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sam_lora_image_encoder.py
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sam_lora_image_encoder.py
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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 = [] # These are linear layers
self.w_Bs = []
# lets freeze first
for param in sam_model.image_encoder.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, batched_input, multimask_output, image_size):
return self.sam(batched_input, multimask_output, image_size)
# def forward(self, x: Tensor) -> Tensor:
# return self.lora_vit(x)
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)))