vit/vision_transformer.py

# Copyright (c) Facebook, Inc. and its affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Mostly copy-paste from timm library.
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
"""
from copy import deepcopy
import math
from functools import partial
from sympy import flatten

import torch
import torch.nn as nn
from torch import Tensor, pixel_shuffle

from einops import rearrange, repeat
from einops.layers.torch import Rearrange
from torch.nn.modules import GELU

# from vit.vision_transformer import Conv3DCrossAttentionBlock

from .utils import trunc_normal_

from pdb import set_trace as st
# import apex
from apex.normalization import FusedRMSNorm as RMSNorm
from apex.normalization import FusedLayerNorm as LayerNorm

try:
    from xformers.ops import memory_efficient_attention, unbind, fmha
    from xformers.ops import MemoryEfficientAttentionFlashAttentionOp
    # from xformers.ops import RMSNorm

    XFORMERS_AVAILABLE = True
except ImportError:
    # logger.warning("xFormers not available")
    XFORMERS_AVAILABLE = False


class Attention(nn.Module):

    def __init__(self,
                 dim,
                 num_heads=8,
                 qkv_bias=False,
                 qk_scale=None,
                 attn_drop=0.,
                 proj_drop=0., 
                 enable_rmsnorm=False,
                 qk_norm=False,):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5

        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)
        # https://github.com/huggingface/pytorch-image-models/blob/5dce71010174ad6599653da4e8ba37fd5f9fa572/timm/models/vision_transformer.py#L79C1-L80C78
        self.q_norm = RMSNorm(head_dim, elementwise_affine=True) if qk_norm else nn.Identity() # sd-3 
        self.k_norm = RMSNorm(head_dim, elementwise_affine=True) if qk_norm else nn.Identity()

        # if qk_norm:
        #     self.q_norm = LayerNorm(dim, eps=1e-5)
        #     self.k_norm = LayerNorm(dim, eps=1e-5)
        self.qk_norm = qk_norm

    def forward(self, x):
        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]

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


class MemEffAttention(Attention):

    def forward(self, x: Tensor, attn_bias=None) -> Tensor:
        if not XFORMERS_AVAILABLE:
            assert attn_bias is None, "xFormers is required for nested tensors usage"
            return super().forward(x)

        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
        q, k, v = unbind(qkv, 2)
        q, k = self.q_norm(q), self.k_norm(k)

        x = memory_efficient_attention(q, k, v, attn_bias=attn_bias) # if not bf16, no flash-attn here.
        # x = memory_efficient_attention(q, k, v, attn_bias=attn_bias, op=MemoryEfficientAttentionFlashAttentionOp) # force flash attention
        x = x.reshape([B, N, C])

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

class MemEffCrossAttention(MemEffAttention):
    # for cross attention, where context serves as k and v
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0, proj_drop=0):
        super().__init__(dim, num_heads, qkv_bias, qk_scale, attn_drop, proj_drop)
        del self.qkv
        self.q = nn.Linear(dim, dim * 1, bias=qkv_bias)
        self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)

    def forward(self, x: Tensor, context: Tensor, attn_bias=None) -> Tensor:
        if not XFORMERS_AVAILABLE:
            assert attn_bias is None, "xFormers is required for nested tensors usage"
            return super().forward(x)

        B, N, C = x.shape
        # qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)

        q = self.q(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
        kv = self.kv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)

        k, v = unbind(kv, 2)

        # x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
        x = memory_efficient_attention(q, k, v, attn_bias=attn_bias, op=MemoryEfficientAttentionFlashAttentionOp)
        x = x.reshape([B, N, C])

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


# https://github.com/IBM/CrossViT/blob/main/models/crossvit.py
class CrossAttention(nn.Module):

    def __init__(self,
                 dim,
                 num_heads=8,
                 qkv_bias=False,
                 qk_scale=None,
                 attn_drop=0.,
                 proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
        self.scale = qk_scale or head_dim**-0.5

        self.wq = nn.Linear(dim, dim, bias=qkv_bias)
        self.wk = nn.Linear(dim, dim, bias=qkv_bias)
        self.wv = nn.Linear(dim, dim, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):

        B, N, C = x.shape
        q = self.wq(x[:,
                      0:1, ...]).reshape(B, 1, self.num_heads,
                                         C // self.num_heads).permute(
                                             0, 2, 1,
                                             3)  # B1C -> B1H(C/H) -> BH1(C/H)
        k = self.wk(x).reshape(B, N,
                               self.num_heads, C // self.num_heads).permute(
                                   0, 2, 1, 3)  # BNC -> BNH(C/H) -> BHN(C/H)
        v = self.wv(x).reshape(B, N,
                               self.num_heads, C // self.num_heads).permute(
                                   0, 2, 1, 3)  # BNC -> BNH(C/H) -> BHN(C/H)

        attn = (q @ k.transpose(
            -2, -1)) * self.scale  # BH1(C/H) @ BH(C/H)N -> BH1N
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(
            B, 1, C)  # (BH1N @ BHN(C/H)) -> BH1(C/H) -> B1H(C/H) -> B1C
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class Conv3D_Aware_CrossAttention(nn.Module):

    def __init__(self,
                 dim,
                 num_heads=8,
                 qkv_bias=False,
                 qk_scale=None,
                 attn_drop=0.,
                 proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
        self.scale = qk_scale or head_dim**-0.5

        self.wq = nn.Linear(dim, dim, bias=qkv_bias)
        self.wk = nn.Linear(dim, dim, bias=qkv_bias)
        self.wv = nn.Linear(dim, dim, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):

        B, group_size, N, C = x.shape  # B 3 N C
        p = int(N**0.5)  # patch size
        assert p**2 == N, 'check input dim, no [cls] needed here'
        assert group_size == 3, 'designed for triplane here'

        x = x.reshape(B, group_size, p, p, C)  # expand patch token dim

        # * init qkv
        # q = torch.empty(B * group_size * N,
        #                 1,
        #                 self.num_heads,
        #                 C // self.num_heads,
        #                 device=x.device).permute(0, 2, 1, 3)
        # k = torch.empty(B * group_size * N,
        #                 2 * p,
        #                 self.num_heads,
        #                 C // self.num_heads,
        #                 device=x.device).permute(0, 2, 1, 3)
        # v = torch.empty_like(k)

        q_x = torch.empty(
            B * group_size * N,
            1,
            # self.num_heads,
            # C // self.num_heads,
            C,
            device=x.device)
        k_x = torch.empty(
            B * group_size * N,
            2 * p,
            # self.num_heads,
            # C // self.num_heads,
            C,
            device=x.device)
        v_x = torch.empty_like(k_x)

        # ! refer to the following plane order
        # N, M, _ = coordinates.shape
        # xy_coords = coordinates[..., [0, 1]]
        # yz_coords = coordinates[..., [1, 2]]
        # zx_coords = coordinates[..., [2, 0]]
        # return torch.stack([xy_coords, yz_coords, zx_coords],
        #                 dim=1).reshape(N * 3, M, 2)

        index_i, index_j = torch.meshgrid(torch.arange(0, p),
                                          torch.arange(0, p),
                                          indexing='ij')  # 16*16
        index_mesh_grid = torch.stack([index_i, index_j], 0).to(
            x.device).unsqueeze(0).repeat_interleave(B,
                                                     0).reshape(B, 2, p,
                                                                p)  # B 2 p p.

        for i in range(group_size):
            q_x[B * i * N:B * (i + 1) * N] = x[:, i:i + 1].permute(
                0, 2, 3, 1, 4).reshape(B * N, 1, C)  # B 1 p p C -> B*N, 1, C

            # TODO, how to batchify gather ops?
            plane_yz = x[:, (i + 1) % group_size:(i + 1) % group_size +
                         1]  # B 1 p p C
            plane_zx = x[:, (i + 2) % group_size:(i + 2) % group_size + 1]

            assert plane_yz.shape == plane_zx.shape == (
                B, 1, p, p, C), 'check sub plane dimensions'

            pooling_plane_yz = torch.gather(
                plane_yz,
                dim=2,
                index=index_mesh_grid[:, 0:1].reshape(B, 1, N, 1, 1).expand(
                    -1, -1, -1, p,
                    C)).permute(0, 2, 1, 3, 4)  # B 1 256 16 C => B 256 1 16 C
            pooling_plane_zx = torch.gather(
                plane_zx,
                dim=3,
                index=index_mesh_grid[:, 1:2].reshape(B, 1, 1, N, 1).expand(
                    -1, -1, p, -1,
                    C)).permute(0, 3, 1, 2, 4)  # B 1 16 256 C => B 256 1 16 C

            k_x[B * i * N:B * (i + 1) *
                N] = v_x[B * i * N:B * (i + 1) * N] = torch.cat(
                    [pooling_plane_yz, pooling_plane_zx],
                    dim=2).reshape(B * N, 2 * p,
                                   C)  # B 256 2 16 C => (B*256) 2*16 C

            # q[B * i * N: B * (i+1) * N] = self.wq(q_x).reshape(B*N, 1, self.num_heads, C // self.num_heads).permute( 0, 2, 1, 3)
            # k[B * i * N: B * (i+1) * N] = self.wk(k_x).reshape(B*N, 2*p, self.num_heads, C // self.num_heads).permute( 0, 2, 1, 3)
            # v[B * i * N: B * (i+1) * N] = self.wv(v_x).reshape(B*N, 2*p, self.num_heads, C // self.num_heads).permute( 0, 2, 1, 3)

        q = self.wq(q_x).reshape(B * group_size * N, 1,
                                 self.num_heads, C // self.num_heads).permute(
                                     0, 2, 1,
                                     3)  # merge num_heads into Batch dimention
        k = self.wk(k_x).reshape(B * group_size * N, 2 * p, self.num_heads,
                                 C // self.num_heads).permute(0, 2, 1, 3)
        v = self.wv(v_x).reshape(B * group_size * N, 2 * p, self.num_heads,
                                 C // self.num_heads).permute(0, 2, 1, 3)

        attn = (q @ k.transpose(
            -2, -1)) * self.scale  # BH1(C/H) @ BH(C/H)N -> BH1N, N=2p here
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(
            B * 3 * N, 1,
            C)  # (BH1N @ BHN(C/H)) -> BH1(C/H) -> B1H(C/H) -> B1C
        x = self.proj(x)
        x = self.proj_drop(x)

        # reshape x back
        x = x.reshape(B, 3, N, C)

        return x


class xformer_Conv3D_Aware_CrossAttention(nn.Module):
    # https://github.dev/facebookresearch/dinov2
    def __init__(self,
                 dim,
                 num_heads=8,
                 qkv_bias=False,
                 qk_scale=None,
                 attn_drop=0.,
                 proj_drop=0.):
        super().__init__()

        # https://pytorch.org/blog/accelerated-generative-diffusion-models/

        self.num_heads = num_heads
        self.wq = nn.Linear(dim, dim * 1, bias=qkv_bias)
        self.w_kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)

        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        self.index_mesh_grid = None

    def forward(self, x, attn_bias=None):

        B, group_size, N, C = x.shape  # B 3 N C
        p = int(N**0.5)  # patch size
        assert p**2 == N, 'check input dim, no [cls] needed here'
        assert group_size == 3, 'designed for triplane here'

        x = x.reshape(B, group_size, p, p, C)  # expand patch token dim

        q_x = torch.empty(B * group_size * N, 1, C, device=x.device)
        context = torch.empty(B * group_size * N, 2 * p, C,
                              device=x.device)  # k_x=v_x

        if self.index_mesh_grid is None:  # further accelerate
            index_i, index_j = torch.meshgrid(torch.arange(0, p),
                                              torch.arange(0, p),
                                              indexing='ij')  # 16*16
            index_mesh_grid = torch.stack([index_i, index_j], 0).to(
                x.device).unsqueeze(0).repeat_interleave(B, 0).reshape(
                    B, 2, p, p)  # B 2 p p.
            self.index_mesh_grid = index_mesh_grid[0:1]
        else:
            index_mesh_grid = self.index_mesh_grid.clone().repeat_interleave(
                B, 0)
            assert index_mesh_grid.shape == (
                B, 2, p, p), 'check index_mesh_grid dimension'

        for i in range(group_size):
            q_x[B * i * N:B * (i + 1) * N] = x[:, i:i + 1].permute(
                0, 2, 3, 1, 4).reshape(B * N, 1, C)  # B 1 p p C -> B*N, 1, C

            # TODO, how to batchify gather ops?
            plane_yz = x[:, (i + 1) % group_size:(i + 1) % group_size +
                         1]  # B 1 p p C
            plane_zx = x[:, (i + 2) % group_size:(i + 2) % group_size + 1]

            assert plane_yz.shape == plane_zx.shape == (
                B, 1, p, p, C), 'check sub plane dimensions'

            pooling_plane_yz = torch.gather(
                plane_yz,
                dim=2,
                index=index_mesh_grid[:, 0:1].reshape(B, 1, N, 1, 1).expand(
                    -1, -1, -1, p,
                    C)).permute(0, 2, 1, 3, 4)  # B 1 256 16 C => B 256 1 16 C
            pooling_plane_zx = torch.gather(
                plane_zx,
                dim=3,
                index=index_mesh_grid[:, 1:2].reshape(B, 1, 1, N, 1).expand(
                    -1, -1, p, -1,
                    C)).permute(0, 3, 1, 2, 4)  # B 1 16 256 C => B 256 1 16 C

            context[B * i * N:B * (i + 1) * N] = torch.cat(
                [pooling_plane_yz, pooling_plane_zx],
                dim=2).reshape(B * N, 2 * p,
                               C)  # B 256 2 16 C => (B*256) 2*16 C

        # B, N, C = x.shape

        q = self.wq(q_x).reshape(B * group_size * N, 1, self.num_heads,
                                 C // self.num_heads)

        kv = self.w_kv(context).reshape(B * group_size * N, 2 * p, 2,
                                        self.num_heads, C // self.num_heads)
        k, v = unbind(kv, 2)

        x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
        # x = memory_efficient_attention(q, k, v, attn_bias=attn_bias, op=MemoryEfficientAttentionFlashAttentionOp)
        x = x.transpose(1, 2).reshape([B * 3 * N, 1, C]).reshape(B, 3, N, C)

        x = self.proj(x)
        x = self.proj_drop(x)

        return x


class xformer_Conv3D_Aware_CrossAttention_xygrid(
        xformer_Conv3D_Aware_CrossAttention):
    """implementation wise clearer, but yields identical results with xformer_Conv3D_Aware_CrossAttention
    """

    def __init__(self,
                 dim,
                 num_heads=8,
                 qkv_bias=False,
                 qk_scale=None,
                 attn_drop=0.0,
                 proj_drop=0.0):
        super().__init__(dim, num_heads, qkv_bias, qk_scale, attn_drop,
                         proj_drop)

    def forward(self, x, attn_bias=None):

        B, group_size, N, C = x.shape  # B 3 N C
        p = int(N**0.5)  # patch size
        assert p**2 == N, 'check input dim, no [cls] needed here'
        assert group_size == 3, 'designed for triplane here'

        x = x.reshape(B, group_size, p, p, C)  # expand patch token dim

        q_x = torch.empty(B * group_size * N, 1, C, device=x.device)
        context = torch.empty(B * group_size * N, 2 * p, C,
                              device=x.device)  # k_x=v_x

        if self.index_mesh_grid is None:  # further accelerate
            index_u, index_v = torch.meshgrid(
                torch.arange(0, p), torch.arange(0, p),
                indexing='xy')  # ! switch to 'xy' here to match uv coordinate
            index_mesh_grid = torch.stack([index_u, index_v], 0).to(
                x.device).unsqueeze(0).repeat_interleave(B, 0).reshape(
                    B, 2, p, p)  # B 2 p p.
            self.index_mesh_grid = index_mesh_grid[0:1]
        else:
            index_mesh_grid = self.index_mesh_grid.clone().repeat_interleave(
                B, 0)
            assert index_mesh_grid.shape == (
                B, 2, p, p), 'check index_mesh_grid dimension'

        for i in range(group_size):
            q_x[B * i * N:B * (i + 1) * N] = x[:, i:i + 1].permute(
                0, 2, 3, 1, 4).reshape(B * N, 1, C)  # B 1 p p C -> B*N, 1, C

            # TODO, how to batchify gather ops?
            plane_yz = x[:, (i + 1) % group_size:(i + 1) % group_size +
                         1]  # B 1 p p C
            plane_zx = x[:, (i + 2) % group_size:(i + 2) % group_size + 1]

            assert plane_yz.shape == plane_zx.shape == (
                B, 1, p, p, C), 'check sub plane dimensions'

            pooling_plane_yz = torch.gather(
                plane_yz,
                dim=2,
                index=index_mesh_grid[:, 1:2].reshape(B, 1, N, 1, 1).expand(
                    -1, -1, -1, p,
                    C)).permute(0, 2, 1, 3, 4)  # B 1 256 16 C => B 256 1 16 C
            pooling_plane_zx = torch.gather(
                plane_zx,
                dim=3,
                index=index_mesh_grid[:, 0:1].reshape(B, 1, 1, N, 1).expand(
                    -1, -1, p, -1,
                    C)).permute(0, 3, 1, 2, 4)  # B 1 16 256 C => B 256 1 16 C

            context[B * i * N:B * (i + 1) * N] = torch.cat(
                [pooling_plane_yz, pooling_plane_zx],
                dim=2).reshape(B * N, 2 * p,
                               C)  # B 256 2 16 C => (B*256) 2*16 C

        # B, N, C = x.shape
        q = self.wq(q_x).reshape(B * group_size * N, 1, self.num_heads,
                                 C // self.num_heads)

        kv = self.w_kv(context).reshape(B * group_size * N, 2 * p, 2,
                                        self.num_heads, C // self.num_heads)
        k, v = unbind(kv, 2)

        x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
        # x = memory_efficient_attention(q, k, v, attn_bias=attn_bias, op=MemoryEfficientAttentionFlashAttentionOp)
        x = x.transpose(1, 2).reshape([B * 3 * N, 1, C]).reshape(B, 3, N, C)

        x = self.proj(x)
        x = self.proj_drop(x)

        return x


class xformer_Conv3D_Aware_CrossAttention_xygrid_withinC(
        xformer_Conv3D_Aware_CrossAttention_xygrid):

    def __init__(self,
                 dim,
                 num_heads=8,
                 qkv_bias=False,
                 qk_scale=None,
                 attn_drop=0,
                 proj_drop=0):
        super().__init__(dim, num_heads, qkv_bias, qk_scale, attn_drop,
                         proj_drop)

    def forward(self, x, attn_bias=None):
        # ! split x: B N C into B 3 N C//3
        B, N, C = x.shape
        x = x.reshape(B, N, C // 3, 3).permute(0, 3, 1,
                                               2)  # B N C 3 -> B 3 N C
        x_out = super().forward(x, attn_bias)  # B 3 N C
        x_out = x_out.permute(0, 2, 3, 1)# B 3 N C -> B N C 3
        x_out = x_out.reshape(*x_out.shape[:2], -1) # B N C 3 -> B N C3
        return x_out.contiguous()

class self_cross_attn(nn.Module):
    def __init__(self, dino_attn, cross_attn, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)
        self.dino_attn = dino_attn
        self.cross_attn = cross_attn
    
    def forward(self, x_norm):
        y = self.dino_attn(x_norm) + x_norm
        return self.cross_attn(y) # will add x in the original code

# class RodinRollOutConv(nn.Module):
#     """implementation wise clearer, but yields identical results with xformer_Conv3D_Aware_CrossAttention
#     Use Group Conv
#     """

#     def __init__(self, in_chans, out_chans=None):
#         super().__init__()
#         # input: B 3C H W
#         if out_chans is None:
#             out_chans = in_chans

#         self.roll_out_convs = nn.Conv2d(in_chans,
#                                         out_chans,
#                                         kernel_size=3,
#                                         groups=3,
#                                         padding=1)

#     def forward(self, x):
#         return self.roll_out_convs(x)


class RodinRollOutConv3D(nn.Module):
    """implementation wise clearer, but yields identical results with xformer_Conv3D_Aware_CrossAttention
    """

    def __init__(self, in_chans, out_chans=None):
        super().__init__()
        if out_chans is None:
            out_chans = in_chans

        self.out_chans = out_chans // 3

        self.roll_out_convs = nn.Conv2d(in_chans,
                                        self.out_chans,
                                        kernel_size=3,
                                        padding=1)

    def forward(self, x):
        # todo, reshape before input?

        B, C3, p, p = x.shape  # B 3C H W
        C = C3 // 3
        group_size = C3 // C
        assert group_size == 3

        x = x.reshape(B, 3, C, p, p)

        roll_out_x = torch.empty(B, group_size * C, p, 3 * p,
                                 device=x.device)  # B, 3C, H, 3W

        for i in range(group_size):
            plane_xy = x[:, i]  # B C H W

            # TODO, simply do the average pooling?
            plane_yz_pooling = x[:, (i + 1) % group_size].mean(
                dim=-1, keepdim=True).repeat_interleave(
                    p, dim=-1)  # B C H W -> B C H 1 -> B C H W, reduce z dim
            plane_zx_pooling = x[:, (i + 2) % group_size].mean(
                dim=-2, keepdim=True).repeat_interleave(
                    p, dim=-2)  # B C H W -> B C 1 W -> B C H W, reduce z dim

            roll_out_x[..., i * p:(i + 1) * p] = torch.cat(
                [plane_xy, plane_yz_pooling, plane_zx_pooling],
                1)  # fill in the 3W dim

        x = self.roll_out_convs(roll_out_x)  # B C H 3W

        x = x.reshape(B, self.out_chans, p, 3, p)
        x = x.permute(0, 3, 1, 2, 4).reshape(B, 3 * self.out_chans, p,
                                             p)  # B 3C H W

        return x


class RodinRollOutConv3D_GroupConv(nn.Module):
    """implementation wise clearer, but yields identical results with xformer_Conv3D_Aware_CrossAttention
    """

    def __init__(self,
                 in_chans,
                 out_chans=None,
                 kernel_size=3,
                 stride=1,
                 padding=1):
        super().__init__()
        if out_chans is None:
            out_chans = in_chans

        self.roll_out_convs = nn.Conv2d(
            in_chans * 3,
            out_chans,
            kernel_size=kernel_size,
            groups=3,  # B 9C H W
            stride=stride,
            padding=padding)

    # @torch.autocast(device_type='cuda')
    def forward(self, x):
        # todo, reshape before input?

        B, C3, p, p = x.shape  # B 3C H W
        C = C3 // 3
        group_size = C3 // C
        assert group_size == 3

        x = x.reshape(B, 3, C, p, p)

        roll_out_x = torch.empty(B, group_size * C * 3, p, p,
                                 device=x.device)  # B, 3C, H, 3W

        for i in range(group_size):
            plane_xy = x[:, i]  # B C H W

            # # TODO, simply do the average pooling?
            plane_yz_pooling = x[:, (i + 1) % group_size].mean(
                dim=-1, keepdim=True).repeat_interleave(
                    p, dim=-1)  # B C H W -> B C H 1 -> B C H W, reduce z dim
            plane_zx_pooling = x[:, (i + 2) % group_size].mean(
                dim=-2, keepdim=True).repeat_interleave(
                    p, dim=-2)  # B C H W -> B C 1 W -> B C H W, reduce z dim

            roll_out_x[:, i * 3 * C:(i + 1) * 3 * C] = torch.cat(
                [plane_xy, plane_yz_pooling, plane_zx_pooling],
                1)  # fill in the 3W dim

            # ! directly cat, avoid intermediate vars
            # ? why OOM
            # roll_out_x[:, i * 3 * C:(i + 1) * 3 * C] = torch.cat(
            #     [
            #         x[:, i],
            #         x[:, (i + 1) % group_size].mean(
            #             dim=-1, keepdim=True).repeat_interleave(p, dim=-1),
            #         x[:, (i + 2) % group_size].mean(
            #             dim=-2, keepdim=True).repeat_interleave(
            #                 p, dim=-2
            #             )  # B C H W -> B C 1 W -> B C H W, reduce z dim
            #     ],
            #     1)  # fill in the 3C dim

        x = self.roll_out_convs(roll_out_x)  # B 3C H W

        return x


class RodinRollOut_GroupConv_noConv3D(nn.Module):
    """only roll out and do Conv on individual planes
    """

    def __init__(self,
                 in_chans,
                 out_chans=None,
                 kernel_size=3,
                 stride=1,
                 padding=1):
        super().__init__()
        if out_chans is None:
            out_chans = in_chans

        self.roll_out_inplane_conv = nn.Conv2d(
            in_chans,
            out_chans,
            kernel_size=kernel_size,
            groups=3,  # B 3C H W
            stride=stride,
            padding=padding)

    def forward(self, x):
        x = self.roll_out_inplane_conv(x)  # B 3C H W
        return x


# class RodinConv3D_SynthesisLayer_withact(nn.Module):
#     def __init__(self, in_chans, out_chans) -> None:
#         super().__init__()

#         self.act = nn.LeakyReLU(inplace=True)
#         self.conv = nn.Sequential(
#             RodinRollOutConv3D_GroupConv(in_chans, out_chans),
#             nn.LeakyReLU(inplace=True),
#         )

#         if in_chans != out_chans:
#             self.short_cut = RodinRollOutConv3D_GroupConv(in_chans, out_chans) # PSNR 13 first iteration.
#         else:
#             self.short_cut = None

# def forward(self, feats):

#     if self.short_cut is not None:
#         res_feats = self.short_cut(feats)
#     else:
#         res_feats = feats

#     # return res_feats + self.conv(feats)
#     feats = res_feats + self.conv(feats)
#     return self.act(feats) # as in resnet, add an act before return


class RodinConv3D_SynthesisLayer_mlp_unshuffle_as_residual(nn.Module):

    def __init__(self, in_chans, out_chans) -> None:
        super().__init__()

        self.act = nn.LeakyReLU(inplace=True)
        self.conv = nn.Sequential(
            RodinRollOutConv3D_GroupConv(in_chans, out_chans),
            nn.LeakyReLU(inplace=True),
        )

        self.out_chans = out_chans
        if in_chans != out_chans:
            # self.short_cut = RodinRollOutConv3D_GroupConv(in_chans, out_chans) # PSNR 13 first iteration.
            self.short_cut = nn.Linear(  # B 3C H W -> B 3C 4H 4W
                in_chans // 3,  # 144 / 3 = 48
                out_chans // 3 * 4 * 4,  # 32 * 16
                bias=True)  # decoder to pat

            # RodinRollOutConv3D_GroupConv(in_chans, out_chans) # PSNR 13 first iteration.
        else:
            self.short_cut = None

    def shortcut_unpatchify_triplane(self,
                                     x,
                                     p=None,
                                     unpatchify_out_chans=None):
        """separate triplane version; x shape: B (3*257) 768
        """

        assert self.short_cut is not None

        # B, L, C = x.shape
        B, C3, h, w = x.shape
        assert h == w
        L = h * w
        x = x.reshape(B, C3 // 3, 3, L).permute(0, 2, 3,
                                                1)  # (B, 3, L // 3, C)

        x = self.short_cut(x)

        p = h * 4

        x = x.reshape(shape=(B, 3, h, w, p, p, unpatchify_out_chans))
        x = torch.einsum('ndhwpqc->ndchpwq',
                         x)  # nplanes, C order in the renderer.py
        x = x.reshape(shape=(B, 3 * self.out_chans, h * p, h * p))
        return x

    def forward(self, feats):

        if self.short_cut is not None:
            res_feats = self.shortcut_unpatchify_triplane(feats)
        else:
            res_feats = feats

        # return res_feats + self.conv(feats)

        feats = res_feats + self.conv(feats)
        return self.act(feats)  # as in resnet, add an act before return


# class RodinConv3D_SynthesisLayer(nn.Module):
#     def __init__(self, in_chans, out_chans) -> None:
#         super().__init__()

#         self.act = nn.LeakyReLU(inplace=True)
#         self.conv = nn.Sequential(
#             RodinRollOutConv3D_GroupConv(in_chans, out_chans),
#             nn.LeakyReLU(inplace=True),
#         )

#         if in_chans != out_chans:
#             self.short_cut = RodinRollOutConv3D_GroupConv(in_chans, out_chans) # PSNR 13 first iteration.
#         else:
#             self.short_cut = None

#     def forward(self, feats):

#         if self.short_cut is not None:
#             res_feats = self.short_cut(feats)
#         else:
#             res_feats = feats

#         # return res_feats + self.conv(feats)

#         feats = res_feats + self.conv(feats)
#         # return self.act(feats) # as in resnet, add an act before return
#         return feats # ! old behaviour, no act


# previous worked version
class RodinConv3D_SynthesisLayer(nn.Module):

    def __init__(self, in_chans, out_chans) -> None:
        super().__init__()
        # x2 SR + 1x1 Conv Residual BLK
        # self.conv3D = RodinRollOutConv3D(in_chans, out_chans)

        self.act = nn.LeakyReLU(inplace=True)
        self.conv = nn.Sequential(
            RodinRollOutConv3D_GroupConv(in_chans, out_chans),
            nn.LeakyReLU(inplace=True),
        )

        if in_chans != out_chans:
            self.short_cut = RodinRollOutConv3D_GroupConv(in_chans, out_chans)
        else:
            self.short_cut = None

    def forward(self, feats):
        feats_out = self.conv(feats)
        if self.short_cut is not None:
            # ! failed below
            feats_out = self.short_cut(
                feats
            ) + feats_out  # ! only difference here, no act() compared with baseline
            # feats_out = self.act(self.short_cut(feats)) + feats_out # ! only difference here, no act() compared with baseline
        else:
            feats_out = feats_out + feats
        return feats_out


class RodinRollOutConv3DSR2X(nn.Module):

    def __init__(self, in_chans, **kwargs) -> None:
        super().__init__()
        self.conv3D = RodinRollOutConv3D_GroupConv(in_chans)
        # self.conv3D = RodinRollOutConv3D(in_chans)
        self.act = nn.LeakyReLU(inplace=True)
        self.input_resolution = 224

    def forward(self, x):
        # x: B 3 112*112 C
        B, C3, p, p = x.shape  # after unpachify triplane
        C = C3 // 3
        group_size = C3 // C

        assert group_size == 3
        # p = int(N**0.5)  # patch size
        # assert p**2 == N, 'check input dim, no [cls] needed here'
        assert group_size == 3, 'designed for triplane here'

        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
                                          p)  # B 3 C N -> B 3C h W

        if x.shape[-1] != self.input_resolution:
            x = torch.nn.functional.interpolate(x,
                                                size=(self.input_resolution,
                                                      self.input_resolution),
                                                mode='bilinear',
                                                align_corners=False,
                                                antialias=True)

        x = x + self.conv3D(x)

        return x


class RodinRollOutConv3DSR4X_lite(nn.Module):

    def __init__(self, in_chans, input_resolutiopn=256, **kwargs) -> None:
        super().__init__()
        self.conv3D_0 = RodinRollOutConv3D_GroupConv(in_chans)
        self.conv3D_1 = RodinRollOutConv3D_GroupConv(in_chans)

        self.act = nn.LeakyReLU(inplace=True)
        self.input_resolution = input_resolutiopn

    def forward(self, x):
        # x: B 3 112*112 C
        B, C3, p, p = x.shape  # after unpachify triplane
        C = C3 // 3
        group_size = C3 // C

        assert group_size == 3
        # p = int(N**0.5)  # patch size
        # assert p**2 == N, 'check input dim, no [cls] needed here'
        assert group_size == 3, 'designed for triplane here'

        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
                                          p)  # B 3 C N -> B 3C h W

        if x.shape[-1] != self.input_resolution:
            x = torch.nn.functional.interpolate(x,
                                                size=(self.input_resolution,
                                                      self.input_resolution),
                                                mode='bilinear',
                                                align_corners=False,
                                                antialias=True)

        # ! still not convering, not bug here?
        # x = x + self.conv3D_0(x)
        # x = x + self.conv3D_1(x)

        x = x + self.act(self.conv3D_0(x))
        x = x + self.act(self.conv3D_1(x))

        # TODO: which is better, bilinear + conv or PixelUnshuffle?

        return x


# class RodinConv3D2X_lite_mlp_as_residual(nn.Module):
#     """lite 4X version, with MLP unshuffle to change the dimention
#     """
#     def __init__(self, in_chans, out_chans, input_resolution=256) -> None:
#         super().__init__()

#         self.act = nn.LeakyReLU(inplace=True)

#         self.conv3D_0 = RodinRollOutConv3D_GroupConv(in_chans, out_chans)
#         self.conv3D_1 = RodinRollOutConv3D_GroupConv(out_chans, out_chans)

#         self.act = nn.LeakyReLU(inplace=True)
#         self.input_resolution = input_resolution

#         self.out_chans = out_chans
#         if in_chans != out_chans: # ! only change the dimension
#             self.short_cut = nn.Linear( # B 3C H W -> B 3C 4H 4W
#                 in_chans//3, # 144 / 3 = 48
#                 out_chans//3, # 32 * 16
#                 bias=True)  # decoder to pat
#         else:
#             self.short_cut = None

#     def shortcut_unpatchify_triplane(self, x, p=None):
#         """separate triplane version; x shape: B (3*257) 768
#         """

#         assert self.short_cut is not None

#         # B, L, C = x.shape
#         B, C3, h, w = x.shape
#         assert h == w
#         L = h*w
#         x = x.reshape(B, C3//3, 3, L).permute(0,2,3,1) # (B, 3, L // 3, C_in)

#         x = self.short_cut(x) # B 3 L//3 C_out

#         x = x.permute(0,1,3,2) # B 3 C_out L//3
#         x = x.reshape(shape=(B, self.out_chans, h, w))

#         # directly resize to the target, no unpatchify here since no 3D ViT is included here
#         if w != self.input_resolution:
#             x = torch.nn.functional.interpolate(x, # 4X SR
#                                                 size=(self.input_resolution,
#                                                       self.input_resolution),
#                                                 mode='bilinear',
#                                                 align_corners=False,
#                                                 antialias=True)

#         return x

#     def forward(self, x):

#         # x: B 3 112*112 C
#         B, C3, p, p = x.shape  # after unpachify triplane
#         C = C3 // 3

#         if self.short_cut is not None:
#             res_feats = self.shortcut_unpatchify_triplane(x)
#         else:
#             res_feats = x

#         """following forward code copied from lite4x version
#         """
#         x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
#                                           p)  # B 3 C N -> B 3C h W

#         if x.shape[-1] != self.input_resolution:
#             x = torch.nn.functional.interpolate(x, # 4X SR
#                                                 size=(self.input_resolution,
#                                                       self.input_resolution),
#                                                 mode='bilinear',
#                                                 align_corners=False,
#                                                 antialias=True)

#         x = res_feats + self.act(self.conv3D_0(x))
#         x = x + self.act(self.conv3D_1(x))

#         return x


class RodinConv3D4X_lite_mlp_as_residual(nn.Module):
    """lite 4X version, with MLP unshuffle to change the dimention
    """

    def __init__(self,
                 in_chans,
                 out_chans,
                 input_resolution=256,
                 interp_mode='bilinear',
                 bcg_triplane=False) -> None:
        super().__init__()

        self.interp_mode = interp_mode

        self.act = nn.LeakyReLU(inplace=True)

        self.conv3D_0 = RodinRollOutConv3D_GroupConv(in_chans, out_chans)
        self.conv3D_1 = RodinRollOutConv3D_GroupConv(out_chans, out_chans)
        self.bcg_triplane = bcg_triplane
        if bcg_triplane:
            self.conv3D_1_bg = RodinRollOutConv3D_GroupConv(
                out_chans, out_chans)

        self.act = nn.LeakyReLU(inplace=True)
        self.input_resolution = input_resolution

        self.out_chans = out_chans
        if in_chans != out_chans:  # ! only change the dimension
            self.short_cut = nn.Linear(  # B 3C H W -> B 3C 4H 4W
                in_chans // 3,  # 144 / 3 = 48
                out_chans // 3,  # 32 * 16
                bias=True)  # decoder to pat
        else:
            self.short_cut = None

    def shortcut_unpatchify_triplane(self, x, p=None):
        """separate triplane version; x shape: B (3*257) 768
        """

        assert self.short_cut is not None

        B, C3, h, w = x.shape
        assert h == w
        L = h * w
        x = x.reshape(B, C3 // 3, 3, L).permute(0, 2, 3,
                                                1)  # (B, 3, L // 3, C_in)

        x = self.short_cut(x)  # B 3 L//3 C_out

        x = x.permute(0, 1, 3, 2)  # B 3 C_out L//3
        x = x.reshape(shape=(B, self.out_chans, h, w))

        # directly resize to the target, no unpatchify here since no 3D ViT is included here
        if w != self.input_resolution:
            x = torch.nn.functional.interpolate(
                x,  # 4X SR
                size=(self.input_resolution, self.input_resolution),
                mode='bilinear',
                align_corners=False,
                antialias=True)

        return x

    def interpolate(self, feats):
        if self.interp_mode == 'bilinear':
            return torch.nn.functional.interpolate(
                feats,  # 4X SR
                size=(self.input_resolution, self.input_resolution),
                mode='bilinear',
                align_corners=False,
                antialias=True)
        else:
            return torch.nn.functional.interpolate(
                feats,  # 4X SR
                size=(self.input_resolution, self.input_resolution),
                mode='nearest',
            )

    def forward(self, x):

        # x: B 3 112*112 C
        B, C3, p, p = x.shape  # after unpachify triplane
        C = C3 // 3

        if self.short_cut is not None:
            res_feats = self.shortcut_unpatchify_triplane(x)
        else:
            res_feats = x
            if res_feats.shape[-1] != self.input_resolution:
                res_feats = self.interpolate(res_feats)
        """following forward code copied from lite4x version
        """
        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
                                          p)  # B 3 C N -> B 3C h W

        if x.shape[-1] != self.input_resolution:
            x = self.interpolate(x)

        x0 = res_feats + self.act(self.conv3D_0(x))  # the base feature
        x = x0 + self.act(self.conv3D_1(x0))
        if self.bcg_triplane:
            x_bcg = x0 + self.act(self.conv3D_1_bg(x0))
            return torch.cat([x, x_bcg], 1)
        else:
            return x


class RodinConv3D4X_lite_mlp_as_residual_litev2(
        RodinConv3D4X_lite_mlp_as_residual):

    def __init__(self,
                 in_chans,
                 out_chans,
                 num_feat=128,
                 input_resolution=256,
                 interp_mode='bilinear',
                 bcg_triplane=False) -> None:
        super().__init__(in_chans, out_chans, input_resolution, interp_mode,
                         bcg_triplane)

        self.conv3D_0 = RodinRollOutConv3D_GroupConv(in_chans, in_chans)
        self.conv_before_upsample = RodinRollOut_GroupConv_noConv3D(
            in_chans, num_feat * 3)
        self.conv3D_1 = RodinRollOut_GroupConv_noConv3D(
            num_feat * 3, num_feat * 3)
        self.conv_last = RodinRollOut_GroupConv_noConv3D(
            num_feat * 3, out_chans)
        self.short_cut = None

    def forward(self, x):

        # x: B 3 112*112 C
        B, C3, p, p = x.shape  # after unpachify triplane
        C = C3 // 3

        # if self.short_cut is not None:
        #     res_feats = self.shortcut_unpatchify_triplane(x)
        # else:
        #     res_feats = x
        #     if res_feats.shape[-1] != self.input_resolution:
        #         res_feats = self.interpolate(res_feats)
        """following forward code copied from lite4x version
        """
        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
                                          p)  # B 3 C N -> B 3C h W

        x = x + self.conv3D_0(x)  # the base feature
        x = self.act(self.conv_before_upsample(x))

        # if x.shape[-1] != self.input_resolution:
        x = self.conv_last(self.act(self.conv3D_1(self.interpolate(x))))

        return x


class RodinConv3D4X_lite_mlp_as_residual_lite(
        RodinConv3D4X_lite_mlp_as_residual):

    def __init__(self,
                 in_chans,
                 out_chans,
                 input_resolution=256,
                 interp_mode='bilinear') -> None:
        super().__init__(in_chans, out_chans, input_resolution, interp_mode)
        """replace the first Rodin Conv 3D with ordinary rollout conv to save memory
        """
        self.conv3D_0 = RodinRollOut_GroupConv_noConv3D(in_chans, out_chans)


class SR3D(nn.Module):
    # https://github.com/SeanChenxy/Mimic3D/blob/77d313656df3cd5536d2c4c5766db3a56208eea6/training/networks_stylegan2.py#L629
    # roll-out and apply two deconv/pixelUnshuffle layer

    def __init__(self, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)


class RodinConv3D4X_lite_mlp_as_residual_improved(nn.Module):

    def __init__(self,
                 in_chans,
                 num_feat,
                 out_chans,
                 input_resolution=256) -> None:
        super().__init__()

        assert in_chans == 4 * out_chans
        assert num_feat == 2 * out_chans
        self.input_resolution = input_resolution

        # refer to https://github.com/JingyunLiang/SwinIR/blob/6545850fbf8df298df73d81f3e8cba638787c8bd/models/network_swinir.py#L750
        self.upscale = 4

        self.conv_after_body = RodinRollOutConv3D_GroupConv(
            in_chans, in_chans, 3, 1, 1)
        self.conv_before_upsample = nn.Sequential(
            RodinRollOutConv3D_GroupConv(in_chans, num_feat, 3, 1, 1),
            nn.LeakyReLU(inplace=True))
        self.conv_up1 = RodinRollOutConv3D_GroupConv(num_feat, num_feat, 3, 1,
                                                     1)
        if self.upscale == 4:
            self.conv_up2 = RodinRollOutConv3D_GroupConv(
                num_feat, num_feat, 3, 1, 1)
        self.conv_hr = RodinRollOutConv3D_GroupConv(num_feat, num_feat, 3, 1,
                                                    1)
        self.conv_last = RodinRollOutConv3D_GroupConv(num_feat, out_chans, 3,
                                                      1, 1)

        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

    def forward(self, x):

        # x: B 3 112*112 C
        B, C3, p, p = x.shape  # after unpachify triplane
        C = C3 // 3
        """following forward code copied from lite4x version
        """
        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
                                          p)  # B 3 C N -> B 3C h W

        # ? nearest or bilinear
        x = self.conv_after_body(x) + x
        x = self.conv_before_upsample(x)
        x = self.lrelu(
            self.conv_up1(
                torch.nn.functional.interpolate(
                    x,
                    scale_factor=2,
                    mode='nearest',
                    # align_corners=False,
                    # antialias=True
                )))
        if self.upscale == 4:
            x = self.lrelu(
                self.conv_up2(
                    torch.nn.functional.interpolate(
                        x,
                        scale_factor=2,
                        mode='nearest',
                        # align_corners=False,
                        # antialias=True
                    )))
        x = self.conv_last(self.lrelu(self.conv_hr(x)))

        assert x.shape[-1] == self.input_resolution

        return x


class RodinConv3D4X_lite_improved_lint_withresidual(nn.Module):

    def __init__(self,
                 in_chans,
                 num_feat,
                 out_chans,
                 input_resolution=256) -> None:
        super().__init__()

        assert in_chans == 4 * out_chans
        assert num_feat == 2 * out_chans
        self.input_resolution = input_resolution

        # refer to https://github.com/JingyunLiang/SwinIR/blob/6545850fbf8df298df73d81f3e8cba638787c8bd/models/network_swinir.py#L750
        self.upscale = 4

        self.conv_after_body = RodinRollOutConv3D_GroupConv(
            in_chans, in_chans, 3, 1, 1)
        self.conv_before_upsample = nn.Sequential(
            RodinRollOutConv3D_GroupConv(in_chans, num_feat, 3, 1, 1),
            nn.LeakyReLU(inplace=True))
        self.conv_up1 = RodinRollOutConv3D_GroupConv(num_feat, num_feat, 3, 1,
                                                     1)
        if self.upscale == 4:
            self.conv_up2 = RodinRollOutConv3D_GroupConv(
                num_feat, num_feat, 3, 1, 1)
        self.conv_hr = RodinRollOutConv3D_GroupConv(num_feat, num_feat, 3, 1,
                                                    1)
        self.conv_last = RodinRollOutConv3D_GroupConv(num_feat, out_chans, 3,
                                                      1, 1)

        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

    def forward(self, x):

        # x: B 3 112*112 C
        B, C3, p, p = x.shape  # after unpachify triplane
        C = C3 // 3
        """following forward code copied from lite4x version
        """
        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
                                          p)  # B 3 C N -> B 3C h W

        # ? nearest or bilinear
        x = self.conv_after_body(x) + x
        x = self.conv_before_upsample(x)
        x = self.lrelu(
            self.conv_up1(
                torch.nn.functional.interpolate(
                    x,
                    scale_factor=2,
                    mode='nearest',
                    # align_corners=False,
                    # antialias=True
                )))
        if self.upscale == 4:
            x = self.lrelu(
                self.conv_up2(
                    torch.nn.functional.interpolate(
                        x,
                        scale_factor=2,
                        mode='nearest',
                        # align_corners=False,
                        # antialias=True
                    )))
        x = self.conv_last(self.lrelu(self.conv_hr(x) + x))

        assert x.shape[-1] == self.input_resolution

        return x


class RodinRollOutConv3DSR_FlexibleChannels(nn.Module):

    def __init__(self,
                 in_chans,
                 num_out_ch=96,
                 input_resolution=256,
                 **kwargs) -> None:
        super().__init__()

        self.block0 = RodinConv3D_SynthesisLayer(in_chans,
                                                 num_out_ch)  # in_chans=48
        self.block1 = RodinConv3D_SynthesisLayer(num_out_ch, num_out_ch)

        self.input_resolution = input_resolution  # 64 -> 256 SR

    def forward(self, x):
        # x: B 3 112*112 C
        B, C3, p, p = x.shape  # after unpachify triplane
        C = C3 // 3
        # group_size = C3 // C

        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
                                          p)  # B 3 C N -> B 3C h W

        if x.shape[-1] != self.input_resolution:
            x = torch.nn.functional.interpolate(x,
                                                size=(self.input_resolution,
                                                      self.input_resolution),
                                                mode='bilinear',
                                                align_corners=False,
                                                antialias=True)

        x = self.block0(x)
        x = self.block1(x)

        return x


# previous worked version
class RodinRollOutConv3DSR4X(nn.Module):
    # follow PixelUnshuffleUpsample

    def __init__(self, in_chans, **kwargs) -> None:
        super().__init__()
        # self.block0 = RodinConv3D_SynthesisLayer(in_chans, 96 * 2) # TODO, match the old behaviour now.
        # self.block1 = RodinConv3D_SynthesisLayer(96 * 2, 96)

        self.block0 = RodinConv3D_SynthesisLayer(in_chans, 96)
        self.block1 = RodinConv3D_SynthesisLayer(
            96, 96)  # baseline choice, validate with no LPIPS loss here

        self.input_resolution = 64  # 64 -> 256

    def forward(self, x):
        # x: B 3 112*112 C
        B, C3, p, p = x.shape  # after unpachify triplane
        C = C3 // 3
        # group_size = C3 // C

        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
                                          p)  # B 3 C N -> B 3C h W

        if x.shape[-1] != self.input_resolution:
            x = torch.nn.functional.interpolate(x,
                                                size=(self.input_resolution,
                                                      self.input_resolution),
                                                mode='bilinear',
                                                align_corners=False,
                                                antialias=True)

        x = self.block0(x)
        x = self.block1(x)

        return x


class Upsample3D(nn.Module):
    """Upsample module.
    Args:
        scale (int): Scale factor. Supported scales: 2^n and 3.
        num_feat (int): Channel number of intermediate features.
    """

    def __init__(self, scale, num_feat):
        super().__init__()

        m_convs = []
        m_pixelshuffle = []

        assert (scale & (scale - 1)) == 0, 'scale = 2^n'
        self.scale = scale

        for _ in range(int(math.log(scale, 2))):
            m_convs.append(
                RodinRollOutConv3D_GroupConv(num_feat, 4 * num_feat, 3, 1, 1))
            m_pixelshuffle.append(nn.PixelShuffle(2))

        self.m_convs = nn.ModuleList(m_convs)
        self.m_pixelshuffle = nn.ModuleList(m_pixelshuffle)

    # @torch.autocast(device_type='cuda')
    def forward(self, x):
        for scale_idx in range(int(math.log(self.scale, 2))):
            x = self.m_convs[scale_idx](x)  # B 3C H W
            # x =
            # B, C3, H, W = x.shape
            x = x.reshape(x.shape[0] * 3, x.shape[1] // 3, *x.shape[2:])
            x = self.m_pixelshuffle[scale_idx](x)
            x = x.reshape(x.shape[0] // 3, x.shape[1] * 3, *x.shape[2:])

        return x


class RodinConv3DPixelUnshuffleUpsample(nn.Module):

    def __init__(self,
                 output_dim,
                 num_feat=32 * 6,
                 num_out_ch=32 * 3,
                 sr_ratio=4,
                 *args,
                 **kwargs) -> None:
        super().__init__()

        self.conv_after_body = RodinRollOutConv3D_GroupConv(
            output_dim, output_dim, 3, 1, 1)
        self.conv_before_upsample = nn.Sequential(
            RodinRollOutConv3D_GroupConv(output_dim, num_feat, 3, 1, 1),
            nn.LeakyReLU(inplace=True))
        self.upsample = Upsample3D(sr_ratio, num_feat)  # 4 time SR
        self.conv_last = RodinRollOutConv3D_GroupConv(num_feat, num_out_ch, 3,
                                                      1, 1)

    # @torch.autocast(device_type='cuda')
    def forward(self, x, input_skip_connection=True, *args, **kwargs):
        # x = self.conv_first(x)
        if input_skip_connection:
            x = self.conv_after_body(x) + x
        else:
            x = self.conv_after_body(x)

        x = self.conv_before_upsample(x)
        x = self.upsample(x)
        x = self.conv_last(x)
        return x


class RodinConv3DPixelUnshuffleUpsample_improvedVersion(nn.Module):

    def __init__(
        self,
        output_dim,
        num_out_ch=32 * 3,
        sr_ratio=4,
        input_resolution=256,
    ) -> None:
        super().__init__()

        self.input_resolution = input_resolution

        # self.conv_first = RodinRollOutConv3D_GroupConv(output_dim, num_out_ch,
        #                                               3, 1, 1)
        self.upsample = Upsample3D(sr_ratio, output_dim)  # 4 time SR
        self.conv_last = RodinRollOutConv3D_GroupConv(output_dim, num_out_ch,
                                                      3, 1, 1)

    def forward(self, x, bilinear_upsample=True):

        B, C3, p, p = x.shape  # after unpachify triplane
        C = C3 // 3
        group_size = C3 // C

        assert group_size == 3, 'designed for triplane here'

        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
                                          p)  # B 3 C N -> B 3C h W

        if bilinear_upsample and x.shape[-1] != self.input_resolution:
            x_bilinear_upsample = torch.nn.functional.interpolate(
                x,
                size=(self.input_resolution, self.input_resolution),
                mode='bilinear',
                align_corners=False,
                antialias=True)
            x = self.upsample(x) + x_bilinear_upsample
        else:
            # x_bilinear_upsample = x
            x = self.upsample(x)

        x = self.conv_last(x)

        return x


class RodinConv3DPixelUnshuffleUpsample_improvedVersion2(nn.Module):
    """removed nearest neighbour residual conenctions, add a conv layer residual conenction
    """

    def __init__(
        self,
        output_dim,
        num_out_ch=32 * 3,
        sr_ratio=4,
        input_resolution=256,
    ) -> None:
        super().__init__()

        self.input_resolution = input_resolution

        self.conv_after_body = RodinRollOutConv3D_GroupConv(
            output_dim, num_out_ch, 3, 1, 1)
        self.upsample = Upsample3D(sr_ratio, output_dim)  # 4 time SR
        self.conv_last = RodinRollOutConv3D_GroupConv(output_dim, num_out_ch,
                                                      3, 1, 1)

    def forward(self, x, input_skip_connection=True):

        B, C3, p, p = x.shape  # after unpachify triplane
        C = C3 // 3
        group_size = C3 // C

        assert group_size == 3, 'designed for triplane here'

        x = x.permute(0, 1, 3, 2).reshape(B, 3 * C, p,
                                          p)  # B 3 C N -> B 3C h W

        if input_skip_connection:
            x = self.conv_after_body(x) + x
        else:
            x = self.conv_after_body(x)

        x = self.upsample(x)
        x = self.conv_last(x)

        return x


class CLSCrossAttentionBlock(nn.Module):

    def __init__(self,
                 dim,
                 num_heads,
                 mlp_ratio=4.,
                 qkv_bias=False,
                 qk_scale=None,
                 drop=0.,
                 attn_drop=0.,
                 drop_path=0.,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm,
                 has_mlp=False):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = CrossAttention(dim,
                                   num_heads=num_heads,
                                   qkv_bias=qkv_bias,
                                   qk_scale=qk_scale,
                                   attn_drop=attn_drop,
                                   proj_drop=drop)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(
            drop_path) if drop_path > 0. else nn.Identity()
        self.has_mlp = has_mlp
        if has_mlp:
            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)

    def forward(self, x):
        x = x[:, 0:1, ...] + self.drop_path(self.attn(self.norm1(x)))
        if self.has_mlp:
            x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x


class Conv3DCrossAttentionBlock(nn.Module):

    def __init__(self,
                 dim,
                 num_heads,
                 mlp_ratio=4.,
                 qkv_bias=False,
                 qk_scale=None,
                 drop=0.,
                 attn_drop=0.,
                 drop_path=0.,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm,
                 has_mlp=False):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Conv3D_Aware_CrossAttention(dim,
                                                num_heads=num_heads,
                                                qkv_bias=qkv_bias,
                                                qk_scale=qk_scale,
                                                attn_drop=attn_drop,
                                                proj_drop=drop)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(
            drop_path) if drop_path > 0. else nn.Identity()
        self.has_mlp = has_mlp
        if has_mlp:
            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)

    def forward(self, x):
        x = x + self.drop_path(self.attn(self.norm1(x)))
        if self.has_mlp:
            x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x


class Conv3DCrossAttentionBlockXformerMHA(Conv3DCrossAttentionBlock):

    def __init__(self,
                 dim,
                 num_heads,
                 mlp_ratio=4,
                 qkv_bias=False,
                 qk_scale=None,
                 drop=0,
                 attn_drop=0,
                 drop_path=0,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm,
                 has_mlp=False):
        super().__init__(dim, num_heads, mlp_ratio, qkv_bias, qk_scale, drop,
                         attn_drop, drop_path, act_layer, norm_layer, has_mlp)
        # self.attn = xformer_Conv3D_Aware_CrossAttention(dim,
        self.attn = xformer_Conv3D_Aware_CrossAttention_xygrid(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop)


class Conv3DCrossAttentionBlockXformerMHANested(
        Conv3DCrossAttentionBlockXformerMHA):

    def __init__(self,
                 dim,
                 num_heads,
                 mlp_ratio=4,
                 qkv_bias=False,
                 qk_scale=None,
                 drop=0.,
                 attn_drop=0.,
                 drop_path=0.,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm,
                 has_mlp=False):
        super().__init__(dim, num_heads, mlp_ratio, qkv_bias, qk_scale, drop,
                         attn_drop, drop_path, act_layer, norm_layer, has_mlp)
        """for in-place replaing the internal attn in Dino ViT.
        """

    def forward(self, x):
        Bx3, N, C = x.shape
        B, group_size = Bx3 // 3, 3
        x = x.reshape(B, group_size, N, C)  # in plane vit
        x = super().forward(x)
        return x.reshape(B * group_size, N,
                         C)  # to match the original attn size


class Conv3DCrossAttentionBlockXformerMHANested_withinC(
        Conv3DCrossAttentionBlockXformerMHANested):

    def __init__(self,
                 dim,
                 num_heads,
                 mlp_ratio=4,
                 qkv_bias=False,
                 qk_scale=None,
                 drop=0,
                 attn_drop=0,
                 drop_path=0,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm,
                 has_mlp=False):
        super().__init__(dim, num_heads, mlp_ratio, qkv_bias, qk_scale, drop,
                         attn_drop, drop_path, act_layer, norm_layer, has_mlp)
        self.attn = xformer_Conv3D_Aware_CrossAttention_xygrid_withinC(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop)

    def forward(self, x):
        # basic TX attention forward function
        x = x + self.drop_path(self.attn(self.norm1(x)))
        if self.has_mlp:
            x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x


class TriplaneFusionBlock(nn.Module):
    """4 ViT blocks + 1 CrossAttentionBlock
    """

    def __init__(self,
                 vit_blks,
                 num_heads,
                 embed_dim,
                 use_fusion_blk=True,
                 cross_attention_blk=CLSCrossAttentionBlock,
                 *args,
                 **kwargs) -> None:
        super().__init__(*args, **kwargs)

        self.num_branches = 3  # triplane
        self.vit_blks = vit_blks

        if use_fusion_blk:
            self.fusion = nn.ModuleList()

            # copied vit settings from https://github.dev/facebookresearch/dinov2
            nh = num_heads
            dim = embed_dim

            mlp_ratio = 4  # defined for all dino2 model
            qkv_bias = True
            norm_layer = partial(nn.LayerNorm, eps=1e-6)
            drop_path_rate = 0.3  # default setting
            attn_drop = proj_drop = 0.0
            qk_scale = None  # TODO, double check

            for d in range(self.num_branches):
                self.fusion.append(
                    cross_attention_blk(
                        dim=dim,
                        num_heads=nh,
                        mlp_ratio=mlp_ratio,
                        qkv_bias=qkv_bias,
                        qk_scale=qk_scale,
                        # drop=drop,
                        drop=proj_drop,
                        attn_drop=attn_drop,
                        drop_path=drop_path_rate,
                        norm_layer=norm_layer,  # type: ignore
                        has_mlp=False))
        else:
            self.fusion = None

    def forward(self, x):
        # modified from https://github.com/IBM/CrossViT/blob/main/models/crossvit.py#L132
        """x: B 3 N C, where N = H*W tokens
        """

        # self attention, by merging the triplane channel into B for parallel computation

        # ! move the below to the front of the first call
        B, group_size, N, C = x.shape  # has [cls] token in N
        assert group_size == 3, 'triplane'
        x = x.view(B * group_size, N, C)

        for blk in self.vit_blks:
            x = blk(x)  # B 3 N C

        if self.fusion is None:
            return x.view(B, group_size, N, C)

        # outs_b = x.view(B, group_size, N,
        #                 C).chunk(chunks=3,
        #                          dim=1)  # 3 * [B, 1, N//3, C] Tensors, for fusion

        outs_b = x.chunk(chunks=3,
                         dim=0)  # 3 * [B, N//3, C] Tensors, for fusion

        # only take the cls token out
        proj_cls_token = [x[:, 0:1] for x in outs_b]
        # cross attention
        outs = []
        for i in range(self.num_branches):
            tmp = torch.cat(
                (proj_cls_token[i], outs_b[(i + 1) % self.num_branches][:, 1:,
                                                                        ...]),
                dim=1)
            tmp = self.fusion[i](tmp)
            # reverted_proj_cls_token = self.revert_projs[i](tmp[:, 0:1, ...])
            reverted_proj_cls_token = tmp[:, 0:1, ...]
            tmp = torch.cat((reverted_proj_cls_token, outs_b[i][:, 1:, ...]),
                            dim=1)
            outs.append(tmp)
        # outs = ? needs to merge back?
        outs = torch.stack(outs, 1)  # B 3 N C
        return outs


class TriplaneFusionBlockv2(nn.Module):
    """4 ViT blocks + 1 CrossAttentionBlock
    """

    def __init__(self,
                 vit_blks,
                 num_heads,
                 embed_dim,
                 use_fusion_blk=True,
                 fusion_ca_blk=Conv3DCrossAttentionBlock,
                 *args,
                 **kwargs) -> None:
        super().__init__(*args, **kwargs)

        self.num_branches = 3  # triplane
        self.vit_blks = vit_blks

        if use_fusion_blk:
            # self.fusion = nn.ModuleList()

            # copied vit settings from https://github.dev/facebookresearch/dinov2
            nh = num_heads
            dim = embed_dim

            mlp_ratio = 4  # defined for all dino2 model
            qkv_bias = True
            norm_layer = partial(nn.LayerNorm, eps=1e-6)
            drop_path_rate = 0.3  # default setting
            attn_drop = proj_drop = 0.0
            qk_scale = None  # TODO, double check

            # for d in range(self.num_branches):
            self.fusion = fusion_ca_blk(  # one fusion is enough
                dim=dim,
                num_heads=nh,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                # drop=drop,
                drop=proj_drop,
                attn_drop=attn_drop,
                drop_path=drop_path_rate,
                norm_layer=norm_layer,  # type: ignore
                has_mlp=False)
        else:
            self.fusion = None

    def forward(self, x):
        # modified from https://github.com/IBM/CrossViT/blob/main/models/crossvit.py#L132
        """x: B 3 N C, where N = H*W tokens
        """

        # self attention, by merging the triplane channel into B for parallel computation

        # ! move the below to the front of the first call
        B, group_size, N, C = x.shape  # has [cls] token in N
        assert group_size == 3, 'triplane'
        x = x.reshape(B * group_size, N, C)

        for blk in self.vit_blks:
            x = blk(x)  # B 3 N C

        if self.fusion is None:
            return x.reshape(B, group_size, N, C)

        x = x.reshape(B, group_size, N, C)  # .chunk(chunks=3,
        #  dim=1)  # 3 * [B, N//3, C] Tensors, for fusion
        return self.fusion(x)


class TriplaneFusionBlockv3(TriplaneFusionBlockv2):

    def __init__(self,
                 vit_blks,
                 num_heads,
                 embed_dim,
                 use_fusion_blk=True,
                 fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHA,
                 *args,
                 **kwargs) -> None:
        super().__init__(vit_blks, num_heads, embed_dim, use_fusion_blk,
                         fusion_ca_blk, *args, **kwargs)


class TriplaneFusionBlockv4(TriplaneFusionBlockv3):

    def __init__(self,
                 vit_blks,
                 num_heads,
                 embed_dim,
                 use_fusion_blk=True,
                 fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHA,
                 *args,
                 **kwargs) -> None:
        super().__init__(vit_blks, num_heads, embed_dim, use_fusion_blk,
                         fusion_ca_blk, *args, **kwargs)
        """OOM? directly replace the atten here
        """

        assert len(vit_blks) == 2
        # del self.vit_blks[1].attn
        del self.vit_blks[1].attn, self.vit_blks[1].ls1, self.vit_blks[1].norm1

    def ffn_residual_func(self, tx_blk, x: Tensor) -> Tensor:
        return tx_blk.ls2(
            tx_blk.mlp(tx_blk.norm2(x))
        )  # https://github.com/facebookresearch/dinov2/blob/c3c2683a13cde94d4d99f523cf4170384b00c34c/dinov2/layers/block.py#L86C1-L87C53

    def forward(self, x):
        """x: B 3 N C, where N = H*W tokens
        """
        assert self.fusion is not None

        B, group_size, N, C = x.shape  # has [cls] token in N
        x = x.reshape(B * group_size, N, C)  # in plane vit

        # in plane self attention
        x = self.vit_blks[0](x)

        # 3D cross attention blk + ffn
        x = x + self.fusion(x.reshape(B, group_size, N, C)).reshape(
            B * group_size, N, C)
        x = x + self.ffn_residual_func(self.vit_blks[1], x)
        return x.reshape(B, group_size, N, C)


class TriplaneFusionBlockv4_nested(nn.Module):

    def __init__(self,
                 vit_blks,
                 num_heads,
                 embed_dim,
                 use_fusion_blk=True,
                 fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHANested,
                 *args,
                 **kwargs) -> None:
        super().__init__()

        self.num_branches = 3  # triplane
        self.vit_blks = vit_blks

        assert use_fusion_blk

        assert len(vit_blks) == 2

        # ! replace vit_blks[1] attn layer with 3D aware attention
        del self.vit_blks[
            1].attn  # , self.vit_blks[1].ls1, self.vit_blks[1].norm1

        # copied vit settings from https://github.dev/facebookresearch/dinov2
        nh = num_heads
        dim = embed_dim

        mlp_ratio = 4  # defined for all dino2 model
        qkv_bias = True
        norm_layer = partial(nn.LayerNorm, eps=1e-6)
        drop_path_rate = 0.3  # default setting
        attn_drop = proj_drop = 0.0
        qk_scale = None  # TODO, double check

        self.vit_blks[1].attn = fusion_ca_blk(  # one fusion is enough
            dim=dim,
            num_heads=nh,
            mlp_ratio=mlp_ratio,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            # drop=drop,
            drop=proj_drop,
            attn_drop=attn_drop,
            drop_path=drop_path_rate,
            norm_layer=norm_layer,  # type: ignore
            has_mlp=False)

    def forward(self, x):
        """x: B 3 N C, where N = H*W tokens
        """

        # self attention, by merging the triplane channel into B for parallel computation

        # ! move the below to the front of the first call
        B, group_size, N, C = x.shape  # has [cls] token in N
        assert group_size == 3, 'triplane'
        x = x.reshape(B * group_size, N, C)

        for blk in self.vit_blks:
            x = blk(x)  # B 3 N C

        # TODO, avoid the reshape overhead?
        return x.reshape(B, group_size, N, C)


class TriplaneFusionBlockv4_nested_init_from_dino(nn.Module):

    def __init__(self,
                 vit_blks,
                 num_heads,
                 embed_dim,
                 use_fusion_blk=True,
                 fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHANested,
                 init_from_dino=True,
                 *args,
                 **kwargs) -> None:
        super().__init__()

        self.num_branches = 3  # triplane
        self.vit_blks = vit_blks

        assert use_fusion_blk

        assert len(vit_blks) == 2

        # copied vit settings from https://github.dev/facebookresearch/dinov2
        nh = num_heads
        dim = embed_dim

        mlp_ratio = 4  # defined for all dino2 model
        qkv_bias = True
        norm_layer = partial(nn.LayerNorm, eps=1e-6)
        drop_path_rate = 0.3  # default setting
        attn_drop = proj_drop = 0.0
        qk_scale = None  # TODO, double check

        attn_3d = fusion_ca_blk(  # one fusion is enough
            dim=dim,
            num_heads=nh,
            mlp_ratio=mlp_ratio,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            # drop=drop,
            drop=proj_drop,
            attn_drop=attn_drop,
            drop_path=drop_path_rate,
            norm_layer=norm_layer,  # type: ignore
            has_mlp=False)

        # ! initialize 3dattn from dino attn
        if init_from_dino:
            merged_qkv_linear = self.vit_blks[1].attn.qkv
            attn_3d.attn.proj.load_state_dict(
                self.vit_blks[1].attn.proj.state_dict())

            # Initialize the Q, K, and V linear layers using the weights of the merged QKV linear layer
            attn_3d.attn.wq.weight.data = merged_qkv_linear.weight.data[:
                                                                        dim, :]
            attn_3d.attn.w_kv.weight.data = merged_qkv_linear.weight.data[
                dim:, :]

            # Optionally, you can initialize the biases as well (if your QKV linear layer has biases)
            if qkv_bias:
                attn_3d.attn.wq.bias.data = merged_qkv_linear.bias.data[:dim]
                attn_3d.attn.w_kv.bias.data = merged_qkv_linear.bias.data[dim:]

        del self.vit_blks[1].attn
        # ! assign
        self.vit_blks[1].attn = attn_3d

    def forward(self, x):
        """x: B 3 N C, where N = H*W tokens
        """

        # self attention, by merging the triplane channel into B for parallel computation

        # ! move the below to the front of the first call
        B, group_size, N, C = x.shape  # has [cls] token in N
        assert group_size == 3, 'triplane'
        x = x.reshape(B * group_size, N, C)

        for blk in self.vit_blks:
            x = blk(x)  # B 3 N C

        # TODO, avoid the reshape overhead?
        return x.reshape(B, group_size, N, C)


class TriplaneFusionBlockv4_nested_init_from_dino_lite(nn.Module):

    def __init__(self,
                 vit_blks,
                 num_heads,
                 embed_dim,
                 use_fusion_blk=True,
                 fusion_ca_blk=None,
                 *args,
                 **kwargs) -> None:
        super().__init__()

        self.num_branches = 3  # triplane
        self.vit_blks = vit_blks

        assert use_fusion_blk

        assert len(vit_blks) == 2

        # copied vit settings from https://github.dev/facebookresearch/dinov2
        nh = num_heads
        dim = embed_dim

        mlp_ratio = 4  # defined for all dino2 model
        qkv_bias = True
        norm_layer = partial(nn.LayerNorm, eps=1e-6)
        drop_path_rate = 0.3  # default setting
        attn_drop = proj_drop = 0.0
        qk_scale = None  # TODO, double check

        attn_3d = xformer_Conv3D_Aware_CrossAttention_xygrid_withinC(  # ! raw 3D attn layer
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=proj_drop)

        del self.vit_blks[1].attn
        # ! assign
        self.vit_blks[1].attn = attn_3d

    def forward(self, x):
        """x: B N C, where N = H*W tokens. Just raw ViT forward pass
        """

        # ! move the below to the front of the first call
        B, N, C = x.shape  # has [cls] token in N

        for blk in self.vit_blks:
            x = blk(x)  # B N C

        return x

class TriplaneFusionBlockv4_nested_init_from_dino_lite_merge(nn.Module):

    def __init__(self,
                 vit_blks,
                 num_heads,
                 embed_dim,
                 use_fusion_blk=True,
                 fusion_ca_blk=None,
                 *args,
                 **kwargs) -> None:
        super().__init__()

        self.vit_blks = vit_blks

        assert use_fusion_blk
        assert len(vit_blks) == 2

        # copied vit settings from https://github.dev/facebookresearch/dinov2
        nh = num_heads
        dim = embed_dim
        qkv_bias = True
        attn_drop = proj_drop = 0.0
        qk_scale = None  # TODO, double check

        if False: # abla
            for blk in self.vit_blks:
                attn_3d = xformer_Conv3D_Aware_CrossAttention_xygrid_withinC(  # ! raw 3D attn layer
                    dim,
                    num_heads=num_heads,
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    attn_drop=attn_drop,
                    proj_drop=proj_drop)
                blk.attn = self_cross_attn(blk.attn, attn_3d)

    def forward(self, x):
        """x: B N C, where N = H*W tokens. Just raw ViT forward pass
        """

        # ! move the below to the front of the first call
        B, N, C = x.shape  # has [cls] token in N

        for blk in self.vit_blks:
            x = blk(x)  # B N C

        return x

class TriplaneFusionBlockv4_nested_init_from_dino_lite_merge_B_3L_C(TriplaneFusionBlockv4_nested_init_from_dino_lite_merge):
    # on roll out + B 3L C
    def __init__(self, vit_blks, num_heads, embed_dim, use_fusion_blk=True, fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHANested, init_from_dino=True, *args, **kwargs) -> None:
        super().__init__(vit_blks, num_heads, embed_dim, use_fusion_blk, fusion_ca_blk, init_from_dino, *args, **kwargs)


    def forward(self, x):
        """x: B 3 N C, where N = H*W tokens
        """

        # ! move the below to the front of the first call

        # B, N, C = x.shape  # has [cls] token in N
        B, group_size, N, C = x.shape  # has [cls] token in N
        x = x.reshape(B, group_size*N, C)

        for blk in self.vit_blks:
            x = blk(x)  # B N C

        x = x.reshape(B, group_size, N, C) # outer loop tradition

        return x

class TriplaneFusionBlockv4_nested_init_from_dino_lite_merge_B_3L_C_withrollout(TriplaneFusionBlockv4_nested_init_from_dino_lite_merge):
    # roll out + B 3L C
    def __init__(self, vit_blks, num_heads, embed_dim, use_fusion_blk=True, fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHANested, init_from_dino=True, *args, **kwargs) -> None:
        super().__init__(vit_blks, num_heads, embed_dim, use_fusion_blk, fusion_ca_blk, init_from_dino, *args, **kwargs)


    def forward(self, x):
        """x: B 3 N C, where N = H*W tokens
        """

        # ! move the below to the front of the first call

        # B, N, C = x.shape  # has [cls] token in N
        B, group_size, N, C = x.shape  # has [cls] token in N
        x = x.reshape(B*group_size, N, C)
        x = self.vit_blks[0](x)

        x = x.reshape(B,group_size*N, C)
        x = self.vit_blks[1](x)

        x = x.reshape(B, group_size, N, C) # outer loop tradition

        return x


class TriplaneFusionBlockv4_nested_init_from_dino_lite_merge_add3DAttn(TriplaneFusionBlockv4_nested_init_from_dino):
    # no roll out + 3D Attention
    def __init__(self, vit_blks, num_heads, embed_dim, use_fusion_blk=True, fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHANested, init_from_dino=True, *args, **kwargs) -> None:
        super().__init__(vit_blks, num_heads, embed_dim, use_fusion_blk, fusion_ca_blk, init_from_dino, *args, **kwargs)


    def forward(self, x):
        """x: B 3 N C, where N = H*W tokens
        """

        B, group_size, N, C = x.shape  # has [cls] token in N
        x = x.reshape(B, group_size*N, C)
        x = self.vit_blks[0](x) # B 3 L C

        # ! move the below to the front of the first call
        x = x.reshape(B, group_size, N, C).reshape(B*group_size, N, C)
        x = self.vit_blks[1](x) # has 3D attention
        return x.reshape(B, group_size, N, C)

        return x


class TriplaneFusionBlockv5_ldm_addCA(nn.Module):

    def __init__(self,
                 vit_blks,
                 num_heads,
                 embed_dim,
                 use_fusion_blk=True,
                 fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHANested,
                 *args,
                 **kwargs) -> None:
        super().__init__()

        self.num_branches = 3  # triplane
        self.vit_blks = vit_blks

        assert use_fusion_blk

        assert len(vit_blks) == 2

        # ! rather than replacing, add a 3D attention block after.
        # del self.vit_blks[
        #     1].attn  # , self.vit_blks[1].ls1, self.vit_blks[1].norm1
        self.norm_for_atten_3d = deepcopy(self.vit_blks[1].norm1)

        # copied vit settings from https://github.dev/facebookresearch/dinov2
        nh = num_heads
        dim = embed_dim

        mlp_ratio = 4  # defined for all dino2 model
        qkv_bias = True
        norm_layer = partial(nn.LayerNorm, eps=1e-6)
        drop_path_rate = 0.3  # default setting
        attn_drop = proj_drop = 0.0
        qk_scale = None  # TODO, double check

        self.attn_3d = xformer_Conv3D_Aware_CrossAttention_xygrid(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=proj_drop)

    def forward(self, x):
        """x: B 3 N C, where N = H*W tokens
        """

        # self attention, by merging the triplane channel into B for parallel computation

        # ! move the below to the front of the first call
        B, group_size, N, C = x.shape  # has [cls] token in N
        assert group_size == 3, 'triplane'

        flatten_token = lambda x: x.reshape(B * group_size, N, C)
        unflatten_token = lambda x: x.reshape(B, group_size, N, C)

        x = flatten_token(x)
        x = self.vit_blks[0](x)

        x = unflatten_token(x)
        x = self.attn_3d(self.norm_for_atten_3d(x)) + x

        x = flatten_token(x)
        x = self.vit_blks[1](x)

        return unflatten_token(x)


class TriplaneFusionBlockv6_ldm_addCA_Init3DAttnfrom2D(
        TriplaneFusionBlockv5_ldm_addCA):

    def __init__(self,
                 vit_blks,
                 num_heads,
                 embed_dim,
                 use_fusion_blk=True,
                 fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHANested,
                 *args,
                 **kwargs) -> None:
        super().__init__(vit_blks, num_heads, embed_dim, use_fusion_blk,
                         fusion_ca_blk, *args, **kwargs)

    def forward(self, x):
        """x: B 3 N C, where N = H*W tokens
        """

        # self attention, by merging the triplane channel into B for parallel computation

        # ! move the below to the front of the first call
        B, group_size, N, C = x.shape  # has [cls] token in N
        assert group_size == 3, 'triplane'

        flatten_token = lambda x: x.reshape(B * group_size, N, C)
        unflatten_token = lambda x: x.reshape(B, group_size, N, C)

        x = flatten_token(x)
        x = self.vit_blks[0](x)

        x = unflatten_token(x)
        x = self.attn_3d(self.norm_for_atten_3d(x)) + x

        x = flatten_token(x)
        x = self.vit_blks[1](x)

        return unflatten_token(x)


class TriplaneFusionBlockv5_ldm_add_dualCA(nn.Module):

    def __init__(self,
                 vit_blks,
                 num_heads,
                 embed_dim,
                 use_fusion_blk=True,
                 fusion_ca_blk=Conv3DCrossAttentionBlockXformerMHANested,
                 *args,
                 **kwargs) -> None:
        super().__init__()

        self.num_branches = 3  # triplane
        self.vit_blks = vit_blks

        assert use_fusion_blk

        assert len(vit_blks) == 2

        # ! rather than replacing, add a 3D attention block after.
        # del self.vit_blks[
        #     1].attn  # , self.vit_blks[1].ls1, self.vit_blks[1].norm1
        self.norm_for_atten_3d_0 = deepcopy(self.vit_blks[0].norm1)
        self.norm_for_atten_3d_1 = deepcopy(self.vit_blks[1].norm1)

        # copied vit settings from https://github.dev/facebookresearch/dinov2
        nh = num_heads
        dim = embed_dim

        mlp_ratio = 4  # defined for all dino2 model
        qkv_bias = True
        norm_layer = partial(nn.LayerNorm, eps=1e-6)
        drop_path_rate = 0.3  # default setting
        attn_drop = proj_drop = 0.0
        qk_scale = None  # TODO, double check

        self.attn_3d_0 = xformer_Conv3D_Aware_CrossAttention_xygrid(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=proj_drop)

        self.attn_3d_1 = deepcopy(self.attn_3d_0)

    def forward(self, x):
        """x: B 3 N C, where N = H*W tokens
        """

        # self attention, by merging the triplane channel into B for parallel computation

        # ! move the below to the front of the first call
        B, group_size, N, C = x.shape  # has [cls] token in N
        assert group_size == 3, 'triplane'

        flatten_token = lambda x: x.reshape(B * group_size, N, C)
        unflatten_token = lambda x: x.reshape(B, group_size, N, C)

        x = flatten_token(x)
        x = self.vit_blks[0](x)

        x = unflatten_token(x)
        x = self.attn_3d_0(self.norm_for_atten_3d_0(x)) + x

        x = flatten_token(x)
        x = self.vit_blks[1](x)

        x = unflatten_token(x)
        x = self.attn_3d_1(self.norm_for_atten_3d_1(x)) + x

        return unflatten_token(x)


def drop_path(x, drop_prob: float = 0., training: bool = False):
    if drop_prob == 0. or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0], ) + (1, ) * (
        x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = keep_prob + torch.rand(
        shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()  # binarize
    output = x.div(keep_prob) * random_tensor
    return output


class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    """

    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)


class Mlp(nn.Module):

    def __init__(self,
                 in_features,
                 hidden_features=None,
                 out_features=None,
                 act_layer=nn.GELU,
                 drop=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


class Block(nn.Module):

    def __init__(self,
                 dim,
                 num_heads,
                 mlp_ratio=4.,
                 qkv_bias=False,
                 qk_scale=None,
                 drop=0.,
                 attn_drop=0.,
                 drop_path=0.,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm):
        super().__init__()
        self.norm1 = norm_layer(dim)
        # self.attn = Attention(dim,
        self.attn = MemEffAttention(dim,
                              num_heads=num_heads,
                              qkv_bias=qkv_bias,
                              qk_scale=qk_scale,
                              attn_drop=attn_drop,
                              proj_drop=drop)
        self.drop_path = DropPath(
            drop_path) if drop_path > 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)

    def forward(self, x, return_attention=False):
        y, attn = self.attn(self.norm1(x))
        if return_attention:
            return attn
        x = x + self.drop_path(y)
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


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

    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
        super().__init__()
        num_patches = (img_size // patch_size) * (img_size // patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches

        self.proj = nn.Conv2d(in_chans,
                              embed_dim,
                              kernel_size=patch_size,
                              stride=patch_size)

    def forward(self, x):
        B, C, H, W = x.shape
        x = self.proj(x).flatten(2).transpose(1, 2)  # B, C, L -> B, L, C
        return x


class VisionTransformer(nn.Module):
    """ Vision Transformer """

    def __init__(self,
                 img_size=[224],
                 patch_size=16,
                 in_chans=3,
                 num_classes=0,
                 embed_dim=768,
                 depth=12,
                 num_heads=12,
                 mlp_ratio=4.,
                 qkv_bias=False,
                 qk_scale=None,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.,
                 norm_layer='nn.LayerNorm',
                 patch_embedding=True,
                 cls_token=True,
                 pixel_unshuffle=False,
                 **kwargs):
        super().__init__()
        self.num_features = self.embed_dim = embed_dim
        self.patch_size = patch_size

        # if norm_layer == 'nn.LayerNorm':
        norm_layer = partial(nn.LayerNorm, eps=1e-6)

        if patch_embedding:
            self.patch_embed = PatchEmbed(img_size=img_size[0],
                                          patch_size=patch_size,
                                          in_chans=in_chans,
                                          embed_dim=embed_dim)
            num_patches = self.patch_embed.num_patches
            self.img_size = self.patch_embed.img_size
        else:
            self.patch_embed = None
            self.img_size = img_size[0]
            num_patches = (img_size[0] // patch_size) * (img_size[0] //
                                                         patch_size)

        if cls_token:
            self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
            self.pos_embed = nn.Parameter(
                torch.zeros(1, num_patches + 1, embed_dim))
        else:
            self.cls_token = None
            self.pos_embed = nn.Parameter(
                torch.zeros(1, num_patches, embed_dim))

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

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)
               ]  # stochastic depth decay rule
        self.blocks = nn.ModuleList([
            Block(dim=embed_dim,
                  num_heads=num_heads,
                  mlp_ratio=mlp_ratio,
                  qkv_bias=qkv_bias,
                  qk_scale=qk_scale,
                  drop=drop_rate,
                  attn_drop=attn_drop_rate,
                  drop_path=dpr[i],
                  norm_layer=norm_layer) for i in range(depth)
        ])
        self.norm = norm_layer(embed_dim)

        # Classifier head
        self.head = nn.Linear(
            embed_dim, num_classes) if num_classes > 0 else nn.Identity()

        trunc_normal_(self.pos_embed, std=.02)
        if cls_token:
            trunc_normal_(self.cls_token, std=.02)
        self.apply(self._init_weights)

        # if pixel_unshuffle:
        #     self.decoder_pred = nn.Linear(embed_dim,
        #                                 patch_size**2 * out_chans,
        #                                 bias=True)  # decoder to patch

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.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)

    def interpolate_pos_encoding(self, x, w, h):
        npatch = x.shape[1] - 1
        N = self.pos_embed.shape[1] - 1
        if npatch == N and w == h:
            return self.pos_embed
        patch_pos_embed = self.pos_embed[:, 1:]
        dim = x.shape[-1]
        w0 = w // self.patch_size
        h0 = h // self.patch_size
        # we add a small number to avoid floating point error in the interpolation
        # see discussion at https://github.com/facebookresearch/dino/issues/8
        w0, h0 = w0 + 0.1, h0 + 0.1

        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)),
                                    dim).permute(0, 3, 1, 2),
            scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)),
            mode='bicubic',
        )
        assert int(w0) == patch_pos_embed.shape[-2] and int(
            h0) == patch_pos_embed.shape[-1]
        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(2, -1, dim)

        if self.cls_token is not None:
            class_pos_embed = self.pos_embed[:, 0]
            return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed),
                             dim=1)
        return patch_pos_embed

    def prepare_tokens(self, x):
        B, nc, w, h = x.shape
        x = self.patch_embed(x)  # patch linear embedding

        # add the [CLS] token to the embed patch tokens
        cls_tokens = self.cls_token.expand(B, -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)

        # add positional encoding to each token
        x = x + self.interpolate_pos_encoding(x, w, h)

        return self.pos_drop(x)

    def forward(self, x):
        x = self.prepare_tokens(x)
        for blk in self.blocks:
            x = blk(x)
        x = self.norm(x)
        return x[:, 1:]  # return spatial feature maps, not the [CLS] token
        # return x[:, 0]

    def get_last_selfattention(self, x):
        x = self.prepare_tokens(x)
        for i, blk in enumerate(self.blocks):
            if i < len(self.blocks) - 1:
                x = blk(x)
            else:
                # return attention of the last block
                return blk(x, return_attention=True)

    def get_intermediate_layers(self, x, n=1):
        x = self.prepare_tokens(x)
        # we return the output tokens from the `n` last blocks
        output = []
        for i, blk in enumerate(self.blocks):
            x = blk(x)
            if len(self.blocks) - i <= n:
                output.append(self.norm(x))
        return output


def vit_tiny(patch_size=16, **kwargs):
    model = VisionTransformer(patch_size=patch_size,
                              embed_dim=192,
                              depth=12,
                              num_heads=3,
                              mlp_ratio=4,
                              qkv_bias=True,
                              norm_layer=partial(nn.LayerNorm, eps=1e-6),
                              **kwargs)
    return model


def vit_small(patch_size=16, **kwargs):
    model = VisionTransformer(
        patch_size=patch_size,
        embed_dim=384,
        depth=12,
        num_heads=6,
        mlp_ratio=4,
        qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6),  # type: ignore
        **kwargs)
    return model


def vit_base(patch_size=16, **kwargs):
    model = VisionTransformer(patch_size=patch_size,
                              embed_dim=768,
                              depth=12,
                              num_heads=12,
                              mlp_ratio=4,
                              qkv_bias=True,
                              norm_layer=partial(nn.LayerNorm, eps=1e-6),
                              **kwargs)
    return model


vits = vit_small
vitb = vit_base