attn(self.norm1(x), rpe_index, mask))
            else:
                x = x + self.drop_path(
                    self.attn(self.norm1(x), rpe_index, mask))
        if self.gamma_2 is not None:
            x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
        else:
            x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


class PatchEmbed(nn.Module):
    """PatchEmbed for HiViT.

    Args:
        img_size (int): Input image size.
        patch_size (int): Patch size. Defaults to 16.
        inner_patches (int): Inner patch. Defaults to 4.
        in_chans (int): Number of image input channels.
        embed_dim (int): Transformer embedding dimension.
        norm_cfg (dict): Config dict for normalization layer.
            Defaults to ``dict(type='LN')``.
        kernel_size (int): Kernel size.
        pad_size (int): Pad size.
    """

    def __init__(self,
                 img_size=224,
                 patch_size=16,
                 inner_patches=4,
                 in_chans=3,
                 embed_dim=128,
                 norm_cfg=None,
                 kernel_size=None,
                 pad_size=None):
        super().__init__()
        img_size = to_2tuple(img_size) if not isinstance(img_size,
                                                         tuple) else img_size
        patch_size = to_2tuple(patch_size)
        patches_resolution = [
            img_size[0] // patch_size[0], img_size[1] // patch_size[1]
        ]
        self.img_size = img_size
        self.patch_size = patch_size
        self.inner_patches = inner_patches
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        conv_size = [size // inner_patches for size in patch_size]
        kernel_size = kernel_size or conv_size
        pad_size = pad_size or 0
        self.proj = nn.Conv2d(
            in_chans,
            embed_dim,
            kernel_size=kernel_size,
            stride=conv_size,
            padding=pad_size)
        if norm_cfg is not None:
            self.norm = build_norm_layer(norm_cfg, embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        B, C, H, W = x.shape
        patches_resolution = (H // self.patch_size[0], W // self.patch_size[1])
        num_patches = patches_resolution[0] * patches_resolution[1]
        x = self.proj(x).view(
            B,
            -1,
            patches_resolution[0],
            self.inner_patches,
            patches_resolution[1],
            self.inner_patches,
        ).permute(0, 2, 4, 3, 5, 1).reshape(B, num_patches, self.inner_patches,
                                            self.inner_patches, -1)
        if self.norm is not None:
            x = self.norm(x)
        return x


class PatchMerge(nn.Module):
    """PatchMerge for HiViT.

    Args:
        dim (int): Number of input channels.
        norm_cfg (dict): Config dict for normalization layer.
    """

    def __init__(self, dim, norm_cfg):
        super().__init__()
        self.norm = build_norm_layer(norm_cfg, dim * 4)
        self.reduction = nn.Linear(dim * 4, dim * 2, bias=False)

    def forward(self, x, *args, **kwargs):
        is_main_stage = len(x.shape) == 3
        if is_main_stage:
            B, N, C = x.shape
            S = int(math.sqrt(N))
            x = x.reshape(B, S // 2, 2, S // 2, 2, C) \
                .permute(0, 1, 3, 2, 4, 5) \
                .reshape(B, -1, 2, 2, C)
        x0 = x[..., 0::2, 0::2, :]
        x1 = x[..., 1::2, 0::2, :]
        x2 = x[..., 0::2, 1::2, :]
        x3 = x[..., 1::2, 1::2, :]

        x = torch.cat([x0, x1, x2, x3], dim=-1)
        x = self.norm(x)
        x = self.reduction(x)

        if is_main_stage:
            x = x[:, :, 0, 0, :]
        return x


[文档]@MODELS.register_module() class HiViT(BaseBackbone): """HiViT. A PyTorch implement of: `HiViT: A Simple and More Efficient Design of Hierarchical Vision Transformer <https://arxiv.org/abs/2205.14949>`_. Args: arch (str | dict): Swin Transformer architecture. If use string, choose from 'tiny', 'small', and'base'. If use dict, it should have below keys: - **embed_dims** (int): The dimensions of embedding. - **depths** (List[int]): The number of blocks in each stage. - **num_heads** (int): The number of heads in attention modules of each stage. Defaults to 'tiny'. img_size (int): Input image size. patch_size (int): Patch size. Defaults to 16. inner_patches (int): Inner patch. Defaults to 4. in_chans (int): Number of image input channels. embed_dim (int): Transformer embedding dimension. depths (list[int]): Number of successive HiViT blocks. num_heads (int): Number of attention heads. stem_mlp_ratio (int): Ratio of MLP hidden dim to embedding dim in the first two stages. mlp_ratio (int): Ratio of MLP hidden dim to embedding dim in the last stage. qkv_bias (bool): Enable bias for qkv projections if True. qk_scale (float): The number of divider after q@k. Default to None. drop_rate (float): Probability of an element to be zeroed after the feed forward layer. Defaults to 0. attn_drop_rate (float): The drop out rate for attention output weights. Defaults to 0. drop_path_rate (float): Stochastic depth rate. Defaults to 0. norm_cfg (dict): Config dict for normalization layer. Defaults to ``dict(type='LN')``. ape (bool): If True, add absolute position embedding to the patch embedding. rpe (bool): If True, add relative position embedding to the patch embedding. patch_norm (bool): If True, use norm_cfg for normalization layer. frozen_stages (int): Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Defaults to -1. kernel_size (int): Kernel size. pad_size (int): Pad size. layer_scale_init_value (float): Layer-scale init values. Defaults to 0. init_cfg (dict, optional): The extra config for initialization. Defaults to None. """ arch_zoo = { **dict.fromkeys(['t', 'tiny'], {'embed_dims': 384, 'depths': [1, 1, 10], 'num_heads': 6}), **dict.fromkeys(['s', 'small'], {'embed_dims': 384, 'depths': [2, 2, 20], 'num_heads': 6}), **dict.fromkeys(['b', 'base'], {'embed_dims': 512, 'depths': [2, 2, 24], 'num_heads': 8}), **dict.fromkeys(['l', 'large'], {'embed_dims': 768, 'depths': [2, 2, 40], 'num_heads': 12}), } # yapf: disable num_extra_tokens = 0 def __init__(self, arch='base', img_size=224, patch_size=16, inner_patches=4, in_chans=3, stem_mlp_ratio=3., mlp_ratio=4., qkv_bias=True, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.0, norm_cfg=dict(type='LN'), out_indices=[23], ape=True, rpe=False, patch_norm=True, frozen_stages=-1, kernel_size=None, pad_size=None, layer_scale_init_value=0.0, init_cfg=None): super(HiViT, self).__init__(init_cfg=init_cfg) if isinstance(arch, str): arch = arch.lower() assert arch in set(self.arch_zoo), \ f'Arch {arch} is not in default archs {set(self.arch_zoo)}' self.arch_settings = self.arch_zoo[arch] else: essential_keys = {'embed_dims', 'depths', 'num_heads'} assert isinstance(arch, dict) and set(arch) == essential_keys, \ f'Custom arch needs a dict with keys {essential_keys}' self.arch_settings = arch self.embed_dims = self.arch_settings['embed_dims'] self.depths = self.arch_settings['depths'] self.num_heads = self.arch_settings['num_heads'] self.num_stages = len(self.depths) self.ape = ape self.rpe = rpe self.patch_size = patch_size self.num_features = self.embed_dims self.mlp_ratio = mlp_ratio self.num_main_blocks = self.depths[-1] self.out_indices = out_indices self.out_indices[-1] = self.depths[-1] - 1 img_size = to_2tuple(img_size) if not isinstance(img_size, tuple) else img_size embed_dim = self.embed_dims // 2**(self.num_stages - 1) # split image into non-overlapping patches self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, inner_patches=inner_patches, in_chans=in_chans, embed_dim=embed_dim, norm_cfg=norm_cfg if patch_norm else None, kernel_size=kernel_size, pad_size=pad_size) num_patches = self.patch_embed.num_patches Hp, Wp = self.patch_embed.patches_resolution if rpe: assert Hp == Wp, 'If you use relative position, make sure H == W ' 'of input size' # absolute position embedding if ape: self.pos_embed = nn.Parameter( torch.zeros(1, num_patches, self.num_features)) trunc_normal_(self.pos_embed, std=.02) if rpe: # get pair-wise relative position index for each token inside the # window coords_h = torch.arange(Hp) coords_w = torch.arange(Wp) coords = torch.stack(torch.meshgrid([coords_h, coords_w])) coords_flatten = torch.flatten(coords, 1) relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] relative_coords = relative_coords.permute(1, 2, 0).contiguous() relative_coords[:, :, 0] += Hp - 1 relative_coords[:, :, 1] += Wp - 1 relative_coords[:, :, 0] *= 2 * Wp - 1 relative_position_index = relative_coords.sum(-1) self.register_buffer('relative_position_index', relative_position_index) self.pos_drop = nn.Dropout(p=drop_rate) # stochastic depth dpr = iter( x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths) + sum(self.depths[:-1]))) # build blocks self.blocks = nn.ModuleList() for stage_i, stage_depth in enumerate(self.depths): is_main_stage = embed_dim == self.num_features nhead = self.num_heads if is_main_stage else 0 ratio = mlp_ratio if is_main_stage else stem_mlp_ratio # every block not in main stage includes two mlp blocks stage_depth = stage_depth if is_main_stage else stage_depth * 2 for _ in range(stage_depth): self.blocks.append( BlockWithRPE( Hp, embed_dim, nhead, ratio, qkv_bias, qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=next(dpr), rpe=rpe, norm_cfg=norm_cfg, layer_scale_init_value=layer_scale_init_value, )) if stage_i + 1 < self.num_stages: self.blocks.append(PatchMerge(embed_dim, norm_cfg)) embed_dim *= 2 self.frozen_stages = frozen_stages if self.frozen_stages > 0: self._freeze_stages() self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.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 interpolate_pos_encoding(self, x, h, w): npatch = x.shape[1] N = self.pos_embed.shape[1] if npatch == N and w == h: return self.pos_embed patch_pos_embed = self.pos_embed dim = x.shape[-1] w0 = w // self.patch_size h0 = h // self.patch_size # we add a small number to avoid floating point error in interpolation # see discussion at https://github.com/facebookresearch/dino/issues/8 w0, h0 = w0 + 0.1, h0 + 0.1 patch_pos_embed = nn.functional.interpolate( patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2), scale_factor=(h0 / math.sqrt(N), w0 / math.sqrt(N)), mode='bicubic', ) assert int(h0) == patch_pos_embed.shape[-2] and int( w0) == patch_pos_embed.shape[-1] patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) return patch_pos_embed def forward(self, x): B, C, H, W = x.shape Hp, Wp = H // self.patch_size, W // self.patch_size x = self.patch_embed(x) outs = [] for i, blk in enumerate(self.blocks[:-self.num_main_blocks]): x = blk(x) if i in self.out_indices: x = x.reshape(B, Hp, Wp, *x.shape[-3:]).permute( 0, 5, 1, 3, 2, 4).reshape(B, -1, Hp * x.shape[-3], Wp * x.shape[-2]).contiguous() outs.append(x) x = x[..., 0, 0, :] if self.ape: x = x + self.interpolate_pos_encoding(x, H, W) x = self.pos_drop(x) rpe_index = True if self.rpe else None for i, blk in enumerate(self.blocks[-self.num_main_blocks:]): x = blk(x, rpe_index) if i in self.out_indices: x = x.transpose(1, 2).view(B, -1, Hp, Wp).contiguous() outs.append(x) return tuple(outs) def _freeze_stages(self): # freeze position embedding if self.pos_embed is not None: self.pos_embed.requires_grad = False # set dropout to eval model self.pos_drop.eval() # freeze patch embedding self.patch_embed.eval() for param in self.patch_embed.parameters(): param.requires_grad = False # freeze layers for i in range(1, self.frozen_stages + 1): m = self.blocks[i - 1] m.eval() for param in m.parameters(): param.requires_grad = False # freeze the last layer norm for param in self.fc_norm.parameters(): param.requires_grad = False
[文档] def get_layer_depth(self, param_name: str, prefix: str = ''): """Get the layer-wise depth of a parameter. Args: param_name (str): The name of the parameter. prefix (str): The prefix for the parameter. Defaults to an empty string. Returns: Tuple[int, int]: The layer-wise depth and the num of layers. Note: The first depth is the stem module (``layer_depth=0``), and the last depth is the subsequent module (``layer_depth=num_layers-1``) """ self.num_layers = len(self.blocks) num_layers = self.num_layers + 2 if not param_name.startswith(prefix): # For subsequent module like head return num_layers - 1, num_layers param_name = param_name[len(prefix):] if param_name in 'pos_embed': layer_depth = 0 elif param_name.startswith('patch_embed'): layer_depth = 0 elif param_name.startswith('layers'): layer_id = int(param_name.split('.')[1]) layer_depth = layer_id + 1 else: layer_depth = num_layers - 1 return layer_depth, num_layers
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