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e4bba57 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 | # Extended SAMs are structured following https://github.com/ziqi-jin/finetune-anything
# but are re-writing for flexibility
from typing import List, Tuple
from torch import nn
from PIL import Image
import numpy as np
import torch
import torch.nn.functional as F
import torchvision.transforms.functional as tv_functional
from utils.torch_utils import fix_params, img2tensor, tensor2img
from .sam.build_sam import sam_model_registry, Sam
from .sam.modeling.mask_decoder import MaskDecoder
from .sam.modeling.prompt_encoder import PromptEncoder
from .sam.modeling.image_encoder import ImageEncoderViT
from .sam.utils.transforms import resize_longside_torch
def pair_params(self, target_model: nn.Module):
src_dict = self.sam_mask_decoder.state_dict()
for name, value in target_model.named_parameters():
if name in src_dict.keys():
value.data.copy_(src_dict[name].data)
class BaseImgEncodeAdapter(nn.Module):
def __init__(self, sam_img_encoder: ImageEncoderViT, fix=False):
super(BaseImgEncodeAdapter, self).__init__()
self.sam_img_encoder = sam_img_encoder
if fix:
fix_params(self.sam_img_encoder)
def forward(self, *args, **kwargs):
return self.sam_img_encoder(*args, **kwargs)
class BaseMaskDecoderAdapter(nn.Module):
'''
multimask_output (bool): If true, the model will return three masks.
For ambiguous input prompts (such as a single click), this will often
produce better masks than a single prediction. If only a single
mask is needed, the model's predicted quality score can be used
to select the best mask. For non-ambiguous prompts, such as multiple
input prompts, multimask_output=False can give better results.
'''
_hidden_param_keywords = ['transformer']
# _hidden_param_exclude_keywords = ['transformer', 'hf_mlp', 'output_hypernetworks_mlps']
# is fix and load params
def __init__(self, sam_mask_decoder: MaskDecoder, fix=False):
super(BaseMaskDecoderAdapter, self).__init__()
# mask_decoder = ori_sam.mask_decoder
self.sam_mask_decoder: MaskDecoder = sam_mask_decoder
if fix:
fix_params(self.sam_mask_decoder) # move to runner to implement
def forward(self, *args, **kwargs):
return self.sam_mask_decoder(*args, **kwargs)
def get_muon_training_params(self):
hidden_weights, nonhidden_params = [], []
for pname, p in self.named_parameters():
if not p.requires_grad:
continue
is_hidden_weights = False
for hidden_param_name in self._hidden_param_keywords:
if hidden_param_name in pname:
is_hidden_weights = True
break
if is_hidden_weights and p.ndim >= 2:
hidden_weights.append(p)
else:
nonhidden_params.append(p)
return hidden_weights, nonhidden_params
class BasePromptEncodeAdapter(nn.Module):
def __init__(self, sam_prompt_encoder: PromptEncoder, fix=False):
super(BasePromptEncodeAdapter, self).__init__()
self.sam_prompt_encoder = sam_prompt_encoder
if fix:
fix_params(self.sam_prompt_encoder)
def forward(self, *args, **kwargs):
return self.sam_prompt_encoder(*args, **kwargs)
class BaseExtendSam(nn.Module):
def __init__(self,
sam: Sam,
fix_img_en=False,
fix_prompt_en=False,
fix_mask_de=False):
super(BaseExtendSam, self).__init__()
# self.ori_sam: Sam = sam
self.img_adapter = BaseImgEncodeAdapter(sam.image_encoder, fix=fix_img_en)
self.prompt_adapter = BasePromptEncodeAdapter(sam.prompt_encoder, fix=fix_prompt_en)
self.mask_adapter = BaseMaskDecoderAdapter(sam.mask_decoder, fix=fix_mask_de)
del sam.mask_decoder
del sam.image_encoder
del sam.prompt_encoder
@property
def img_size(self):
return self.img_adapter.sam_img_encoder.img_size
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 inference(self, batch_imgs, normalize=True, output_dtype='tensor'):
if isinstance(batch_imgs, (Image.Image, np.ndarray)):
batch_imgs = [batch_imgs]
preprocess_lst = []
_batch_imgs = []
device = self.device
dtype = self.dtype
for x in batch_imgs:
if isinstance(x, (Image.Image, np.ndarray)):
x = img2tensor(x)
ori_sz = x.shape[-2:]
x = resize_longside_torch(x, target_length=self.img_size)
h, w = x.shape[-2:]
padh = self.img_size - h
padw = self.img_size - w
x1 = padw // 2
y1 = padh // 2
preprocess_lst.append(((y1, x1, ori_sz[0], ori_sz[1]), (h, w)))
if padh > 0 or padw > 0:
x = F.pad(x, (x1, padw - x1, y1, padh - y1))
_batch_imgs.append(x)
_batch_imgs = torch.cat(_batch_imgs).to(device=self.device, dtype=self.dtype)
batch_imgs = _batch_imgs
if normalize:
batch_imgs = tv_functional.normalize(batch_imgs, mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375])
rst, _ = self(batch_imgs)
rst_imgs = []
for ii, pred in enumerate(rst):
pred = F.interpolate(
pred[None],
(self.img_size, self.img_size),
mode="bilinear",
align_corners=False,
)
ori_sz, input_size = preprocess_lst[ii]
pred = pred[..., ori_sz[0]: ori_sz[0] + input_size[0], ori_sz[1]: ori_sz[1] + input_size[1]]
pred = F.interpolate(pred, (ori_sz[2], ori_sz[3]), mode="bilinear", align_corners=False)[0]
if output_dtype == 'numpy':
pred = pred.to(device='cpu', dtype=torch.float32).numpy()
rst_imgs.append(pred)
return rst_imgs
def forward(
self,
img,
hq_token_only=False,
multimask_output=True
):
image_embeddings, interm_embeddings = self.img_adapter(img, get_interm_embeds=self.mask_adapter.is_hq)
points = None
boxes = None
masks = None
sparse_embeddings, dense_embeddings = self.prompt_adapter(
points=points,
boxes=boxes,
masks=masks,
)
image_pe = self.prompt_adapter.sam_prompt_encoder.get_dense_pe()
low_res_masks, iou_predictions = self.mask_adapter(
image_embeddings=image_embeddings,
image_pe=image_pe,
sparse_prompt_embeddings=sparse_embeddings,
dense_prompt_embeddings=dense_embeddings,
multimask_output=multimask_output,
hq_token_only=hq_token_only,
interm_embeddings=interm_embeddings,
)
return low_res_masks, iou_predictions
@property
def dtype(self):
return next(self.parameters()).dtype
@property
def device(self):
return next(self.parameters()).device
def get_muon_training_params(self):
def _get_adapter_muon_params(adapter):
if hasattr(adapter, 'get_muon_training_params'):
return adapter.get_muon_training_params()
else:
hidden_weights = [p for p in adapter.parameters() if p.ndim >= 2 and p.requires_grad]
hidden_gains_biases = [p for p in adapter.parameters() if p.ndim < 2 and p.requires_grad]
return hidden_weights, hidden_gains_biases
hidden_weights = []
nonhidden_params = []
for module_name in ['img_adapter', 'prompt_adapter', 'mask_adapter']:
h, n = _get_adapter_muon_params(getattr(self, module_name))
hidden_weights += h
nonhidden_params += n
return hidden_weights, nonhidden_params
# hidden_weights = [p for p in model.body.parameters() if p.ndim >= 2]
# hidden_gains_biases = [p for p in model.body.parameters() if p.ndim < 2]
# nonhidden_params = [*model.head.parameters(), *model.embed.parameters()] |