mmpretrain.models.backbones.vit_sam 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Tuple

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn.bricks.transformer import FFN, PatchEmbed
from mmengine.model import BaseModule, ModuleList
from mmengine.model.weight_init import trunc_normal_

from mmpretrain.registry import MODELS
from ..utils import LayerNorm2d, build_norm_layer, resize_pos_embed, to_2tuple
from .base_backbone import BaseBackbone

def window_partition(x: torch.Tensor,
                     window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
    """Partition into non-overlapping windows with padding if needed.

    Borrowed from

        x (torch.Tensor): Input tokens with [B, H, W, C].
        window_size (int): Window size.

        Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]

        - ``windows``: Windows after partition with
        [B * num_windows, window_size, window_size, C].
        - ``(Hp, Wp)``: Padded height and width before partition
    B, H, W, C = x.shape

    pad_h = (window_size - H % window_size) % window_size
    pad_w = (window_size - W % window_size) % window_size
    if pad_h > 0 or pad_w > 0:
        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
    Hp, Wp = H + pad_h, W + pad_w

    x = x.view(B, Hp // window_size, window_size, Wp // window_size,
               window_size, C)
    windows = x.permute(0, 1, 3, 2, 4,
                        5).contiguous().view(-1, window_size, window_size, C)
    return windows, (Hp, Wp)

def window_unpartition(windows: torch.Tensor, window_size: int,
                       pad_hw: Tuple[int, int],
                       hw: Tuple[int, int]) -> torch.Tensor:
    """Window unpartition into original sequences and removing padding.

    Borrowed from

        x (torch.Tensor): Input tokens with
            [B * num_windows, window_size, window_size, C].
        window_size (int): Window size.
        pad_hw (tuple): Padded height and width (Hp, Wp).
        hw (tuple): Original height and width (H, W) before padding.

        torch.Tensor: Unpartitioned sequences with [B, H, W, C].
    Hp, Wp = pad_hw
    H, W = hw
    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
    x = windows.view(B, Hp // window_size, Wp // window_size, window_size,
                     window_size, -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)

    if Hp > H or Wp > W:
        x = x[:, :H, :W, :].contiguous()
    return x

def get_rel_pos(q_size: int, k_size: int,
                rel_pos: torch.Tensor) -> torch.Tensor:
    """Get relative positional embeddings according to the relative positions
    of query and key sizes.

    Borrowed from

        q_size (int): Size of query q.
        k_size (int): Size of key k.
        rel_pos (torch.Tensor): Relative position embeddings (L, C).

        torch.Tensor: Extracted positional embeddings according to relative
    max_rel_dist = int(2 * max(q_size, k_size) - 1)
    # Interpolate rel pos if needed.
    if rel_pos.shape[0] != max_rel_dist:
        # Interpolate rel pos.
        rel_pos_resized = F.interpolate(
            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
        rel_pos_resized = rel_pos_resized.reshape(-1,
                                                  max_rel_dist).permute(1, 0)
        rel_pos_resized = rel_pos

    # Scale the coords with short length if shapes for q and k are different.
    q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
    k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
    relative_coords = (q_coords -
                       k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)

    return rel_pos_resized[relative_coords.long()]

def add_decomposed_rel_pos(
    attn: torch.Tensor,
    q: torch.Tensor,
    rel_pos_h: torch.Tensor,
    rel_pos_w: torch.Tensor,
    q_size: Tuple[int, int],
    k_size: Tuple[int, int],
) -> torch.Tensor:
    """Borrowed from

    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.

        attn (torch.Tensor): Attention map.
        q (torch.Tensor): Query q in the attention layer with shape
            (B, q_h * q_w, C).
        rel_pos_h (torch.Tensor): Relative position embeddings (Lh, C) for
            height axis.
        rel_pos_w (torch.Tensor): Relative position embeddings (Lw, C) for
            width axis.
        q_size (tuple): Spatial sequence size of query q with (q_h, q_w).
        k_size (tuple): Spatial sequence size of key k with (k_h, k_w).

        torch.Tensor: Attention map with added relative positional embeddings.
    q_h, q_w = q_size
    k_h, k_w = k_size
    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
    Rw = get_rel_pos(q_w, k_w, rel_pos_w)

    B, _, dim = q.shape
    r_q = q.reshape(B, q_h, q_w, dim)
    rel_h = torch.einsum('bhwc,hkc->bhwk', r_q, Rh)
    rel_w = torch.einsum('bhwc,wkc->bhwk', r_q, Rw)

    attn = (attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] +
            rel_w[:, :, :, None, :]).view(B, q_h * q_w, k_h * k_w)

    return attn

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

    Borrowed from

        embed_dims (int): The embedding dimension.
        num_heads (int): Parallel attention heads.
        qkv_bias (bool): If True, add a learnable bias to q, k, v.
            Defaults to True.
        use_rel_pos (bool):Whether to use relative position embedding.
            Defaults to False.
        input_size (int, optional): Input resolution for calculating the
            relative positional parameter size. Defaults to None.

    def __init__(
        embed_dims: int,
        num_heads: int = 8,
        qkv_bias: bool = True,
        use_rel_pos: bool = False,
        input_size: Optional[Tuple[int, int]] = None,
    ) -> None:
        self.num_heads = num_heads
        head_embed_dims = embed_dims // num_heads
        self.scale = head_embed_dims**-0.5

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

        self.use_rel_pos = use_rel_pos
        if self.use_rel_pos:
            assert (input_size is not None), \
                'Input size must be provided if using relative position embed.'
            # initialize relative positional embeddings
            self.rel_pos_h = nn.Parameter(
                torch.zeros(2 * input_size[0] - 1, head_embed_dims))
            self.rel_pos_w = nn.Parameter(
                torch.zeros(2 * input_size[1] - 1, head_embed_dims))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        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 TransformerEncoderLayer(BaseModule):
    """Encoder layer with window attention in Vision Transformer.

        embed_dims (int): The feature dimension
        num_heads (int): Parallel attention heads
        feedforward_channels (int): The hidden dimension for FFNs
        drop_rate (float): Probability of an element to be zeroed
            after the feed forward layer. Defaults to 0.
        drop_path_rate (float): Stochastic depth rate. Defaults to 0.
        num_fcs (int): The number of fully-connected layers for FFNs.
            Defaults to 2.
        qkv_bias (bool): enable bias for qkv if True. Defaults to True.
        act_cfg (dict): The activation config for FFNs.
            Defaults to ``dict(type='GELU')``.
        norm_cfg (dict): Config dict for normalization layer.
            Defaults to ``dict(type='LN')``.
        use_rel_pos (bool):Whether to use relative position embedding.
            Defaults to False.
        window_size (int): Window size for window attention. Defaults to 0.
        input_size (int, optional): Input resolution for calculating the
            relative positional parameter size. Defaults to None.
        init_cfg (dict, optional): Initialization config dict.
            Defaults to None.

    def __init__(self,
                 embed_dims: int,
                 num_heads: int,
                 feedforward_channels: int,
                 drop_rate: float = 0.,
                 drop_path_rate: float = 0.,
                 num_fcs: int = 2,
                 qkv_bias: bool = True,
                 act_cfg: dict = dict(type='GELU'),
                 norm_cfg: dict = dict(type='LN'),
                 use_rel_pos: bool = False,
                 window_size: int = 0,
                 input_size: Optional[Tuple[int, int]] = None,

        self.embed_dims = embed_dims
        self.window_size = window_size

        self.ln1 = build_norm_layer(norm_cfg, self.embed_dims)

        self.attn = Attention(
            input_size=input_size if window_size == 0 else
            (window_size, window_size),

        self.ln2 = build_norm_layer(norm_cfg, self.embed_dims)

        self.ffn = FFN(
            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),

    def norm1(self):
        return self.ln1

    def norm2(self):
        return self.ln2

    def forward(self, x):
        shortcut = x
        x = self.ln1(x)
        # 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 + x

        x = self.ffn(self.ln2(x), identity=x)
        return x

[文档]@MODELS.register_module() class ViTSAM(BaseBackbone): """Vision Transformer as image encoder used in SAM. A PyTorch implement of backbone: `Segment Anything <>`_ Args: arch (str | dict): Vision Transformer architecture. If use string, choose from 'base', 'large', 'huge'. If use dict, it should have below keys: - **embed_dims** (int): The dimensions of embedding. - **num_layers** (int): The number of transformer encoder layers. - **num_heads** (int): The number of heads in attention modules. - **feedforward_channels** (int): The hidden dimensions in feedforward modules. - **global_attn_indexes** (int): The index of layers with global attention. Defaults to 'base'. img_size (int | tuple): The expected input image shape. Because we support dynamic input shape, just set the argument to the most common input image shape. Defaults to 224. patch_size (int | tuple): The patch size in patch embedding. Defaults to 16. in_channels (int): The num of input channels. Defaults to 3. out_channels (int): The num of output channels, if equal to 0, the channel reduction layer is disabled. Defaults to 256. out_indices (Sequence | int): Output from which stages. Defaults to -1, means the last stage. out_type (str): The type of output features. Please choose from - ``"raw"`` or ``"featmap"``: The feature map tensor from the patch tokens with shape (B, C, H, W). - ``"avg_featmap"``: The global averaged feature map tensor with shape (B, C). Defaults to ``"raw"``. drop_rate (float): Probability of an element to be zeroed. Defaults to 0. drop_path_rate (float): stochastic depth rate. Defaults to 0. qkv_bias (bool): Whether to add bias for qkv in attention modules. Defaults to True. use_abs_pos (bool): Whether to use absolute position embedding. Defaults to True. use_rel_pos (bool):Whether to use relative position embedding. Defaults to True. window_size (int): Window size for window attention. Defaults to 14. norm_cfg (dict): Config dict for normalization layer. Defaults to ``dict(type='LN')``. frozen_stages (int): Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Defaults to -1. interpolate_mode (str): Select the interpolate mode for position embeding vector resize. Defaults to "bicubic". patch_cfg (dict): Configs of patch embeding. Defaults to an empty dict. layer_cfgs (Sequence | dict): Configs of each transformer layer in encoder. Defaults to an empty dict. init_cfg (dict, optional): Initialization config dict. Defaults to None. """ arch_zoo = { **dict.fromkeys( ['b', 'base'], { 'embed_dims': 768, 'num_layers': 12, 'num_heads': 12, 'feedforward_channels': 3072, 'global_attn_indexes': [2, 5, 8, 11] }), **dict.fromkeys( ['l', 'large'], { 'embed_dims': 1024, 'num_layers': 24, 'num_heads': 16, 'feedforward_channels': 4096, 'global_attn_indexes': [5, 11, 17, 23] }), **dict.fromkeys( ['h', 'huge'], { 'embed_dims': 1280, 'num_layers': 32, 'num_heads': 16, 'feedforward_channels': 5120, 'global_attn_indexes': [7, 15, 23, 31] }), } OUT_TYPES = {'raw', 'featmap', 'avg_featmap'} def __init__(self, arch: str = 'base', img_size: int = 224, patch_size: int = 16, in_channels: int = 3, out_channels: int = 256, out_indices: int = -1, out_type: str = 'raw', drop_rate: float = 0., drop_path_rate: float = 0., qkv_bias: bool = True, use_abs_pos: bool = True, use_rel_pos: bool = True, window_size: int = 14, norm_cfg: dict = dict(type='LN', eps=1e-6), frozen_stages: int = -1, interpolate_mode: str = 'bicubic', patch_cfg: dict = dict(), layer_cfgs: dict = dict(), init_cfg: Optional[dict] = None): super().__init__(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', 'num_layers', 'num_heads', 'feedforward_channels' } assert isinstance(arch, dict) and essential_keys <= set(arch), \ f'Custom arch needs a dict with keys {essential_keys}' self.arch_settings = arch self.embed_dims = self.arch_settings['embed_dims'] self.num_layers = self.arch_settings['num_layers'] self.global_attn_indexes = self.arch_settings['global_attn_indexes'] self.img_size = to_2tuple(img_size) # Set patch embedding _patch_cfg = dict( in_channels=in_channels, input_size=img_size, embed_dims=self.embed_dims, conv_type='Conv2d', kernel_size=patch_size, stride=patch_size, ) _patch_cfg.update(patch_cfg) self.patch_embed = PatchEmbed(**_patch_cfg) self.patch_resolution = self.patch_embed.init_out_size # Set out type if out_type not in self.OUT_TYPES: raise ValueError(f'Unsupported `out_type` {out_type}, please ' f'choose from {self.OUT_TYPES}') self.out_type = out_type self.use_abs_pos = use_abs_pos self.interpolate_mode = interpolate_mode if use_abs_pos: # Set position embedding self.pos_embed = nn.Parameter( torch.zeros(1, *self.patch_resolution, self.embed_dims)) self.drop_after_pos = nn.Dropout(p=drop_rate) self._register_load_state_dict_pre_hook(self._prepare_pos_embed) if use_rel_pos: self._register_load_state_dict_pre_hook( self._prepare_relative_position) if isinstance(out_indices, int): out_indices = [out_indices] assert isinstance(out_indices, Sequence), \ f'"out_indices" must by a sequence or int, ' \ f'get {type(out_indices)} instead.' for i, index in enumerate(out_indices): if index < 0: out_indices[i] = self.num_layers + index assert 0 <= out_indices[i] <= self.num_layers, \ f'Invalid out_indices {index}' self.out_indices = out_indices # stochastic depth decay rule dpr = np.linspace(0, drop_path_rate, self.num_layers) self.layers = ModuleList() if isinstance(layer_cfgs, dict): layer_cfgs = [layer_cfgs] * self.num_layers for i in range(self.num_layers): _layer_cfg = dict( embed_dims=self.embed_dims, num_heads=self.arch_settings['num_heads'], feedforward_channels=self. arch_settings['feedforward_channels'], drop_rate=drop_rate, drop_path_rate=dpr[i], qkv_bias=qkv_bias, window_size=window_size if i not in self.global_attn_indexes else 0, input_size=self.patch_resolution, use_rel_pos=use_rel_pos, norm_cfg=norm_cfg) _layer_cfg.update(layer_cfgs[i]) self.layers.append(TransformerEncoderLayer(**_layer_cfg)) self.out_channels = out_channels if self.out_channels > 0: self.channel_reduction = nn.Sequential( nn.Conv2d( self.embed_dims, out_channels, kernel_size=1, bias=False, ), LayerNorm2d(out_channels, eps=1e-6), nn.Conv2d( out_channels, out_channels, kernel_size=3, padding=1, bias=False, ), LayerNorm2d(out_channels, eps=1e-6), ) # freeze stages only when self.frozen_stages > 0 self.frozen_stages = frozen_stages if self.frozen_stages > 0: self._freeze_stages() def init_weights(self): super().init_weights() if not (isinstance(self.init_cfg, dict) and self.init_cfg['type'] == 'Pretrained'): if self.pos_embed is not None: trunc_normal_(self.pos_embed, std=0.02) 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.drop_after_pos.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.layers[i - 1] m.eval() for param in m.parameters(): param.requires_grad = False # freeze channel_reduction module if self.frozen_stages == self.num_layers and self.out_channels > 0: m = self.channel_reduction m.eval() for param in m.parameters(): param.requires_grad = False def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor]: B = x.shape[0] x, patch_resolution = self.patch_embed(x) x = x.view(B, patch_resolution[0], patch_resolution[1], self.embed_dims) if self.use_abs_pos: # 'resize_pos_embed' only supports 'pos_embed' with ndim==3, but # in ViTSAM, the 'pos_embed' has 4 dimensions (1, H, W, C), so it # is flattened. Besides, ViTSAM doesn't have any extra token. resized_pos_embed = resize_pos_embed( self.pos_embed.flatten(1, 2), self.patch_resolution, patch_resolution, mode=self.interpolate_mode, num_extra_tokens=0) x = x + resized_pos_embed.view(1, *patch_resolution, self.embed_dims) x = self.drop_after_pos(x) outs = [] for i, layer in enumerate(self.layers): x = layer(x) if i in self.out_indices: # (B, H, W, C) -> (B, C, H, W) x_reshape = x.permute(0, 3, 1, 2) if self.out_channels > 0: x_reshape = self.channel_reduction(x_reshape) outs.append(self._format_output(x_reshape)) return tuple(outs) def _format_output(self, x) -> torch.Tensor: if self.out_type == 'raw' or self.out_type == 'featmap': return x elif self.out_type == 'avg_featmap': # (B, C, H, W) -> (B, C, N) -> (B, N, C) x = x.flatten(2).permute(0, 2, 1) return x.mean(dim=1) def _prepare_pos_embed(self, state_dict, prefix, *args, **kwargs): name = prefix + 'pos_embed' if name not in state_dict.keys(): return ckpt_pos_embed_shape = state_dict[name].shape if self.pos_embed.shape != ckpt_pos_embed_shape: from mmengine.logging import MMLogger logger = MMLogger.get_current_instance() f'Resize the pos_embed shape from {ckpt_pos_embed_shape} ' f'to {self.pos_embed.shape}.') ckpt_pos_embed_shape = ckpt_pos_embed_shape[1:3] pos_embed_shape = self.patch_embed.init_out_size flattened_pos_embed = state_dict[name].flatten(1, 2) resized_pos_embed = resize_pos_embed(flattened_pos_embed, ckpt_pos_embed_shape, pos_embed_shape, self.interpolate_mode, 0) state_dict[name] = resized_pos_embed.view(1, *pos_embed_shape, self.embed_dims) def _prepare_relative_position(self, state_dict, prefix, *args, **kwargs): state_dict_model = self.state_dict() all_keys = list(state_dict_model.keys()) for key in all_keys: if 'rel_pos_' in key: ckpt_key = prefix + key if ckpt_key not in state_dict: continue relative_position_pretrained = state_dict[ckpt_key] relative_position_current = state_dict_model[key] L1, _ = relative_position_pretrained.size() L2, _ = relative_position_current.size() if L1 != L2: new_rel_pos = F.interpolate( relative_position_pretrained.reshape(1, L1, -1).permute( 0, 2, 1), size=L2, mode='linear', ) new_rel_pos = new_rel_pos.reshape(-1, L2).permute(1, 0) from mmengine.logging import MMLogger logger = MMLogger.get_current_instance()'Resize the {ckpt_key} from ' f'{state_dict[ckpt_key].shape} to ' f'{new_rel_pos.shape}') state_dict[ckpt_key] = new_rel_pos
[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``) """ 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 ('cls_token', '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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