maskrcnn_benchmark/modeling/backbone/swint_v2_vl.py

# --------------------------------------------------------
# Swin Transformer
# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py
# --------------------------------------------------------

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
from einops import rearrange
from timm.models.layers import DropPath, to_2tuple, trunc_normal_


class Mlp(nn.Module):
    """Multilayer perceptron."""

    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


def window_partition(x, window_size):
    """

    Args:

        x: (B, H, W, C)

        window_size (int): window size

    Returns:

        windows: (num_windows*B, window_size, window_size, C)

    """
    B, H, W, C = x.shape
    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().view(-1, window_size, window_size, C)
    return windows


def window_reverse(windows, window_size, H, W):
    """

    Args:

        windows: (num_windows*B, window_size, window_size, C)

        window_size (int): Window size

        H (int): Height of image

        W (int): Width of image

    Returns:

        x: (B, H, W, C)

    """
    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


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

    It supports both of shifted and non-shifted window.

    Args:

        dim (int): Number of input channels.

        window_size (tuple[int]): The height and width of the window.

        num_heads (int): Number of attention heads.

        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

        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0

        proj_drop (float, optional): Dropout ratio of output. Default: 0.0

    """

    def __init__(

        self,

        dim,

        window_size,

        num_heads,

        qkv_bias=True,

        qk_scale=None,

        attn_drop=0.0,

        proj_drop=0.0,

        ntext=None,

        dim_text=None,

    ):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-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

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size[0])
        coords_w = torch.arange(self.window_size[1])
        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        self.register_buffer("relative_position_index", relative_position_index)

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

        trunc_normal_(self.relative_position_bias_table, std=0.02)
        self.softmax = nn.Softmax(dim=-1)

        if ntext is not None:
            self.qkv_text = nn.Linear(dim_text, dim * 3, bias=qkv_bias)
            self.proj_text = nn.Linear(dim, dim_text)

            self.i2t_relative_position_bias = nn.Parameter(torch.zeros(2, num_heads, ntext))  # (2, nH, ntext)
            self.t2t_relative_position_bias = nn.Parameter(torch.zeros(num_heads, ntext, ntext))  # (nH, ntext, ntext)
            trunc_normal_(self.i2t_relative_position_bias, std=0.02)
            trunc_normal_(self.t2t_relative_position_bias, std=0.02)

    def forward(self, x, mask=None, x_text=None, mask_text=None):
        """Forward function.

        Args:

            x: input features with shape of (num_windows*B, N, C)

            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None

            x_text: input text features with shape of (B_text, N_text, C_text)

            mask_text: (0/-inf) mask with shape of (B_text, N_text) or None; TODO: support casual mask

        """
        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)
        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

        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)

        if x_text is not None:
            B_text, N_text, C_text = x_text.shape
            nW = B_ // B_text  # number of windows
            assert B_text * nW == B_, "B_ is not a multiplier of B_text in window attention"
            # notice that after qkv_text, the hidden dimension is C instead of C_text
            qkv_text = (
                self.qkv_text(x_text)
                .reshape(B_text, N_text, 3, self.num_heads, C // self.num_heads)
                .permute(2, 0, 3, 1, 4)
            )
            q_text, k_text, v_text = (
                qkv_text[0],
                qkv_text[1],
                qkv_text[2],
            )  # make torchscript happy (cannot use tensor as tuple)

            # image to text attention
            attn_i2t = q @ torch.repeat_interleave(k_text, nW, dim=0).transpose(-2, -1)  # B_, nH, N, N_text
            # add image to text bias and text_mask
            if mask_text is not None:
                mask_and_i2t_bias = mask_text.view(B_text, 1, 1, N_text) + self.i2t_relative_position_bias[:1].expand(
                    B_text, -1, -1
                ).unsqueeze(
                    -2
                )  # B_text, nH, 1, N_text
            else:
                mask_and_i2t_bias = (
                    self.i2t_relative_position_bias[:1].expand(B_text, -1, -1).unsqueeze(-2)
                )  # B_text, nH, 1, N_text
            attn_i2t = attn_i2t + torch.repeat_interleave(mask_and_i2t_bias, nW, dim=0)

            attn = torch.cat((attn, attn_i2t), dim=-1)  # B_, nH, N, N+N_text

        attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        if x_text is None:
            x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        else:
            x = (
                (attn @ torch.cat((v, torch.repeat_interleave(v_text, nW, dim=0)), dim=-2))
                .transpose(1, 2)
                .reshape(B_, N, C)
            )

            # compute attn_t2i
            q_text = q_text * self.scale

            kv = qkv[1:].reshape(2, B_text, nW, self.num_heads, N, C // self.num_heads).transpose(2, 3)
            k, v = kv[0].reshape(B_text, self.num_heads, nW * N, -1), kv[1].reshape(B_text, self.num_heads, nW * N, -1)
            attn_t2i = q_text @ k.transpose(-2, -1)
            mask_t2i = self.i2t_relative_position_bias[1:].expand(B_text, -1, -1).unsqueeze(-1)  # B_text, nH, N_text, 1
            attn_t2i = attn_t2i + mask_t2i

            attn_t2t = q_text @ k_text.transpose(-2, -1)
            # add relative positional bias
            attn_t2t = attn_t2t + self.t2t_relative_position_bias.unsqueeze(0)
            if mask_text is not None:
                attn_t2t = attn_t2t + mask_text.view(B_text, 1, 1, N_text)

            attn_t = torch.cat((attn_t2i, attn_t2t), dim=-1)  # B_text, nH, N_text, N+N_text
            attn_t = self.softmax(attn_t)
            attn_t = self.attn_drop(attn_t)

            x_text = (attn_t @ torch.cat((v, v_text), dim=-2)).transpose(1, 2).reshape(B_text, N_text, C)

            x_text = self.proj_text(x_text)
            x_text = self.proj_drop(x_text)

        x = self.proj(x)
        x = self.proj_drop(x)
        return x, x_text


class SwinTransformerBlock(nn.Module):
    """Swin Transformer Block.

    Args:

        dim (int): Number of input channels.

        num_heads (int): Number of attention heads.

        window_size (int): Window size.

        shift_size (int): Shift size for SW-MSA.

        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.

        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.

        drop (float, optional): Dropout rate. Default: 0.0

        attn_drop (float, optional): Attention dropout rate. Default: 0.0

        drop_path (float, optional): Stochastic depth rate. Default: 0.0

        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU

        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm

    """

    def __init__(

        self,

        dim,

        num_heads,

        window_size=7,

        shift_size=0,

        mlp_ratio=4.0,

        qkv_bias=True,

        qk_scale=None,

        drop=0.0,

        attn_drop=0.0,

        drop_path=0.0,

        act_layer=nn.GELU,

        norm_layer=nn.LayerNorm,

        layer_scale=False,

        ntext=None,

        dim_text=None,

    ):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim,
            window_size=to_2tuple(self.window_size),
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop,
            ntext=ntext,
            dim_text=dim_text,
        )

        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

        self.H = None
        self.W = None

        self.gamma = 1.0
        if layer_scale:
            self.gamma = nn.Parameter(1e-4 * torch.ones(dim), requires_grad=True)

        if dim_text is not None:
            self.norm1_text = norm_layer(dim_text)
            self.norm2_text = norm_layer(dim_text)
            mlp_hidden_dim_text = int(dim_text * mlp_ratio)
            self.mlp_text = Mlp(
                in_features=dim_text, hidden_features=mlp_hidden_dim_text, act_layer=act_layer, drop=drop
            )
            self.gamma_text = 1.0
            if layer_scale:
                self.gamma_text = nn.Parameter(1e-4 * torch.ones(dim_text), requires_grad=True)

    def forward(self, x, mask_matrix, x_text, mask_text):
        """Forward function.

        Args:

            x: Input feature, tensor size (B, H*W, C).

            H, W: Spatial resolution of the input feature.

            mask_matrix: Attention mask for cyclic shift.

            x_text: Input text feature, tensor size (B, L_text, C_text). L_text: Number of text tokens.

            mask_text: text mask (vector right now).

        """
        B, L, C = x.shape
        H, W = self.H, self.W
        assert L == H * W, "input feature has wrong size"

        if x_text is not None:
            B, L_text, C_text = x_text.shape
            shortcut_text = x_text
            x_text = self.norm1_text(x_text)

        shortcut = x
        x = self.norm1(x)
        x = x.view(B, H, W, C)

        # pad feature maps to multiples of window size
        pad_l = pad_t = 0
        pad_r = (self.window_size - W % self.window_size) % self.window_size
        pad_b = (self.window_size - H % self.window_size) % self.window_size
        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
        _, Hp, Wp, _ = x.shape

        # cyclic shift
        if self.shift_size > 0:
            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
            attn_mask = mask_matrix
        else:
            shifted_x = x
            attn_mask = None

        # partition windows
        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C

        # W-MSA/SW-MSA
        attn_windows, x_text = self.attn(
            x_windows, mask=attn_mask, x_text=x_text, mask_text=mask_text
        )  # nW*B, window_size*window_size, C

        # merge windows
        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C

        # 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 > 0:
            x = x[:, :H, :W, :].contiguous()

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

        # FFN
        x = shortcut + self.drop_path(self.gamma * x)
        x = x + self.drop_path(self.gamma * self.mlp(self.norm2(x)))

        if x_text is not None:
            x_text = shortcut_text + self.drop_path(self.gamma_text * x_text)
            x_text = x_text + self.drop_path(self.gamma_text * self.mlp_text(self.norm2_text(x_text)))

        return x, x_text


class PatchMerging(nn.Module):
    """Patch Merging Layer

    Args:

        dim (int): Number of input channels.

        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm

    """

    def __init__(self, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def forward(self, x, H, W):
        """Forward function.

        Args:

            x: Input feature, tensor size (B, H*W, C).

            H, W: Spatial resolution of the input feature.

        """
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

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

        # padding
        pad_input = (H % 2 == 1) or (W % 2 == 1)
        if pad_input:
            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C

        x = self.norm(x)
        x = self.reduction(x)

        return x


class BasicLayer(nn.Module):
    """A basic Swin Transformer layer for one stage.

    Args:

        dim (int): Number of feature channels

        depth (int): Depths of this stage.

        num_heads (int): Number of attention head.

        window_size (int): Local window size. Default: 7.

        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.

        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.

        drop (float, optional): Dropout rate. Default: 0.0

        attn_drop (float, optional): Attention dropout rate. Default: 0.0

        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0

        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm

        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None

        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.

    """

    def __init__(

        self,

        dim,

        depth,

        num_heads,

        window_size=7,

        mlp_ratio=4.0,

        qkv_bias=True,

        qk_scale=None,

        drop=0.0,

        attn_drop=0.0,

        drop_path=0.0,

        norm_layer=nn.LayerNorm,

        downsample=None,

        use_checkpoint=False,

        layer_scale=False,

        ntext=None,

        dim_text=None,

    ):
        super().__init__()
        self.window_size = window_size
        self.shift_size = window_size // 2
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList(
            [
                SwinTransformerBlock(
                    dim=dim,
                    num_heads=num_heads,
                    window_size=window_size,
                    shift_size=0 if (i % 2 == 0) else window_size // 2,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=drop,
                    attn_drop=attn_drop,
                    drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                    norm_layer=norm_layer,
                    layer_scale=layer_scale,
                    ntext=ntext,
                    dim_text=dim_text,
                )
                for i in range(depth)
            ]
        )

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(
                patch_size=3, in_chans=dim, embed_dim=dim * 2, stride=2, padding=1, norm_layer=norm_layer
            )
        else:
            self.downsample = None

    def forward(self, x, H, W, x_text=None, mask_text=None):
        """Forward function.

        Args:

            x: Input feature, tensor size (B, H*W, C).

            H, W: Spatial resolution of the input feature.

            x_text: input text features with shape of (B_text, N_text, C_text)

            mask_text: (0/-inf) mask with shape of (B_text, N_text) or None;

        """

        # calculate attention mask for SW-MSA
        Hp = int(np.ceil(H / self.window_size)) * self.window_size
        Wp = int(np.ceil(W / self.window_size)) * self.window_size
        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
        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

        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
        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))

        for blk in self.blocks:
            blk.H, blk.W = H, W
            if self.use_checkpoint:
                x, x_text = checkpoint.checkpoint(blk, x, attn_mask, x_text, mask_text)
            else:
                x, x_text = blk(x, attn_mask, x_text, mask_text)
        if self.downsample is not None:
            x_down = self.downsample(x, H, W)
            Wh, Ww = (H + 1) // 2, (W + 1) // 2
            return x, H, W, x_down, Wh, Ww, x_text
        else:
            return x, H, W, x, H, W, x_text


# class PatchEmbed(nn.Module):
#     """ Image to Patch Embedding
#     Args:
#         patch_size (int): Patch token size. Default: 4.
#         in_chans (int): Number of input image channels. Default: 3.
#         embed_dim (int): Number of linear projection output channels. Default: 96.
#         norm_layer (nn.Module, optional): Normalization layer. Default: None
#     """
#
#     def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
#         super().__init__()
#         patch_size = to_2tuple(patch_size)
#         self.patch_size = patch_size
#
#         self.in_chans = in_chans
#         self.embed_dim = embed_dim
#
#         self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
#         if norm_layer is not None:
#             self.norm = norm_layer(embed_dim)
#         else:
#             self.norm = None
#
#     def forward(self, x):
#         """Forward function."""
#         # padding
#         _, _, H, W = x.size()
#         if W % self.patch_size[1] != 0:
#             x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
#         if H % self.patch_size[0] != 0:
#             x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
#
#         x = self.proj(x)  # B C Wh Ww
#         if self.norm is not None:
#             Wh, Ww = x.size(2), x.size(3)
#             x = x.flatten(2).transpose(1, 2)
#             x = self.norm(x)
#             x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
#
#         return x


class ConvEmbed(nn.Module):
    """Image to Patch Embedding"""

    def __init__(self, patch_size=7, in_chans=3, embed_dim=64, stride=4, padding=2, norm_layer=None):
        super().__init__()
        self.patch_size = patch_size
        self.embed_dim = embed_dim

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride, padding=padding)
        self.norm = norm_layer(embed_dim) if norm_layer else None

    def forward(self, x, H=None, W=None):
        restore_hw = False
        if H is None and W is None and len(x.size()) == 4:
            _, _, H, W = x.size()
            if W % self.patch_size != 0:
                x = F.pad(x, (0, self.patch_size - W % self.patch_size))
            if H % self.patch_size != 0:
                x = F.pad(x, (0, 0, 0, self.patch_size - H % self.patch_size))
            restore_hw = True

        if len(x.size()) == 3:
            x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W)
        x = self.proj(x)  # B C Wh Ww
        B, C, Wh, Ww = x.shape
        x = rearrange(x, "b c h w -> b (h w) c")
        if self.norm:
            x = self.norm(x)

        if restore_hw:
            x = rearrange(x, "b (h w) c -> b c h w", h=Wh, w=Ww)

        return x


class SwinTransformer(nn.Module):
    """Swin Transformer backbone.

        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -

          https://arxiv.org/pdf/2103.14030

    Args:

        pretrain_img_size (int): Input image size for training the pretrained model,

            used in absolute postion embedding. Default 224.

        patch_size (int | tuple(int)): Patch size. Default: 4.

        in_chans (int): Number of input image channels. Default: 3.

        embed_dim (int): Number of linear projection output channels. Default: 96.

        depths (tuple[int]): Depths of each Swin Transformer stage.

        num_heads (tuple[int]): Number of attention head of each stage.

        window_size (int): Window size. Default: 7.

        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.

        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True

        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.

        drop_rate (float): Dropout rate.

        attn_drop_rate (float): Attention dropout rate. Default: 0.

        drop_path_rate (float): Stochastic depth rate. Default: 0.2.

        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.

        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.

        patch_norm (bool): If True, add normalization after patch embedding. Default: True.

        out_indices (Sequence[int]): Output from which stages.

        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).

            -1 means not freezing any parameters.

        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.

    """

    def __init__(

        self,

        pretrain_img_size=224,

        patch_size=7,

        patch_padding=2,

        patch_stride=4,

        in_chans=3,

        embed_dim=96,

        depths=[2, 2, 6, 2],

        num_heads=[3, 6, 12, 24],

        window_size=7,

        mlp_ratio=4.0,

        qkv_bias=True,

        qk_scale=None,

        drop_rate=0.0,

        attn_drop_rate=0.0,

        drop_path_rate=0.2,

        norm_layer=nn.LayerNorm,

        ape=False,

        patch_norm=True,

        frozen_stages=-1,

        use_checkpoint=False,

        layer_scale=False,

        out_features=["stage2", "stage3", "stage4", "stage5"],

        out_norm=True,

        backbone_arch="SWINT-FPN-RETINANET",

        max_query_len=None,

        lang_dim=None,

    ):
        super(SwinTransformer, self).__init__()

        print("VISION BACKBONE USE GRADIENT CHECKPOINTING: ", use_checkpoint)

        self.pretrain_img_size = pretrain_img_size
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.frozen_stages = frozen_stages

        self.out_features = out_features
        self.out_norm = out_norm

        # split image into non-overlapping patches
        # self.patch_embed = PatchEmbed(
        #     patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
        #     norm_layer=norm_layer if self.patch_norm else None)
        self.patch_embed = ConvEmbed(
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
            padding=patch_padding,
            norm_layer=norm_layer if self.patch_norm else None,
        )

        # absolute position embedding
        if self.ape:
            pretrain_img_size = to_2tuple(pretrain_img_size)
            patch_size = to_2tuple(patch_size)
            patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]

            self.absolute_pos_embed = nn.Parameter(
                torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])
            )
            trunc_normal_(self.absolute_pos_embed, std=0.02)

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

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

        self._out_feature_strides = {}
        self._out_feature_channels = {}

        # build layers
        self.layers = nn.ModuleList()
        for i_layer in range(self.num_layers):
            if i_layer < self.num_layers - 1:
                ntext, dim_text = None, None
            else:
                ntext, dim_text = max_query_len, lang_dim
            layer = BasicLayer(
                dim=int(embed_dim * 2**i_layer),
                depth=depths[i_layer],
                num_heads=num_heads[i_layer],
                window_size=window_size,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop_rate,
                attn_drop=attn_drop_rate,
                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
                norm_layer=norm_layer,
                downsample=ConvEmbed if (i_layer < self.num_layers - 1) else None,
                use_checkpoint=use_checkpoint and i_layer > self.frozen_stages - 1,
                layer_scale=layer_scale,
                ntext=ntext,
                dim_text=dim_text,
            )
            self.layers.append(layer)

            stage = f"stage{i_layer + 2}"
            if stage in self.out_features:
                self._out_feature_channels[stage] = embed_dim * 2**i_layer
                self._out_feature_strides[stage] = 4 * 2**i_layer

        num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
        self.num_features = num_features

        # add a norm layer for each output
        if self.out_norm:
            for i_layer in range(self.num_layers):
                stage = f"stage{i_layer + 2}"
                if stage in self.out_features:
                    if i_layer == 0 and backbone_arch.endswith("RETINANET"):
                        layer = nn.Identity()
                    else:
                        layer = norm_layer(num_features[i_layer])
                    layer_name = f"norm{i_layer}"
                    self.add_module(layer_name, layer)

        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.frozen_stages >= 1 and self.ape:
            self.absolute_pos_embed.requires_grad = False

        if self.frozen_stages >= 2:
            self.pos_drop.eval()
            for i in range(0, self.frozen_stages - 1):
                m = self.layers[i]
                m.eval()
                for param in m.parameters():
                    param.requires_grad = False

    def init_weights(self, pretrained=None):
        """Initialize the weights in backbone.

        Args:

            pretrained (str, optional): Path to pre-trained weights.

                Defaults to None.

        """

        def _init_weights(m):
            if isinstance(m, nn.Linear):
                trunc_normal_(m.weight, std=0.02)
                if isinstance(m, nn.Linear) and m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.LayerNorm):
                nn.init.constant_(m.bias, 0)
                nn.init.constant_(m.weight, 1.0)

        self.apply(_init_weights)

    def forward(self, inputs):
        """Forward function."""
        x = inputs["img"]
        language_dict_features = inputs["lang"]

        x = self.patch_embed(x)

        Wh, Ww = x.size(2), x.size(3)
        if self.ape:
            # interpolate the position embedding to the corresponding size
            absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic")
            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
        else:
            x = x.flatten(2).transpose(1, 2)
        x = self.pos_drop(x)

        x_text = language_dict_features["hidden"]
        if "masks" in language_dict_features:
            mask_text = 1.0 - language_dict_features["masks"]  # (B, N_text) 0 means not to be masked out
            mask_text.masked_fill_(mask_text.bool(), -float("inf"))
        else:
            mask_text = None

        outs = []
        for i in range(self.num_layers):
            layer = self.layers[i]
            if i < self.num_layers - 1:
                x_out, H, W, x, Wh, Ww, _ = layer(x, Wh, Ww, x_text=None, mask_text=None)
            else:
                x_out, H, W, x, Wh, Ww, x_text = layer(x, Wh, Ww, x_text=x_text, mask_text=mask_text)
            name = f"stage{i + 2}"
            if name in self.out_features:
                if self.out_norm:
                    norm_layer = getattr(self, f"norm{i}")
                    x_out = norm_layer(x_out)
                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
                outs.append(out)

        # the backbone only update the "hidden" field, currently
        language_dict_features["hidden"] = x_text

        return outs, language_dict_features

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


def build_swint_backbone(cfg):
    """

    Create a SwinT instance from config.



    Returns:

        VoVNet: a :class:`VoVNet` instance.

    """
    return SwinTransformer(
        patch_size=7,
        patch_padding=2,
        patch_stride=4,
        in_chans=3,
        embed_dim=cfg.MODEL.SWINT.EMBED_DIM,
        depths=cfg.MODEL.SWINT.DEPTHS,
        num_heads=cfg.MODEL.SWINT.NUM_HEADS,
        window_size=cfg.MODEL.SWINT.WINDOW_SIZE,
        mlp_ratio=cfg.MODEL.SWINT.MLP_RATIO,
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=cfg.MODEL.SWINT.DROP_PATH_RATE,
        norm_layer=nn.LayerNorm,
        ape=cfg.MODEL.SWINT.APE,
        patch_norm=True,
        frozen_stages=cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT,
        backbone_arch=cfg.MODEL.BACKBONE.CONV_BODY,
        use_checkpoint=cfg.MODEL.BACKBONE.USE_CHECKPOINT,
        layer_scale=cfg.MODEL.SWINT.LAYER_SCALE,
        out_features=cfg.MODEL.BACKBONE.OUT_FEATURES,
        out_norm=cfg.MODEL.SWINT.OUT_NORM,
        max_query_len=cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN,
        lang_dim=cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM,
    )