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mmpretrain.models.backbones.swin_transformer_v2 源代码

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

import numpy as np
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import build_norm_layer
from mmcv.cnn.bricks.transformer import FFN, PatchEmbed
from mmengine.model import BaseModule, ModuleList
from mmengine.model.weight_init import trunc_normal_
from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm

from ..builder import MODELS
from ..utils import (PatchMerging, ShiftWindowMSA, WindowMSAV2,
                     resize_pos_embed, to_2tuple)
from .base_backbone import BaseBackbone


class SwinBlockV2(BaseModule):
    """Swin Transformer V2 block. Use post normalization.

    Args:
        embed_dims (int): Number of input channels.
        num_heads (int): Number of attention heads.
        window_size (int): The height and width of the window. Defaults to 7.
        shift (bool): Shift the attention window or not. Defaults to False.
        extra_norm (bool): Whether add extra norm at the end of main branch.
        ffn_ratio (float): The expansion ratio of feedforward network hidden
            layer channels. Defaults to 4.
        drop_path (float): The drop path rate after attention and ffn.
            Defaults to 0.
        pad_small_map (bool): If True, pad the small feature map to the window
            size, which is common used in detection and segmentation. If False,
            avoid shifting window and shrink the window size to the size of
            feature map, which is common used in classification.
            Defaults to False.
        attn_cfgs (dict): The extra config of Shift Window-MSA.
            Defaults to empty dict.
        ffn_cfgs (dict): The extra config of FFN. Defaults to empty dict.
        norm_cfg (dict): The config of norm layers.
            Defaults to ``dict(type='LN')``.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Defaults to False.
        pretrained_window_size (int): Window size in pretrained.
        init_cfg (dict, optional): The extra config for initialization.
            Defaults to None.
    """

    def __init__(self,
                 embed_dims,
                 num_heads,
                 window_size=8,
                 shift=False,
                 extra_norm=False,
                 ffn_ratio=4.,
                 drop_path=0.,
                 pad_small_map=False,
                 attn_cfgs=dict(),
                 ffn_cfgs=dict(),
                 norm_cfg=dict(type='LN'),
                 with_cp=False,
                 pretrained_window_size=0,
                 init_cfg=None):

        super(SwinBlockV2, self).__init__(init_cfg)
        self.with_cp = with_cp
        self.extra_norm = extra_norm

        _attn_cfgs = {
            'embed_dims': embed_dims,
            'num_heads': num_heads,
            'shift_size': window_size // 2 if shift else 0,
            'window_size': window_size,
            'dropout_layer': dict(type='DropPath', drop_prob=drop_path),
            'pad_small_map': pad_small_map,
            **attn_cfgs
        }
        # use V2 attention implementation
        _attn_cfgs.update(
            window_msa=WindowMSAV2,
            pretrained_window_size=to_2tuple(pretrained_window_size))
        self.attn = ShiftWindowMSA(**_attn_cfgs)
        self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1]

        _ffn_cfgs = {
            'embed_dims': embed_dims,
            'feedforward_channels': int(embed_dims * ffn_ratio),
            'num_fcs': 2,
            'ffn_drop': 0,
            'dropout_layer': dict(type='DropPath', drop_prob=drop_path),
            'act_cfg': dict(type='GELU'),
            'add_identity': False,
            **ffn_cfgs
        }
        self.ffn = FFN(**_ffn_cfgs)
        self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1]

        # add extra norm for every n blocks in huge and giant model
        if self.extra_norm:
            self.norm3 = build_norm_layer(norm_cfg, embed_dims)[1]

    def forward(self, x, hw_shape):

        def _inner_forward(x):
            # Use post normalization
            identity = x
            x = self.attn(x, hw_shape)
            x = self.norm1(x)
            x = x + identity

            identity = x
            x = self.ffn(x)
            x = self.norm2(x)
            x = x + identity

            if self.extra_norm:
                x = self.norm3(x)

            return x

        if self.with_cp and x.requires_grad:
            x = cp.checkpoint(_inner_forward, x)
        else:
            x = _inner_forward(x)

        return x


class SwinBlockV2Sequence(BaseModule):
    """Module with successive Swin Transformer blocks and downsample layer.

    Args:
        embed_dims (int): Number of input channels.
        depth (int): Number of successive swin transformer blocks.
        num_heads (int): Number of attention heads.
        window_size (int): The height and width of the window. Defaults to 7.
        downsample (bool): Downsample the output of blocks by patch merging.
            Defaults to False.
        downsample_cfg (dict): The extra config of the patch merging layer.
            Defaults to empty dict.
        drop_paths (Sequence[float] | float): The drop path rate in each block.
            Defaults to 0.
        block_cfgs (Sequence[dict] | dict): The extra config of each block.
            Defaults to empty dicts.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Defaults to False.
        pad_small_map (bool): If True, pad the small feature map to the window
            size, which is common used in detection and segmentation. If False,
            avoid shifting window and shrink the window size to the size of
            feature map, which is common used in classification.
            Defaults to False.
        extra_norm_every_n_blocks (int): Add extra norm at the end of main
            branch every n blocks. Defaults to 0, which means no needs for
            extra norm layer.
        pretrained_window_size (int): Window size in pretrained.
        init_cfg (dict, optional): The extra config for initialization.
            Defaults to None.
    """

    def __init__(self,
                 embed_dims,
                 depth,
                 num_heads,
                 window_size=8,
                 downsample=False,
                 downsample_cfg=dict(),
                 drop_paths=0.,
                 block_cfgs=dict(),
                 with_cp=False,
                 pad_small_map=False,
                 extra_norm_every_n_blocks=0,
                 pretrained_window_size=0,
                 init_cfg=None):
        super().__init__(init_cfg)

        if not isinstance(drop_paths, Sequence):
            drop_paths = [drop_paths] * depth

        if not isinstance(block_cfgs, Sequence):
            block_cfgs = [deepcopy(block_cfgs) for _ in range(depth)]

        if downsample:
            self.out_channels = 2 * embed_dims
            _downsample_cfg = {
                'in_channels': embed_dims,
                'out_channels': self.out_channels,
                'norm_cfg': dict(type='LN'),
                **downsample_cfg
            }
            self.downsample = PatchMerging(**_downsample_cfg)
        else:
            self.out_channels = embed_dims
            self.downsample = None

        self.blocks = ModuleList()
        for i in range(depth):
            extra_norm = True if extra_norm_every_n_blocks and \
                (i + 1) % extra_norm_every_n_blocks == 0 else False
            _block_cfg = {
                'embed_dims': self.out_channels,
                'num_heads': num_heads,
                'window_size': window_size,
                'shift': False if i % 2 == 0 else True,
                'extra_norm': extra_norm,
                'drop_path': drop_paths[i],
                'with_cp': with_cp,
                'pad_small_map': pad_small_map,
                'pretrained_window_size': pretrained_window_size,
                **block_cfgs[i]
            }
            block = SwinBlockV2(**_block_cfg)
            self.blocks.append(block)

    def forward(self, x, in_shape):
        if self.downsample:
            x, out_shape = self.downsample(x, in_shape)
        else:
            out_shape = in_shape

        for block in self.blocks:
            x = block(x, out_shape)

        return x, out_shape


[文档]@MODELS.register_module() class SwinTransformerV2(BaseBackbone): """Swin Transformer V2. A PyTorch implement of : `Swin Transformer V2: Scaling Up Capacity and Resolution <https://arxiv.org/abs/2111.09883>`_ Inspiration from https://github.com/microsoft/Swin-Transformer Args: arch (str | dict): Swin Transformer architecture. If use string, choose from 'tiny', 'small', 'base' and 'large'. 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** (List[int]): The number of heads in attention modules of each stage. - **extra_norm_every_n_blocks** (int): Add extra norm at the end of main branch every n blocks. Defaults to 'tiny'. 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 4. in_channels (int): The num of input channels. Defaults to 3. window_size (int | Sequence): The height and width of the window. Defaults to 7. drop_rate (float): Dropout rate after embedding. Defaults to 0. drop_path_rate (float): Stochastic depth rate. Defaults to 0.1. use_abs_pos_embed (bool): If True, add absolute position embedding to the patch embedding. Defaults to False. interpolate_mode (str): Select the interpolate mode for absolute position embeding vector resize. Defaults to "bicubic". with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Defaults to False. frozen_stages (int): Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Defaults to -1. norm_eval (bool): Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Defaults to False. pad_small_map (bool): If True, pad the small feature map to the window size, which is common used in detection and segmentation. If False, avoid shifting window and shrink the window size to the size of feature map, which is common used in classification. Defaults to False. norm_cfg (dict): Config dict for normalization layer for all output features. Defaults to ``dict(type='LN')`` stage_cfgs (Sequence[dict] | dict): Extra config dict for each stage. Defaults to an empty dict. patch_cfg (dict): Extra config dict for patch embedding. Defaults to an empty dict. pretrained_window_sizes (tuple(int)): Pretrained window sizes of each layer. init_cfg (dict, optional): The Config for initialization. Defaults to None. Examples: >>> from mmpretrain.models import SwinTransformerV2 >>> import torch >>> extra_config = dict( >>> arch='tiny', >>> stage_cfgs=dict(downsample_cfg={'kernel_size': 3, >>> 'padding': 'same'})) >>> self = SwinTransformerV2(**extra_config) >>> inputs = torch.rand(1, 3, 224, 224) >>> output = self.forward(inputs) >>> print(output.shape) (1, 2592, 4) """ arch_zoo = { **dict.fromkeys(['t', 'tiny'], {'embed_dims': 96, 'depths': [2, 2, 6, 2], 'num_heads': [3, 6, 12, 24], 'extra_norm_every_n_blocks': 0}), **dict.fromkeys(['s', 'small'], {'embed_dims': 96, 'depths': [2, 2, 18, 2], 'num_heads': [3, 6, 12, 24], 'extra_norm_every_n_blocks': 0}), **dict.fromkeys(['b', 'base'], {'embed_dims': 128, 'depths': [2, 2, 18, 2], 'num_heads': [4, 8, 16, 32], 'extra_norm_every_n_blocks': 0}), **dict.fromkeys(['l', 'large'], {'embed_dims': 192, 'depths': [2, 2, 18, 2], 'num_heads': [6, 12, 24, 48], 'extra_norm_every_n_blocks': 0}), # head count not certain for huge, and is employed for another # parallel study about self-supervised learning. **dict.fromkeys(['h', 'huge'], {'embed_dims': 352, 'depths': [2, 2, 18, 2], 'num_heads': [8, 16, 32, 64], 'extra_norm_every_n_blocks': 6}), **dict.fromkeys(['g', 'giant'], {'embed_dims': 512, 'depths': [2, 2, 42, 4], 'num_heads': [16, 32, 64, 128], 'extra_norm_every_n_blocks': 6}), } # yapf: disable _version = 1 num_extra_tokens = 0 def __init__(self, arch='tiny', img_size=256, patch_size=4, in_channels=3, window_size=8, drop_rate=0., drop_path_rate=0.1, out_indices=(3, ), use_abs_pos_embed=False, interpolate_mode='bicubic', with_cp=False, frozen_stages=-1, norm_eval=False, pad_small_map=False, norm_cfg=dict(type='LN'), stage_cfgs=dict(), patch_cfg=dict(), pretrained_window_sizes=[0, 0, 0, 0], init_cfg=None): super(SwinTransformerV2, 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', 'extra_norm_every_n_blocks' } 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.extra_norm_every_n_blocks = self.arch_settings[ 'extra_norm_every_n_blocks'] self.num_layers = len(self.depths) self.out_indices = out_indices self.use_abs_pos_embed = use_abs_pos_embed self.interpolate_mode = interpolate_mode self.frozen_stages = frozen_stages if isinstance(window_size, int): self.window_sizes = [window_size for _ in range(self.num_layers)] elif isinstance(window_size, Sequence): assert len(window_size) == self.num_layers, \ f'Length of window_sizes {len(window_size)} is not equal to '\ f'length of stages {self.num_layers}.' self.window_sizes = window_size else: raise TypeError('window_size should be a Sequence or int.') _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, norm_cfg=dict(type='LN'), ) _patch_cfg.update(patch_cfg) self.patch_embed = PatchEmbed(**_patch_cfg) self.patch_resolution = self.patch_embed.init_out_size if self.use_abs_pos_embed: num_patches = self.patch_resolution[0] * self.patch_resolution[1] self.absolute_pos_embed = nn.Parameter( torch.zeros(1, num_patches, self.embed_dims)) self._register_load_state_dict_pre_hook( self._prepare_abs_pos_embed) self._register_load_state_dict_pre_hook(self._delete_reinit_params) self.drop_after_pos = nn.Dropout(p=drop_rate) self.norm_eval = norm_eval # stochastic depth total_depth = sum(self.depths) dpr = [ x.item() for x in torch.linspace(0, drop_path_rate, total_depth) ] # stochastic depth decay rule self.stages = ModuleList() embed_dims = [self.embed_dims] for i, (depth, num_heads) in enumerate(zip(self.depths, self.num_heads)): if isinstance(stage_cfgs, Sequence): stage_cfg = stage_cfgs[i] else: stage_cfg = deepcopy(stage_cfgs) downsample = True if i > 0 else False _stage_cfg = { 'embed_dims': embed_dims[-1], 'depth': depth, 'num_heads': num_heads, 'window_size': self.window_sizes[i], 'downsample': downsample, 'drop_paths': dpr[:depth], 'with_cp': with_cp, 'pad_small_map': pad_small_map, 'extra_norm_every_n_blocks': self.extra_norm_every_n_blocks, 'pretrained_window_size': pretrained_window_sizes[i], 'downsample_cfg': dict(use_post_norm=True), **stage_cfg } stage = SwinBlockV2Sequence(**_stage_cfg) self.stages.append(stage) dpr = dpr[depth:] embed_dims.append(stage.out_channels) for i in out_indices: if norm_cfg is not None: norm_layer = build_norm_layer(norm_cfg, embed_dims[i + 1])[1] else: norm_layer = nn.Identity() self.add_module(f'norm{i}', norm_layer) def init_weights(self): super(SwinTransformerV2, self).init_weights() if (isinstance(self.init_cfg, dict) and self.init_cfg['type'] == 'Pretrained'): # Suppress default init if use pretrained model. return if self.use_abs_pos_embed: trunc_normal_(self.absolute_pos_embed, std=0.02) def forward(self, x): x, hw_shape = self.patch_embed(x) if self.use_abs_pos_embed: x = x + resize_pos_embed( self.absolute_pos_embed, self.patch_resolution, hw_shape, self.interpolate_mode, self.num_extra_tokens) x = self.drop_after_pos(x) outs = [] for i, stage in enumerate(self.stages): x, hw_shape = stage(x, hw_shape) if i in self.out_indices: norm_layer = getattr(self, f'norm{i}') out = norm_layer(x) out = out.view(-1, *hw_shape, stage.out_channels).permute(0, 3, 1, 2).contiguous() outs.append(out) return tuple(outs) def _freeze_stages(self): if self.frozen_stages >= 0: self.patch_embed.eval() for param in self.patch_embed.parameters(): param.requires_grad = False for i in range(0, self.frozen_stages + 1): m = self.stages[i] m.eval() for param in m.parameters(): param.requires_grad = False for i in self.out_indices: if i <= self.frozen_stages: for param in getattr(self, f'norm{i}').parameters(): param.requires_grad = False def train(self, mode=True): super(SwinTransformerV2, self).train(mode) self._freeze_stages() if mode and self.norm_eval: for m in self.modules(): # trick: eval have effect on BatchNorm only if isinstance(m, _BatchNorm): m.eval() def _prepare_abs_pos_embed(self, state_dict, prefix, *args, **kwargs): name = prefix + 'absolute_pos_embed' if name not in state_dict.keys(): return ckpt_pos_embed_shape = state_dict[name].shape if self.absolute_pos_embed.shape != ckpt_pos_embed_shape: from mmengine.logging import MMLogger logger = MMLogger.get_current_instance() logger.info( 'Resize the absolute_pos_embed shape from ' f'{ckpt_pos_embed_shape} to {self.absolute_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) def _delete_reinit_params(self, state_dict, prefix, *args, **kwargs): # delete relative_position_index since we always re-init it from mmengine.logging import MMLogger logger = MMLogger.get_current_instance() logger.info( 'Delete `relative_position_index` and `relative_coords_table` ' 'since we always re-init these params according to the ' '`window_size`, which might cause unwanted but unworried ' 'warnings when loading checkpoint.') relative_position_index_keys = [ k for k in state_dict.keys() if 'relative_position_index' in k ] for k in relative_position_index_keys: del state_dict[k] # delete relative_coords_table since we always re-init it relative_position_index_keys = [ k for k in state_dict.keys() if 'relative_coords_table' in k ] for k in relative_position_index_keys: del state_dict[k]
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