networks/utils.py

import math
import copy
import random
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F

from safetensors.torch import save_file
from safetensors import safe_open
from torch.nn.parameter import Parameter

from depth_anything_v2_metric.depth_anything_v2.dpt import DepthAnythingV2

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

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

        pip install safetensor if you do not have one installed yet.
        """
        assert filename.endswith(".safetensors")
        _in = self.lora_vit.head.in_features
        _out = self.lora_vit.head.out_features
        fc_tensors = {f"fc_{_in}in_{_out}out": self.lora_vit.head.weight}
        save_file(fc_tensors, filename)

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

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

        assert filename.endswith(".safetensors")
        _in = self.lora_vit.head.in_features
        _out = self.lora_vit.head.out_features
        with safe_open(filename, framework="pt") as f:
            saved_key = f"fc_{_in}in_{_out}out"
            try:
                saved_tensor = f.get_tensor(saved_key)
                self.lora_vit.head.weight = Parameter(saved_tensor)
            except ValueError:
                print("this fc weight is not for this 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(".safetensors")

        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)}
        
        _in = self.lora_vit.head.in_features
        _out = self.lora_vit.head.out_features
        fc_tensors = {f"fc_{_in}in_{_out}out": self.lora_vit.head.weight}
        
        merged_dict = {**a_tensors, **b_tensors, **fc_tensors}
        save_file(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(".safetensors")

        with safe_open(filename, framework="pt") as f:
            for i, w_A_linear in enumerate(self.w_As):
                saved_key = f"w_a_{i:03d}"
                saved_tensor = f.get_tensor(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 = f.get_tensor(saved_key)
                w_B_linear.weight = Parameter(saved_tensor)
                
            _in = self.lora_vit.head.in_features
            _out = self.lora_vit.head.out_features
            saved_key = f"fc_{_in}in_{_out}out"
            try:
                saved_tensor = f.get_tensor(saved_key)
                self.lora_vit.head.weight = Parameter(saved_tensor)
            except ValueError:
                print("this fc weight is not for this model")

    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)

class LoRA_Depth_Anything_v2(LoRA):
    """Applies low-rank adaptation to a Depth Anything 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, da_model: DepthAnythingV2, r: int, lora_layer=None):
        super(LoRA_Depth_Anything_v2, 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(da_model.pretrained.blocks)))
        # create for storage, then we can init them or load weights
        self.w_As = []  # These are linear layers
        self.w_Bs = []

        # lets freeze first
        for param in da_model.pretrained.parameters():
            param.requires_grad = False

        # Here, we do the surgery
        for t_layer_i, blk in enumerate(da_model.pretrained.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.lora_vit = da_model