sam_lora_image_encoder.py

from segment_anything import build_sam, SamPredictor
from segment_anything import sam_model_registry

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torch.nn.parameter import Parameter
from segment_anything.modeling import Sam
from safetensors import safe_open
from safetensors.torch import save_file

from icecream import ic


class _LoRA_qkv(nn.Module):
    """In Sam it is implemented as
    self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
    B, N, C = x.shape
    qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
    q, k, v = qkv.unbind(0)
    """

    def __init__(
            self,
            qkv: nn.Module,
            linear_a_q: nn.Module,
            linear_b_q: nn.Module,
            linear_a_v: nn.Module,
            linear_b_v: nn.Module,
    ):
        super().__init__()
        self.qkv = qkv
        self.linear_a_q = linear_a_q
        self.linear_b_q = linear_b_q
        self.linear_a_v = linear_a_v
        self.linear_b_v = linear_b_v
        self.dim = qkv.in_features
        self.w_identity = torch.eye(qkv.in_features)

    def forward(self, x):
        qkv = self.qkv(x)  # B,N,N,3*org_C
        new_q = self.linear_b_q(self.linear_a_q(x))
        new_v = self.linear_b_v(self.linear_a_v(x))
        qkv[:, :, :, : self.dim] += new_q
        qkv[:, :, :, -self.dim:] += new_v
        return qkv


class LoRA_Sam(nn.Module):
    """Applies low-rank adaptation to a Sam model's image encoder.

    Args:
        sam_model: a vision transformer model, see base_vit.py
        r: rank of LoRA
        num_classes: how many classes the model output, default to the vit model
        lora_layer: which layer we apply LoRA.

    Examples::
        >>> model = ViT('B_16_imagenet1k')
        >>> lora_model = LoRA_ViT(model, r=4)
        >>> preds = lora_model(img)
        >>> print(preds.shape)
        torch.Size([1, 1000])
    """

    def __init__(self, sam_model: Sam, r: int, lora_layer=None):
        super(LoRA_Sam, self).__init__()

        assert r > 0
        # base_vit_dim = sam_model.image_encoder.patch_embed.proj.out_channels
        # dim = base_vit_dim
        if lora_layer: self.lora_layer = lora_layer
        else: self.lora_layer = list(range(len(sam_model.image_encoder.blocks)))  # Only apply lora to the image encoder by default
        # create for storage, then we can init them or load weights
        self.w_As, self.w_Bs = [], []  # These are linear layers

        # lets freeze first
        for param in sam_model.image_encoder.parameters(): param.requires_grad = False
        
        for param in sam_model.prompt_encoder.parameters(): param.requires_grad = False
        for param in sam_model.mask_decoder.parameters(): param.requires_grad = False

        # Here, we do the surgery
        for t_layer_i, blk in enumerate(sam_model.image_encoder.blocks):
            # If we only want few lora layer instead of all
            # if t_layer_i not in self.lora_layer: continue
            w_qkv_linear = blk.attn.qkv
            self.dim = w_qkv_linear.in_features
            w_a_linear_q = nn.Linear(self.dim, r, bias=False)
            w_b_linear_q = nn.Linear(r, self.dim, bias=False)
            w_a_linear_v = nn.Linear(self.dim, r, bias=False)
            w_b_linear_v = nn.Linear(r, self.dim, bias=False)
            self.w_As.append(w_a_linear_q)
            self.w_Bs.append(w_b_linear_q)
            self.w_As.append(w_a_linear_v)
            self.w_Bs.append(w_b_linear_v)
            blk.attn.qkv = _LoRA_qkv(
                w_qkv_linear,
                w_a_linear_q,
                w_b_linear_q,
                w_a_linear_v,
                w_b_linear_v,
            )
        self.reset_parameters()
        self.sam = sam_model

    def save_lora_parameters(self, filename: str) -> None:
        r"""Only safetensors is supported now.

        pip install safetensor if you do not have one installed yet.

        save both lora and fc parameters.
        """

        assert filename.endswith(".pt") or filename.endswith('.pth')

        num_layer = len(self.w_As)  # actually, it is half
        a_tensors = {f"w_a_{i:03d}": self.w_As[i].weight for i in range(num_layer)}
        b_tensors = {f"w_b_{i:03d}": self.w_Bs[i].weight for i in range(num_layer)}
        prompt_encoder_tensors = {}
        mask_decoder_tensors = {}

        # save prompt encoder, only `state_dict`, the `named_parameter` is not permitted
        if isinstance(self.sam, torch.nn.DataParallel) or isinstance(self.sam, torch.nn.parallel.DistributedDataParallel):
            state_dict = self.sam.module.state_dict()
        else:
            state_dict = self.sam.state_dict()
        for key, value in state_dict.items():
            if 'prompt_encoder' in key: prompt_encoder_tensors[key] = value
            if 'mask_decoder' in key: mask_decoder_tensors[key] = value

        merged_dict = {**a_tensors, **b_tensors, **prompt_encoder_tensors, **mask_decoder_tensors}
        torch.save(merged_dict, filename)

    def load_lora_parameters(self, filename: str) -> None:
        r"""Only safetensors is supported now.

        pip install safetensor if you do not have one installed yet.\

        load both lora and fc parameters.
        """

        assert filename.endswith(".pt") or filename.endswith('.pth')

        state_dict = torch.load(filename)

        for i, w_A_linear in enumerate(self.w_As):
            saved_key = f"w_a_{i:03d}"
            saved_tensor = state_dict[saved_key]
            w_A_linear.weight = Parameter(saved_tensor)

        for i, w_B_linear in enumerate(self.w_Bs):
            saved_key = f"w_b_{i:03d}"
            saved_tensor = state_dict[saved_key]
            w_B_linear.weight = Parameter(saved_tensor)

        sam_dict = self.sam.state_dict()
        sam_keys = sam_dict.keys()

        # load prompt encoder
        prompt_encoder_keys = [k for k in sam_keys if 'prompt_encoder' in k]
        prompt_encoder_values = [state_dict[k] for k in prompt_encoder_keys]
        prompt_encoder_new_state_dict = {k: v for k, v in zip(prompt_encoder_keys, prompt_encoder_values)}
        sam_dict.update(prompt_encoder_new_state_dict)

        # load mask decoder
        mask_decoder_keys = [k for k in sam_keys if 'mask_decoder' in k]
        mask_decoder_values = [state_dict[k] for k in mask_decoder_keys]
        mask_decoder_new_state_dict = {k: v for k, v in zip(mask_decoder_keys, mask_decoder_values)}
        sam_dict.update(mask_decoder_new_state_dict)
        self.sam.load_state_dict(sam_dict)

    def reset_parameters(self) -> None:
        for w_A in self.w_As: nn.init.kaiming_uniform_(w_A.weight, a=math.sqrt(5))
        for w_B in self.w_Bs: nn.init.zeros_(w_B.weight)

    def forward(self, images, original_size, point_coords, point_labels, multimask_output=False):
        input_size = tuple(images.shape[-2:])
        images = self.sam.preprocess(images)
        image_features = self.sam.image_encoder(images) # shape: 1, 256, 64, 64, range: 0-1

        sparse_embeddings, dense_embeddings = self.sam.prompt_encoder(
            points=(point_coords, point_labels),
            boxes=None,
            masks=None,
        )

        # Predict masks
        low_res_masks, iou_predictions = self.sam.mask_decoder(
            image_embeddings=image_features,
            image_pe=self.sam.prompt_encoder.get_dense_pe(),
            sparse_prompt_embeddings=sparse_embeddings,
            dense_prompt_embeddings=dense_embeddings,
            multimask_output=multimask_output,
        )

        # Upscale the masks to the original image resolution
        masks = self.sam.postprocess_masks(low_res_masks, input_size, original_size)
        return nn.Sigmoid()(masks)




# if __name__ == "__main__":
#     sam = sam_model_registry["vit_b"](checkpoint="sam_vit_b_01ec64.pth")
#     lora_sam = LoRA_Sam(sam, 4)
#     lora_sam.sam.image_encoder(torch.rand(size=(1, 3, 1024, 1024)))