549 lines
22 KiB
Python
549 lines
22 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from copy import deepcopy
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from typing import Sequence
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.utils.checkpoint as cp
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from mmcv.cnn import build_norm_layer
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from mmcv.cnn.bricks.transformer import FFN, PatchEmbed, PatchMerging
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from mmcv.cnn.utils.weight_init import trunc_normal_
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from mmcv.runner.base_module import BaseModule, ModuleList
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from mmcv.utils.parrots_wrapper import _BatchNorm
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from ..builder import BACKBONES
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from ..utils import (ShiftWindowMSA, resize_pos_embed,
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resize_relative_position_bias_table, to_2tuple)
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from .base_backbone import BaseBackbone
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class SwinBlock(BaseModule):
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"""Swin Transformer block.
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Args:
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embed_dims (int): Number of input channels.
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num_heads (int): Number of attention heads.
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window_size (int): The height and width of the window. Defaults to 7.
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shift (bool): Shift the attention window or not. Defaults to False.
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ffn_ratio (float): The expansion ratio of feedforward network hidden
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layer channels. Defaults to 4.
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drop_path (float): The drop path rate after attention and ffn.
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Defaults to 0.
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pad_small_map (bool): If True, pad the small feature map to the window
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size, which is common used in detection and segmentation. If False,
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avoid shifting window and shrink the window size to the size of
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feature map, which is common used in classification.
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Defaults to False.
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attn_cfgs (dict): The extra config of Shift Window-MSA.
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Defaults to empty dict.
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ffn_cfgs (dict): The extra config of FFN. Defaults to empty dict.
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norm_cfg (dict): The config of norm layers.
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Defaults to ``dict(type='LN')``.
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with_cp (bool): Use checkpoint or not. Using checkpoint will save some
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memory while slowing down the training speed. Defaults to False.
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init_cfg (dict, optional): The extra config for initialization.
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Defaults to None.
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"""
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def __init__(self,
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embed_dims,
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num_heads,
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window_size=7,
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shift=False,
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ffn_ratio=4.,
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drop_path=0.,
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pad_small_map=False,
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attn_cfgs=dict(),
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ffn_cfgs=dict(),
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norm_cfg=dict(type='LN'),
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with_cp=False,
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init_cfg=None):
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super(SwinBlock, self).__init__(init_cfg)
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self.with_cp = with_cp
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_attn_cfgs = {
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'embed_dims': embed_dims,
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'num_heads': num_heads,
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'shift_size': window_size // 2 if shift else 0,
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'window_size': window_size,
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'dropout_layer': dict(type='DropPath', drop_prob=drop_path),
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'pad_small_map': pad_small_map,
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**attn_cfgs
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}
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self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1]
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self.attn = ShiftWindowMSA(**_attn_cfgs)
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_ffn_cfgs = {
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'embed_dims': embed_dims,
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'feedforward_channels': int(embed_dims * ffn_ratio),
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'num_fcs': 2,
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'ffn_drop': 0,
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'dropout_layer': dict(type='DropPath', drop_prob=drop_path),
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'act_cfg': dict(type='GELU'),
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**ffn_cfgs
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}
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self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1]
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self.ffn = FFN(**_ffn_cfgs)
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def forward(self, x, hw_shape):
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def _inner_forward(x):
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identity = x
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x = self.norm1(x)
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x = self.attn(x, hw_shape)
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x = x + identity
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identity = x
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x = self.norm2(x)
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x = self.ffn(x, identity=identity)
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return x
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if self.with_cp and x.requires_grad:
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x = cp.checkpoint(_inner_forward, x)
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else:
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x = _inner_forward(x)
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return x
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class SwinBlockSequence(BaseModule):
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"""Module with successive Swin Transformer blocks and downsample layer.
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Args:
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embed_dims (int): Number of input channels.
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depth (int): Number of successive swin transformer blocks.
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num_heads (int): Number of attention heads.
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window_size (int): The height and width of the window. Defaults to 7.
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downsample (bool): Downsample the output of blocks by patch merging.
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Defaults to False.
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downsample_cfg (dict): The extra config of the patch merging layer.
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Defaults to empty dict.
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drop_paths (Sequence[float] | float): The drop path rate in each block.
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Defaults to 0.
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block_cfgs (Sequence[dict] | dict): The extra config of each block.
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Defaults to empty dicts.
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with_cp (bool): Use checkpoint or not. Using checkpoint will save some
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memory while slowing down the training speed. Defaults to False.
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pad_small_map (bool): If True, pad the small feature map to the window
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size, which is common used in detection and segmentation. If False,
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avoid shifting window and shrink the window size to the size of
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feature map, which is common used in classification.
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Defaults to False.
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init_cfg (dict, optional): The extra config for initialization.
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Defaults to None.
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"""
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def __init__(self,
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embed_dims,
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depth,
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num_heads,
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window_size=7,
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downsample=False,
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downsample_cfg=dict(),
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drop_paths=0.,
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block_cfgs=dict(),
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with_cp=False,
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pad_small_map=False,
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init_cfg=None):
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super().__init__(init_cfg)
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if not isinstance(drop_paths, Sequence):
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drop_paths = [drop_paths] * depth
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if not isinstance(block_cfgs, Sequence):
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block_cfgs = [deepcopy(block_cfgs) for _ in range(depth)]
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self.embed_dims = embed_dims
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self.blocks = ModuleList()
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for i in range(depth):
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_block_cfg = {
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'embed_dims': embed_dims,
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'num_heads': num_heads,
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'window_size': window_size,
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'shift': False if i % 2 == 0 else True,
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'drop_path': drop_paths[i],
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'with_cp': with_cp,
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'pad_small_map': pad_small_map,
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**block_cfgs[i]
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}
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block = SwinBlock(**_block_cfg)
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self.blocks.append(block)
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if downsample:
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_downsample_cfg = {
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'in_channels': embed_dims,
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'out_channels': 2 * embed_dims,
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'norm_cfg': dict(type='LN'),
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**downsample_cfg
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}
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self.downsample = PatchMerging(**_downsample_cfg)
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else:
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self.downsample = None
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def forward(self, x, in_shape, do_downsample=True):
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for block in self.blocks:
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x = block(x, in_shape)
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if self.downsample is not None and do_downsample:
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x, out_shape = self.downsample(x, in_shape)
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else:
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out_shape = in_shape
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return x, out_shape
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@property
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def out_channels(self):
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if self.downsample:
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return self.downsample.out_channels
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else:
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return self.embed_dims
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@BACKBONES.register_module()
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class SwinTransformer(BaseBackbone):
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"""Swin Transformer.
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A PyTorch implement of : `Swin Transformer:
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Hierarchical Vision Transformer using Shifted Windows
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<https://arxiv.org/abs/2103.14030>`_
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Inspiration from
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https://github.com/microsoft/Swin-Transformer
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Args:
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arch (str | dict): Swin Transformer architecture. If use string, choose
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from 'tiny', 'small', 'base' and 'large'. If use dict, it should
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have below keys:
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- **embed_dims** (int): The dimensions of embedding.
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- **depths** (List[int]): The number of blocks in each stage.
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- **num_heads** (List[int]): The number of heads in attention
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modules of each stage.
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Defaults to 'tiny'.
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img_size (int | tuple): The expected input image shape. Because we
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support dynamic input shape, just set the argument to the most
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common input image shape. Defaults to 224.
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patch_size (int | tuple): The patch size in patch embedding.
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Defaults to 4.
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in_channels (int): The num of input channels. Defaults to 3.
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window_size (int): The height and width of the window. Defaults to 7.
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drop_rate (float): Dropout rate after embedding. Defaults to 0.
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drop_path_rate (float): Stochastic depth rate. Defaults to 0.1.
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out_after_downsample (bool): Whether to output the feature map of a
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stage after the following downsample layer. Defaults to False.
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use_abs_pos_embed (bool): If True, add absolute position embedding to
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the patch embedding. Defaults to False.
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interpolate_mode (str): Select the interpolate mode for absolute
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position embeding vector resize. Defaults to "bicubic".
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with_cp (bool): Use checkpoint or not. Using checkpoint will save some
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memory while slowing down the training speed. Defaults to False.
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frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
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-1 means not freezing any parameters. Defaults to -1.
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norm_eval (bool): Whether to set norm layers to eval mode, namely,
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freeze running stats (mean and var). Note: Effect on Batch Norm
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and its variants only. Defaults to False.
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pad_small_map (bool): If True, pad the small feature map to the window
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size, which is common used in detection and segmentation. If False,
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avoid shifting window and shrink the window size to the size of
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feature map, which is common used in classification.
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Defaults to False.
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norm_cfg (dict): Config dict for normalization layer for all output
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features. Defaults to ``dict(type='LN')``
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stage_cfgs (Sequence[dict] | dict): Extra config dict for each
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stage. Defaults to an empty dict.
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patch_cfg (dict): Extra config dict for patch embedding.
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Defaults to an empty dict.
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init_cfg (dict, optional): The Config for initialization.
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Defaults to None.
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Examples:
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>>> from mmcls.models import SwinTransformer
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>>> import torch
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>>> extra_config = dict(
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>>> arch='tiny',
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>>> stage_cfgs=dict(downsample_cfg={'kernel_size': 3,
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>>> 'expansion_ratio': 3}))
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>>> self = SwinTransformer(**extra_config)
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>>> inputs = torch.rand(1, 3, 224, 224)
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>>> output = self.forward(inputs)
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>>> print(output.shape)
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(1, 2592, 4)
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"""
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arch_zoo = {
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**dict.fromkeys(['t', 'tiny'],
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{'embed_dims': 96,
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'depths': [2, 2, 6, 2],
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'num_heads': [3, 6, 12, 24]}),
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**dict.fromkeys(['s', 'small'],
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{'embed_dims': 96,
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'depths': [2, 2, 18, 2],
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'num_heads': [3, 6, 12, 24]}),
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**dict.fromkeys(['b', 'base'],
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{'embed_dims': 128,
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'depths': [2, 2, 18, 2],
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'num_heads': [4, 8, 16, 32]}),
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**dict.fromkeys(['l', 'large'],
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{'embed_dims': 192,
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'depths': [2, 2, 18, 2],
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'num_heads': [6, 12, 24, 48]}),
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} # yapf: disable
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_version = 3
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num_extra_tokens = 0
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def __init__(self,
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arch='tiny',
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img_size=224,
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patch_size=4,
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in_channels=3,
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window_size=7,
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drop_rate=0.,
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drop_path_rate=0.1,
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out_indices=(3, ),
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out_after_downsample=False,
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use_abs_pos_embed=False,
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interpolate_mode='bicubic',
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with_cp=False,
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frozen_stages=-1,
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norm_eval=False,
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pad_small_map=False,
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norm_cfg=dict(type='LN'),
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stage_cfgs=dict(),
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patch_cfg=dict(),
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init_cfg=None):
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super(SwinTransformer, self).__init__(init_cfg=init_cfg)
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if isinstance(arch, str):
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arch = arch.lower()
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assert arch in set(self.arch_zoo), \
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f'Arch {arch} is not in default archs {set(self.arch_zoo)}'
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self.arch_settings = self.arch_zoo[arch]
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else:
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essential_keys = {'embed_dims', 'depths', 'num_heads'}
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assert isinstance(arch, dict) and set(arch) == essential_keys, \
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f'Custom arch needs a dict with keys {essential_keys}'
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self.arch_settings = arch
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self.embed_dims = self.arch_settings['embed_dims']
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self.depths = self.arch_settings['depths']
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self.num_heads = self.arch_settings['num_heads']
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self.num_layers = len(self.depths)
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self.out_indices = out_indices
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self.out_after_downsample = out_after_downsample
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self.use_abs_pos_embed = use_abs_pos_embed
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self.interpolate_mode = interpolate_mode
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self.frozen_stages = frozen_stages
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_patch_cfg = dict(
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in_channels=in_channels,
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input_size=img_size,
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embed_dims=self.embed_dims,
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conv_type='Conv2d',
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kernel_size=patch_size,
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stride=patch_size,
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norm_cfg=dict(type='LN'),
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)
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_patch_cfg.update(patch_cfg)
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self.patch_embed = PatchEmbed(**_patch_cfg)
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self.patch_resolution = self.patch_embed.init_out_size
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if self.use_abs_pos_embed:
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num_patches = self.patch_resolution[0] * self.patch_resolution[1]
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self.absolute_pos_embed = nn.Parameter(
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torch.zeros(1, num_patches, self.embed_dims))
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self._register_load_state_dict_pre_hook(
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self._prepare_abs_pos_embed)
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self._register_load_state_dict_pre_hook(
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self._prepare_relative_position_bias_table)
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self.drop_after_pos = nn.Dropout(p=drop_rate)
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self.norm_eval = norm_eval
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# stochastic depth
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total_depth = sum(self.depths)
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dpr = [
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x.item() for x in torch.linspace(0, drop_path_rate, total_depth)
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] # stochastic depth decay rule
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self.stages = ModuleList()
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embed_dims = [self.embed_dims]
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for i, (depth,
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num_heads) in enumerate(zip(self.depths, self.num_heads)):
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if isinstance(stage_cfgs, Sequence):
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stage_cfg = stage_cfgs[i]
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else:
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stage_cfg = deepcopy(stage_cfgs)
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downsample = True if i < self.num_layers - 1 else False
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_stage_cfg = {
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'embed_dims': embed_dims[-1],
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'depth': depth,
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'num_heads': num_heads,
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'window_size': window_size,
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'downsample': downsample,
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'drop_paths': dpr[:depth],
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'with_cp': with_cp,
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'pad_small_map': pad_small_map,
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**stage_cfg
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}
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stage = SwinBlockSequence(**_stage_cfg)
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self.stages.append(stage)
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dpr = dpr[depth:]
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embed_dims.append(stage.out_channels)
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if self.out_after_downsample:
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self.num_features = embed_dims[1:]
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else:
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self.num_features = embed_dims[:-1]
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for i in out_indices:
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if norm_cfg is not None:
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norm_layer = build_norm_layer(norm_cfg,
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self.num_features[i])[1]
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else:
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norm_layer = nn.Identity()
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self.add_module(f'norm{i}', norm_layer)
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def init_weights(self):
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super(SwinTransformer, self).init_weights()
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if (isinstance(self.init_cfg, dict)
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and self.init_cfg['type'] == 'Pretrained'):
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# Suppress default init if use pretrained model.
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return
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if self.use_abs_pos_embed:
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trunc_normal_(self.absolute_pos_embed, std=0.02)
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def forward(self, x):
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x, hw_shape = self.patch_embed(x)
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if self.use_abs_pos_embed:
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x = x + resize_pos_embed(
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self.absolute_pos_embed, self.patch_resolution, hw_shape,
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self.interpolate_mode, self.num_extra_tokens)
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x = self.drop_after_pos(x)
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outs = []
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for i, stage in enumerate(self.stages):
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x, hw_shape = stage(
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x, hw_shape, do_downsample=self.out_after_downsample)
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if i in self.out_indices:
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norm_layer = getattr(self, f'norm{i}')
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out = norm_layer(x)
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out = out.view(-1, *hw_shape,
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self.num_features[i]).permute(0, 3, 1,
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2).contiguous()
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outs.append(out)
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if stage.downsample is not None and not self.out_after_downsample:
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x, hw_shape = stage.downsample(x, hw_shape)
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return tuple(outs)
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def _load_from_state_dict(self, state_dict, prefix, local_metadata, *args,
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**kwargs):
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"""load checkpoints."""
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# Names of some parameters in has been changed.
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version = local_metadata.get('version', None)
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if (version is None
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or version < 2) and self.__class__ is SwinTransformer:
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final_stage_num = len(self.stages) - 1
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state_dict_keys = list(state_dict.keys())
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for k in state_dict_keys:
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if k.startswith('norm.') or k.startswith('backbone.norm.'):
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convert_key = k.replace('norm.', f'norm{final_stage_num}.')
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state_dict[convert_key] = state_dict[k]
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del state_dict[k]
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if (version is None
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or version < 3) and self.__class__ is SwinTransformer:
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state_dict_keys = list(state_dict.keys())
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for k in state_dict_keys:
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if 'attn_mask' in k:
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del state_dict[k]
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super()._load_from_state_dict(state_dict, prefix, local_metadata,
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*args, **kwargs)
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def _freeze_stages(self):
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if self.frozen_stages >= 0:
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self.patch_embed.eval()
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for param in self.patch_embed.parameters():
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param.requires_grad = False
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for i in range(0, self.frozen_stages + 1):
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m = self.stages[i]
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m.eval()
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for param in m.parameters():
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param.requires_grad = False
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for i in self.out_indices:
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if i <= self.frozen_stages:
|
|
for param in getattr(self, f'norm{i}').parameters():
|
|
param.requires_grad = False
|
|
|
|
def train(self, mode=True):
|
|
super(SwinTransformer, 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 mmcls.utils import get_root_logger
|
|
logger = get_root_logger()
|
|
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 _prepare_relative_position_bias_table(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 'relative_position_bias_table' in key:
|
|
ckpt_key = prefix + key
|
|
if ckpt_key not in state_dict:
|
|
continue
|
|
relative_position_bias_table_pretrained = state_dict[ckpt_key]
|
|
relative_position_bias_table_current = state_dict_model[key]
|
|
L1, nH1 = relative_position_bias_table_pretrained.size()
|
|
L2, nH2 = relative_position_bias_table_current.size()
|
|
if L1 != L2:
|
|
src_size = int(L1**0.5)
|
|
dst_size = int(L2**0.5)
|
|
new_rel_pos_bias = resize_relative_position_bias_table(
|
|
src_size, dst_size,
|
|
relative_position_bias_table_pretrained, nH1)
|
|
from mmcls.utils import get_root_logger
|
|
logger = get_root_logger()
|
|
logger.info('Resize the relative_position_bias_table from '
|
|
f'{state_dict[ckpt_key].shape} to '
|
|
f'{new_rel_pos_bias.shape}')
|
|
state_dict[ckpt_key] = new_rel_pos_bias
|
|
|
|
# The index buffer need to be re-generated.
|
|
index_buffer = ckpt_key.replace('bias_table', 'index')
|
|
del state_dict[index_buffer]
|