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mmpretrain.models.backbones.vision_transformer 源代码

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

import numpy as np
import torch
import torch.nn as nn
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 (MultiheadAttention, SwiGLUFFNFused, build_norm_layer,
                     resize_pos_embed, to_2tuple)
from .base_backbone import BaseBackbone


class TransformerEncoderLayer(BaseModule):
    """Implements one encoder layer in Vision Transformer.

    Args:
        embed_dims (int): The feature dimension
        num_heads (int): Parallel attention heads
        feedforward_channels (int): The hidden dimension for FFNs
        layer_scale_init_value (float or torch.Tensor): Init value of layer
            scale. Defaults to 0.
        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.
        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.
        ffn_type (str): Select the type of ffn layers. Defaults to 'origin'.
        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')``.
        init_cfg (dict, optional): Initialization config dict.
            Defaults to None.
    """

    def __init__(self,
                 embed_dims,
                 num_heads,
                 feedforward_channels,
                 layer_scale_init_value=0.,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.,
                 num_fcs=2,
                 qkv_bias=True,
                 ffn_type='origin',
                 act_cfg=dict(type='GELU'),
                 norm_cfg=dict(type='LN'),
                 init_cfg=None):
        super(TransformerEncoderLayer, self).__init__(init_cfg=init_cfg)

        self.embed_dims = embed_dims

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

        self.attn = MultiheadAttention(
            embed_dims=embed_dims,
            num_heads=num_heads,
            attn_drop=attn_drop_rate,
            proj_drop=drop_rate,
            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
            qkv_bias=qkv_bias,
            layer_scale_init_value=layer_scale_init_value)

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

        if ffn_type == 'origin':
            self.ffn = FFN(
                embed_dims=embed_dims,
                feedforward_channels=feedforward_channels,
                num_fcs=num_fcs,
                ffn_drop=drop_rate,
                dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
                act_cfg=act_cfg,
                layer_scale_init_value=layer_scale_init_value)
        elif ffn_type == 'swiglu_fused':
            self.ffn = SwiGLUFFNFused(
                embed_dims=embed_dims,
                feedforward_channels=feedforward_channels,
                layer_scale_init_value=layer_scale_init_value)
        else:
            raise NotImplementedError

    @property
    def norm1(self):
        return self.ln1

    @property
    def norm2(self):
        return self.ln2

    def init_weights(self):
        super(TransformerEncoderLayer, self).init_weights()
        for m in self.ffn.modules():
            if isinstance(m, nn.Linear):
                nn.init.xavier_uniform_(m.weight)
                nn.init.normal_(m.bias, std=1e-6)

    def forward(self, x):
        x = x + self.attn(self.ln1(x))
        x = self.ffn(self.ln2(x), identity=x)
        return x


[文档]@MODELS.register_module() class VisionTransformer(BaseBackbone): """Vision Transformer. A PyTorch implement of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`_ Args: arch (str | dict): Vision Transformer architecture. If use string, choose from 'small', 'base', 'large', 'deit-tiny', 'deit-small' and 'deit-base'. 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. 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_indices (Sequence | int): Output from which stages. Defaults to -1, means the last stage. 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. norm_cfg (dict): Config dict for normalization layer. Defaults to ``dict(type='LN')``. final_norm (bool): Whether to add a additional layer to normalize final feature map. Defaults to True. out_type (str): The type of output features. Please choose from - ``"cls_token"``: The class token tensor with shape (B, C). - ``"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). - ``"raw"``: The raw feature tensor includes patch tokens and class tokens with shape (B, L, C). Defaults to ``"cls_token"``. with_cls_token (bool): Whether concatenating class token into image tokens as transformer input. Defaults to True. 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". layer_scale_init_value (float or torch.Tensor): Init value of layer scale. Defaults to 0. 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( ['s', 'small'], { 'embed_dims': 768, 'num_layers': 8, 'num_heads': 8, 'feedforward_channels': 768 * 3, }), **dict.fromkeys( ['b', 'base'], { 'embed_dims': 768, 'num_layers': 12, 'num_heads': 12, 'feedforward_channels': 3072 }), **dict.fromkeys( ['l', 'large'], { 'embed_dims': 1024, 'num_layers': 24, 'num_heads': 16, 'feedforward_channels': 4096 }), **dict.fromkeys( ['h', 'huge'], { # The same as the implementation in MAE # <https://arxiv.org/abs/2111.06377> 'embed_dims': 1280, 'num_layers': 32, 'num_heads': 16, 'feedforward_channels': 5120 }), **dict.fromkeys( ['eva-g', 'eva-giant'], { # The implementation in EVA # <https://arxiv.org/abs/2211.07636> 'embed_dims': 1408, 'num_layers': 40, 'num_heads': 16, 'feedforward_channels': 6144 }), **dict.fromkeys( ['deit-t', 'deit-tiny'], { 'embed_dims': 192, 'num_layers': 12, 'num_heads': 3, 'feedforward_channels': 192 * 4 }), **dict.fromkeys( ['deit-s', 'deit-small', 'dinov2-s', 'dinov2-small'], { 'embed_dims': 384, 'num_layers': 12, 'num_heads': 6, 'feedforward_channels': 384 * 4 }), **dict.fromkeys( ['deit-b', 'deit-base'], { 'embed_dims': 768, 'num_layers': 12, 'num_heads': 12, 'feedforward_channels': 768 * 4 }), **dict.fromkeys( ['dinov2-g', 'dinov2-giant'], { 'embed_dims': 1536, 'num_layers': 40, 'num_heads': 24, 'feedforward_channels': 6144 }), } num_extra_tokens = 1 # class token OUT_TYPES = {'raw', 'cls_token', 'featmap', 'avg_featmap'} def __init__(self, arch='base', img_size=224, patch_size=16, in_channels=3, out_indices=-1, drop_rate=0., drop_path_rate=0., qkv_bias=True, norm_cfg=dict(type='LN', eps=1e-6), final_norm=True, out_type='cls_token', with_cls_token=True, frozen_stages=-1, interpolate_mode='bicubic', layer_scale_init_value=0., patch_cfg=dict(), layer_cfgs=dict(), pre_norm=False, init_cfg=None): super(VisionTransformer, self).__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.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, bias=not pre_norm, # disable bias if pre_norm is used(e.g., CLIP) ) _patch_cfg.update(patch_cfg) self.patch_embed = PatchEmbed(**_patch_cfg) self.patch_resolution = self.patch_embed.init_out_size num_patches = self.patch_resolution[0] * self.patch_resolution[1] # 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 # Set cls token self.with_cls_token = with_cls_token if with_cls_token: self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dims)) elif out_type != 'cls_token': self.cls_token = None self.num_extra_tokens = 0 else: raise ValueError( 'with_cls_token must be True when `out_type="cls_token"`.') # Set position embedding self.interpolate_mode = interpolate_mode self.pos_embed = nn.Parameter( torch.zeros(1, num_patches + self.num_extra_tokens, self.embed_dims)) self._register_load_state_dict_pre_hook(self._prepare_pos_embed) self.drop_after_pos = nn.Dropout(p=drop_rate) 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'], layer_scale_init_value=layer_scale_init_value, drop_rate=drop_rate, drop_path_rate=dpr[i], qkv_bias=qkv_bias, norm_cfg=norm_cfg) _layer_cfg.update(layer_cfgs[i]) self.layers.append(TransformerEncoderLayer(**_layer_cfg)) self.frozen_stages = frozen_stages if pre_norm: self.pre_norm = build_norm_layer(norm_cfg, self.embed_dims) else: self.pre_norm = nn.Identity() self.final_norm = final_norm if final_norm: self.ln1 = build_norm_layer(norm_cfg, self.embed_dims) if self.out_type == 'avg_featmap': self.ln2 = build_norm_layer(norm_cfg, self.embed_dims) # freeze stages only when self.frozen_stages > 0 if self.frozen_stages > 0: self._freeze_stages() @property def norm1(self): return self.ln1 @property def norm2(self): return self.ln2 def init_weights(self): super(VisionTransformer, self).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 _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 (not self.with_cls_token and ckpt_pos_embed_shape[1] == self.pos_embed.shape[1] + 1): # Remove cls token from state dict if it's not used. state_dict[name] = state_dict[name][:, 1:] 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() logger.info( f'Resize the pos_embed shape from {ckpt_pos_embed_shape} ' f'to {self.pos_embed.shape}.') ckpt_pos_embed_shape = to_2tuple( int(np.sqrt(ckpt_pos_embed_shape[1] - self.num_extra_tokens))) pos_embed_shape = self.patch_embed.init_out_size state_dict[name] = resize_pos_embed(state_dict[name], ckpt_pos_embed_shape, pos_embed_shape, self.interpolate_mode, self.num_extra_tokens)
[文档] @staticmethod def resize_pos_embed(*args, **kwargs): """Interface for backward-compatibility.""" return resize_pos_embed(*args, **kwargs)
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 pre-norm for param in self.pre_norm.parameters(): param.requires_grad = False # freeze cls_token if self.cls_token is not None: self.cls_token.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 the last layer norm if self.frozen_stages == len(self.layers): if self.final_norm: self.ln1.eval() for param in self.ln1.parameters(): param.requires_grad = False if self.out_type == 'avg_featmap': self.ln2.eval() for param in self.ln2.parameters(): param.requires_grad = False def forward(self, x): B = x.shape[0] x, patch_resolution = self.patch_embed(x) if self.cls_token is not None: # stole cls_tokens impl from Phil Wang, thanks cls_token = self.cls_token.expand(B, -1, -1) x = torch.cat((cls_token, x), dim=1) x = x + resize_pos_embed( self.pos_embed, self.patch_resolution, patch_resolution, mode=self.interpolate_mode, num_extra_tokens=self.num_extra_tokens) x = self.drop_after_pos(x) x = self.pre_norm(x) outs = [] for i, layer in enumerate(self.layers): x = layer(x) if i == len(self.layers) - 1 and self.final_norm: x = self.ln1(x) if i in self.out_indices: outs.append(self._format_output(x, patch_resolution)) return tuple(outs) def _format_output(self, x, hw): if self.out_type == 'raw': return x if self.out_type == 'cls_token': return x[:, 0] patch_token = x[:, self.num_extra_tokens:] if self.out_type == 'featmap': B = x.size(0) # (B, N, C) -> (B, H, W, C) -> (B, C, H, W) return patch_token.reshape(B, *hw, -1).permute(0, 3, 1, 2) if self.out_type == 'avg_featmap': return self.ln2(patch_token.mean(dim=1))
[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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