models/swin_neck.py

from typing import Sequence
import math
from models import register
from einops import rearrange

import warnings
from collections import OrderedDict
from copy import deepcopy

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import build_norm_layer, constant_init, trunc_normal_init, build_conv_layer
from mmcv.cnn.bricks.transformer import FFN, build_dropout
from mmcv.cnn.utils.weight_init import trunc_normal_
from mmcv.runner import BaseModule, ModuleList, _load_checkpoint
from mmcv.utils import to_2tuple


@register('Swintransformer_neck')
class SwinTransformerNeck(nn.Module):
    def __init__(self, in_dim, out_dim=256, depth=4, w_neck=True, drop_path_rate=0.1):
        super().__init__()
        self.input_proj = nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1)
        self.global_op = nn.Sequential(
            nn.AdaptiveAvgPool2d(output_size=(1, 1)),
            nn.Conv2d(out_dim, out_dim, kernel_size=1)
        )
        self.w_neck = w_neck
        if w_neck:
            dpr = [
                x.item() for x in torch.linspace(0, drop_path_rate, depth)
            ]

            self.stage = SwinBlockSequence(
                embed_dims=out_dim,
                num_heads=8,
                feedforward_channels=out_dim * 2,
                depth=depth,
                window_size=4,
                drop_path_rate=dpr,
                downsample=None
            )

            self.norm_layer = build_norm_layer(dict(type='LN'), out_dim)[1]

        self.out_dim = out_dim

    def forward(self, x):
        x = self.input_proj(x)  # x: BxCxHxW
        hw = x.shape[-2:]
        global_content = self.global_op(x)  # BxCx1x1
        if self.w_neck:
            x = rearrange(x, ' B C H W -> B (H W) C')
            x, hw_shape, out, out_hw_shape = self.stage(x, hw)
            out = self.norm_layer(out)
            x_rep = out.view(-1, *out_hw_shape, self.out_dim).permute(0, 3, 1, 2).contiguous()
        else:
            x_rep = x
        global_content = rearrange(global_content, 'B C H W -> B (H W) C')
        return global_content, x_rep


class WindowMSA(BaseModule):
    """Window based multi-head self-attention (W-MSA) module with relative
    position bias.

    Args:
        embed_dims (int): Number of input channels.
        num_heads (int): Number of attention heads.
        window_size (tuple[int]): The height and width of the window.
        qkv_bias (bool, optional):  If True, add a learnable bias to q, k, v.
            Default: True.
        qk_scale (float | None, optional): Override default qk scale of
            head_dim ** -0.5 if set. Default: None.
        attn_drop_rate (float, optional): Dropout ratio of attention weight.
            Default: 0.0
        proj_drop_rate (float, optional): Dropout ratio of output. Default: 0.
        init_cfg (dict | None, optional): The Config for initialization.
            Default: None.
    """

    def __init__(self,
                 embed_dims,
                 num_heads,
                 window_size,
                 qkv_bias=True,
                 qk_scale=None,
                 attn_drop_rate=0.,
                 proj_drop_rate=0.,
                 init_cfg=None):

        super().__init__()
        self.embed_dims = embed_dims
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_embed_dims = embed_dims // num_heads
        self.scale = qk_scale or head_embed_dims**-0.5
        self.init_cfg = init_cfg

        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1),
                        num_heads))  # 2*Wh-1 * 2*Ww-1, nH

        # About 2x faster than original impl
        Wh, Ww = self.window_size
        rel_index_coords = self.double_step_seq(2 * Ww - 1, Wh, 1, Ww)
        rel_position_index = rel_index_coords + rel_index_coords.T
        rel_position_index = rel_position_index.flip(1).contiguous()
        self.register_buffer('relative_position_index', rel_position_index)

        self.qkv = nn.Linear(embed_dims, embed_dims * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop_rate)
        self.proj = nn.Linear(embed_dims, embed_dims)
        self.proj_drop = nn.Dropout(proj_drop_rate)

        self.softmax = nn.Softmax(dim=-1)

    def init_weights(self):
        trunc_normal_(self.relative_position_bias_table, std=0.02)

    def forward(self, x, mask=None):
        """
        Args:

            x (tensor): input features with shape of (num_windows*B, N, C)
            mask (tensor | None, Optional): mask with shape of (num_windows,
                Wh*Ww, Wh*Ww), value should be between (-inf, 0].
        """
        # import pdb
        # pdb.set_trace()

        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        # make torchscript happy (cannot use tensor as tuple)
        q, k, v = qkv[0], qkv[1], qkv[2]

        q = q * self.scale
        attn = (q @ k.transpose(-2, -1))

        relative_position_bias = self.relative_position_bias_table[
            self.relative_position_index.view(-1)].view(
                self.window_size[0] * self.window_size[1],
                self.window_size[0] * self.window_size[1],
                -1)  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(
            2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
        attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B // nW, nW, self.num_heads, N,
                             N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
        attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

    @staticmethod
    def double_step_seq(step1, len1, step2, len2):
        seq1 = torch.arange(0, step1 * len1, step1)
        seq2 = torch.arange(0, step2 * len2, step2)
        return (seq1[:, None] + seq2[None, :]).reshape(1, -1)


class ShiftWindowMSA(BaseModule):
    """Shifted Window Multihead Self-Attention Module.

    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.
        shift_size (int, optional): The shift step of each window towards
            right-bottom. If zero, act as regular window-msa. Defaults to 0.
        qkv_bias (bool, optional): If True, add a learnable bias to q, k, v.
            Default: True
        qk_scale (float | None, optional): Override default qk scale of
            head_dim ** -0.5 if set. Defaults: None.
        attn_drop_rate (float, optional): Dropout ratio of attention weight.
            Defaults: 0.
        proj_drop_rate (float, optional): Dropout ratio of output.
            Defaults: 0.
        dropout_layer (dict, optional): The dropout_layer used before output.
            Defaults: dict(type='DropPath', drop_prob=0.).
        init_cfg (dict, optional): The extra config for initialization.
            Default: None.
    """

    def __init__(self,
                 embed_dims,
                 num_heads,
                 window_size,
                 shift_size=0,
                 qkv_bias=True,
                 qk_scale=None,
                 attn_drop_rate=0,
                 proj_drop_rate=0,
                 dropout_layer=dict(type='DropPath', drop_prob=0.),
                 init_cfg=None):
        super().__init__(init_cfg)

        self.window_size = window_size
        self.shift_size = shift_size
        assert 0 <= self.shift_size < self.window_size

        self.w_msa = WindowMSA(
            embed_dims=embed_dims,
            num_heads=num_heads,
            window_size=to_2tuple(window_size),
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop_rate=attn_drop_rate,
            proj_drop_rate=proj_drop_rate,
            init_cfg=None)

        self.drop = build_dropout(dropout_layer)

    def forward(self, query, hw_shape):
        B, L, C = query.shape
        H, W = hw_shape
        assert L == H * W, 'input feature has wrong size'
        query = query.view(B, H, W, C)

        # pad feature maps to multiples of window size
        pad_r = (self.window_size - W % self.window_size) % self.window_size
        pad_b = (self.window_size - H % self.window_size) % self.window_size
        query = F.pad(query, (0, 0, 0, pad_r, 0, pad_b))
        H_pad, W_pad = query.shape[1], query.shape[2]

        # cyclic shift
        if self.shift_size > 0:
            shifted_query = torch.roll(
                query,
                shifts=(-self.shift_size, -self.shift_size),
                dims=(1, 2))

            # calculate attention mask for SW-MSA
            img_mask = torch.zeros((1, H_pad, W_pad, 1), device=query.device)
            h_slices = (slice(0, -self.window_size),
                        slice(-self.window_size,
                              -self.shift_size), slice(-self.shift_size, None))
            w_slices = (slice(0, -self.window_size),
                        slice(-self.window_size,
                              -self.shift_size), slice(-self.shift_size, None))
            cnt = 0
            for h in h_slices:
                for w in w_slices:
                    img_mask[:, h, w, :] = cnt
                    cnt += 1

            # nW, window_size, window_size, 1
            mask_windows = self.window_partition(img_mask)
            mask_windows = mask_windows.view(
                -1, self.window_size * self.window_size)
            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
            attn_mask = attn_mask.masked_fill(attn_mask != 0,
                                              float(-100.0)).masked_fill(
                                                  attn_mask == 0, float(0.0))
        else:
            shifted_query = query
            attn_mask = None

        # nW*B, window_size, window_size, C
        query_windows = self.window_partition(shifted_query)
        # nW*B, window_size*window_size, C
        query_windows = query_windows.view(-1, self.window_size**2, C)

        # W-MSA/SW-MSA (nW*B, window_size*window_size, C)
        attn_windows = self.w_msa(query_windows, mask=attn_mask)

        # merge windows
        attn_windows = attn_windows.view(-1, self.window_size,
                                         self.window_size, C)

        # B H' W' C
        shifted_x = self.window_reverse(attn_windows, H_pad, W_pad)
        # reverse cyclic shift
        if self.shift_size > 0:
            x = torch.roll(
                shifted_x,
                shifts=(self.shift_size, self.shift_size),
                dims=(1, 2))
        else:
            x = shifted_x

        if pad_r > 0 or pad_b:
            x = x[:, :H, :W, :].contiguous()

        x = x.view(B, H * W, C)

        x = self.drop(x)
        return x

    def window_reverse(self, windows, H, W):
        """
        Args:
            windows: (num_windows*B, window_size, window_size, C)
            H (int): Height of image
            W (int): Width of image
        Returns:
            x: (B, H, W, C)
        """
        window_size = self.window_size
        B = int(windows.shape[0] / (H * W / window_size / window_size))
        x = windows.view(B, H // window_size, W // window_size, window_size,
                         window_size, -1)
        x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
        return x

    def window_partition(self, x):
        """
        Args:
            x: (B, H, W, C)
        Returns:
            windows: (num_windows*B, window_size, window_size, C)
        """
        B, H, W, C = x.shape
        window_size = self.window_size
        x = x.view(B, H // window_size, window_size, W // window_size,
                   window_size, C)
        windows = x.permute(0, 1, 3, 2, 4, 5).contiguous()
        windows = windows.view(-1, window_size, window_size, C)
        return windows


class SwinBlock(BaseModule):
    """"
    Args:
        embed_dims (int): The feature dimension.
        num_heads (int): Parallel attention heads.
        feedforward_channels (int): The hidden dimension for FFNs.
        window_size (int, optional): The local window scale. Default: 7.
        shift (bool, optional): whether to shift window or not. Default False.
        qkv_bias (bool, optional): enable bias for qkv if True. Default: True.
        qk_scale (float | None, optional): Override default qk scale of
            head_dim ** -0.5 if set. Default: None.
        drop_rate (float, optional): Dropout rate. Default: 0.
        attn_drop_rate (float, optional): Attention dropout rate. Default: 0.
        drop_path_rate (float, optional): Stochastic depth rate. Default: 0.
        act_cfg (dict, optional): The config dict of activation function.
            Default: dict(type='GELU').
        norm_cfg (dict, optional): The config dict of normalization.
            Default: dict(type='LN').
        with_cp (bool, optional): Use checkpoint or not. Using checkpoint
            will save some memory while slowing down the training speed.
            Default: False.
        init_cfg (dict | list | None, optional): The init config.
            Default: None.
    """

    def __init__(self,
                 embed_dims,
                 num_heads,
                 feedforward_channels,
                 window_size=7,
                 shift=False,
                 qkv_bias=True,
                 qk_scale=None,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.,
                 act_cfg=dict(type='GELU'),
                 norm_cfg=dict(type='LN'),
                 with_cp=False,
                 init_cfg=None):

        super(SwinBlock, self).__init__()

        self.init_cfg = init_cfg
        self.with_cp = with_cp

        self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1]
        self.attn = ShiftWindowMSA(
            embed_dims=embed_dims,
            num_heads=num_heads,
            window_size=window_size,
            shift_size=window_size // 2 if shift else 0,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop_rate=attn_drop_rate,
            proj_drop_rate=drop_rate,
            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
            init_cfg=None)

        self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1]
        self.ffn = FFN(
            embed_dims=embed_dims,
            feedforward_channels=feedforward_channels,
            num_fcs=2,
            ffn_drop=drop_rate,
            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
            act_cfg=act_cfg,
            add_identity=True,
            init_cfg=None)

    def forward(self, x, hw_shape):

        def _inner_forward(x):
            identity = x
            x = self.norm1(x)
            x = self.attn(x, hw_shape)

            x = x + identity

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

            return x

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

        return x


class SwinBlockSequence(BaseModule):
    """Implements one stage in Swin Transformer.

    Args:
        embed_dims (int): The feature dimension.
        num_heads (int): Parallel attention heads.
        feedforward_channels (int): The hidden dimension for FFNs.
        depth (int): The number of blocks in this stage.
        window_size (int, optional): The local window scale. Default: 7.
        qkv_bias (bool, optional): enable bias for qkv if True. Default: True.
        qk_scale (float | None, optional): Override default qk scale of
            head_dim ** -0.5 if set. Default: None.
        drop_rate (float, optional): Dropout rate. Default: 0.
        attn_drop_rate (float, optional): Attention dropout rate. Default: 0.
        drop_path_rate (float | list[float], optional): Stochastic depth
            rate. Default: 0.
        downsample (BaseModule | None, optional): The downsample operation
            module. Default: None.
        act_cfg (dict, optional): The config dict of activation function.
            Default: dict(type='GELU').
        norm_cfg (dict, optional): The config dict of normalization.
            Default: dict(type='LN').
        with_cp (bool, optional): Use checkpoint or not. Using checkpoint
            will save some memory while slowing down the training speed.
            Default: False.
        init_cfg (dict | list | None, optional): The init config.
            Default: None.
    """

    def __init__(self,
                 embed_dims,
                 num_heads,
                 feedforward_channels,
                 depth,
                 window_size=7,
                 qkv_bias=True,
                 qk_scale=None,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.,
                 downsample=None,
                 act_cfg=dict(type='GELU'),
                 norm_cfg=dict(type='LN'),
                 with_cp=False,
                 init_cfg=None):
        super().__init__(init_cfg=init_cfg)

        if isinstance(drop_path_rate, list):
            drop_path_rates = drop_path_rate
            assert len(drop_path_rates) == depth
        else:
            drop_path_rates = [deepcopy(drop_path_rate) for _ in range(depth)]

        self.blocks = ModuleList()
        for i in range(depth):
            block = SwinBlock(
                embed_dims=embed_dims,
                num_heads=num_heads,
                feedforward_channels=feedforward_channels,
                window_size=window_size,
                shift=False if i % 2 == 0 else True,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop_rate=drop_rate,
                attn_drop_rate=attn_drop_rate,
                drop_path_rate=drop_path_rates[i],
                act_cfg=act_cfg,
                norm_cfg=norm_cfg,
                with_cp=with_cp,
                init_cfg=None)
            self.blocks.append(block)

        self.downsample = downsample

    def forward(self, x, hw_shape):
        for block in self.blocks:
            x = block(x, hw_shape)

        if self.downsample:
            x_down, down_hw_shape = self.downsample(x, hw_shape)
            return x_down, down_hw_shape, x, hw_shape
        else:
            return x, hw_shape, x, hw_shape



class SwinTransformer(BaseModule):
    """ Swin Transformer
    A PyTorch implement of : `Swin Transformer:
    Hierarchical Vision Transformer using Shifted Windows`  -
        https://arxiv.org/abs/2103.14030

    Inspiration from
    https://github.com/microsoft/Swin-Transformer

    Args:
        pretrain_img_size (int | tuple[int]): The size of input image when
            pretrain. Defaults: 224.
        in_channels (int): The num of input channels.
            Defaults: 3.
        embed_dims (int): The feature dimension. Default: 96.
        patch_size (int | tuple[int]): Patch size. Default: 4.
        window_size (int): Window size. Default: 7.
        mlp_ratio (int): Ratio of mlp hidden dim to embedding dim.
            Default: 4.
        depths (tuple[int]): Depths of each Swin Transformer stage.
            Default: (2, 2, 6, 2).
        num_heads (tuple[int]): Parallel attention heads of each Swin
            Transformer stage. Default: (3, 6, 12, 24).
        strides (tuple[int]): The patch merging or patch embedding stride of
            each Swin Transformer stage. (In swin, we set kernel size equal to
            stride.) Default: (4, 2, 2, 2).
        out_indices (tuple[int]): Output from which stages.
            Default: (0, 1, 2, 3).
        qkv_bias (bool, optional): If True, add a learnable bias to query, key,
            value. Default: True
        qk_scale (float | None, optional): Override default qk scale of
            head_dim ** -0.5 if set. Default: None.
        patch_norm (bool): If add a norm layer for patch embed and patch
            merging. Default: True.
        drop_rate (float): Dropout rate. Defaults: 0.
        attn_drop_rate (float): Attention dropout rate. Default: 0.
        drop_path_rate (float): Stochastic depth rate. Defaults: 0.1.
        use_abs_pos_embed (bool): If True, add absolute position embedding to
            the patch embedding. Defaults: False.
        act_cfg (dict): Config dict for activation layer.
            Default: dict(type='LN').
        norm_cfg (dict): Config dict for normalization layer at
            output of backone. Defaults: dict(type='LN').
        with_cp (bool, optional): Use checkpoint or not. Using checkpoint
            will save some memory while slowing down the training speed.
            Default: False.
        pretrained (str, optional): model pretrained path. Default: None.
        convert_weights (bool): The flag indicates whether the
            pre-trained model is from the original repo. We may need
            to convert some keys to make it compatible.
            Default: False.
        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
            Default: -1 (-1 means not freezing any parameters).
        init_cfg (dict, optional): The Config for initialization.
            Defaults to None.
    """

    def __init__(self,
                 pretrain_img_size=224,
                 in_channels=3,
                 embed_dims=96,
                 patch_size=4,
                 window_size=7,
                 mlp_ratio=4,
                 depths=(2, 2, 6, 2),
                 num_heads=(3, 6, 12, 24),
                 strides=(4, 2, 2, 2),
                 out_indices=(0, 1, 2, 3),
                 qkv_bias=True,
                 qk_scale=None,
                 patch_norm=True,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.1,
                 use_abs_pos_embed=False,
                 act_cfg=dict(type='GELU'),
                 norm_cfg=dict(type='LN'),
                 with_cp=False,
                 pretrained=None,
                 convert_weights=False,
                 frozen_stages=-1,
                 init_cfg=None):
        self.convert_weights = convert_weights
        self.frozen_stages = frozen_stages
        if isinstance(pretrain_img_size, int):
            pretrain_img_size = to_2tuple(pretrain_img_size)
        elif isinstance(pretrain_img_size, tuple):
            if len(pretrain_img_size) == 1:
                pretrain_img_size = to_2tuple(pretrain_img_size[0])
            assert len(pretrain_img_size) == 2, \
                f'The size of image should have length 1 or 2, ' \
                f'but got {len(pretrain_img_size)}'

        assert not (init_cfg and pretrained), \
            'init_cfg and pretrained cannot be specified at the same time'
        if isinstance(pretrained, str):
            warnings.warn('DeprecationWarning: pretrained is deprecated, '
                          'please use "init_cfg" instead')
            self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
        elif pretrained is None:
            self.init_cfg = init_cfg
        else:
            raise TypeError('pretrained must be a str or None')

        super(SwinTransformer, self).__init__(init_cfg=init_cfg)

        num_layers = len(depths)
        self.out_indices = out_indices
        self.use_abs_pos_embed = use_abs_pos_embed

        assert strides[0] == patch_size, 'Use non-overlapping patch embed.'

        self.patch_embed = PatchEmbed(
            in_channels=in_channels,
            embed_dims=embed_dims,
            conv_type='Conv2d',
            kernel_size=patch_size,
            stride=strides[0],
            norm_cfg=norm_cfg if patch_norm else None,
            init_cfg=None)

        if self.use_abs_pos_embed:
            patch_row = pretrain_img_size[0] // patch_size
            patch_col = pretrain_img_size[1] // patch_size
            num_patches = patch_row * patch_col
            self.absolute_pos_embed = nn.Parameter(
                torch.zeros((1, num_patches, embed_dims)))

        self.drop_after_pos = nn.Dropout(p=drop_rate)

        # set stochastic depth decay rule
        total_depth = sum(depths)
        dpr = [
            x.item() for x in torch.linspace(0, drop_path_rate, total_depth)
        ]

        self.stages = ModuleList()
        in_channels = embed_dims
        for i in range(num_layers):
            if i < num_layers - 1:
                downsample = PatchMerging(
                    in_channels=in_channels,
                    out_channels=2 * in_channels,
                    stride=strides[i + 1],
                    norm_cfg=norm_cfg if patch_norm else None,
                    init_cfg=None)
            else:
                downsample = None

            stage = SwinBlockSequence(
                embed_dims=in_channels,
                num_heads=num_heads[i],
                feedforward_channels=mlp_ratio * in_channels,
                depth=depths[i],
                window_size=window_size,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop_rate=drop_rate,
                attn_drop_rate=attn_drop_rate,
                drop_path_rate=dpr[sum(depths[:i]):sum(depths[:i + 1])],
                downsample=downsample,
                act_cfg=act_cfg,
                norm_cfg=norm_cfg,
                with_cp=with_cp,
                init_cfg=None)
            self.stages.append(stage)
            if downsample:
                in_channels = downsample.out_channels

        self.num_features = [int(embed_dims * 2**i) for i in range(num_layers)]
        # Add a norm layer for each output
        for i in out_indices:
            layer = build_norm_layer(norm_cfg, self.num_features[i])[1]
            layer_name = f'norm{i}'
            self.add_module(layer_name, layer)

    def train(self, mode=True):
        """Convert the model into training mode while keep layers freezed."""
        super(SwinTransformer, self).train(mode)
        self._freeze_stages()

    def _freeze_stages(self):
        if self.frozen_stages >= 0:
            self.patch_embed.eval()
            for param in self.patch_embed.parameters():
                param.requires_grad = False
            if self.use_abs_pos_embed:
                self.absolute_pos_embed.requires_grad = False
            self.drop_after_pos.eval()

        for i in range(1, self.frozen_stages + 1):

            if (i - 1) in self.out_indices:
                norm_layer = getattr(self, f'norm{i-1}')
                norm_layer.eval()
                for param in norm_layer.parameters():
                    param.requires_grad = False

            m = self.stages[i - 1]
            m.eval()
            for param in m.parameters():
                param.requires_grad = False

    def init_weights(self):
        logger = get_root_logger()
        if self.init_cfg is None:
            logger.warn(f'No pre-trained weights for '
                        f'{self.__class__.__name__}, '
                        f'training start from scratch')
            if self.use_abs_pos_embed:
                trunc_normal_(self.absolute_pos_embed, std=0.02)
            for m in self.modules():
                if isinstance(m, nn.Linear):
                    trunc_normal_init(m, std=.02, bias=0.)
                elif isinstance(m, nn.LayerNorm):
                    constant_init(m, 1.0)
        else:
            assert 'checkpoint' in self.init_cfg, f'Only support ' \
                                                  f'specify `Pretrained` in ' \
                                                  f'`init_cfg` in ' \
                                                  f'{self.__class__.__name__} '
            ckpt = _load_checkpoint(
                self.init_cfg.checkpoint, logger=logger, map_location='cpu')
            if 'state_dict' in ckpt:
                _state_dict = ckpt['state_dict']
            elif 'model' in ckpt:
                _state_dict = ckpt['model']
            else:
                _state_dict = ckpt
            if self.convert_weights:
                # supported loading weight from original repo,
                _state_dict = swin_converter(_state_dict)

            state_dict = OrderedDict()
            for k, v in _state_dict.items():
                if k.startswith('backbone.'):
                    state_dict[k[9:]] = v

            # strip prefix of state_dict
            if list(state_dict.keys())[0].startswith('module.'):
                state_dict = {k[7:]: v for k, v in state_dict.items()}

            # reshape absolute position embedding
            if state_dict.get('absolute_pos_embed') is not None:
                absolute_pos_embed = state_dict['absolute_pos_embed']
                N1, L, C1 = absolute_pos_embed.size()
                N2, C2, H, W = self.absolute_pos_embed.size()
                if N1 != N2 or C1 != C2 or L != H * W:
                    logger.warning('Error in loading absolute_pos_embed, pass')
                else:
                    state_dict['absolute_pos_embed'] = absolute_pos_embed.view(
                        N2, H, W, C2).permute(0, 3, 1, 2).contiguous()

            # interpolate position bias table if needed
            relative_position_bias_table_keys = [
                k for k in state_dict.keys()
                if 'relative_position_bias_table' in k
            ]
            for table_key in relative_position_bias_table_keys:
                table_pretrained = state_dict[table_key]
                table_current = self.state_dict()[table_key]
                L1, nH1 = table_pretrained.size()
                L2, nH2 = table_current.size()
                if nH1 != nH2:
                    logger.warning(f'Error in loading {table_key}, pass')
                elif L1 != L2:
                    S1 = int(L1**0.5)
                    S2 = int(L2**0.5)
                    table_pretrained_resized = F.interpolate(
                        table_pretrained.permute(1, 0).reshape(1, nH1, S1, S1),
                        size=(S2, S2),
                        mode='bicubic')
                    state_dict[table_key] = table_pretrained_resized.view(
                        nH2, L2).permute(1, 0).contiguous()

            # load state_dict
            self.load_state_dict(state_dict, False)

    def forward(self, x):
        x, hw_shape = self.patch_embed(x)

        if self.use_abs_pos_embed:
            x = x + self.absolute_pos_embed
        x = self.drop_after_pos(x)

        outs = []
        for i, stage in enumerate(self.stages):
            x, hw_shape, out, out_hw_shape = stage(x, hw_shape)
            if i in self.out_indices:
                norm_layer = getattr(self, f'norm{i}')
                out = norm_layer(out)
                out = out.view(-1, *out_hw_shape,
                               self.num_features[i]).permute(0, 3, 1,
                                                             2).contiguous()
                outs.append(out)

        return outs

class PatchEmbed(BaseModule):
    """Image to Patch Embedding.

    We use a conv layer to implement PatchEmbed.

    Args:
        in_channels (int): The num of input channels. Default: 3
        embed_dims (int): The dimensions of embedding. Default: 768
        conv_type (str): The config dict for embedding
            conv layer type selection. Default: "Conv2d.
        kernel_size (int): The kernel_size of embedding conv. Default: 16.
        stride (int): The slide stride of embedding conv.
            Default: None (Would be set as `kernel_size`).
        padding (int | tuple | string ): The padding length of
            embedding conv. When it is a string, it means the mode
            of adaptive padding, support "same" and "corner" now.
            Default: "corner".
        dilation (int): The dilation rate of embedding conv. Default: 1.
        bias (bool): Bias of embed conv. Default: True.
        norm_cfg (dict, optional): Config dict for normalization layer.
            Default: None.
        input_size (int | tuple | None): The size of input, which will be
            used to calculate the out size. Only work when `dynamic_size`
            is False. Default: None.
        init_cfg (`mmcv.ConfigDict`, optional): The Config for initialization.
            Default: None.
    """

    def __init__(
        self,
        in_channels=3,
        embed_dims=768,
        conv_type='Conv2d',
        kernel_size=16,
        stride=16,
        padding='corner',
        dilation=1,
        bias=True,
        norm_cfg=None,
        input_size=None,
        init_cfg=None,
    ):
        super(PatchEmbed, self).__init__(init_cfg=init_cfg)

        self.embed_dims = embed_dims
        if stride is None:
            stride = kernel_size

        kernel_size = to_2tuple(kernel_size)
        stride = to_2tuple(stride)
        dilation = to_2tuple(dilation)

        if isinstance(padding, str):
            self.adap_padding = AdaptivePadding(
                kernel_size=kernel_size,
                stride=stride,
                dilation=dilation,
                padding=padding)
            # disable the padding of conv
            padding = 0
        else:
            self.adap_padding = None
        padding = to_2tuple(padding)

        self.projection = build_conv_layer(
            dict(type=conv_type),
            in_channels=in_channels,
            out_channels=embed_dims,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            bias=bias)

        if norm_cfg is not None:
            self.norm = build_norm_layer(norm_cfg, embed_dims)[1]
        else:
            self.norm = None

        if input_size:
            input_size = to_2tuple(input_size)
            # `init_out_size` would be used outside to
            # calculate the num_patches
            # when `use_abs_pos_embed` outside
            self.init_input_size = input_size
            if self.adap_padding:
                pad_h, pad_w = self.adap_padding.get_pad_shape(input_size)
                input_h, input_w = input_size
                input_h = input_h + pad_h
                input_w = input_w + pad_w
                input_size = (input_h, input_w)

            # https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html
            h_out = (input_size[0] + 2 * padding[0] - dilation[0] *
                     (kernel_size[0] - 1) - 1) // stride[0] + 1
            w_out = (input_size[1] + 2 * padding[1] - dilation[1] *
                     (kernel_size[1] - 1) - 1) // stride[1] + 1
            self.init_out_size = (h_out, w_out)
        else:
            self.init_input_size = None
            self.init_out_size = None

    def forward(self, x):
        """
        Args:
            x (Tensor): Has shape (B, C, H, W). In most case, C is 3.

        Returns:
            tuple: Contains merged results and its spatial shape.

                - x (Tensor): Has shape (B, out_h * out_w, embed_dims)
                - out_size (tuple[int]): Spatial shape of x, arrange as
                    (out_h, out_w).
        """

        if self.adap_padding:
            x = self.adap_padding(x)

        x = self.projection(x)
        out_size = (x.shape[2], x.shape[3])
        x = x.flatten(2).transpose(1, 2)
        if self.norm is not None:
            x = self.norm(x)
        return x, out_size


class PatchMerging(BaseModule):
    """Merge patch feature map.

    This layer groups feature map by kernel_size, and applies norm and linear
    layers to the grouped feature map. Our implementation uses `nn.Unfold` to
    merge patch, which is about 25% faster than original implementation.
    Instead, we need to modify pretrained models for compatibility.

    Args:
        in_channels (int): The num of input channels.
            to gets fully covered by filter and stride you specified..
            Default: True.
        out_channels (int): The num of output channels.
        kernel_size (int | tuple, optional): the kernel size in the unfold
            layer. Defaults to 2.
        stride (int | tuple, optional): the stride of the sliding blocks in the
            unfold layer. Default: None. (Would be set as `kernel_size`)
        padding (int | tuple | string ): The padding length of
            embedding conv. When it is a string, it means the mode
            of adaptive padding, support "same" and "corner" now.
            Default: "corner".
        dilation (int | tuple, optional): dilation parameter in the unfold
            layer. Default: 1.
        bias (bool, optional): Whether to add bias in linear layer or not.
            Defaults: False.
        norm_cfg (dict, optional): Config dict for normalization layer.
            Default: dict(type='LN').
        init_cfg (dict, optional): The extra config for initialization.
            Default: None.
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size=2,
                 stride=None,
                 padding='corner',
                 dilation=1,
                 bias=False,
                 norm_cfg=dict(type='LN'),
                 init_cfg=None):
        super().__init__(init_cfg=init_cfg)
        self.in_channels = in_channels
        self.out_channels = out_channels
        if stride:
            stride = stride
        else:
            stride = kernel_size

        kernel_size = to_2tuple(kernel_size)
        stride = to_2tuple(stride)
        dilation = to_2tuple(dilation)

        if isinstance(padding, str):
            self.adap_padding = AdaptivePadding(
                kernel_size=kernel_size,
                stride=stride,
                dilation=dilation,
                padding=padding)
            # disable the padding of unfold
            padding = 0
        else:
            self.adap_padding = None

        padding = to_2tuple(padding)
        self.sampler = nn.Unfold(
            kernel_size=kernel_size,
            dilation=dilation,
            padding=padding,
            stride=stride)

        sample_dim = kernel_size[0] * kernel_size[1] * in_channels

        if norm_cfg is not None:
            self.norm = build_norm_layer(norm_cfg, sample_dim)[1]
        else:
            self.norm = None

        self.reduction = nn.Linear(sample_dim, out_channels, bias=bias)

    def forward(self, x, input_size):
        """
        Args:
            x (Tensor): Has shape (B, H*W, C_in).
            input_size (tuple[int]): The spatial shape of x, arrange as (H, W).
                Default: None.

        Returns:
            tuple: Contains merged results and its spatial shape.

                - x (Tensor): Has shape (B, Merged_H * Merged_W, C_out)
                - out_size (tuple[int]): Spatial shape of x, arrange as
                    (Merged_H, Merged_W).
        """
        B, L, C = x.shape
        assert isinstance(input_size, Sequence), f'Expect ' \
                                                 f'input_size is ' \
                                                 f'`Sequence` ' \
                                                 f'but get {input_size}'

        H, W = input_size
        assert L == H * W, 'input feature has wrong size'

        x = x.view(B, H, W, C).permute([0, 3, 1, 2])  # B, C, H, W
        # Use nn.Unfold to merge patch. About 25% faster than original method,
        # but need to modify pretrained model for compatibility

        if self.adap_padding:
            x = self.adap_padding(x)
            H, W = x.shape[-2:]

        x = self.sampler(x)
        # if kernel_size=2 and stride=2, x should has shape (B, 4*C, H/2*W/2)

        out_h = (H + 2 * self.sampler.padding[0] - self.sampler.dilation[0] *
                 (self.sampler.kernel_size[0] - 1) -
                 1) // self.sampler.stride[0] + 1
        out_w = (W + 2 * self.sampler.padding[1] - self.sampler.dilation[1] *
                 (self.sampler.kernel_size[1] - 1) -
                 1) // self.sampler.stride[1] + 1

        output_size = (out_h, out_w)
        x = x.transpose(1, 2)  # B, H/2*W/2, 4*C
        x = self.norm(x) if self.norm else x
        x = self.reduction(x)
        return x, output_size


class AdaptivePadding(nn.Module):
    """Applies padding to input (if needed) so that input can get fully covered
    by filter you specified. It support two modes "same" and "corner". The
    "same" mode is same with "SAME" padding mode in TensorFlow, pad zero around
    input. The "corner"  mode would pad zero to bottom right.

    Args:
        kernel_size (int | tuple): Size of the kernel:
        stride (int | tuple): Stride of the filter. Default: 1:
        dilation (int | tuple): Spacing between kernel elements.
            Default: 1
        padding (str): Support "same" and "corner", "corner" mode
            would pad zero to bottom right, and "same" mode would
            pad zero around input. Default: "corner".
    Example:
        >>> kernel_size = 16
        >>> stride = 16
        >>> dilation = 1
        >>> input = torch.rand(1, 1, 15, 17)
        >>> adap_pad = AdaptivePadding(
        >>>     kernel_size=kernel_size,
        >>>     stride=stride,
        >>>     dilation=dilation,
        >>>     padding="corner")
        >>> out = adap_pad(input)
        >>> assert (out.shape[2], out.shape[3]) == (16, 32)
        >>> input = torch.rand(1, 1, 16, 17)
        >>> out = adap_pad(input)
        >>> assert (out.shape[2], out.shape[3]) == (16, 32)
    """

    def __init__(self, kernel_size=1, stride=1, dilation=1, padding='corner'):

        super(AdaptivePadding, self).__init__()

        assert padding in ('same', 'corner')

        kernel_size = to_2tuple(kernel_size)
        stride = to_2tuple(stride)
        padding = to_2tuple(padding)
        dilation = to_2tuple(dilation)

        self.padding = padding
        self.kernel_size = kernel_size
        self.stride = stride
        self.dilation = dilation

    def get_pad_shape(self, input_shape):
        input_h, input_w = input_shape
        kernel_h, kernel_w = self.kernel_size
        stride_h, stride_w = self.stride
        output_h = math.ceil(input_h / stride_h)
        output_w = math.ceil(input_w / stride_w)
        pad_h = max((output_h - 1) * stride_h +
                    (kernel_h - 1) * self.dilation[0] + 1 - input_h, 0)
        pad_w = max((output_w - 1) * stride_w +
                    (kernel_w - 1) * self.dilation[1] + 1 - input_w, 0)
        return pad_h, pad_w

    def forward(self, x):
        pad_h, pad_w = self.get_pad_shape(x.size()[-2:])
        if pad_h > 0 or pad_w > 0:
            if self.padding == 'corner':
                x = F.pad(x, [0, pad_w, 0, pad_h])
            elif self.padding == 'same':
                x = F.pad(x, [
                    pad_w // 2, pad_w - pad_w // 2, pad_h // 2,
                    pad_h - pad_h // 2
                ])
        return x