Delete modeling_m3d_lamed.py

modeling_m3d_lamed.py

@@ -1,2105 +0,0 @@
-from __future__ import annotations
-from typing import Union
-from transformers import Phi3Config, Phi3Model, Phi3ForCausalLM
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.generation.utils import GenerateOutput
-from .configuration_m3d_lamed import LamedPhi3Config
-from abc import ABC, abstractmethod
-from torch import Tensor
-import math
-from typing import Any, Dict, List
-import torch
-import torch.nn as nn
-from typing import Optional, Tuple, Type
-from monai.networks.blocks import PatchEmbed
-import numpy as np
-import torch.nn.functional as F
-
-from einops import rearrange
-from einops.layers.torch import Rearrange
-from collections.abc import Sequence
-from monai.networks.blocks.patchembedding import PatchEmbeddingBlock
-from monai.networks.blocks.transformerblock import TransformerBlock
-from monai.networks.nets import ViT
-
-
-class BinaryDiceLoss(nn.Module):
-    def __init__(self, smooth=1, p=2, reduction='mean'):
-        super(BinaryDiceLoss, self).__init__()
-        self.smooth = smooth
-        self.p = p
-        self.reduction = reduction
-
-    def forward(self, predict, target):
-        predict = torch.sigmoid(predict)
-        target_ = target.clone().float()
-        target_[target == -1] = 0
-        assert predict.shape[0] == target.shape[0], "predict & target batch size don't match\n" + str(predict.shape) + '\n' + str(target.shape[0])
-        predict = predict.contiguous().view(predict.shape[0], -1)
-        target_ = target_.contiguous().view(target_.shape[0], -1)
-
-        num = torch.sum(torch.mul(predict, target_), dim=1)
-        den = torch.sum(predict, dim=1) + torch.sum(target_, dim=1) + self.smooth
-
-        dice_score = 2*num / den
-        dice_loss = 1 - dice_score
-
-        # dice_loss_avg = dice_loss[target[:,0]!=-1].sum() / dice_loss[target[:,0]!=-1].shape[0]
-        dice_loss_avg = dice_loss.sum() / dice_loss.shape[0]
-
-        return dice_loss_avg
-
-class BCELoss(nn.Module):
-    def __init__(self):
-        super(BCELoss, self).__init__()
-        self.criterion = nn.BCEWithLogitsLoss()
-
-    def forward(self, predict, target):
-        assert predict.shape == target.shape, 'predict & target shape do not match\n' + str(predict.shape) + '\n' + str(target.shape)
-        target_ = target.clone()
-        target_[target == -1] = 0
-
-        ce_loss = self.criterion(predict, target_.float())
-
-        return ce_loss
-
-
-
-class LayerNorm2d(nn.Module):
-    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(num_channels))
-        self.bias = nn.Parameter(torch.zeros(num_channels))
-        self.eps = eps
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        u = x.mean(1, keepdim=True)
-        s = (x - u).pow(2).mean(1, keepdim=True)
-        x = (x - u) / torch.sqrt(s + self.eps)
-        x = self.weight[:, None, None] * x + self.bias[:, None, None]
-        return x
-
-
-class MLPBlock(nn.Module):
-    def __init__(
-            self,
-            embedding_dim: int,
-            mlp_dim: int,
-            act: Type[nn.Module] = nn.GELU,
-    ) -> None:
-        super().__init__()
-        self.lin1 = nn.Linear(embedding_dim, mlp_dim)
-        self.lin2 = nn.Linear(mlp_dim, embedding_dim)
-        self.act = act()
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.lin2(self.act(self.lin1(x)))
-
-
-class TwoWayTransformer(nn.Module):
-    def __init__(
-            self,
-            depth: int,
-            embedding_dim: int,
-            num_heads: int,
-            mlp_dim: int,
-            activation: Type[nn.Module] = nn.ReLU,
-            attention_downsample_rate: int = 2,
-    ) -> None:
-        """
-        A transformer decoder that attends to an input image using
-        queries whose positional embedding is supplied.
-
-        Args:
-          depth (int): number of layers in the transformer
-          embedding_dim (int): the channel dimension for the input embeddings
-          num_heads (int): the number of heads for multihead attention. Must
-            divide embedding_dim
-          mlp_dim (int): the channel dimension internal to the MLP block
-          activation (nn.Module): the activation to use in the MLP block
-        """
-        super().__init__()
-        self.depth = depth
-        self.embedding_dim = embedding_dim
-        self.num_heads = num_heads
-        self.mlp_dim = mlp_dim
-        self.layers = nn.ModuleList()
-
-        for i in range(depth):
-            self.layers.append(
-                TwoWayAttentionBlock(
-                    embedding_dim=embedding_dim,
-                    num_heads=num_heads,
-                    mlp_dim=mlp_dim,
-                    activation=activation,
-                    attention_downsample_rate=attention_downsample_rate,
-                    skip_first_layer_pe=(i == 0),
-                )
-            )
-
-        self.final_attn_token_to_image = Attention(
-            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
-        )
-        self.norm_final_attn = nn.LayerNorm(embedding_dim)
-
-    def forward(
-            self,
-            image_embedding: Tensor,
-            image_pe: Tensor,
-            point_embedding: Tensor,
-    ) -> Tuple[Tensor, Tensor]:
-        """
-        Args:
-          image_embedding (torch.Tensor): image to attend to. Should be shape
-            B x embedding_dim x h x w for any h and w.
-          image_pe (torch.Tensor): the positional encoding to add to the image. Must
-            have the same shape as image_embedding.
-          point_embedding (torch.Tensor): the embedding to add to the query points.
-            Must have shape B x N_points x embedding_dim for any N_points.
-
-        Returns:
-          torch.Tensor: the processed point_embedding
-          torch.Tensor: the processed image_embedding
-        """
-        # BxCxHxW -> BxHWxC == B x N_image_tokens x C
-        bs, c, h, w, d = image_embedding.shape
-        image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
-        image_pe = image_pe.flatten(2).permute(0, 2, 1)
-
-        # Prepare queries
-        queries = point_embedding
-        keys = image_embedding
-
-        # Apply transformer blocks and final layernorm
-        for layer in self.layers:
-            queries, keys = layer(
-                queries=queries,
-                keys=keys,
-                query_pe=point_embedding,
-                key_pe=image_pe,
-            )
-
-        # Apply the final attention layer from the points to the image
-        q = queries + point_embedding
-        k = keys + image_pe
-        attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
-        queries = queries + attn_out
-        queries = self.norm_final_attn(queries)
-
-        return queries, keys
-
-
-class TwoWayAttentionBlock(nn.Module):
-    def __init__(
-            self,
-            embedding_dim: int,
-            num_heads: int,
-            mlp_dim: int = 2048,
-            activation: Type[nn.Module] = nn.ReLU,
-            attention_downsample_rate: int = 2,
-            skip_first_layer_pe: bool = False,
-    ) -> None:
-        """
-        A transformer block with four layers: (1) self-attention of sparse
-        inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp
-        block on sparse inputs, and (4) cross attention of dense inputs to sparse
-        inputs.
-
-        Arguments:
-          embedding_dim (int): the channel dimension of the embeddings
-          num_heads (int): the number of heads in the attention layers
-          mlp_dim (int): the hidden dimension of the mlp block
-          activation (nn.Module): the activation of the mlp block
-          skip_first_layer_pe (bool): skip the PE on the first layer
-        """
-        super().__init__()
-        self.self_attn = Attention(embedding_dim, num_heads)
-        self.norm1 = nn.LayerNorm(embedding_dim)
-
-        self.cross_attn_token_to_image = Attention(
-            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
-        )
-        self.norm2 = nn.LayerNorm(embedding_dim)
-
-        self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
-        self.norm3 = nn.LayerNorm(embedding_dim)
-
-        self.norm4 = nn.LayerNorm(embedding_dim)
-        self.cross_attn_image_to_token = Attention(
-            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
-        )
-
-        self.skip_first_layer_pe = skip_first_layer_pe
-
-    def forward(
-            self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor
-    ) -> Tuple[Tensor, Tensor]:
-        # Self attention block
-        if self.skip_first_layer_pe:
-            queries = self.self_attn(q=queries, k=queries, v=queries)
-        else:
-            q = queries + query_pe
-            attn_out = self.self_attn(q=q, k=q, v=queries)
-            queries = queries + attn_out
-        queries = self.norm1(queries)
-
-        # Cross attention block, tokens attending to image embedding
-        q = queries + query_pe
-        k = keys + key_pe
-        attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
-        queries = queries + attn_out
-        queries = self.norm2(queries)
-
-        # MLP block
-        mlp_out = self.mlp(queries)
-        queries = queries + mlp_out
-        queries = self.norm3(queries)
-
-        # Cross attention block, image embedding attending to tokens
-        q = queries + query_pe
-        k = keys + key_pe
-        attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
-        keys = keys + attn_out
-        keys = self.norm4(keys)
-
-        return queries, keys
-
-
-class Attention(nn.Module):
-    """
-    An attention layer that allows for downscaling the size of the embedding
-    after projection to queries, keys, and values.
-    """
-
-    def __init__(
-            self,
-            embedding_dim: int,
-            num_heads: int,
-            downsample_rate: int = 1,
-    ) -> None:
-        super().__init__()
-        self.embedding_dim = embedding_dim
-        self.internal_dim = embedding_dim // downsample_rate
-        self.num_heads = num_heads
-        assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim."
-
-        self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
-        self.k_proj = nn.Linear(embedding_dim, self.internal_dim)
-        self.v_proj = nn.Linear(embedding_dim, self.internal_dim)
-        self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
-
-    def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
-        b, n, c = x.shape
-        x = x.reshape(b, n, num_heads, c // num_heads)
-        return x.transpose(1, 2)  # B x N_heads x N_tokens x C_per_head
-
-    def _recombine_heads(self, x: Tensor) -> Tensor:
-        b, n_heads, n_tokens, c_per_head = x.shape
-        x = x.transpose(1, 2)
-        return x.reshape(b, n_tokens, n_heads * c_per_head)  # B x N_tokens x C
-
-    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
-        # Input projections
-        q = self.q_proj(q)
-        k = self.k_proj(k)
-        v = self.v_proj(v)
-
-        # Separate into heads
-        q = self._separate_heads(q, self.num_heads)
-        k = self._separate_heads(k, self.num_heads)
-        v = self._separate_heads(v, self.num_heads)
-
-        # Attention
-        _, _, _, c_per_head = q.shape
-        attn = q @ k.permute(0, 1, 3, 2)  # B x N_heads x N_tokens x N_tokens
-        attn = attn / math.sqrt(c_per_head)
-        attn = torch.softmax(attn, dim=-1)
-
-        # Get output
-        out = attn @ v
-        out = self._recombine_heads(out)
-        out = self.out_proj(out)
-
-        return out
-
-
-
-# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
-class ImageEncoderViT(nn.Module):
-    def __init__(
-            self,
-            img_size: int = 1024,
-            patch_size: int = 16,
-            in_chans: int = 1,
-            embed_dim: int = 768,
-            depth: int = 12,
-            num_heads: int = 12,
-            mlp_ratio: float = 4.0,
-            out_chans: int = 256,
-            qkv_bias: bool = True,
-            norm_layer: Type[nn.Module] = nn.LayerNorm,
-            act_layer: Type[nn.Module] = nn.GELU,
-            use_abs_pos: bool = True,
-            use_rel_pos: bool = False,
-            rel_pos_zero_init: bool = True,
-            window_size: int = 0,
-            global_attn_indexes: Tuple[int, ...] = (),
-    ) -> None:
-        """
-        Args:
-            img_size (int): Input image size.
-            patch_size (int): Patch size.
-            in_chans (int): Number of input image channels.
-            embed_dim (int): Patch embedding dimension.
-            depth (int): Depth of ViT.
-            num_heads (int): Number of attention heads in each ViT block.
-            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-            qkv_bias (bool): If True, add a learnable bias to query, key, value.
-            norm_layer (nn.Module): Normalization layer.
-            act_layer (nn.Module): Activation layer.
-            use_abs_pos (bool): If True, use absolute positional embeddings.
-            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
-            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
-            window_size (int): Window size for window attention blocks.
-            global_attn_indexes (list): Indexes for blocks using global attention.
-        """
-        super().__init__()
-        self.img_size = img_size
-
-        # self.patch_embed = PatchEmbed(
-        #     kernel_size=(patch_size, patch_size),
-        #     stride=(patch_size, patch_size),
-        #     in_chans=in_chans,
-        #     embed_dim=embed_dim,
-        # )
-
-        self.patch_embed = PatchEmbed(
-            patch_size=patch_size,
-            in_chans=in_chans,
-            embed_dim=embed_dim,
-            spatial_dims=3,
-        )
-
-        self.pos_embed: Optional[nn.Parameter] = None
-        if use_abs_pos:
-            # Initialize absolute positional embedding with pretrain image size.
-            self.pos_embed = nn.Parameter(
-                torch.zeros(1, img_size // patch_size, img_size // patch_size, img_size // patch_size, embed_dim)
-            )
-
-        self.blocks = nn.ModuleList()
-        for i in range(depth):
-            block = Block(
-                dim=embed_dim,
-                num_heads=num_heads,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                norm_layer=norm_layer,
-                act_layer=act_layer,
-                use_rel_pos=use_rel_pos,
-                rel_pos_zero_init=rel_pos_zero_init,
-                window_size=window_size if i not in global_attn_indexes else 0,
-                input_size=(img_size // patch_size, img_size // patch_size),
-            )
-            self.blocks.append(block)
-
-        self.neck = nn.Sequential(
-            nn.Conv2d(
-                embed_dim,
-                out_chans,
-                kernel_size=1,
-                bias=False,
-            ),
-            LayerNorm2d(out_chans),
-            nn.Conv2d(
-                out_chans,
-                out_chans,
-                kernel_size=3,
-                padding=1,
-                bias=False,
-            ),
-            LayerNorm2d(out_chans),
-        )
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.patch_embed(x)
-        print('patch embedded shape: ', x.shape)  # embedded: [8, 768, 6, 6, 6]
-        if self.pos_embed is not None:
-            x = x + self.pos_embed
-
-        for blk in self.blocks:
-            x = blk(x)
-
-        x = self.neck(x.permute(0, 3, 1, 2))
-
-        return x
-
-
-class Block(nn.Module):
-    """Transformer blocks with support of window attention and residual propagation blocks"""
-
-    def __init__(
-            self,
-            dim: int,
-            num_heads: int,
-            mlp_ratio: float = 4.0,
-            qkv_bias: bool = True,
-            norm_layer: Type[nn.Module] = nn.LayerNorm,
-            act_layer: Type[nn.Module] = nn.GELU,
-            use_rel_pos: bool = False,
-            rel_pos_zero_init: bool = True,
-            window_size: int = 0,
-            input_size: Optional[Tuple[int, int]] = None,
-    ) -> None:
-        """
-        Args:
-            dim (int): Number of input channels.
-            num_heads (int): Number of attention heads in each ViT block.
-            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-            qkv_bias (bool): If True, add a learnable bias to query, key, value.
-            norm_layer (nn.Module): Normalization layer.
-            act_layer (nn.Module): Activation layer.
-            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
-            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
-            window_size (int): Window size for window attention blocks. If it equals 0, then
-                use global attention.
-            input_size (tuple(int, int) or None): Input resolution for calculating the relative
-                positional parameter size.
-        """
-        super().__init__()
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention2(
-            dim,
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            use_rel_pos=use_rel_pos,
-            rel_pos_zero_init=rel_pos_zero_init,
-            input_size=input_size if window_size == 0 else (window_size, window_size),
-        )
-
-        self.norm2 = norm_layer(dim)
-        self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
-
-        self.window_size = window_size
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        shortcut = x
-        x = self.norm1(x)
-        # Window partition
-        if self.window_size > 0:
-            H, W = x.shape[1], x.shape[2]
-            x, pad_hw = window_partition(x, self.window_size)
-
-        x = self.attn(x)
-        # Reverse window partition
-        if self.window_size > 0:
-            x = window_unpartition(x, self.window_size, pad_hw, (H, W))
-
-        x = shortcut + x
-        x = x + self.mlp(self.norm2(x))
-
-        return x
-
-
-class Attention2(nn.Module):
-    """Multi-head Attention block with relative position embeddings."""
-
-    def __init__(
-            self,
-            dim: int,
-            num_heads: int = 8,
-            qkv_bias: bool = True,
-            use_rel_pos: bool = False,
-            rel_pos_zero_init: bool = True,
-            input_size: Optional[Tuple[int, int]] = None,
-    ) -> None:
-        """
-        Args:
-            dim (int): Number of input channels.
-            num_heads (int): Number of attention heads.
-            qkv_bias (bool):  If True, add a learnable bias to query, key, value.
-            rel_pos (bool): If True, add relative positional embeddings to the attention map.
-            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
-            input_size (tuple(int, int) or None): Input resolution for calculating the relative
-                positional parameter size.
-        """
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = head_dim ** -0.5
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.proj = nn.Linear(dim, dim)
-
-        self.use_rel_pos = use_rel_pos
-        if self.use_rel_pos:
-            assert (
-                    input_size is not None
-            ), "Input size must be provided if using relative positional encoding."
-            # initialize relative positional embeddings
-            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
-            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        B, H, W, _ = x.shape
-        # qkv with shape (3, B, nHead, H * W, C)
-        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
-        # q, k, v with shape (B * nHead, H * W, C)
-        q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
-
-        attn = (q * self.scale) @ k.transpose(-2, -1)
-
-        if self.use_rel_pos:
-            attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
-
-        attn = attn.softmax(dim=-1)
-        x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
-        x = self.proj(x)
-
-        return x
-
-
-def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
-    """
-    Partition into non-overlapping windows with padding if needed.
-    Args:
-        x (tensor): input tokens with [B, H, W, C].
-        window_size (int): window size.
-
-    Returns:
-        windows: windows after partition with [B * num_windows, window_size, window_size, C].
-        (Hp, Wp): padded height and width before partition
-    """
-    B, H, W, C = x.shape
-
-    pad_h = (window_size - H % window_size) % window_size
-    pad_w = (window_size - W % window_size) % window_size
-    if pad_h > 0 or pad_w > 0:
-        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
-    Hp, Wp = H + pad_h, W + pad_w
-
-    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows, (Hp, Wp)
-
-
-def window_unpartition(
-        windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
-) -> torch.Tensor:
-    """
-    Window unpartition into original sequences and removing padding.
-    Args:
-        windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
-        window_size (int): window size.
-        pad_hw (Tuple): padded height and width (Hp, Wp).
-        hw (Tuple): original height and width (H, W) before padding.
-
-    Returns:
-        x: unpartitioned sequences with [B, H, W, C].
-    """
-    Hp, Wp = pad_hw
-    H, W = hw
-    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
-    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
-
-    if Hp > H or Wp > W:
-        x = x[:, :H, :W, :].contiguous()
-    return x
-
-
-def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
-    """
-    Get relative positional embeddings according to the relative positions of
-        query and key sizes.
-    Args:
-        q_size (int): size of query q.
-        k_size (int): size of key k.
-        rel_pos (Tensor): relative position embeddings (L, C).
-
-    Returns:
-        Extracted positional embeddings according to relative positions.
-    """
-    max_rel_dist = int(2 * max(q_size, k_size) - 1)
-    # Interpolate rel pos if needed.
-    if rel_pos.shape[0] != max_rel_dist:
-        # Interpolate rel pos.
-        rel_pos_resized = F.interpolate(
-            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
-            size=max_rel_dist,
-            mode="linear",
-        )
-        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
-    else:
-        rel_pos_resized = rel_pos
-
-    # Scale the coords with short length if shapes for q and k are different.
-    q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
-    k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
-    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
-
-    return rel_pos_resized[relative_coords.long()]
-
-
-def add_decomposed_rel_pos(
-        attn: torch.Tensor,
-        q: torch.Tensor,
-        rel_pos_h: torch.Tensor,
-        rel_pos_w: torch.Tensor,
-        q_size: Tuple[int, int],
-        k_size: Tuple[int, int],
-) -> torch.Tensor:
-    """
-    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
-    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py   # noqa B950
-    Args:
-        attn (Tensor): attention map.
-        q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
-        rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
-        rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
-        q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
-        k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
-
-    Returns:
-        attn (Tensor): attention map with added relative positional embeddings.
-    """
-    q_h, q_w = q_size
-    k_h, k_w = k_size
-    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
-    Rw = get_rel_pos(q_w, k_w, rel_pos_w)
-
-    B, _, dim = q.shape
-    r_q = q.reshape(B, q_h, q_w, dim)
-    rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
-    rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
-
-    attn = (
-            attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
-    ).view(B, q_h * q_w, k_h * k_w)
-
-    return attn
-
-
-class PromptEncoder(nn.Module):
-    def __init__(
-            self,
-            embed_dim: int,
-            image_embedding_size: Tuple[int, int, int],
-            input_image_size: Tuple[int, int, int],
-            mask_in_chans: int,
-            activation: Type[nn.Module] = nn.GELU,
-    ) -> None:
-        """
-        Encodes prompts for input to SAM's mask decoder.
-
-        Arguments:
-          embed_dim (int): The prompts' embedding dimension
-          image_embedding_size (tuple(int, int)): The spatial size of the
-            image embedding, as (H, W).
-          input_image_size (int): The padded size of the image as input
-            to the image encoder, as (H, W).
-          mask_in_chans (int): The number of hidden channels used for
-            encoding input masks.
-          activation (nn.Module): The activation to use when encoding
-            input masks.
-        """
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.input_image_size = input_image_size
-        self.image_embedding_size = image_embedding_size
-        self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
-
-        self.num_point_embeddings: int = 4  # pos/neg point + 2 box corners
-        point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
-        self.point_embeddings = nn.ModuleList(point_embeddings)
-        self.not_a_point_embed = nn.Embedding(1, embed_dim)
-
-        self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1], 4 * image_embedding_size[2])
-        self.mask_downscaling = nn.Sequential(
-            nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
-            LayerNorm2d(mask_in_chans // 4),
-            activation(),
-            nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
-            LayerNorm2d(mask_in_chans),
-            activation(),
-            nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
-        )
-        self.no_mask_embed = nn.Embedding(1, embed_dim)
-
-    def get_dense_pe(self) -> torch.Tensor:
-        """
-        Returns the positional encoding used to encode point prompts,
-        applied to a dense set of points the shape of the image encoding.
-
-        Returns:
-          torch.Tensor: Positional encoding with shape
-            1x(embed_dim)x(embedding_h)x(embedding_w)
-        """
-        return self.pe_layer(self.image_embedding_size).unsqueeze(0)
-
-    def _embed_points(
-            self,
-            points: torch.Tensor,
-            labels: torch.Tensor,
-            pad: bool,
-    ) -> torch.Tensor:
-        """Embeds point prompts."""
-        points = points + 0.5  # Shift to center of pixel
-        if pad:
-            padding_point = torch.zeros((points.shape[0], 1, 3), device=points.device)
-            padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
-            points = torch.cat([points, padding_point], dim=1)
-            labels = torch.cat([labels, padding_label], dim=1)
-        point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
-        point_embedding[labels == -1] = 0.0
-        point_embedding[labels == -1] += self.not_a_point_embed.weight
-        point_embedding[labels == 0] += self.point_embeddings[0].weight
-        point_embedding[labels == 1] += self.point_embeddings[1].weight
-        return point_embedding
-
-    def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
-        """Embeds box prompts."""
-        boxes = boxes + 0.5  # Shift to center of pixel
-        coords = boxes.reshape(-1, 2, 3)
-        corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
-        corner_embedding[:, 0, :] += self.point_embeddings[2].weight
-        corner_embedding[:, 1, :] += self.point_embeddings[3].weight
-        return corner_embedding
-
-    def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
-        """Embeds mask inputs."""
-        mask_embedding = self.mask_downscaling(masks)
-        return mask_embedding
-
-    def _get_batch_size(
-            self,
-            points: Optional[Tuple[torch.Tensor, torch.Tensor]],
-            boxes: Optional[torch.Tensor],
-            masks: Optional[torch.Tensor],
-            text_embedding: Optional[torch.Tensor],
-    ) -> int:
-        """
-        Gets the batch size of the output given the batch size of the input prompts.
-        """
-        if points is not None:
-            return points[0].shape[0]
-        elif boxes is not None:
-            return boxes.shape[0]
-        elif masks is not None:
-            return masks.shape[0]
-        elif text_embedding is not None:
-            return text_embedding.shape[0]
-        else:
-            return 1
-
-    def _get_device(self) -> torch.device:
-        return self.point_embeddings[0].weight.device
-
-    def forward(
-            self,
-            points: Optional[Tuple[torch.Tensor, torch.Tensor]],
-            boxes: Optional[torch.Tensor],
-            masks: Optional[torch.Tensor],
-            text_embedding: Optional[torch.Tensor],
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        Embeds different types of prompts, returning both sparse and dense
-        embeddings.
-
-        Arguments:
-          points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
-            and labels to embed.
-          boxes (torch.Tensor or none): boxes to embed
-          masks (torch.Tensor or none): masks to embed
-          text: test prompt (B, 768)
-
-        Returns:
-          torch.Tensor: sparse embeddings for the points and boxes, with shape
-            BxNx(embed_dim), where N is determined by the number of input points
-            and boxes.
-          torch.Tensor: dense embeddings for the masks, in the shape
-            Bx(embed_dim)x(embed_H)x(embed_W)
-        """
-        # print('prompt encoder here...')
-
-        bs = self._get_batch_size(points, boxes, masks, text_embedding)
-        sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device(),
-                                        dtype=self.point_embeddings[0].weight.dtype)
-        # print('sparse_embeddings ', sparse_embeddings.shape)
-        if points is not None:
-            coords, labels = points
-            point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
-            sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
-
-        if boxes is not None:
-            box_embeddings = self._embed_boxes(boxes)
-            sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
-
-        if text_embedding is not None:
-            sparse_embeddings = torch.cat([sparse_embeddings, text_embedding.unsqueeze(dim=1)], dim=1)
-
-        # print('box_embeddings ', box_embeddings.shape)
-        # print('sparse_embeddings after box/point/text', sparse_embeddings.shape)
-
-        if masks is not None:
-            dense_embeddings = self._embed_masks(masks)
-        else:
-            dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1, 1).expand(
-                bs, -1, int(self.image_embedding_size[0]), int(self.image_embedding_size[1]),
-                int(self.image_embedding_size[2])
-            )
-        return sparse_embeddings, dense_embeddings
-
-
-class PositionEmbeddingRandom(nn.Module):
-    """
-    Positional encoding using random spatial frequencies.
-    """
-
-    def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
-        super().__init__()
-        if scale is None or scale <= 0.0:
-            scale = 1.0
-        self.register_buffer(
-            "positional_encoding_gaussian_matrix",
-            scale * torch.randn((3, num_pos_feats)),
-        )
-
-    def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
-        """Positionally encode points that are normalized to [0,1]."""
-        # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
-        coords = 2 * coords - 1
-        coords = coords @ self.positional_encoding_gaussian_matrix
-        coords = 2 * np.pi * coords
-        # outputs d_1 x ... x d_n x C shape
-        return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
-
-    def forward(self, size: Tuple[int, int, int]) -> torch.Tensor:
-        """Generate positional encoding for a grid of the specified size."""
-        h, w, d = size
-        device: Any = self.positional_encoding_gaussian_matrix.device
-        dtype = self.positional_encoding_gaussian_matrix.dtype
-        grid = torch.ones((h, w, d), device=device, dtype=dtype)
-        y_embed = grid.cumsum(dim=0) - 0.5
-        x_embed = grid.cumsum(dim=1) - 0.5
-        z_embed = grid.cumsum(dim=2) - 0.5
-        y_embed = y_embed / h
-        x_embed = x_embed / w
-        z_embed = z_embed / d
-
-        pe = self._pe_encoding(torch.stack([x_embed, y_embed, z_embed], dim=-1))
-        return pe.permute(3, 0, 1, 2)  # C x H x W x D
-
-    def forward_with_coords(
-            self, coords_input: torch.Tensor, image_size: Tuple[int, int]
-    ) -> torch.Tensor:
-        """Positionally encode points that are not normalized to [0,1]."""
-        coords = coords_input.clone()
-        coords[:, :, 0] = coords[:, :, 0] / image_size[1]
-        coords[:, :, 1] = coords[:, :, 1] / image_size[0]
-        coords[:, :, 2] = coords[:, :, 2] / image_size[2]
-        return self._pe_encoding(coords.to(torch.float))  # B x N x C
-
-
-class MaskDecoder(nn.Module):
-    def __init__(
-            self,
-            *,
-            image_encoder_type: str,
-            transformer_dim: int,
-            transformer: nn.Module,
-            num_multimask_outputs: int = 3,
-            activation: Type[nn.Module] = nn.GELU,
-            iou_head_depth: int = 3,
-            iou_head_hidden_dim: int = 256,
-            image_size,
-            patch_size,
-    ) -> None:
-        """
-        Predicts masks given an image and prompt embeddings, using a
-        transformer architecture.
-
-        Arguments:
-          transformer_dim (int): the channel dimension of the transformer
-          transformer (nn.Module): the transformer used to predict masks
-          num_multimask_outputs (int): the number of masks to predict
-            when disambiguating masks
-          activation (nn.Module): the type of activation to use when
-            upscaling masks
-          iou_head_depth (int): the depth of the MLP used to predict
-            mask quality
-          iou_head_hidden_dim (int): the hidden dimension of the MLP
-            used to predict mask quality
-        """
-        super().__init__()
-        self.transformer_dim = transformer_dim
-        self.transformer = transformer
-
-        self.num_multimask_outputs = num_multimask_outputs
-
-        self.iou_token = nn.Embedding(1, transformer_dim)
-        self.num_mask_tokens = num_multimask_outputs + 1
-        self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
-
-        if image_encoder_type == 'swin_vit':
-            self.feat_shape = image_size / patch_size
-            self.output_upscaling = nn.Sequential(
-                nn.ConvTranspose3d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
-                nn.LayerNorm(
-                    (transformer_dim // 4, int(self.feat_shape[0]), int(self.feat_shape[1]), int(self.feat_shape[2]))),
-                # swin
-                activation(),
-                nn.ConvTranspose3d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),  # swin
-                # nn.Conv3d(transformer_dim // 4, transformer_dim // 8, kernel_size=3, stride=1, padding=1),    # vit
-                activation(),
-            )
-        else:
-            self.feat_shape = image_size / patch_size * 2
-            self.output_upscaling = nn.Sequential(
-                nn.ConvTranspose3d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
-                nn.LayerNorm(
-                    (transformer_dim // 4, int(self.feat_shape[0]), int(self.feat_shape[1]), int(self.feat_shape[2]))),
-                # vit
-                activation(),
-                nn.ConvTranspose3d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
-                # nn.Conv3d(transformer_dim // 4, transformer_dim // 8, kernel_size=3, stride=1, padding=1),
-                activation(),
-            )
-        self.output_hypernetworks_mlps = nn.ModuleList(
-            [
-                MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
-                for i in range(self.num_mask_tokens)
-            ]
-        )
-
-        self.iou_prediction_head = MLP(
-            transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
-        )
-
-        self.txt_align_upscaled_embedding = nn.Linear(768, 96)
-
-    def forward(
-            self,
-            image_embeddings: torch.Tensor,
-            text_embedding: Optional[torch.Tensor],
-            image_pe: torch.Tensor,
-            sparse_prompt_embeddings: torch.Tensor,
-            dense_prompt_embeddings: torch.Tensor,
-            multimask_output: bool,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        Predict masks given image and prompt embeddings.
-
-        Arguments:
-          image_embeddings (torch.Tensor): the embeddings from the image encoder
-          image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
-          sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
-          dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
-          multimask_output (bool): Whether to return multiple masks or a single
-            mask.
-
-        Returns:
-          torch.Tensor: batched predicted masks
-          torch.Tensor: batched predictions of mask quality
-        """
-        # print('--------------decoder here--------------')
-        masks, iou_pred = self.predict_masks(
-            image_embeddings=image_embeddings,
-            text_embedding=text_embedding,
-            image_pe=image_pe,
-            sparse_prompt_embeddings=sparse_prompt_embeddings,
-            dense_prompt_embeddings=dense_prompt_embeddings,
-        )
-
-        # Select the correct mask or masks for output
-        if multimask_output:
-            mask_slice = slice(1, None)
-        else:
-            mask_slice = slice(0, 1)
-        masks = masks[:, mask_slice, :, :, :]
-        iou_pred = iou_pred[:, mask_slice]
-
-        # Prepare output
-        return masks, iou_pred
-
-    def predict_masks(
-            self,
-            image_embeddings: torch.Tensor,
-            text_embedding: torch.Tensor,
-            image_pe: torch.Tensor,
-            sparse_prompt_embeddings: torch.Tensor,
-            dense_prompt_embeddings: torch.Tensor,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """Predicts masks. See 'forward' for more details."""
-        # Concatenate output tokens
-        output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
-        output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
-        tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)  # [2, 7=(5+2), 256]
-        # Expand per-image data in batch direction to be per-mask
-        if image_embeddings.shape[0] != tokens.shape[0]:
-            src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
-        else:
-            src = image_embeddings
-
-        src = src + dense_prompt_embeddings
-        pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
-        b, c, h, w, d = src.shape
-
-        # Run the transformer
-        hs, src = self.transformer(src, pos_src, tokens)
-        iou_token_out = hs[:, 0, :]
-        mask_tokens_out = hs[:, 1: (1 + self.num_mask_tokens), :]
-
-        # Upscale mask embeddings and predict masks using the mask tokens
-        src = src.transpose(1, 2).view(b, c, h, w, d)
-        # print('src ', src.shape) # vit:[B, 768, 12, 12, 6], swin: [B, 6, 6, 3]
-        upscaled_embedding = self.output_upscaling(src)
-        hyper_in_list: List[torch.Tensor] = []
-        for i in range(self.num_mask_tokens):
-            hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
-        hyper_in = torch.stack(hyper_in_list, dim=1)
-        b, c, h, w, d = upscaled_embedding.shape
-        # print('hyper_in ', hyper_in.shape)    # [2, 4, 96]
-        # print('upscaled_embedding ', upscaled_embedding.shape)    # [2, 96, 24, 24, 12]*
-        masks = (hyper_in @ upscaled_embedding.view(b, c, h * w * d)).view(b, -1, h, w, d)
-        # print('masks here ', masks.shape) # [2, 4, 24, 24, 12]
-
-        if text_embedding is not None:
-            # text_embedding: B x 768, upscaled_embedding: B x c x h x w x d => B x 1 x h x w x d
-            text_embedding_down = self.txt_align_upscaled_embedding(text_embedding).unsqueeze(dim=1)
-            upscaled_embedding = upscaled_embedding.view(b, c, h * w * d)
-            # print('text_embedding_down ', text_embedding_down.shape)  # [2, 1, 96]
-            # text_embedding_norm = F.normalize(text_embedding_down, dim=-1)
-            # upscaled_embedding_norm = F.normalize(upscaled_embedding, dim=1)
-            # sim = (text_embedding_norm @ upscaled_embedding_norm).view(b, -1, h, w, d)
-            # print(text_embedding_down.shape, upscaled_embedding.shape)
-            sim = (text_embedding_down @ upscaled_embedding).view(b, -1, h, w, d)
-            # print('sim ', sim.shape)  # [B, 1, 24, 24, 12]
-            sim = sim.repeat(1, masks.shape[1], 1, 1, 1)
-            # print('sim after', sim.shape) # [B, 4, 24, 24, 12]
-            masks = masks + sim
-        # Generate mask quality predictions
-        iou_pred = self.iou_prediction_head(iou_token_out)
-
-        return masks, iou_pred
-
-
-# Lightly adapted from
-# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
-class MLP(nn.Module):
-    def __init__(
-            self,
-            input_dim: int,
-            hidden_dim: int,
-            output_dim: int,
-            num_layers: int,
-            sigmoid_output: bool = False,
-    ) -> None:
-        super().__init__()
-        self.num_layers = num_layers
-        h = [hidden_dim] * (num_layers - 1)
-        self.layers = nn.ModuleList(
-            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
-        )
-        self.sigmoid_output = sigmoid_output
-
-    def forward(self, x):
-        for i, layer in enumerate(self.layers):
-            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
-        if self.sigmoid_output:
-            x = F.sigmoid(x)
-        return x
-
-
-class Sam(nn.Module):
-    mask_threshold: float = 0.0
-    image_format: str = "RGB"
-
-    def __init__(
-            self,
-            image_encoder: ImageEncoderViT,
-            prompt_encoder: PromptEncoder,
-            mask_decoder: MaskDecoder,
-            pixel_mean: List[float] = [123.675, 116.28, 103.53],
-            pixel_std: List[float] = [58.395, 57.12, 57.375],
-    ) -> None:
-        """
-        SAM predicts object masks from an image and input prompts.
-
-        Arguments:
-          image_encoder (ImageEncoderViT): The backbone used to encode the
-            image into image embeddings that allow for efficient mask prediction.
-          prompt_encoder (PromptEncoder): Encodes various types of input prompts.
-          mask_decoder (MaskDecoder): Predicts masks from the image embeddings
-            and encoded prompts.
-          pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
-          pixel_std (list(float)): Std values for normalizing pixels in the input image.
-        """
-        super().__init__()
-        self.image_encoder = image_encoder
-        self.prompt_encoder = prompt_encoder
-        self.mask_decoder = mask_decoder
-        self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
-        self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
-
-    @property
-    def device(self) -> Any:
-        return self.pixel_mean.device
-
-    @torch.no_grad()
-    def forward(
-            self,
-            batched_input: List[Dict[str, Any]],
-            multimask_output: bool,
-    ) -> List[Dict[str, torch.Tensor]]:
-        """
-        Predicts masks end-to-end from provided images and prompts.
-        If prompts are not known in advance, using SamPredictor is
-        recommended over calling the model directly.
-
-        Arguments:
-          batched_input (list(dict)): A list over input images, each a
-            dictionary with the following keys. A prompt key can be
-            excluded if it is not present.
-              'image': The image as a torch tensor in 3xHxW format,
-                already transformed for input to the model.
-              'original_size': (tuple(int, int)) The original size of
-                the image before transformation, as (H, W).
-              'point_coords': (torch.Tensor) Batched point prompts for
-                this image, with shape BxNx2. Already transformed to the
-                input frame of the model.
-              'point_labels': (torch.Tensor) Batched labels for point prompts,
-                with shape BxN.
-              'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
-                Already transformed to the input frame of the model.
-              'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
-                in the form Bx1xHxW.
-          multimask_output (bool): Whether the model should predict multiple
-            disambiguating masks, or return a single mask.
-
-        Returns:
-          (list(dict)): A list over input images, where each element is
-            as dictionary with the following keys.
-              'masks': (torch.Tensor) Batched binary mask predictions,
-                with shape BxCxHxW, where B is the number of input prompts,
-                C is determined by multimask_output, and (H, W) is the
-                original size of the image.
-              'iou_predictions': (torch.Tensor) The model's predictions
-                of mask quality, in shape BxC.
-              'low_res_logits': (torch.Tensor) Low resolution logits with
-                shape BxCxHxW, where H=W=256. Can be passed as mask input
-                to subsequent iterations of prediction.
-        """
-        input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
-        image_embeddings = self.image_encoder(input_images)
-
-        outputs = []
-        for image_record, curr_embedding in zip(batched_input, image_embeddings):
-            if "point_coords" in image_record:
-                points = (image_record["point_coords"], image_record["point_labels"])
-            else:
-                points = None
-            sparse_embeddings, dense_embeddings = self.prompt_encoder(
-                points=points,
-                boxes=image_record.get("boxes", None),
-                masks=image_record.get("mask_inputs", None),
-            )
-            low_res_masks, iou_predictions = self.mask_decoder(
-                image_embeddings=curr_embedding.unsqueeze(0),
-                image_pe=self.prompt_encoder.get_dense_pe(),
-                sparse_prompt_embeddings=sparse_embeddings,
-                dense_prompt_embeddings=dense_embeddings,
-                multimask_output=multimask_output,
-            )
-            masks = self.postprocess_masks(
-                low_res_masks,
-                input_size=image_record["image"].shape[-2:],
-                original_size=image_record["original_size"],
-            )
-            masks = masks > self.mask_threshold
-            outputs.append(
-                {
-                    "masks": masks,
-                    "iou_predictions": iou_predictions,
-                    "low_res_logits": low_res_masks,
-                }
-            )
-        return outputs
-
-    def postprocess_masks(
-            self,
-            masks: torch.Tensor,
-            input_size: Tuple[int, ...],
-            original_size: Tuple[int, ...],
-    ) -> torch.Tensor:
-        """
-        Remove padding and upscale masks to the original image size.
-
-        Arguments:
-          masks (torch.Tensor): Batched masks from the mask_decoder,
-            in BxCxHxW format.
-          input_size (tuple(int, int)): The size of the image input to the
-            model, in (H, W) format. Used to remove padding.
-          original_size (tuple(int, int)): The original size of the image
-            before resizing for input to the model, in (H, W) format.
-
-        Returns:
-          (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
-            is given by original_size.
-        """
-        masks = F.interpolate(
-            masks,
-            (self.image_encoder.img_size, self.image_encoder.img_size),
-            mode="bilinear",
-            align_corners=False,
-        )
-        masks = masks[..., : input_size[0], : input_size[1]]
-        masks = F.interpolate(masks, original_size, mode="bilinear", align_corners=False)
-        return masks
-
-    def preprocess(self, x: torch.Tensor) -> torch.Tensor:
-        """Normalize pixel values and pad to a square input."""
-        # Normalize colors
-        # TODO
-        x = (x - self.pixel_mean) / self.pixel_std
-
-        # Pad
-        h, w = x.shape[-2:]
-        padh = self.image_encoder.img_size - h
-        padw = self.image_encoder.img_size - w
-        x = F.pad(x, (0, padw, 0, padh))
-        return x
-
-
-"""
-Examples::
-            # for 3D single channel input with size (96,96,96), 4-channel output and feature size of 48.
-            >>> net = SwinUNETR(img_size=(96,96,96), in_channels=1, out_channels=4, feature_size=48)
-            # for 3D 4-channel input with size (128,128,128), 3-channel output and (2,4,2,2) layers in each stage.
-            >>> net = SwinUNETR(img_size=(128,128,128), in_channels=4, out_channels=3, depths=(2,4,2,2))
-            # for 2D single channel input with size (96,96), 2-channel output and gradient checkpointing.
-            >>> net = SwinUNETR(img_size=(96,96), in_channels=3, out_channels=2, use_checkpoint=True, spatial_dims=2)
-"""
-
-
-def build_sam_vit_3d(args, checkpoint=None):
-    print('build_sam_vit_3d...')
-    return _build_sam(
-        image_encoder_type='vit',
-        embed_dim=768,
-        patch_size=args.patch_size,
-        checkpoint=checkpoint,
-        image_size=args.image_size,
-    )
-
-
-sam_model_registry = {
-    "vit": build_sam_vit_3d,
-}
-
-
-def _build_sam(
-        image_encoder_type,
-        embed_dim,
-        patch_size,
-        checkpoint,
-        image_size,
-):
-    mlp_dim = 3072
-    num_layers = 12
-    num_heads = 12
-    pos_embed = 'perceptron'
-    dropout_rate = 0.0
-
-    image_encoder = ViT(
-        in_channels=1,
-        img_size=image_size,
-        patch_size=patch_size,
-        hidden_size=embed_dim,
-        mlp_dim=mlp_dim,
-        num_layers=num_layers,
-        num_heads=num_heads,
-        pos_embed=pos_embed,
-        classification=False,
-        dropout_rate=dropout_rate,
-    )
-    image_embedding_size = [int(item) for item in (np.array(image_size) / np.array(patch_size))]
-
-    if checkpoint is not None:
-        with open(checkpoint, "rb") as f:
-            state_dict = torch.load(f, map_location='cpu')['state_dict']
-            encoder_dict = {k.replace('model.encoder.', ''): v for k, v in state_dict.items() if 'model.encoder.' in k}
-        image_encoder.load_state_dict(encoder_dict)
-        print(f'===> image_encoder.load_param: {checkpoint}')
-    sam = Sam(
-        image_encoder=image_encoder,
-        prompt_encoder=PromptEncoder(
-            embed_dim=embed_dim,
-            image_embedding_size=image_embedding_size,
-            input_image_size=image_size,
-            mask_in_chans=16,
-        ),
-        mask_decoder=MaskDecoder(
-            image_encoder_type=image_encoder_type,
-            num_multimask_outputs=3,
-            transformer=TwoWayTransformer(
-                depth=2,
-                embedding_dim=embed_dim,
-                mlp_dim=2048,
-                num_heads=8,
-            ),
-            transformer_dim=embed_dim,
-            iou_head_depth=3,
-            iou_head_hidden_dim=256,
-            image_size=np.array(image_size),
-            patch_size=np.array(patch_size),
-        ),
-        pixel_mean=[123.675, 116.28, 103.53],
-        pixel_std=[58.395, 57.12, 57.375],
-    )
-    sam.eval()
-    return sam
-
-class SegVol(nn.Module):
-    def __init__(self,
-                image_encoder,
-                mask_decoder,
-                prompt_encoder,
-                roi_size,
-                patch_size,
-                ):
-        super().__init__()
-        self.image_encoder = image_encoder
-        self.mask_decoder = mask_decoder
-        self.prompt_encoder = prompt_encoder
-        self.feat_shape = np.array(roi_size)/np.array(patch_size)
-
-    def forward(self, image, text_emb=None, text=None, boxes=None, points=None):
-        bs = image.shape[0]
-        img_shape = (image.shape[2], image.shape[3], image.shape[4])
-        image_embedding, _ = self.image_encoder(image)
-
-        image_embedding = image_embedding.transpose(1, 2).view(bs, -1,
-            int(self.feat_shape[0]), int(self.feat_shape[1]), int(self.feat_shape[2]))
-
-        logits = self.forward_decoder(image_embedding, img_shape, text_emb=text_emb, text=text, boxes=boxes, points=points)
-
-        return logits
-
-    def forward_decoder(self, image_embedding, img_shape, text_emb=None, text=None, boxes=None, points=None):
-        text_embedding = text_emb
-        sparse_embeddings, dense_embeddings = self.prompt_encoder(
-            points=None,
-            boxes=None,
-            masks=None,
-            text_embedding=text_embedding,
-        )
-
-        dense_pe = self.prompt_encoder.get_dense_pe()
-
-        low_res_masks, _ = self.mask_decoder(
-            image_embeddings=image_embedding,
-            text_embedding = text_embedding,
-            image_pe=dense_pe,
-            sparse_prompt_embeddings=sparse_embeddings,
-            dense_prompt_embeddings=dense_embeddings,
-            multimask_output=False,
-          )
-        logits = F.interpolate(low_res_masks, size=img_shape, mode='trilinear', align_corners=False)
-
-        return logits
-
-
-def build_segmentation_module(config, **kwargs):
-    segmentation_module = getattr(config, 'segmentation_module')
-    if 'segvol' in segmentation_module.lower():
-        sam_model = sam_model_registry['vit'](args=config, checkpoint=None)
-        seg_model = SegVol(
-            image_encoder=sam_model.image_encoder,
-            mask_decoder=sam_model.mask_decoder,
-            prompt_encoder=sam_model.prompt_encoder,
-            roi_size=config.image_size,
-            patch_size=config.patch_size,
-        )
-        return seg_model
-    else:
-        raise ValueError(f'Unknown segmentation module: {segmentation_module}')
-
-
-class IdentityMap(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, x, *args, **kwargs):
-        return x
-
-    @property
-    def config(self):
-        return {"mm_projector_type": 'identity'}
-
-
-
-class SpatialPoolingProjector(nn.Module):
-    def __init__(self, image_size, patch_size, in_dim, out_dim, layer_type, layer_num, pooling_type='spatial', pooling_size=2):
-        super().__init__()
-        self.in_dim = in_dim
-        self.pooling_size = pooling_size
-
-        self.num_patches_pre = [img // pch for img, pch in zip(image_size, patch_size)]
-        self.num_patches_post = [num // pooling_size for num in self.num_patches_pre]
-
-        if layer_type == 'linear':
-            depth = int(layer_num)
-            modules = [nn.Linear(in_dim, out_dim)]
-            for _ in range(1, depth):
-                modules.append(nn.Linear(out_dim, out_dim))
-            self.projector = nn.Sequential(*modules)
-        elif layer_type == 'mlp':
-            depth = int(layer_num)
-            modules = [nn.Linear(in_dim, out_dim)]
-            for _ in range(1, depth):
-                modules.append(nn.GELU())
-                modules.append(nn.Linear(out_dim, out_dim))
-            self.projector = nn.Sequential(*modules)
-        else:
-            print("Projector error!")
-
-        self.pooling_type = pooling_type
-
-    def forward(self, x):
-        B = x.shape[0] # B*N*D
-
-        if self.pooling_type == 'spatial':
-            to_3d = Rearrange("b (p1 p2 p3) d -> b d p1 p2 p3", b=B, d=self.in_dim, p1=self.num_patches_pre[0], p2=self.num_patches_pre[1], p3=self.num_patches_pre[2])
-            x = to_3d(x)
-            x = F.avg_pool3d(x, kernel_size=self.pooling_size, stride=self.pooling_size)
-            to_seq = Rearrange("b d p1 p2 p3 -> b (p1 p2 p3) d", b=B, d=self.in_dim, p1=self.num_patches_post[0], p2=self.num_patches_post[1], p3=self.num_patches_post[2])
-            x = to_seq(x)
-        elif self.pooling_type == 'sequence':
-            x = x.permute(0, 2, 1) #b d n
-            x = F.avg_pool1d(x, kernel_size=self.pooling_size**3, stride=self.pooling_size**3)
-            x = x.permute(0, 2, 1) #b n d
-
-        x = rearrange(x, "b n d -> (b n) d")
-        x = self.projector(x)
-        x = rearrange(x, "(b n) d -> b n d", b=B)
-
-        return x
-
-    @property
-    def proj_out_num(self):
-        num = 1
-        for n in self.num_patches_post:
-            num *= n
-        return num
-
-
-class Minigpt(nn.Module):
-    def __init__(self, config=None):
-        super(Minigpt, self).__init__()
-        # c*4 is the input size, and c is the output size for the linear layer
-        inc, ouc = config.mm_hidden_size, config.hidden_size
-        self.linear = nn.Linear(inc * 4, ouc)
-
-    def forward(self, x):
-        # x is the input tensor with shape [b, num_tokens, c]
-        b, num_tokens, c = x.shape
-
-        # Check if num_tokens is divisible by 4
-        if num_tokens % 4 != 0:
-            raise ValueError("num_tokens must be divisible by 4")
-
-        # Reshape x to [b, num_tokens/4, c*4]
-        x = x.view(b, num_tokens // 4, c * 4)
-
-        # Apply the linear transformation
-        x = self.linear(x)
-        return x
-
-
-class Vanilla(nn.Module):
-    def __init__(self, config=None):
-        super(Vanilla, self).__init__()
-        # c*4 is the input size, and c is the output size for the linear layer
-        inc, ouc = config.mm_hidden_size, config.hidden_size
-        self.linear = nn.Linear(inc * 4, ouc)
-
-    def forward(self, x):
-        b, num_tokens, c = x.shape
-
-        # Check if num_tokens is divisible by 4
-        if num_tokens % 4 != 0:
-            raise ValueError("num_tokens must be divisible by 4")
-
-        # First, reshape to [b, num_tokens//4, 4, c]
-        x = x.view(b, num_tokens // 4, 4, c)
-
-        # Then, permute to interleave the tokens
-        x = x.permute(0, 1, 3, 2).contiguous()
-
-        # Finally, reshape to [b, num_tokens//4, c*4] to interleave features of 4 tokens
-        x = x.view(b, num_tokens // 4, c * 4)
-
-        # Apply the linear transformation
-        x = self.linear(x)
-        return x
-
-
-def build_mm_projector(config, delay_load=False, **kwargs):
-    projector_type = getattr(config, 'mm_projector_type')
-
-    if projector_type == 'linear':
-        return nn.Linear(config.mm_hidden_size, config.hidden_size)
-
-
-    elif projector_type == 'spp':
-        return SpatialPoolingProjector(image_size=config.image_size,
-                                        patch_size=config.patch_size,
-                                        in_dim=config.mm_hidden_size,
-                                        out_dim=config.hidden_size,
-                                        layer_type=config.proj_layer_type,
-                                        layer_num=config.proj_layer_num,
-                                        pooling_type=config.proj_pooling_type,
-                                        pooling_size=config.proj_pooling_size)
-
-
-    elif projector_type == 'identity':
-        return IdentityMap()
-    else:
-        raise ValueError(f'Unknown projector type: {projector_type}')
-
-
-
-class myViT(nn.Module):
-    """
-    Vision Transformer (ViT), based on: "Dosovitskiy et al.,
-    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>"
-
-    ViT supports Torchscript but only works for Pytorch after 1.8.
-    """
-
-    def __init__(
-        self,
-        in_channels: int,
-        img_size: Sequence[int] | int,
-        patch_size: Sequence[int] | int,
-        hidden_size: int = 768,
-        mlp_dim: int = 3072,
-        num_layers: int = 12,
-        num_heads: int = 12,
-        pos_embed: str = "conv",
-        classification: bool = False,
-        num_classes: int = 2,
-        dropout_rate: float = 0.0,
-        spatial_dims: int = 3,
-        post_activation="Tanh",
-        qkv_bias: bool = False,
-        save_attn: bool = False,
-    ) -> None:
-        """
-        Args:
-            in_channels (int): dimension of input channels.
-            img_size (Union[Sequence[int], int]): dimension of input image.
-            patch_size (Union[Sequence[int], int]): dimension of patch size.
-            hidden_size (int, optional): dimension of hidden layer. Defaults to 768.
-            mlp_dim (int, optional): dimension of feedforward layer. Defaults to 3072.
-            num_layers (int, optional): number of transformer blocks. Defaults to 12.
-            num_heads (int, optional): number of attention heads. Defaults to 12.
-            pos_embed (str, optional): position embedding layer type. Defaults to "conv".
-            classification (bool, optional): bool argument to determine if classification is used. Defaults to False.
-            num_classes (int, optional): number of classes if classification is used. Defaults to 2.
-            dropout_rate (float, optional): faction of the input units to drop. Defaults to 0.0.
-            spatial_dims (int, optional): number of spatial dimensions. Defaults to 3.
-            post_activation (str, optional): add a final acivation function to the classification head
-                when `classification` is True. Default to "Tanh" for `nn.Tanh()`.
-                Set to other values to remove this function.
-            qkv_bias (bool, optional): apply bias to the qkv linear layer in self attention block. Defaults to False.
-            save_attn (bool, optional): to make accessible the attention in self attention block. Defaults to False.
-
-        Examples::
-
-            # for single channel input with image size of (96,96,96), conv position embedding and segmentation backbone
-            >>> net = ViT(in_channels=1, img_size=(96,96,96), pos_embed='conv')
-
-            # for 3-channel with image size of (128,128,128), 24 layers and classification backbone
-            >>> net = ViT(in_channels=3, img_size=(128,128,128), pos_embed='conv', classification=True)
-
-            # for 3-channel with image size of (224,224), 12 layers and classification backbone
-            >>> net = ViT(in_channels=3, img_size=(224,224), pos_embed='conv', classification=True, spatial_dims=2)
-
-        """
-
-        super().__init__()
-
-        if not (0 <= dropout_rate <= 1):
-            raise ValueError("dropout_rate should be between 0 and 1.")
-
-        if hidden_size % num_heads != 0:
-            raise ValueError("hidden_size should be divisible by num_heads.")
-        self.hidden_size = hidden_size
-        self.classification = classification
-        self.patch_embedding = PatchEmbeddingBlock(
-            in_channels=in_channels,
-            img_size=img_size,
-            patch_size=patch_size,
-            hidden_size=hidden_size,
-            num_heads=num_heads,
-            pos_embed=pos_embed,
-            dropout_rate=dropout_rate,
-            spatial_dims=spatial_dims,
-        )
-        self.blocks = nn.ModuleList(
-            [
-                TransformerBlock(hidden_size, mlp_dim, num_heads, dropout_rate, qkv_bias, save_attn)
-                for i in range(num_layers)
-            ]
-        )
-        self.norm = nn.LayerNorm(hidden_size)
-        if self.classification:
-            self.cls_token = nn.Parameter(torch.zeros(1, 1, hidden_size))
-            # if post_activation == "Tanh":
-            #     self.classification_head = nn.Sequential(nn.Linear(hidden_size, num_classes), nn.Tanh())
-            # else:
-            #     self.classification_head = nn.Linear(hidden_size, num_classes)  # type: ignore
-
-    def forward(self, x):
-        x = self.patch_embedding(x)
-        if hasattr(self, "cls_token"):
-            cls_token = self.cls_token.expand(x.shape[0], -1, -1)
-            x = torch.cat((cls_token, x), dim=1)
-        hidden_states_out = []
-        for blk in self.blocks:
-            x = blk(x)
-            hidden_states_out.append(x)
-        x = self.norm(x)
-        # if hasattr(self, "classification_head"):
-        #     x = self.classification_head(x[:, 0])
-        return x, hidden_states_out
-
-
-class ViT3DTower(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.select_layer = config.vision_select_layer
-        self.select_feature = config.vision_select_feature
-
-        self.vision_tower = myViT(
-            in_channels=self.config.image_channel,
-            img_size=self.config.image_size,
-            patch_size=self.config.patch_size,
-            pos_embed="perceptron",
-            spatial_dims=len(self.config.patch_size),
-            classification=True,
-        )
-
-    def forward(self, images):
-        last_feature, hidden_states = self.vision_tower(images)
-        if self.select_layer == -1:
-            image_features = last_feature
-        elif self.select_layer < -1:
-            image_features = hidden_states[self.select_feature]
-        else:
-            raise ValueError(f'Unexpected select layer: {self.select_layer}')
-
-        if self.select_feature == 'patch':
-            image_features = image_features[:, 1:]
-        elif self.select_feature == 'cls_patch':
-            image_features = image_features
-        else:
-            raise ValueError(f'Unexpected select feature: {self.select_feature}')
-
-        return image_features
-
-    @property
-    def dtype(self):
-        return self.vision_tower.dtype
-
-    @property
-    def device(self):
-        return self.vision_tower.device
-
-    @property
-    def hidden_size(self):
-        return self.vision_tower.hidden_size
-
-
-def build_vision_tower(config, **kwargs):
-    vision_tower = getattr(config, 'vision_tower', None)
-    if 'vit3d' in vision_tower.lower():
-        return ViT3DTower(config, **kwargs)
-    else:
-        raise ValueError(f'Unknown vision tower: {vision_tower}')
-
-class LamedMetaModel:
-    def __init__(self, config):
-        super(LamedMetaModel, self).__init__(config)
-
-        self.config = config
-        self.seg_enable = False
-
-        if hasattr(config, "vision_tower"):
-            self.vision_tower = build_vision_tower(config)
-            self.mm_projector = build_mm_projector(config)
-
-        if hasattr(config, "segmentation_module") and config.segmentation_module is not None:
-            self.seg_enable = True
-            self.seg_module = build_segmentation_module(config)
-
-            self.seg_projector = nn.Sequential(
-                nn.Linear(config.hidden_size, config.hidden_size),
-                nn.ReLU(inplace=True),
-                nn.Linear(config.hidden_size, config.mm_hidden_size),
-                nn.Dropout(0.1),
-            )
-
-            self.dice_loss = BinaryDiceLoss()
-            self.bce_loss = BCELoss()
-
-    def get_vision_tower(self):
-        vision_tower = getattr(self, 'vision_tower', None)
-        return vision_tower
-
-    def initialize_vision_modules(self, model_args):
-        self.config.image_channel = model_args.image_channel
-        self.config.image_size = model_args.image_size
-        self.config.patch_size = model_args.patch_size
-
-        self.config.vision_tower = model_args.vision_tower
-        self.config.vision_select_layer = model_args.vision_select_layer
-        self.config.vision_select_feature = model_args.vision_select_feature
-
-        self.config.mm_projector_type = model_args.mm_projector_type
-        self.config.proj_layer_type = model_args.proj_layer_type
-        self.config.proj_layer_num = model_args.proj_layer_num
-        self.config.proj_pooling_type = model_args.proj_pooling_type
-        self.config.proj_pooling_size = model_args.proj_pooling_size
-
-        # vision tower
-        if self.get_vision_tower() is None:
-            self.vision_tower = build_vision_tower(self.config)
-            # If you have a more robust vision encoder, try freezing the vision tower by requires_grad_(False)
-
-
-        if model_args.pretrain_vision_model is not None:
-            vision_model_weights = torch.load(model_args.pretrain_vision_model, map_location='cpu')
-            self.vision_tower.vision_tower.load_state_dict(vision_model_weights, strict=True)
-
-        self.config.mm_hidden_size = self.vision_tower.hidden_size
-
-        # mm_projector
-        if getattr(self, 'mm_projector', None) is None:
-            self.mm_projector = build_mm_projector(self.config)
-
-        if model_args.pretrain_mm_mlp_adapter is not None:
-            mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
-            def get_w(weights, keyword):
-                return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
-            self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'), strict=True)
-
-    def initialize_seg_modules(self, model_args):
-        self.config.segmentation_module = model_args.segmentation_module
-
-        # segmentation_module
-        if getattr(self, 'segmentation_module', None) is None:
-            self.seg_module = build_segmentation_module(self.config)
-            self.seg_projector = nn.Sequential(
-                nn.Linear(self.config.hidden_size, self.config.hidden_size),
-                nn.ReLU(inplace=True),
-                nn.Linear(self.config.hidden_size, self.config.mm_hidden_size),
-                nn.Dropout(0.1),
-            )
-            self.seg_enable = True
-
-        if model_args.pretrain_seg_module is not None:
-            seg_module_weights = torch.load(model_args.pretrain_seg_module, map_location='cpu')
-            self.seg_module.load_state_dict(seg_module_weights, strict=True)
-
-        self.dice_loss = BinaryDiceLoss()
-        self.bce_loss = BCELoss()
-
-class LamedMetaForCausalLM(ABC):
-    @abstractmethod
-    def get_model(self):
-        pass
-
-    def get_vision_tower(self):
-        return self.get_model().get_vision_tower()
-
-    def encode_images(self, images):
-        image_features = self.get_model().get_vision_tower()(images)
-        image_features = self.get_model().mm_projector(image_features)
-        return image_features
-
-    def prepare_inputs_for_multimodal(
-        self, input_ids, position_ids, attention_mask, past_key_values, labels,
-        images,
-    ):
-        vision_tower = self.get_vision_tower()
-        if vision_tower is None or images is None or input_ids.shape[1] == 1:
-            return input_ids, position_ids, attention_mask, past_key_values, None, labels
-        else:
-            image_features = self.encode_images(images)
-            inputs_embeds = self.get_model().embed_tokens(input_ids)
-            inputs_embeds = torch.cat(
-                (inputs_embeds[:, :1, :], image_features, inputs_embeds[:, (image_features.shape[1] + 1):, :]), dim=1)
-        return None, position_ids, attention_mask, past_key_values, inputs_embeds, labels
-
-    def initialize_vision_tokenizer(self, model_args, tokenizer):
-        num_new_tokens = model_args.num_new_tokens
-
-        self.resize_token_embeddings(len(tokenizer))
-
-        if num_new_tokens > 0:
-            input_embeddings = self.get_input_embeddings().weight.data
-            output_embeddings = self.get_output_embeddings().weight.data
-
-            input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
-                dim=0, keepdim=True)
-            output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
-                dim=0, keepdim=True)
-
-            input_embeddings[-num_new_tokens:] = input_embeddings_avg
-            output_embeddings[-num_new_tokens:] = output_embeddings_avg
-
-            if model_args.tune_mm_mlp_adapter:
-                for p in self.get_input_embeddings().parameters():
-                    p.requires_grad = True
-                for p in self.get_output_embeddings().parameters():
-                    p.requires_grad = False
-            else:
-                # we add 4 new tokens
-                # if new tokens need input, please train input_embeddings
-                for p in self.get_input_embeddings().parameters():
-                    p.requires_grad = True
-                # if new tokens need predict, please train output_embeddings
-                for p in self.get_output_embeddings().parameters():
-                    p.requires_grad = True
-
-        if model_args.pretrain_mm_mlp_adapter:
-            mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
-            embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
-
-            if input_embeddings.shape == embed_tokens_weight.shape:
-                input_embeddings = embed_tokens_weight
-            elif embed_tokens_weight.shape[0] == num_new_tokens:
-                input_embeddings[-num_new_tokens:] = embed_tokens_weight
-            else:
-                raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
-
-
-
-class LamedPhi3Model(LamedMetaModel, Phi3Model):
-    config_class = LamedPhi3Config
-    def __init__(self, config: Phi3Config):
-        super(LamedPhi3Model, self).__init__(config)
-
-
-class LamedPhi3ForCausalLM(LamedMetaForCausalLM, Phi3ForCausalLM):
-    config_class = LamedPhi3Config
-
-    def __init__(self, config):
-        super(LamedPhi3ForCausalLM, self).__init__(config)
-        self.model = LamedPhi3Model(config)
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_model(self):
-        return self.model
-
-    def forward(
-            self,
-            images: Optional[torch.FloatTensor] = None,
-            input_ids: torch.LongTensor = None,
-            labels: Optional[torch.LongTensor] = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            segs: Optional[torch.FloatTensor] = None,
-
-            position_ids: Optional[torch.LongTensor] = None,
-            past_key_values: Optional[List[torch.FloatTensor]] = None,
-            inputs_embeds: Optional[torch.FloatTensor] = None,
-            use_cache: Optional[bool] = None,
-            output_attentions: Optional[bool] = None,
-            output_hidden_states: Optional[bool] = None,
-            return_dict: Optional[bool] = None,
-            cache_position: Optional[torch.LongTensor] = None,
-            **kwargs,  # <<<<<<<<<<<<<< 加上这行！
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-
-        input_ids_pre = input_ids
-
-        if inputs_embeds is None:
-            (
-                input_ids,
-                position_ids,
-                attention_mask,
-                past_key_values,
-                inputs_embeds,
-                labels
-            ) = self.prepare_inputs_for_multimodal(
-                input_ids,
-                position_ids,
-                attention_mask,
-                past_key_values,
-                labels,
-                images,
-            )
-
-        try:
-            seg_ids = torch.nonzero(torch.sum(segs, dim=(1, 2, 3, 4))).flatten().tolist()
-        except:
-            seg_ids = []
-
-        if self.get_model().seg_enable and seg_ids:
-            outputs = super().forward(
-                                    input_ids=input_ids,
-                                    inputs_embeds=inputs_embeds,
-                                    attention_mask=attention_mask,
-                                    labels=labels,
-                                    output_hidden_states=True,
-
-                                    position_ids=position_ids,
-                                    past_key_values=past_key_values,
-                                    use_cache=use_cache,
-                                    output_attentions=output_attentions,
-                                    return_dict=return_dict
-                                )
-
-            output_hidden_states = outputs.hidden_states
-
-            last_hidden_state = output_hidden_states[-1]
-
-            seg_token_mask = input_ids_pre[:, 1:] == self.config.seg_token_id
-            seg_token_mask = torch.cat(
-                [
-                    seg_token_mask,
-                    torch.zeros((seg_token_mask.shape[0], 1), dtype=seg_token_mask.dtype).cuda(),
-                ],
-                dim=1,
-            )
-
-            seg_prompts = []
-            for i in seg_ids:
-                if torch.sum(seg_token_mask[i]) == 1:
-                    seg_token = last_hidden_state[i][seg_token_mask[i]]
-                    seg_prompt = self.get_model().seg_projector(seg_token)
-                elif torch.sum(seg_token_mask[i]) > 1:
-                    seg_tokens = last_hidden_state[i][seg_token_mask[i]]
-                    seg_token = torch.mean(seg_tokens, dim=0, keepdim=True)
-                    seg_prompt = self.get_model().seg_projector(seg_token)
-                else:
-                    seg_prompt = torch.zeros([1, self.config.mm_hidden_size], dtype=last_hidden_state.dtype,
-                                             device=last_hidden_state.device)
-                seg_prompts.append(seg_prompt)
-
-            seg_prompts = torch.cat(seg_prompts, dim=0)
-            logits = self.get_model().seg_module(images[seg_ids], text_emb=seg_prompts)
-            loss_dice = self.get_model().dice_loss(logits, segs[seg_ids])
-            loss_bce = self.get_model().bce_loss(logits, segs[seg_ids])
-            seg_loss = loss_dice + loss_bce
-            outputs.loss = outputs.loss + seg_loss
-            return outputs
-        else:
-            return super().forward(
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                position_ids=position_ids,
-                past_key_values=past_key_values,
-                inputs_embeds=inputs_embeds,
-                labels=labels,
-                use_cache=use_cache,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict
-            )
-
-
-    @torch.no_grad()
-    def generate(
-        self,
-        images: Optional[torch.Tensor] = None,
-        inputs: Optional[torch.Tensor] = None,
-        seg_enable: bool = False,
-        **kwargs,
-    ) -> Union[GenerateOutput, torch.LongTensor, Any]:
-        position_ids = kwargs.pop("position_ids", None)
-        attention_mask = kwargs.pop("attention_mask", None)
-        if "inputs_embeds" in kwargs:
-            raise NotImplementedError("`inputs_embeds` is not supported")
-
-        if images is not None:
-            (
-                inputs,
-                position_ids,
-                attention_mask,
-                _,
-                inputs_embeds,
-                _
-            ) = self.prepare_inputs_for_multimodal(
-                inputs,
-                position_ids,
-                attention_mask,
-                None,
-                None,
-                images,
-            )
-        else:
-            inputs_embeds = self.get_model().embed_tokens(inputs)
-
-        if seg_enable:
-            outputs = super().generate(
-                inputs_embeds=inputs_embeds,
-                output_hidden_states=True,
-                return_dict_in_generate=True,
-                **kwargs
-            )
-
-            output_hidden_states = outputs.hidden_states
-            output_ids = outputs.sequences
-
-            seg_token_mask = output_ids[:, 1:] == self.config.seg_token_id
-
-            last_tensors = [tuple[-1] for tuple in output_hidden_states]
-            last_hidden_state = torch.cat(last_tensors[1:], dim=1)
-
-            seg_prompts = []
-            noseg_ids = []
-            for i in range(len(seg_token_mask)):
-                if torch.sum(seg_token_mask[i]) == 1:
-                    seg_token = last_hidden_state[i][seg_token_mask[i]]
-                    seg_prompt = self.get_model().seg_projector(seg_token)
-                elif torch.sum(seg_token_mask[i]) > 1:
-                    seg_tokens = last_hidden_state[i][seg_token_mask[i]]
-                    seg_token = torch.mean(seg_tokens, dim=0, keepdim=True)
-                    seg_prompt = self.get_model().seg_projector(seg_token)
-                else:
-                    noseg_ids.append(i)
-                    seg_prompt = torch.zeros([1, self.config.mm_hidden_size], dtype=last_hidden_state.dtype,
-                                             device=last_hidden_state.device)
-                seg_prompts.append(seg_prompt)
-
-            seg_prompts = torch.cat(seg_prompts, dim=0)
-            logits = self.get_model().seg_module(images, seg_prompts)
-            logits[noseg_ids] = -torch.inf
-
-            return output_ids, logits
-        else:
-            output_ids = super().generate(
-                inputs_embeds=inputs_embeds,
-                **kwargs
-            )
-            return output_ids
-
-
-    def prepare_inputs_for_generation(self, input_ids, past_key_values=None,
-                                      inputs_embeds=None, **kwargs):
-        images = kwargs.pop("images", None)
-        inputs = super().prepare_inputs_for_generation(
-            input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs
-        )
-        if images is not None:
-            inputs['images'] = images
-        return inputs