maskrcnn_benchmark/modeling/backbone/fusion_swin_transformer_v2.py

""" Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
    - https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
"""
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import logging
import math
from copy import deepcopy
from typing import Optional

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
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, 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)

        # dim_text = 768
        if dim_text is not None:
            self.qkv_text_i2t = nn.Linear(dim_text, dim * 2, bias=qkv_bias)
            self.qkv_i2t = nn.Linear(dim, dim, bias=qkv_bias)
            self.attn_drop_i2t = nn.Dropout(attn_drop)
            self.proj_i2t = nn.Linear(dim, dim)
            self.proj_drop_i2t = nn.Dropout(proj_drop)
            # self.proj_i2t = nn.Linear(dim, dim)
            self.alpha_i2t = nn.Parameter(torch.Tensor([0]))

            # self.gate_i2t = nn.Linear(2*dim, 1)
            # self.gate_i2t = nn.Linear(2*dim, dim)
            # self.sigmoid_i2t = nn.Sigmoid()

            """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=.02)
            trunc_normal_(self.t2t_relative_position_bias, std=.02)#"""

    def forward(self, x, mask: Optional[torch.Tensor] = None, y=None, y_mask=None):
        """
        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
        """
        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)

        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)

        if y is not None:
            B_text, N_text, C_text = y.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_i2t(y)
                .reshape(B_text, N_text, 2, self.num_heads, C // self.num_heads)
                .permute(2, 0, 3, 1, 4)
            )
            k_text, v_text = qkv_text[0], qkv_text[1]

            k_text = torch.repeat_interleave(k_text, nW, dim=0)
            v_text = torch.repeat_interleave(v_text, nW, dim=0)
            # TODO: remove q_text
            q_i2t = self.qkv_i2t(x).reshape(B_, N, 1, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
            q_i2t = q_i2t[0]

            # image to text attention
            # attn_i2t = (q_i2t @ torch.repeat_interleave(k_text, nW, dim=0).transpose(-2, -1))  # B_, nH, N, N_text
            # print(q_i2t.size())
            # print(k_text.size())
            # torch.Size([4096, 4, 49, 32])
            # torch.Size([4096, 4, 50, 32])
            text_scale = k_text.size(-1) ** -0.5
            q_i2t = q_i2t * text_scale
            attn_i2t = q_i2t @ k_text.transpose(-2, -1)  # B_, nH, N, N_text
            # add image to text bias and text_mask
            if y_mask is not None:
                mask_and_i2t_bias = y_mask.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
                attn_i2t = attn_i2t + torch.repeat_interleave(mask_and_i2t_bias, nW, dim=0)

            attn_i2t = self.softmax(attn_i2t)
            attn_i2t = self.attn_drop_i2t(attn_i2t)
            # torch.Size([4096, 4, 49, 50])
            # torch.Size([64, 4, 50, 32])
            # print(attn_i2t.size())
            # print(v_text.size())
            # 1/0
            y = (attn_i2t @ v_text).transpose(1, 2).reshape(B_, N, C)
            y = self.proj_i2t(y)
            y = self.proj_drop_i2t(y)

            # g = torch.cat([x, y], dim=-1)
            # g = (self.gate_i2t(g))
            # g = self.sigmoid_i2t(self.gate_i2t(g))
            # x = x+g*y

            x = x + self.alpha_i2t * y

        return x


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
        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,
        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,
            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

    def forward(self, x, mask_matrix, x_text=None, mask_text=None):
        B, L, C = x.shape
        H, W = self.H, self.W
        assert L == H * W, "input feature has wrong size"

        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 = self.attn(
            x_windows, mask=attn_mask, y=x_text, y_mask=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(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x


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"
        # TODO: Keep?
        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."

        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

    # TODO: Keep?
    # def extra_repr(self) -> str:
    #     return f"input_resolution={self.input_resolution}, dim={self.dim}"
    #
    # def flops(self):
    #     H, W = self.input_resolution
    #     flops = H * W * self.dim
    #     flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
    #     return flops


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,
        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,
        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,
                    dim_text=(768 if i >= 14 else dim_text),
                )
                for i in range(depth)
            ]
        )

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
        else:
            self.downsample = None

    def get_attention_mask(self, H, W, device):
        # 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=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))

        return attn_mask

    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;
        """
        attn_mask = self.get_attention_mask(H, W, x.device)

        for blk in self.blocks:
            blk.H, blk.W = H, W
            if not torch.jit.is_scripting() and self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x, attn_mask, x_text, mask_text)
            else:
                x = blk(x, mask_matrix=attn_mask, x_text=x_text, mask_text=mask_text)
            # print(x.size())
        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
        else:
            return x, H, W, x, H, W

    # TODO: Keep?
    # def extra_repr(self) -> str:
    #     return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"


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

        # 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,
        )

        # 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):
            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=PatchMerging if (i_layer < self.num_layers - 1) else None,
                use_checkpoint=use_checkpoint and i_layer > self.frozen_stages - 1,
                dim_text=(768 if i_layer == 3 else None),
            )  # TODO: Make this general : lang_dim not 768
            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

        # TODO : need this?
        # assert weight_init in ('jax', 'jax_nlhb', 'nlhb', '')
        # head_bias = -math.log(self.num_classes) if 'nlhb' in weight_init else 0.
        # if weight_init.startswith('jax'):
        #     for n, m in self.named_modules():
        #         _init_vit_weights(m, n, head_bias=head_bias, jax_impl=True)
        # else:
        #     self.apply(_init_vit_weights)

        # add a norm layer for each output
        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 layer_i, layer in enumerate(self.layers):
            # if layer_i > 1:
            # if layer_i > 2:
            if layer_i > -1:
                x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww, x_text=x_text, mask_text=mask_text)
            else:
                x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww, x_text=None, mask_text=None)
            name = f"stage{layer_i + 2}"
            if name in self.out_features:
                norm_layer = getattr(self, f"norm{layer_i}")
                x_out = norm_layer(x_out)
                out = x_out.view(-1, H, W, self.num_features[layer_i]).permute(0, 3, 1, 2).contiguous()
                outs.append(out)

        # Here the text features are just combined directly with the image features, so language_dict_features is unchanged
        return outs, language_dict_features

    @torch.jit.ignore
    def no_weight_decay(self):
        return {"absolute_pos_embed"}

    @torch.jit.ignore
    def no_weight_decay_keywords(self):
        return {"relative_position_bias_table"}

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


class FusionSwinTransformer(nn.Module):
    def __init__(self, vision_backbone, language_backbone, add_linear_layer=False):
        super().__init__()
        self.backbone = vision_backbone
        self.language_backbone = language_backbone
        # self.cross_modal_image_transform2 = nn.Linear(1024, 768)
        # self.cross_modal_image_transform3 = nn.Linear(1024, 768)
        self.add_linear_layer = add_linear_layer
        if self.add_linear_layer:
            self.tunable_linear = torch.nn.Linear(
                self.language_backbone.body.cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM, 1000, bias=False
            )
            self.tunable_linear.weight.data.fill_(0.0)

    def forward(
        self,
        tokenizer_input,
        images,
    ):

        # Fusion in the backbone forward - interleaves the passed through the langauge and image backbone.
        x = images.tensors

        # Embed the image
        x = self.backbone.body.patch_embed(x)
        Wh, Ww = x.size(2), x.size(3)

        if self.backbone.body.ape:
            # interpolate the position embedding to the corresponding size
            absolute_pos_embed = F.interpolate(self.backbone.body.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)
        image_embeds = self.backbone.body.pos_drop(x)

        # Embed the text
        text_embeds = self.language_backbone.body.model.embeddings(input_ids=tokenizer_input["input_ids"])
        input_shape = tokenizer_input["attention_mask"].size()
        extended_text_masks = self.language_backbone.body.model.get_extended_attention_mask(
            tokenizer_input["attention_mask"], input_shape, device=tokenizer_input["attention_mask"].device
        )

        if self.add_linear_layer:
            text_embeds = self.tunable_linear.weight[: text_embeds.size(1), :].unsqueeze(0) + text_embeds

        outs = []
        # Pass the text through the first 10 layers
        num_pre_text = 6
        for layer_i, layer in enumerate(self.language_backbone.body.model.encoder.layer[:num_pre_text]):
            text_embeds = layer(text_embeds, extended_text_masks)[0]

        # Pass through first 2 image backbone layers
        num_pre_vision = 2
        for layer_i, layer in enumerate(self.backbone.body.layers[:num_pre_vision]):
            x_out, H, W, image_embeds, Wh, Ww = layer(image_embeds, Wh, Ww, x_text=None, mask_text=None)
            name = f"stage{layer_i + 2}"
            if name in self.backbone.body.out_features:
                norm_layer = getattr(self.backbone.body, f"norm{layer_i}")
                x_out = norm_layer(x_out)
                out = x_out.view(-1, H, W, self.backbone.body.num_features[layer_i]).permute(0, 3, 1, 2).contiguous()
                outs.append(out)

        num_pre_block = 14
        # Get the attention mask for the third layer:
        attn_mask = self.backbone.body.layers[num_pre_vision].get_attention_mask(Wh, Ww, image_embeds.device)
        for blk_cnt, blk in enumerate(self.backbone.body.layers[num_pre_vision].blocks):
            blk.H, blk.W = Wh, Ww
            if blk_cnt < num_pre_block:
                if not torch.jit.is_scripting() and self.backbone.body.layers[num_pre_vision].use_checkpoint:
                    image_embeds = checkpoint.checkpoint(blk, image_embeds, attn_mask)
                else:
                    image_embeds = blk(image_embeds, attn_mask)
            else:
                if not torch.jit.is_scripting() and self.backbone.body.layers[num_pre_vision].use_checkpoint:
                    fused_image_embeds = checkpoint.checkpoint(
                        blk, image_embeds, attn_mask, text_embeds, extended_text_masks
                    )
                else:
                    fused_image_embeds = blk(image_embeds, attn_mask, text_embeds, extended_text_masks)
                text_embeds = self.language_backbone.body.model.encoder.layer[blk_cnt - num_pre_block + num_pre_text](
                    text_embeds, extended_text_masks, encoder_hidden_states=(image_embeds)
                )[0]
                image_embeds = fused_image_embeds

        # Apply layer norm after 3rd layer and take output
        name = f"stage{num_pre_vision + 2}"
        if name in self.backbone.body.out_features:
            norm_layer = getattr(self.backbone.body, f"norm{num_pre_vision}")
            x_out = norm_layer(image_embeds)
            out = (
                x_out.view(-1, Wh, Ww, self.backbone.body.num_features[num_pre_vision]).permute(0, 3, 1, 2).contiguous()
            )
            outs.append(out)

        # Apply downsampling if we need to at the output of third layer for input to next layer
        if self.backbone.body.layers[num_pre_vision].downsample is not None:
            image_embeds = self.backbone.body.layers[num_pre_vision].downsample(image_embeds, Wh, Ww)
            Wh, Ww = (Wh + 1) // 2, (Ww + 1) // 2

        # Final layer

        # Get attention mask for 4th layer
        attn_mask = self.backbone.body.layers[num_pre_vision + 1].get_attention_mask(Wh, Ww, image_embeds.device)
        blk = self.backbone.body.layers[num_pre_vision + 1].blocks[0]
        blk.H, blk.W = Wh, Ww

        fuse_image_embeds = blk(
            x=image_embeds, mask_matrix=attn_mask, x_text=text_embeds, mask_text=extended_text_masks
        )

        fuse_text_embeds = self.language_backbone.body.model.encoder.layer[-2](
            text_embeds, extended_text_masks, encoder_hidden_states=(image_embeds)
        )[0]
        text_embeds, image_embeds = fuse_text_embeds, fuse_image_embeds

        blk = self.backbone.body.layers[num_pre_vision + 1].blocks[1]
        blk.H, blk.W = Wh, Ww
        fuse_image_embeds = self.backbone.body.layers[num_pre_vision + 1].blocks[1](
            x=image_embeds, mask_matrix=attn_mask, x_text=text_embeds, mask_text=extended_text_masks
        )

        fuse_text_embeds = self.language_backbone.body.model.encoder.layer[-1](
            text_embeds, extended_text_masks, encoder_hidden_states=(image_embeds)
        )[0]
        text_embeds, image_embeds = fuse_text_embeds, fuse_image_embeds

        # Apply layer norm after 4th layer and take output
        name = f"stage{num_pre_vision + 1 + 2}"
        if name in self.backbone.body.out_features:
            norm_layer = getattr(self.backbone.body, f"norm{num_pre_vision + 1}")
            x_out = norm_layer(image_embeds)
            out = (
                x_out.view(-1, Wh, Ww, self.backbone.body.num_features[num_pre_vision + 1])
                .permute(0, 3, 1, 2)
                .contiguous()
            )
            outs.append(out)

        language_dict_features = self.language_backbone.body.get_aggregated_output(
            text_embeds, tokenizer_input["input_ids"], tokenizer_input["attention_mask"]
        )

        # Apply fpn
        visual_features = self.backbone.fpn(outs)

        # None for now, need to add if we want to add shallow contrastive loss?
        swint_feature_c4 = None

        return visual_features, language_dict_features, swint_feature_c4


def build_swint_backbone(cfg):
    """
    Create a SwinT instance from config.

    Returns:
        VoVNet: a :class:`VoVNet` instance.
    """
    return SwinTransformer(
        patch_size=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,
        out_features=cfg.MODEL.BACKBONE.OUT_FEATURES,
        max_query_len=cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN,
        lang_dim=cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM,
    )


def build_combined_backbone(vision_backbone, language_backbone, add_linear_layer=False):
    return FusionSwinTransformer(vision_backbone, language_backbone, add_linear_layer=add_linear_layer)