Upload LamedPhi3ForCausalLM

config.json

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+{
+  "_name_or_path": "/home/baifan/PycharmProjects/M3D_hf/M3D_LaMed_phi3/weight/Phi-3-mini-4k-instruct",
+  "architectures": [
+    "LamedPhi3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_m3d_lamed.LamedPhi3Config",
+    "AutoModelForCausalLM": "modeling_m3d_lamed.LamedPhi3ForCausalLM"
+  },
+  "bos_token_id": 1,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 32000,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "image_channel": 1,
+  "image_size": [
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+  ],
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 4096,
+  "mm_hidden_size": 768,
+  "mm_projector_type": "spp",
+  "model_type": "lamed_phi3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "original_max_position_embeddings": 4096,
+  "pad_token_id": 32000,
+  "patch_size": [
+    4,
+    16,
+    16
+  ],
+  "proj_layer_num": 2,
+  "proj_layer_type": "mlp",
+  "proj_pooling_size": 2,
+  "proj_pooling_type": "spatial",
+  "resid_pdrop": 0.0,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "seg_token_id": 32014,
+  "segmentation_module": "segvol",
+  "sliding_window": 2047,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.0.dev0",
+  "use_cache": true,
+  "vision_select_feature": "patch",
+  "vision_select_layer": -1,
+  "vision_tower": "vit3d",
+  "vocab_size": 32015
+}

configuration_m3d_lamed.py

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+from transformers import Phi3Config
+
+
+class LamedPhi3Config(Phi3Config):
+    model_type = "lamed_phi3"

generation_config.json

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+{
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+  "bos_token_id": 1,
+  "eos_token_id": [
+    32000,
+    32001,
+    32007
+  ],
+  "pad_token_id": 32000,
+  "transformers_version": "4.42.0.dev0"
+}

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+}

modeling_m3d_lamed.py

@@ -0,0 +1,2104 @@
+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,
+    ) -> 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