diff --git a/pcdet/models/backbones_image/swin.py b/pcdet/models/backbones_image/swin.py
new file mode 100644
index 0000000..d428c27
--- /dev/null
+++ b/pcdet/models/backbones_image/swin.py
@@ -0,0 +1,736 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""
+Mostly copy-paste from 
+    https://github.com/open-mmlab/mmdetection/blob/main/mmdet/models/backbones/swin.py 
+    
+"""
+
+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 ..model_utils.swin_utils import swin_converter
+from ..model_utils.swin_utils import PatchEmbed, PatchMerging
+from ..model_utils.swin_utils import FFN, DropPath, to_2tuple, trunc_normal_, trunc_normal_init, constant_init
+
+
+class WindowMSA(nn.Module):
+    """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.
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 num_heads,
+                 window_size,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 attn_drop_rate=0.,
+                 proj_drop_rate=0.):
+
+        super().__init__()
+        self._is_init = False
+
+        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
+
+        # 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].
+        """
+        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(nn.Module):
+    """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.).
+    """
+
+    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.)):
+        super().__init__()
+        self._is_init = False
+
+        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,)
+        self.drop = DropPath(dropout_layer['drop_prob'])
+
+    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(nn.Module):
+    """"
+    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.
+    """
+
+    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,):
+        super(SwinBlock, self).__init__()
+        self._is_init = False
+
+        self.with_cp = with_cp
+
+        self.norm1 = nn.LayerNorm(embed_dims)
+        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),)
+
+        self.norm2 = nn.LayerNorm(embed_dims)
+        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,)
+
+    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(nn.Module):
+    """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.
+    """
+
+    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):
+        super().__init__()
+        self._is_init = False
+
+        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 = nn.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,)
+            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(nn.Module):
+    """ Swin Transformer
+    A PyTorch implement of : `Swin Transformer:
+    Hierarchical Vision Transformer using Shifted Windows`  -
+        https://arxiv.org/abs/2103.14030
+
+    This code is adapted from https://github.com/open-mmlab/mmdetection/blob/main/mmdet/models/backbones/swin.py 
+    with minimal modifications.
+
+    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='GELU').
+        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, model_cfg):
+        
+        self.model_cfg = model_cfg
+        pretrain_img_size = self.model_cfg.get('PRETRAIN_IMG_SIZE', 224)
+        init_cfg = self.model_cfg.get('INIT_CFG', None)
+        depths = self.model_cfg.DEPTHS
+        in_channels = self.model_cfg.get('IN_CHANNELS', 3)
+        strides = self.model_cfg.get('STRIDES', (4, 2, 2, 2))
+        patch_size = self.model_cfg.get('PATCH_SIZE', 4)
+        embed_dims = self.model_cfg.EMBED_DIMS
+        num_heads = self.model_cfg.NUM_HEADS
+        window_size = self.model_cfg.WINDOW_SIZE
+        mlp_ratio = self.model_cfg.MLP_RATIO
+        qkv_bias = self.model_cfg.get('QKV_BIAS', True)
+        qk_scale = self.model_cfg.get('QK_SCALE', None)
+        drop_rate = self.model_cfg.DROP_RATE
+        attn_drop_rate = self.model_cfg.ATTN_DROP_RATE
+        drop_path_rate = self.model_cfg.DROP_PATH_RATE
+        patch_norm = self.model_cfg.get('PATCH_NORM', True)
+        out_indices = self.model_cfg.get('OUT_INDICES', [0, 1, 2, 3])
+        with_cp = self.model_cfg.get('WITH_CP', False)
+        use_abs_pos_embed = self.model_cfg.get('USE_ABS_POS_EMBED', False)
+        act_cfg=dict(type='GELU')
+        norm_cfg=dict(type='LN')
+
+        self.convert_weights = self.model_cfg.get('CONVERT_WEIGHTS', False)
+        self.frozen_stages = self.model_cfg.get('FROZEN_STAGES', -1)
+
+        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)}'
+
+        super(SwinTransformer, self).__init__()
+        self.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)
+
+        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 = nn.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)
+            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)
+            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 =  nn.LayerNorm(self.num_features[i])
+            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):
+        if self.init_cfg is None:
+            print(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 = torch.load(self.init_cfg.checkpoint, 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:
+                    print('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:
+                    print(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, batch_dict):
+        x = batch_dict['camera_imgs']
+        B, N, C, H, W = x.size()
+        x = x.view(B * N, C, H, W)
+        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)
+        batch_dict['image_features'] = outs
+        return batch_dict