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mmpretrain.models.backbones.vit_eva02 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn.bricks.drop import build_dropout
from mmengine.model import BaseModule, ModuleList

from mmpretrain.registry import MODELS
from ..utils import (RotaryEmbeddingFast, SwiGLUFFN, build_norm_layer,
                     resize_pos_embed)
from .vision_transformer import VisionTransformer


class AttentionWithRoPE(BaseModule):
    """Multi-head Attention Module with 2D sincos position embedding (RoPE).

    Args:
        embed_dims (int): The embedding dimension.
        num_heads (int): Parallel attention heads.
        attn_drop (float): Dropout rate of the dropout layer after the
            attention calculation of query and key. Defaults to 0.
        proj_drop (float): Dropout rate of the dropout layer after the
            output projection. Defaults to 0.
        qkv_bias (bool): If True, add a learnable bias to q and v. Note
            that we follows the official implementation where ``k_bias``
            is 0. Defaults to True.
        qk_scale (float, optional): Override default qk scale of
            ``head_dim ** -0.5`` if set. Defaults to None.
        proj_bias (bool) If True, add a learnable bias to output projection.
            Defaults to True.
        rope (:obj:`torch.nn.Module`, optional): If it is an object of the
            ``RotaryEmbedding``, the rotation of the token position will be
            performed before the softmax. Defaults to None.
        with_cls_token (bool): Whether concatenating class token into image
            tokens as transformer input. Defaults to True.
        init_cfg (dict, optional): The Config for initialization.
            Defaults to None.
    """

    def __init__(self,
                 embed_dims,
                 num_heads,
                 attn_drop=0.,
                 proj_drop=0.,
                 qkv_bias=True,
                 qk_scale=None,
                 proj_bias=True,
                 rope=None,
                 with_cls_token=True,
                 init_cfg=None):
        super(AttentionWithRoPE, self).__init__(init_cfg=init_cfg)

        self.embed_dims = embed_dims
        self.num_heads = num_heads
        self.head_dims = embed_dims // num_heads
        self.scale = qk_scale or self.head_dims**-0.5
        self.qkv = nn.Linear(embed_dims, embed_dims * 3, bias=qkv_bias)

        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(embed_dims, embed_dims, bias=proj_bias)
        self.proj_drop = nn.Dropout(proj_drop)

        self.with_cls_token = with_cls_token

        self.rope = rope

    def forward(self, x, patch_resolution):
        B, N, _ = x.shape

        qkv = self.qkv(x)
        qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
        q, k, v = qkv.unbind(dim=0)

        if self.rope:
            if self.with_cls_token:
                q_t = q[:, :, 1:, :]
                ro_q_t = self.rope(q_t, patch_resolution)
                q = torch.cat((q[:, :, :1, :], ro_q_t), -2).type_as(v)

                k_t = k[:, :, 1:, :] if self.with_cls_token else k
                ro_k_t = self.rope(k_t, patch_resolution)
                k = torch.cat((k[:, :, :1, :], ro_k_t), -2).type_as(v)
            else:
                q = self.rope(q, patch_resolution)
                k = self.rope(k, patch_resolution)

        q = q * self.scale

        attn = (q @ k.transpose(-2, -1))
        attn = attn.softmax(dim=-1).type_as(x)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)

        x = self.proj(x)
        x = self.proj_drop(x)

        return x


class EVA02EndcoderLayer(BaseModule):
    """Implements one encoder EVA02EndcoderLayer in EVA02.

    Args:
        embed_dims (int): The feature dimension
        num_heads (int): Parallel attention heads
        feedforward_channels (int): The hidden dimension of FFNs.
        sub_ln (bool): Whether to add the sub layer normalization
            in the attention module. Defaults to False.
        attn_drop (float): Dropout rate of the dropout layer after the
            attention calculation of query and key. Defaults to 0.
        proj_drop (float): Dropout rate of the dropout layer after the
            output projection. Defaults to 0.
        qkv_bias (bool): enable bias for qkv if True. Defaults to True.
        qk_scale (float, optional): Override default qk scale of
            ``head_dim ** -0.5`` if set. Defaults to None.
        proj_bias (bool): enable bias for projection in the attention module
            if True. Defaults to True.
        rope (:obj:`torch.nn.Module`, optional): RotaryEmbedding object
            in the attention module. Defaults to None.
        drop_rate (float): Dropout rate in the mlp module. 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')``.
        init_cfg (dict, optional): Initialization config dict.
            Defaults to None.
    """

    def __init__(self,
                 embed_dims,
                 num_heads,
                 feedforward_channels,
                 sub_ln=False,
                 attn_drop=0.,
                 proj_drop=0.,
                 qkv_bias=False,
                 qk_scale=None,
                 proj_bias=True,
                 rope=None,
                 with_cls_token=True,
                 drop_rate=0.,
                 drop_path_rate=0.,
                 norm_cfg=dict(type='LN'),
                 init_cfg=None):
        super(EVA02EndcoderLayer, self).__init__(init_cfg=init_cfg)

        self.norm1 = build_norm_layer(norm_cfg, embed_dims)

        self.attn = AttentionWithRoPE(
            embed_dims=embed_dims,
            num_heads=num_heads,
            attn_drop=attn_drop,
            proj_drop=proj_drop,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            proj_bias=proj_bias,
            rope=rope,
            with_cls_token=with_cls_token)

        self.drop_path = build_dropout(
            dict(type='DropPath', drop_prob=drop_path_rate))

        self.norm2 = build_norm_layer(norm_cfg, embed_dims)

        if drop_rate > 0:
            dropout_layer = dict(type='Dropout', drop_prob=drop_rate)
        else:
            dropout_layer = None

        if sub_ln:
            ffn_norm = norm_cfg
        else:
            ffn_norm = None

        self.mlp = SwiGLUFFN(
            embed_dims=embed_dims,
            feedforward_channels=feedforward_channels,
            dropout_layer=dropout_layer,
            norm_cfg=ffn_norm,
            add_identity=False,
        )

    def forward(self, x, patch_resolution):
        inputs = x
        x = self.norm1(x)
        x = self.attn(x, patch_resolution)
        x = self.drop_path(x)
        x = inputs + x

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

        return x


[文档]@MODELS.register_module() class ViTEVA02(VisionTransformer): """EVA02 Vision Transformer. A PyTorch implement of : `EVA-02: A Visual Representation for Neon Genesis <https://arxiv.org/abs/2303.11331>`_ Args: arch (str | dict): Vision Transformer architecture. If use string, choose from 'tiny', 'small', 'base', 'large'. 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. - **mlp_ratio** (float): The ratio of the mlp module. Defaults to 'tiny'. sub_ln (bool): Whether to add the sub layer normalization in swiglu. Defaults to False. drop_rate (float): Probability of an element to be zeroed in the mlp module. Defaults to 0. attn_drop_rate (float): Probability of an element to be zeroed after the softmax in the attention. Defaults to 0. proj_drop_rate (float): Probability of an element to be zeroed after projection in the attention. 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')``. with_cls_token (bool): Whether concatenating class token into image tokens as transformer input. Defaults to True. layer_cfgs (Sequence | dict): Configs of each transformer layer in encoder. Defaults to an empty dict. **kwargs(dict, optional): Other args for Vision Transformer. """ arch_zoo = { **dict.fromkeys( ['t', 'ti', 'tiny'], { 'embed_dims': 192, 'num_layers': 12, 'num_heads': 3, 'feedforward_channels': int(192 * 4 * 2 / 3) }), **dict.fromkeys( ['s', 'small'], { 'embed_dims': 384, 'num_layers': 12, 'num_heads': 6, 'feedforward_channels': int(384 * 4 * 2 / 3) }), **dict.fromkeys( ['b', 'base'], { 'embed_dims': 768, 'num_layers': 12, 'num_heads': 12, 'feedforward_channels': int(768 * 4 * 2 / 3) }), **dict.fromkeys( ['l', 'large'], { 'embed_dims': 1024, 'num_layers': 24, 'num_heads': 16, 'feedforward_channels': int(1024 * 4 * 2 / 3) }) } num_extra_tokens = 1 # class token OUT_TYPES = {'raw', 'cls_token', 'featmap', 'avg_featmap'} def __init__(self, arch='tiny', sub_ln=False, drop_rate=0., attn_drop_rate=0., proj_drop_rate=0., drop_path_rate=0., qkv_bias=True, norm_cfg=dict(type='LN'), with_cls_token=True, layer_cfgs=dict(), **kwargs): # set essential args for Vision Transformer kwargs.update( arch=arch, drop_rate=drop_rate, drop_path_rate=drop_path_rate, norm_cfg=norm_cfg, with_cls_token=with_cls_token) super(ViTEVA02, self).__init__(**kwargs) self.num_heads = self.arch_settings['num_heads'] # Set RoPE head_dim = self.embed_dims // self.num_heads self.rope = RotaryEmbeddingFast( embed_dims=head_dim, patch_resolution=self.patch_resolution) # 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.num_heads, feedforward_channels=self. arch_settings['feedforward_channels'], sub_ln=sub_ln, norm_cfg=norm_cfg, proj_drop=proj_drop_rate, attn_drop=attn_drop_rate, drop_rate=drop_rate, qkv_bias=qkv_bias, rope=self.rope, with_cls_token=with_cls_token, drop_path_rate=dpr[i]) _layer_cfg.update(layer_cfgs[i]) self.layers.append(EVA02EndcoderLayer(**_layer_cfg)) 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_tokens = self.cls_token.expand(B, -1, -1) x = torch.cat((cls_tokens, 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, patch_resolution) 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)
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