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new sam model files

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jixingyue 2025-09-29 12:20:47 +08:00
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95
config/semantic_seg.yaml Normal file
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train:
experiment_name: 'semantic_sam'
# Model
model:
sam_name: 'sem_sam'
params:
# Fix the a part of parameters in SAM
fix_img_en: True
fix_prompt_en: True
fix_mask_de: False
ckpt_path: '/home/sweet/trt-finetune-anything/sam_ckpts/sam_vit_b_16.pth'
# class_num: 2
class_num: 3 # [background, lettuce, weed] [0, 1, 2]
model_type: 'vit_b' # type should be in [vit_h, vit_b, vit_l, default]
# Dataset
dataset:
name: 'torch_voc_sem'
params:
root: '/data/jinziqi/DATASETS/'
year: '2012'
image_set: 'train'
transforms:
resize:
params:
size: [1024, 1024]
to_tensor:
params: ~
target_transforms:
resize:
params:
size: [1024, 1024]
# Losses
losses:
ce:
weight: 0.5
params: # ~ means None type, the initial params of loss could be identified here
ignore_index: 255
label_one_hot: False
# Optimizer
opt_params:
lr_default: 1e-3
wd_default: 1e-4
momentum: 0.9
lr_list: [ 1e-2, ]
group_keys: [ [ 'mask_adapter.decoder_head', ], ]
wd_list: [ 0.0, ]
opt_name: 'sgd' # 'sgd'
scheduler_name: 'cosine'
# Runner
max_iter: 100000
log_iter: 20
eval_iter: 100
runner_name: 'sem_runner'
# Dataloader
bs: 2 # 8
num_workers: 2
drop_last: True
# Logger
use_tensorboard: True
tensorboard_folder: './experiment/tensorboard'
log_folder: './experiment/log'
model_folder: './experiment/model'
val:
# Dataset
dataset:
name: 'torch_voc_sem'
params:
root: '/data/jinziqi/DATASETS/'
year: '2012'
image_set: 'train'
transforms:
resize:
params:
size: [1024, 1024]
to_tensor:
params: ~
target_transforms:
resize:
params:
size: [1024, 1024]
bs: 2
num_workers: 2
drop_last: True
test:
need_test: False

60
datasets/__init__.py Normal file
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from .detection import BaseDetectionDataset
from .instance_seg import BaseInstanceDataset
from .semantic_seg import BaseSemanticDataset, VOCSemanticDataset, TorchVOCSegmentation, LettuceSegDataset, construct_LettuceSegDataset
from .transforms import get_transforms
from torchvision.datasets import VOCSegmentation
from sklearn.model_selection import train_test_split
import glob
import os
segment_datasets = {'base_ins': BaseInstanceDataset, 'base_sem': BaseSemanticDataset,
'voc_sem': VOCSemanticDataset, 'torch_voc_sem': TorchVOCSegmentation, 'lettuce_sem':construct_LettuceSegDataset}
det_dataset = {'base_det': BaseDetectionDataset, }
def get_lettuce_dataset():
# all_image_paths = sorted(glob.glob(os.path.join('/home/sweetai/large_model/sam_finetune/lettuce_data', "*.JPG")))
all_image_paths = sorted(glob.glob(os.path.join('/home/sweetai/large_model/sam_finetune_multi_class/weed_data_bak', "*.jpg")))
# JPG_image_paths = sorted(glob.glob(os.path.join('/home/sweetai/large_model/sam_finetune/lettuce_data', "*.JPG")))
# jpg_image_paths = sorted(glob.glob(os.path.join('/home/sweetai/large_model/sam_finetune/lettuce_data', "*.jpg")))
# all_image_paths = JPG_image_paths + jpg_image_paths
train_image_paths, val_image_paths = train_test_split(
all_image_paths, test_size=0.2, random_state=42
)
print(f"训练集数量: {len(train_image_paths)}")
print(f"测试集数量: {len(val_image_paths)}")
# train_dataset = LettuceSegDataset(train_image_paths, width=1024, height=1024)
# val_dataset = LettuceSegDataset(val_image_paths, width=1024, height=1024)
train_dataset = construct_LettuceSegDataset(train_image_paths, width=1024, height=1024)
val_dataset = construct_LettuceSegDataset(val_image_paths, width=1024, height=1024)
return train_dataset,val_dataset
def get_dataset(cfg):
name = cfg.name
assert name in segment_datasets or name in det_dataset, \
print('{name} is not supported, please implement it first.'.format(name=name))
# TODO customized dataset params:
# customized dataset params example:
# if xxx:
# param1 = cfg.xxx
# param2 = cfg.xxx
# return name_dict[name](path, model, param1, param2, ...)
transform = get_transforms(cfg.transforms)
if name in det_dataset:
return det_dataset[name](**cfg.params, transform=transform)
target_transform = get_transforms(cfg.target_transforms)
return segment_datasets[name](**cfg.params, transform=transform, target_transform=target_transform)
class Iterator:
def __init__(self, loader):
self.loader = loader
self.init()
def init(self):
self.iterator = iter(self.loader)
def get(self):
try:
data = next(self.iterator)
except StopIteration:
self.init()
data = next(self.iterator)
return data

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datasets/detection.py Normal file
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from torch.utils.data import Dataset
class BaseDetectionDataset(Dataset):
def __init__(self):
assert False, print('BaseDetectionDataset is not Unimplemented.')
def __getitem__(self, item):
pass

9
datasets/instance_seg.py Normal file
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from torch.utils.data import Dataset
class BaseInstanceDataset(Dataset):
def __init__(self):
assert False, print("Unimplement Dataset.")
def __getitem__(self, item):
pass

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datasets/matting.py Normal file
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import os
from PIL import Image
from torch.utils.data import Dataset
from torchvision.datasets import VisionDataset
import numpy as np
class BaseMattingDataset(VisionDataset):
"""
if you want to customize a new dataset to train the matting task,
the img and mask file need be arranged as this sturcture.
├── data
│ ├── my_dataset
│ │ ├── img
│ │ │ ├── train
│ │ │ │ ├── xxx{img_suffix}
│ │ │ │ ├── yyy{img_suffix}
│ │ │ │ ├── zzz{img_suffix}
│ │ │ ├── val
│ │ ├── trimap
│ │ │ ├── train
│ │ │ │ ├── xxx{img_suffix}
│ │ │ │ ├── yyy{img_suffix}
│ │ │ │ ├── zzz{img_suffix}
│ │ │ ├── val
│ │ ├── ann
│ │ │ ├── train
│ │ │ │ ├── xxx{ann_suffix}
│ │ │ │ ├── yyy{ann_suffix}
│ │ │ │ ├── zzz{ann_suffix}
│ │ │ ├── val
"""
def __init__(self, metainfo, dataset_dir, transform, target_transform,
trimap_transform=None,
image_set='train',
img_suffix='.jpg',
ann_suffix='.png',
trimap_suffix=None,
data_prefix: dict = dict(img_path='img', ann_path='ann', trimap_path='trimap_pth'),
return_dict=False):
'''
:param metainfo: meta data in original dataset, e.g. class_names
:param dataset_dir: the path of your dataset, e.g. data/my_dataset/ by the stucture tree above
:param image_set: 'train' or 'val'
:param img_suffix: your image suffix
:param ann_suffix: your annotation suffix
:param data_prefix: data folder name, as the tree shows above, the data_prefix of my_dataset: img_path='img' , ann_path='ann'
:param return_dict: return dict() or tuple(img, ann)
'''
super(BaseMattingDataset, self).__init__(root=dataset_dir, transform=transform,
target_transform=target_transform)
self.class_names = metainfo['class_names']
self.img_path = os.path.join(dataset_dir, data_prefix['img_path'], image_set)
self.ann_path = os.path.join(dataset_dir, data_prefix['ann_path'], image_set)
print('img_folder_name: {img_folder_name}, ann_folder_name: {ann_folder_name}'.format(
img_folder_name=self.img_path, ann_folder_name=self.ann_path))
self.img_names = [img_name.split(img_suffix)[0] for img_name in os.listdir(self.img_path) if
img_name.endswith(img_suffix)]
self.has_trimap = trimap_suffix is not None
if self.has_trimap:
self.trimap_path = os.path.join(dataset_dir, data_prefix['trimap_pth'], image_set)
print('trimap_folder_name: {trimap_folder_name}'.format(trimap_folder_name=self.trimap_path))
self.img_suffix = img_suffix
self.ann_suffix = ann_suffix
self.return_dict = return_dict
self.trimap_transform = trimap_transform
def __getitem__(self, index):
img = Image.open(os.path.join(self.img_path, self.img_names[index] + self.img_suffix))
ann = Image.open(os.path.join(self.ann_path, self.img_names[index] + self.ann_suffix))
if self.transforms is not None:
img, ann = self.transforms(img, ann)
ann = np.array(ann)
if self.has_trimap:
## return for self.has_trimpa==True
trimap = Image.open(os.path.join(self.trimap_path, self.img_names[index] + self.trimap_suffix))
if self.trimap_transform:
trimap = self.trimap_transform(trimap)
else:
print("Warnning: you may need set transform function for trimap input")
if self.return_dict:
data = dict(img_name=self.img_names[index], img=img, ann=ann, trimap=trimap,
img_path=os.path.join(self.img_path, self.img_names[index] + self.img_suffix),
ann_path=os.path.join(self.ann_path, self.img_names[index] + self.ann_suffix),
trimap_path=os.path.join(self.trimap_path, self.img_names[index] + self.trimap_suffix))
return data
return img, ann, trimap
else:
## return for self.has_trimpa==False
if self.return_dict:
data = dict(img_name=self.img_names[index], img=img, ann=ann,
img_path=os.path.join(self.img_path, self.img_names[index] + self.img_suffix),
ann_path=os.path.join(self.ann_path, self.img_names[index] + self.ann_suffix))
return data
return img, ann
def __len__(self):
return len(self.img_names)

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datasets/semantic_seg.py Normal file
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import os
from PIL import Image
from torch.utils.data import Dataset
from torchvision.datasets import VOCSegmentation, VisionDataset
import numpy as np
import cv2
import json
import torch
class BaseSemanticDataset(VisionDataset):
"""
if you want to customize a new dataset to train the segmentation task,
the img and mask file need be arranged as this sturcture.
├── data
│ ├── my_dataset
│ │ ├── img
│ │ │ ├── train
│ │ │ │ ├── xxx{img_suffix}
│ │ │ │ ├── yyy{img_suffix}
│ │ │ │ ├── zzz{img_suffix}
│ │ │ ├── val
│ │ ├── ann
│ │ │ ├── train
│ │ │ │ ├── xxx{ann_suffix}
│ │ │ │ ├── yyy{ann_suffix}
│ │ │ │ ├── zzz{ann_suffix}
│ │ │ ├── val
"""
def __init__(self, metainfo, dataset_dir, transform, target_transform,
image_set='train',
img_suffix='.jpg',
ann_suffix='.png',
data_prefix: dict = dict(img_path='img', ann_path='ann'),
return_dict=False):
'''
:param metainfo: meta data in original dataset, e.g. class_names
:param dataset_dir: the path of your dataset, e.g. data/my_dataset/ by the stucture tree above
:param image_set: 'train' or 'val'
:param img_suffix: your image suffix
:param ann_suffix: your annotation suffix
:param data_prefix: data folder name, as the tree shows above, the data_prefix of my_dataset: img_path='img' , ann_path='ann'
:param return_dict: return dict() or tuple(img, ann)
'''
super(BaseSemanticDataset, self).__init__(root=dataset_dir, transform=transform,
target_transform=target_transform)
self.class_names = metainfo['class_names']
self.img_path = os.path.join(dataset_dir, data_prefix['img_path'], image_set)
self.ann_path = os.path.join(dataset_dir, data_prefix['ann_path'], image_set)
print('img_folder_name: {img_folder_name}, ann_folder_name: {ann_folder_name}'.format(
img_folder_name=self.img_path, ann_folder_name=self.ann_path))
self.img_names = [img_name.split(img_suffix)[0] for img_name in os.listdir(self.img_path) if
img_name.endswith(img_suffix)]
self.img_suffix = img_suffix
self.ann_suffix = ann_suffix
self.return_dict = return_dict
def __getitem__(self, index):
img = Image.open(os.path.join(self.img_path, self.img_names[index] + self.img_suffix))
ann = Image.open(os.path.join(self.ann_path, self.img_names[index] + self.ann_suffix))
if self.transforms is not None:
img, ann = self.transforms(img, ann)
ann = np.array(ann)
if self.return_dict:
data = dict(img_name=self.img_names[index], img=img, ann=ann,
img_path=os.path.join(self.img_path, self.img_names[index] + self.img_suffix),
ann_path=os.path.join(self.ann_path, self.img_names[index] + self.ann_suffix))
return data
return img, ann
def __len__(self):
return len(self.img_names)
class VOCSemanticDataset(Dataset):
def __init__(self, root_dir, domain, transform, with_id=False, with_mask=False):
super(VOCSemanticDataset, self).__init__()
self.root_dir = root_dir
self.image_dir = self.root_dir + 'JPEGImages/'
self.xml_dir = self.root_dir + 'Annotations/'
self.mask_dir = self.root_dir + 'SegmentationClass/'
self.image_id_list = [image_id.strip() for image_id in open('./data/%s.txt' % domain).readlines()]
self.transform = transform
self.with_id = with_id
self.with_mask = with_mask
self.class_names = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
'bus', 'car', 'cat', 'chair', 'cow',
'diningtable', 'dog', 'horse', 'motorbike', 'person',
'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
def __len__(self):
return len(self.image_id_list)
def get_image(self, image_id):
image = Image.open(self.image_dir + image_id + '.jpg').convert('RGB')
if self.transform is not None:
image = self.transform(image)
return image
def get_mask(self, image_id):
mask_path = self.mask_dir + image_id + '.png'
if os.path.isfile(mask_path):
mask = Image.open(mask_path)
else:
mask = None
return mask
def __getitem__(self, index):
image_id = self.image_id_list[index]
data_list = [self.get_image(image_id)]
if self.with_id:
data_list.append(image_id)
if self.with_mask:
data_list.append(self.get_mask(image_id))
return data_list
class TorchVOCSegmentation(VOCSegmentation):
def __init__(self, root, year='2012', image_set='train', download=False, transform=None, target_transform=None):
super(TorchVOCSegmentation, self).__init__(root=root, year=year, image_set=image_set, download=download,
transform=transform, target_transform=target_transform)
self.class_names = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
'bus', 'car', 'cat', 'chair', 'cow',
'diningtable', 'dog', 'horse', 'motorbike', 'person',
'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
def __getitem__(self, index: int):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is the image segmentation.
"""
img = Image.open(self.images[index]).convert('RGB')
target = Image.open(self.masks[index])
if self.transforms is not None:
img, target = self.transforms(img, target)
target = np.array(target)
return img, target
class LettuceSegDataset(Dataset):
def __init__(self,
file_list,
transform=None,
# image_suffix=".JPG",
image_suffix=".jpg",
label_suffix=".json",
width=None,
height=None):
super().__init__()
self.file_list = file_list
self.transform = transform
self.image_suffix = image_suffix
self.label_suffix = label_suffix
self.width = width
self.height = height
# self.class_names = ['background', 'lettuce']
self.class_names = ['background', 'lettuce', 'weed']
def __len__(self):
return len(self.file_list)
def __getitem__(self, idx):
image_path = self.file_list[idx]
json_path = image_path.replace(self.image_suffix, self.label_suffix)
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
h, w, _ = image.shape
mask = np.zeros((h, w), dtype=np.uint8)
with open(json_path, 'r', encoding='utf-8') as f:
data = json.load(f)
for shape in data.get("shapes", []):
label_name = shape["label"]
polygon = np.array(shape["points"], dtype=np.int32).reshape((-1,1,2))
# if label_name == "lettuce":
if label_name == "weed":
cv2.fillPoly(mask, [polygon], 1)
if self.width is not None and self.height is not None:
image = cv2.resize(image, (self.width, self.height))
mask = cv2.resize(mask, (self.width, self.height), interpolation=cv2.INTER_NEAREST)
image = torch.from_numpy(image.transpose(2, 0, 1)).float()
mask = torch.from_numpy(mask[np.newaxis, ...]).long()
return image, mask, image_path
class construct_LettuceSegDataset(Dataset):
def __init__(self,
file_list,
transform=None,
# image_suffix=".JPG",
image_suffix=".jpg",
label_suffix=".json",
width=None,
height=None):
super().__init__()
self.file_list = file_list
self.transform = transform
self.image_suffix = image_suffix
self.label_suffix = label_suffix
self.width = width
self.height = height
# 添加 'weed' 类别
self.class_names = ['background', 'lettuce', 'weed']
# 定义类别到索引的映射
self.class_to_idx = {
'background': 0,
'lettuce': 1,
'weed': 2
}
def __len__(self):
return len(self.file_list)
def __getitem__(self, idx):
image_path = self.file_list[idx]
json_path = image_path.replace(self.image_suffix, self.label_suffix)
# 读取并转换图像
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
h, w, _ = image.shape
# 创建掩码，初始化为背景类别(0)
mask = np.zeros((h, w), dtype=np.uint8)
# 读取标注文件
with open(json_path, 'r', encoding='utf-8') as f:
data = json.load(f)
# 处理所有标注的形状
for shape in data.get("shapes", []):
label_name = shape["label"]
polygon = np.array(shape["points"], dtype=np.int32).reshape((-1,1,2))
# 根据类别填充不同的值
if label_name in self.class_to_idx:
cv2.fillPoly(mask, [polygon], self.class_to_idx[label_name])
# 如果需要调整大小
if self.width is not None and self.height is not None:
image = cv2.resize(image, (self.width, self.height))
mask = cv2.resize(mask, (self.width, self.height),
interpolation=cv2.INTER_NEAREST)
# 转换为张量
image = torch.from_numpy(image.transpose(2, 0, 1)).float()
mask = torch.from_numpy(mask[np.newaxis, ...]).long()
return image, mask, image_path

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datasets/transforms.py Normal file
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import torchvision.transforms as T
from omegaconf.dictconfig import DictConfig
import torch.nn as nn
AVIAL_TRANSFORM = {'resize': T.Resize, 'to_tensor': T.ToTensor}
def get_transforms(transforms: DictConfig):
T_list = []
for t_name in transforms.keys():
assert t_name in AVIAL_TRANSFORM, "{T_name} is not supported transform, please implement it and add it to " \
"AVIAL_TRANSFORM first.".format(T_name=t_name)
if transforms[t_name].params is not None:
T_list.append(AVIAL_TRANSFORM[t_name](**transforms[t_name].params))
else:
T_list.append(AVIAL_TRANSFORM[t_name]())
return T.Compose(T_list)
class CustomTransform(nn.Module):
def __init__(self):
pass
def forward(self):
pass

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experiment/log/semantic_sam/log_file.txt Normal file
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iteration : 19, ce : 1.752346932888031, total_loss : 0.8761734664440155, time : 4
iteration : 39, ce : 0.43678617626428606, total_loss : 0.21839308813214303, time : 4
iteration : 59, ce : 0.22363422363996505, total_loss : 0.11181711181998252, time : 3
iteration : 79, ce : 0.1457903351634741, total_loss : 0.07289516758173704, time : 3
iteration : 99, ce : 0.13958008363842964, total_loss : 0.06979004181921482, time : 3
saved model in ./experiment/model/semantic_sam/model.pth
iteration : 99, mIoU : 86.78554050143299, best_valid_mIoU : 86.78554050143299, time : 60
iteration : 119, ce : 0.11194387599825859, total_loss : 0.05597193799912929, time : 64
iteration : 139, ce : 0.13336510248482228, total_loss : 0.06668255124241114, time : 4
iteration : 159, ce : 0.1312786541879177, total_loss : 0.06563932709395885, time : 3
iteration : 179, ce : 0.12188598942011594, total_loss : 0.06094299471005797, time : 3
iteration : 199, ce : 0.11663060411810874, total_loss : 0.05831530205905437, time : 4
saved model in ./experiment/model/semantic_sam/model.pth
iteration : 199, mIoU : 89.69729538718849, best_valid_mIoU : 89.69729538718849, time : 59
iteration : 219, ce : 0.1029241617769003, total_loss : 0.05146208088845015, time : 63
iteration : 239, ce : 0.12284142505377531, total_loss : 0.061420712526887654, time : 4
iteration : 259, ce : 0.11378218494355678, total_loss : 0.05689109247177839, time : 3
iteration : 279, ce : 0.11434118337929249, total_loss : 0.05717059168964624, time : 4
iteration : 299, ce : 0.11516949944198132, total_loss : 0.05758474972099066, time : 3
saved model in ./experiment/model/semantic_sam/model.pth
iteration : 299, mIoU : 90.45565191317206, best_valid_mIoU : 90.45565191317206, time : 60
iteration : 319, ce : 0.11855659522116184, total_loss : 0.05927829761058092, time : 64
iteration : 339, ce : 0.10724063962697983, total_loss : 0.053620319813489914, time : 4
iteration : 359, ce : 0.10985201448202134, total_loss : 0.05492600724101067, time : 3
iteration : 379, ce : 0.09583570621907711, total_loss : 0.047917853109538555, time : 4
iteration : 399, ce : 0.10013786368072033, total_loss : 0.05006893184036017, time : 4
saved model in ./experiment/model/semantic_sam/model.pth
iteration : 399, mIoU : 90.56240643946504, best_valid_mIoU : 90.56240643946504, time : 62
iteration : 419, ce : 0.09867587257176638, total_loss : 0.04933793628588319, time : 67
iteration : 439, ce : 0.10385298412293195, total_loss : 0.051926492061465976, time : 4
iteration : 459, ce : 0.10420440025627613, total_loss : 0.052102200128138064, time : 4
iteration : 479, ce : 0.09501391369849443, total_loss : 0.04750695684924722, time : 3
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
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saved model in ./experiment/model/semantic_sam/model.pth
iteration : 399, mIoU : 83.12914567271814, best_valid_mIoU : 83.12914567271814, time : 48
iteration : 419, ce : 0.07650328939780593, total_loss : 0.038251644698902965, time : 59
iteration : 439, ce : 0.12737306039780377, total_loss : 0.06368653019890189, time : 10
iteration : 459, ce : 0.09591937027871608, total_loss : 0.04795968513935804, time : 10
iteration : 479, ce : 0.10537517564371228, total_loss : 0.05268758782185614, time : 10
iteration : 499, ce : 0.08678049889858812, total_loss : 0.04339024944929406, time : 10
saved model in ./experiment/model/semantic_sam/model.pth
iteration : 499, mIoU : 86.71075383127464, best_valid_mIoU : 86.71075383127464, time : 48
iteration : 519, ce : 0.065172965452075, total_loss : 0.0325864827260375, time : 58
iteration : 539, ce : 0.07847554692998529, total_loss : 0.039237773464992645, time : 10
iteration : 559, ce : 0.11086338181048631, total_loss : 0.05543169090524316, time : 10
iteration : 579, ce : 0.11131466701626777, total_loss : 0.055657333508133885, time : 10
iteration : 599, ce : 0.09892227221280336, total_loss : 0.04946113610640168, time : 10
iteration : 599, mIoU : 83.43759111648548, best_valid_mIoU : 86.71075383127464, time : 49
iteration : 619, ce : 0.060881080804392695, total_loss : 0.030440540402196348, time : 59
iteration : 639, ce : 0.06826045289635659, total_loss : 0.03413022644817829, time : 10
iteration : 659, ce : 0.08870259951800108, total_loss : 0.04435129975900054, time : 10
iteration : 679, ce : 0.11652187979780138, total_loss : 0.05826093989890069, time : 10
iteration : 699, ce : 0.07042193752713502, total_loss : 0.03521096876356751, time : 10
iteration : 699, mIoU : 83.23095958793452, best_valid_mIoU : 86.71075383127464, time : 48
iteration : 719, ce : 0.0663936838041991, total_loss : 0.03319684190209955, time : 58
iteration : 739, ce : 0.06597791106905788, total_loss : 0.03298895553452894, time : 10
iteration : 759, ce : 0.06343856947496533, total_loss : 0.031719284737482666, time : 10
iteration : 779, ce : 0.09711240408942104, total_loss : 0.04855620204471052, time : 10
iteration : 799, ce : 0.07680428037419915, total_loss : 0.03840214018709957, time : 10
iteration : 799, mIoU : 81.43817219158842, best_valid_mIoU : 86.71075383127464, time : 48
iteration : 819, ce : 0.07191853327676653, total_loss : 0.03595926663838327, time : 58
iteration : 839, ce : 0.08352819001302123, total_loss : 0.04176409500651061, time : 10
iteration : 859, ce : 0.07599039357155561, total_loss : 0.037995196785777806, time : 10
iteration : 879, ce : 0.10239242473617197, total_loss : 0.05119621236808598, time : 10
iteration : 899, ce : 0.07294631809927524, total_loss : 0.03647315904963762, time : 10
saved model in ./experiment/model/semantic_sam/model.pth
iteration : 899, mIoU : 88.7458099910793, best_valid_mIoU : 88.7458099910793, time : 48
iteration : 919, ce : 0.0750368535052985, total_loss : 0.03751842675264925, time : 59
iteration : 939, ce : 0.07469065655022859, total_loss : 0.037345328275114296, time : 10
iteration : 959, ce : 0.09910964691080153, total_loss : 0.049554823455400764, time : 10
iteration : 979, ce : 0.07515906286425889, total_loss : 0.03757953143212944, time : 10
iteration : 999, ce : 0.04880384765565395, total_loss : 0.024401923827826976, time : 10
iteration : 999, mIoU : 87.94771838428038, best_valid_mIoU : 88.7458099910793, time : 48
iteration : 1019, ce : 0.0596143594942987, total_loss : 0.02980717974714935, time : 59
iteration : 1039, ce : 0.07017137254588306, total_loss : 0.03508568627294153, time : 10
iteration : 1059, ce : 0.04953117328695953, total_loss : 0.024765586643479765, time : 10
iteration : 1079, ce : 0.06962326178327202, total_loss : 0.03481163089163601, time : 10
iteration : 1099, ce : 0.1142594498116523, total_loss : 0.05712972490582615, time : 10
saved model in ./experiment/model/semantic_sam/model.pth
iteration : 1099, mIoU : 90.2042178204663, best_valid_mIoU : 90.2042178204663, time : 49
iteration : 1119, ce : 0.06606756038963794, total_loss : 0.03303378019481897, time : 59
iteration : 1139, ce : 0.08854892421513796, total_loss : 0.04427446210756898, time : 10
iteration : 1159, ce : 0.07104225680232049, total_loss : 0.03552112840116024, time : 10
iteration : 1179, ce : 0.08063898030668497, total_loss : 0.040319490153342484, time : 10
iteration : 1199, ce : 0.06300937542691827, total_loss : 0.031504687713459135, time : 10
iteration : 1199, mIoU : 87.81258921438592, best_valid_mIoU : 90.2042178204663, time : 48
iteration : 1219, ce : 0.06756734945811331, total_loss : 0.033783674729056655, time : 58
iteration : 1239, ce : 0.08060045670717955, total_loss : 0.040300228353589776, time : 10
iteration : 1259, ce : 0.06920733847655355, total_loss : 0.03460366923827678, time : 10

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# copyright ziqi-jin
import torch
from .extend_sam import BaseExtendSam, SemanticSam
from .runner import BaseRunner, SemRunner
# from .optimizer import BaseOptimizer
from .scheduler import WarmupMultiStepLR
from .utils import get_opt_pamams
AVAI_SCH = ["single_step", "multi_step", "warmup_multi_step", "cosine", "linear"]
AVAI_MODEL = {'base_sam': BaseExtendSam, 'sem_sam': SemanticSam}
# AVAI_OPT = {'base_opt': BaseOptimizer, 'sgd': torch.optim.SGD, 'adam': torch.optim.Adam}
AVAI_OPT = {'sgd': torch.optim.SGD, 'adam': torch.optim.Adam, 'adamw': torch.optim.AdamW}
AVAI_RUNNER = {'base_runner': BaseRunner, 'sem_runner': SemRunner}
def get_model(model_name, **kwargs):
if model_name not in AVAI_MODEL:
print('not supported model name, please implement it first.')
return AVAI_MODEL[model_name](**kwargs).cuda()
def get_optimizer(opt_name, **kwargs):
if opt_name not in AVAI_OPT:
print('not supported optimizer name, please implement it first.')
return AVAI_OPT[opt_name](**{k: v for k, v in kwargs.items() if v is not None})
def get_runner(runner_name):
if runner_name not in AVAI_RUNNER:
print('not supported runner name, please implement it first.')
return AVAI_RUNNER[runner_name]
def get_scheduler(
optimizer,
lr_scheduler="single_step",
stepsize=1,
gamma=0.1,
warmup_factor=0.01,
warmup_steps=10,
max_epoch=1,
n_epochs_init=50,
n_epochs_decay=50,
):
"""A function wrapper for building a learning rate scheduler.
Args:
optimizer (Optimizer): an Optimizer.
lr_scheduler (str, optional): learning rate scheduler method. Default is
single_step.
stepsize (int or list, optional): step size to decay learning rate.
When ``lr_scheduler`` is "single_step", ``stepsize`` should be an integer.
When ``lr_scheduler`` is "multi_step", ``stepsize`` is a list. Default is 1.
gamma (float, optional): decay rate. Default is 0.1.
max_epoch (int, optional): maximum epoch (for cosine annealing). Default is 1.
Examples::
>>> # Decay learning rate by every 20 epochs.
>>> scheduler = get_scheduler(
>>> optimizer, lr_scheduler='single_step', stepsize=20
>>> )
>>> # Decay learning rate at 30, 50 and 55 epochs.
>>> scheduler = get_scheduler(
>>> optimizer, lr_scheduler='multi_step', stepsize=[30, 50, 55]
>>> )
"""
if lr_scheduler not in AVAI_SCH:
raise ValueError(
"Unsupported scheduler: {}. Must be one of {}".format(
lr_scheduler, AVAI_SCH
)
)
if lr_scheduler == "single_step":
if isinstance(stepsize, list):
stepsize = stepsize[-1]
if not isinstance(stepsize, int):
raise TypeError(
"For single_step lr_scheduler, stepsize must "
"be an integer, but got {}".format(type(stepsize))
)
scheduler = torch.optim.lr_scheduler.StepLR(
optimizer, step_size=stepsize, gamma=gamma
)
elif lr_scheduler == "multi_step":
if not isinstance(stepsize, list):
raise TypeError(
"For multi_step lr_scheduler, stepsize must "
"be a list, but got {}".format(type(stepsize))
)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=stepsize, gamma=gamma
)
elif lr_scheduler == "warmup_multi_step":
if not isinstance(stepsize, list):
raise TypeError(
"For warmup multi_step lr_scheduler, stepsize must "
"be a list, but got {}".format(type(stepsize))
)
scheduler = WarmupMultiStepLR(
optimizer,
milestones=stepsize,
gamma=gamma,
warmup_factor=warmup_factor,
warmup_iters=warmup_steps,
)
elif lr_scheduler == "cosine":
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, int(max_epoch)
)
elif lr_scheduler == "linear":
def lambda_rule(epoch):
lr_l = 1.0 - max(0, epoch - n_epochs_init) / float(n_epochs_decay + 1)
return lr_l
scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer, lr_lambda=lambda_rule
)
return scheduler

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# copyright ziqi-jin
import torch
import torch.nn as nn
from .segment_anything_ori import sam_model_registry
from .image_encoder_adapter import BaseImgEncodeAdapter
from .mask_decoder_adapter import BaseMaskDecoderAdapter, SemMaskDecoderAdapter
from .prompt_encoder_adapter import BasePromptEncodeAdapter
class BaseExtendSam(nn.Module):
def __init__(self, ckpt_path=None, fix_img_en=False, fix_prompt_en=False, fix_mask_de=False, model_type='vit_b'):
super(BaseExtendSam, self).__init__()
assert model_type in ['default', 'vit_b', 'vit_l', 'vit_h'], print(
"Wrong model_type, SAM only can be built as vit_b, vot_l, vit_h and default ")
self.ori_sam = sam_model_registry[model_type](ckpt_path)
self.img_adapter = BaseImgEncodeAdapter(self.ori_sam, fix=fix_img_en)
self.prompt_adapter = BasePromptEncodeAdapter(self.ori_sam, fix=fix_prompt_en)
self.mask_adapter = BaseMaskDecoderAdapter(self.ori_sam, fix=fix_mask_de)
def forward(self, img):
x = self.img_adapter(img)
points = None
boxes = None
masks = None
sparse_embeddings, dense_embeddings = self.prompt_adapter(
points=points,
boxes=boxes,
masks=masks,
)
multimask_output = True
low_res_masks, iou_predictions = self.mask_adapter(
image_embeddings=x,
prompt_adapter=self.prompt_adapter,
sparse_embeddings=sparse_embeddings,
dense_embeddings=dense_embeddings,
multimask_output=multimask_output,
)
return low_res_masks, iou_predictions
class SemanticSam(BaseExtendSam):
def __init__(self, ckpt_path=None, fix_img_en=False, fix_prompt_en=False, fix_mask_de=False, class_num=20, model_type='vit_b'):
super().__init__(ckpt_path=ckpt_path, fix_img_en=fix_img_en, fix_prompt_en=fix_prompt_en,
fix_mask_de=fix_mask_de, model_type=model_type)
self.mask_adapter = SemMaskDecoderAdapter(self.ori_sam, fix=fix_mask_de, class_num=class_num)

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import torch.nn as nn
from .segment_anything_ori.modeling.sam import Sam
from .utils import fix_params
class BaseImgEncodeAdapter(nn.Module):
def __init__(self, ori_sam: Sam, fix=False):
super(BaseImgEncodeAdapter, self).__init__()
self.sam_img_encoder = ori_sam.image_encoder
if fix:
fix_params(self.sam_img_encoder)
def forward(self, x):
x = self.sam_img_encoder(x)
return x

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# @copyright ziqi-jin
import torch.nn as nn
import torch
from .segment_anything_ori.modeling.sam import Sam
from .utils import fix_params
from .segment_anything_ori.modeling.mask_decoder import MaskDecoder
from typing import List, Tuple
from torch.nn import functional as F
from .mask_decoder_heads import SemSegHead
from .mask_decoder_neck import MaskDecoderNeck
class BaseMaskDecoderAdapter(MaskDecoder):
'''
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.
'''
# is fix and load params
def __init__(self, ori_sam: Sam, fix=False):
super(BaseMaskDecoderAdapter, self).__init__(transformer_dim=ori_sam.mask_decoder.transformer_dim,
transformer=ori_sam.mask_decoder.transformer)
self.sam_mask_decoder = ori_sam.mask_decoder
if fix:
fix_params(self.sam_mask_decoder) # move to runner to implement
def forward(self, image_embeddings, prompt_adapter, sparse_embeddings, dense_embeddings, multimask_output=True):
low_res_masks, iou_predictions = self.sam_mask_decoder(image_embeddings=image_embeddings,
image_pe=prompt_adapter.sam_prompt_encoder.get_dense_pe(),
sparse_prompt_embeddings=sparse_embeddings,
dense_prompt_embeddings=dense_embeddings,
multimask_output=multimask_output, )
return low_res_masks, iou_predictions
class SemMaskDecoderAdapter(BaseMaskDecoderAdapter):
def __init__(self, ori_sam: Sam, fix=False, class_num=20):
super(SemMaskDecoderAdapter, self).__init__(ori_sam, fix)
self.decoder_neck = MaskDecoderNeck(transformer_dim=self.sam_mask_decoder.transformer_dim,
transformer=self.sam_mask_decoder.transformer,
num_multimask_outputs=self.sam_mask_decoder.num_multimask_outputs)
self.decoder_head = SemSegHead(transformer_dim=self.sam_mask_decoder.transformer_dim,
num_multimask_outputs=self.sam_mask_decoder.num_multimask_outputs,
iou_head_depth=self.sam_mask_decoder.iou_head_depth,
iou_head_hidden_dim=self.sam_mask_decoder.iou_head_hidden_dim,
class_num=class_num)
# pair the params between ori mask_decoder and new mask_decoder_adapter
self.pair_params(self.decoder_neck)
self.pair_params(self.decoder_head)
def forward(self, image_embeddings, prompt_adapter, sparse_embeddings, dense_embeddings, multimask_output=True,
scale=1):
src, iou_token_out, mask_tokens_out, src_shape = self.decoder_neck(image_embeddings=image_embeddings,
image_pe=prompt_adapter.sam_prompt_encoder.get_dense_pe(),
sparse_prompt_embeddings=sparse_embeddings,
dense_prompt_embeddings=dense_embeddings,
multimask_output=multimask_output, )
masks, iou_pred = self.decoder_head(src, iou_token_out, mask_tokens_out, src_shape, mask_scale=scale)
return masks, iou_pred
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)
# 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

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import torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type
from .segment_anything_ori.modeling.common import LayerNorm2d
class OriHead(nn.Module):
def __init__(
self,
*,
transformer_dim: int,
num_multimask_outputs: int = 3,
activation: Type[nn.Module] = nn.GELU,
iou_head_depth: int = 3,
iou_head_hidden_dim: int = 256,
) -> None:
"""
Predicts masks given an image and prompt embeddings, using a
tranformer architecture.
Arguments:
transformer_dim (int): the channel dimension of the transformer
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.num_multimask_outputs = num_multimask_outputs
self.num_mask_tokens = num_multimask_outputs + 1
self.output_upscaling = nn.Sequential(
nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
LayerNorm2d(transformer_dim // 4),
activation(),
nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
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
)
def forward(
self,
src: torch.Tensor,
iou_token_out: torch.Tensor,
mask_tokens_out: 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
"""
b, c, h, w = src.shape
# Upscale mask embeddings and predict masks using the mask tokens
src = src.transpose(1, 2).view(b, c, h, w)
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 = upscaled_embedding.shape
masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
# Generate mask quality predictions
iou_pred = self.iou_prediction_head(iou_token_out)
# Select the correct mask or masks for outptu
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
class SemSegHead(nn.Module):
def __init__(
self,
*,
transformer_dim: int,
num_multimask_outputs: int = 3,
activation: Type[nn.Module] = nn.GELU,
iou_head_depth: int = 3,
iou_head_hidden_dim: int = 256,
class_num: int = 20,
) -> None:
"""
Predicts masks given an image and prompt embeddings, using a
tranformer architecture.
Arguments:
transformer_dim (int): the channel dimension of the transformer
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.num_multimask_outputs = num_multimask_outputs
self.num_mask_tokens = num_multimask_outputs + 1
self.class_num = class_num
self.output_upscaling = nn.Sequential(
nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
LayerNorm2d(transformer_dim // 4),
activation(),
nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
activation(),
)
self.output_hypernetworks_mlps = nn.ModuleList(
[
MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
for _ in range(self.class_num)
]
)
self.iou_prediction_head = MLP(
transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
)
def forward(
self,
src: torch.Tensor,
iou_token_out: torch.Tensor,
mask_tokens_out: torch.Tensor,
src_shape,
mask_scale=1,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Predict masks given image and prompt embeddings.
Arguments:
src (torch.Tensor): The tensor contains image embedding and sparse prompt embedding
iou_token_out (torch.Tensor): Tokens of iou prediction from neck module
mask_tokens_out (torch.Tensor): Tokens of mask prediction form neck module
mask_scale (int): Original SAM output 3 masks which is from local to global as default
This Class use one of three mask tokens to transform it into class-ware
semantic segmentation prediction
Returns:
torch.Tensor: batched predicted semantic masks
torch.Tensor: batched predictions of mask quality
"""
b, c, h, w = src_shape
# Upscale mask embeddings and predict masks using the mask tokens
src = src.transpose(1, 2).view(b, c, h, w)
upscaled_embedding = self.output_upscaling(src)
hyper_in_list: List[torch.Tensor] = []
for i in range(self.class_num):
hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, mask_scale, :]))
hyper_in = torch.stack(hyper_in_list, dim=1)
b, c, h, w = upscaled_embedding.shape
masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w) # B N H W, N is num of category
# Generate mask quality predictions
iou_pred = self.iou_prediction_head(iou_token_out) # B N H W, N is num of category
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

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type
from .segment_anything_ori.modeling.common import LayerNorm2d
'''
This file save the mask_decoder's neck class,
which is the former part of original mask decoder of SAM.
Then the mask_decoder_heads can be used with the neck.
'''
class MaskDecoderNeck(nn.Module):
def __init__(
self,
*,
transformer_dim: int,
transformer: nn.Module,
num_multimask_outputs: int = 3,
activation: Type[nn.Module] = nn.GELU,
) -> None:
"""
Predicts masks given an image and prompt embeddings, using a
tranformer 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
"""
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)
self.output_upscaling = nn.Sequential(
nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
LayerNorm2d(transformer_dim // 4),
activation(),
nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
activation(),
)
def forward(
self,
image_embeddings: 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: The tensor contains image embedding and sparse prompt embedding
torch.Tensor: Tokens of iou prediction
torch.Tensor: Tokens of mask prediction
"""
# 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)
# Expand per-image data in batch direction to be per-mask
src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
src = src + dense_prompt_embeddings
pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
src_shape = 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), :]
return src, iou_token_out, mask_tokens_out, src_shape

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# copyright ziqi-jin
import torch.nn as nn
from .segment_anything_ori.modeling.sam import Sam
from .utils import fix_params
class BasePromptEncodeAdapter(nn.Module):
def __init__(self, ori_sam: Sam, fix=False):
super(BasePromptEncodeAdapter, self).__init__()
self.sam_prompt_encoder = ori_sam.prompt_encoder
if fix:
fix_params(self.sam_prompt_encoder)
def forward(self, points=None, boxes=None, masks=None):
sparse_embeddings, dense_embeddings = self.sam_prompt_encoder(points, boxes, masks)
return sparse_embeddings, dense_embeddings

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from datasets import Iterator
from .utils import Average_Meter, Timer, print_and_save_log, mIoUOnline, get_numpy_from_tensor, save_model, write_log, \
check_folder, one_hot_embedding_3d
import torch
import cv2
import torch.nn.functional as F
import os
import torch.nn as nn
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.colors import ListedColormap, BoundaryNorm
import numpy as np
class BaseRunner():
def __init__(self, model, optimizer, losses, train_loader, val_loader, scheduler):
self.optimizer = optimizer
self.losses = losses
self.train_loader = train_loader
self.val_loader = val_loader
self.model = model
self.scheduler = scheduler
self.train_timer = Timer()
self.eval_timer = Timer()
try:
use_gpu = os.environ['CUDA_VISIBLE_DEVICES']
except KeyError:
use_gpu = '0'
self.the_number_of_gpu = len(use_gpu.split(','))
self.original_size = self.model.img_adapter.sam_img_encoder.img_size
if self.the_number_of_gpu > 1:
self.model = nn.DataParallel(self.model)
class SemRunner(BaseRunner):
# def __init__(self, **kwargs):
# super().__init__(kwargs)
def __init__(self, model, optimizer, losses, train_loader, val_loader, scheduler):
super().__init__(model, optimizer, losses, train_loader, val_loader, scheduler)
self.exist_status = ['train', 'eval', 'test']
def train(self, cfg):
# initial identify
train_meter = Average_Meter(list(self.losses.keys()) + ['total_loss'])
train_iterator = Iterator(self.train_loader)
best_valid_mIoU = -1
model_path = "{cfg.model_folder}/{cfg.experiment_name}/model.pth".format(cfg=cfg)
log_path = "{cfg.log_folder}/{cfg.experiment_name}/log_file.txt".format(cfg=cfg)
check_folder(model_path)
check_folder(log_path)
writer = None
if cfg.use_tensorboard is True:
tensorboard_dir = "{cfg.tensorboard_folder}/{cfg.experiment_name}/tensorboard/".format(cfg=cfg)
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter(tensorboard_dir)
# train
train_losses = []
for iteration in range(cfg.max_iter):
images, labels,_ = train_iterator.get()
images, labels = images.cuda(), labels.cuda().long()
labels = labels.squeeze(1)
masks_pred, iou_pred = self.model(images)
masks_pred = F.interpolate(masks_pred, self.original_size, mode="bilinear", align_corners=False)
total_loss = torch.zeros(1).cuda()
loss_dict = {}
self._compute_loss(total_loss, loss_dict, masks_pred, labels, cfg)
self.optimizer.zero_grad()
total_loss.backward()
self.optimizer.step()
self.scheduler.step()
loss_dict['total_loss'] = total_loss.item()
train_losses.append(total_loss.item())
train_meter.add(loss_dict)
# log
if (iteration + 1) % cfg.log_iter == 0:
write_log(iteration=iteration, log_path=log_path, log_data=train_meter.get(clear=True),
status=self.exist_status[0],
writer=writer, timer=self.train_timer)
# eval
if (iteration + 1) % cfg.eval_iter == 0:
mIoU, _ = self._eval()
if best_valid_mIoU == -1 or best_valid_mIoU < mIoU:
best_valid_mIoU = mIoU
save_model(self.model, model_path, parallel=self.the_number_of_gpu > 1)
print_and_save_log("saved model in {model_path}".format(model_path=model_path), path=log_path)
log_data = {'mIoU': mIoU, 'best_valid_mIoU': best_valid_mIoU}
write_log(iteration=iteration, log_path=log_path, log_data=log_data, status=self.exist_status[1],
writer=writer, timer=self.eval_timer)
plt.figure(figsize=(8, 5))
plt.plot(train_losses, label="Train Loss")
plt.xlabel("Iteration")
plt.ylabel("Loss")
plt.title(f"Loss Curve up to Iter {iteration}")
plt.legend()
# 保存
save_path = os.path.join('./', f"loss_iter_{iteration}.png")
plt.savefig(save_path, dpi=150)
plt.close() # 关闭当前 figure，释放内存
# final process
save_model(self.model, model_path, is_final=True, parallel=self.the_number_of_gpu > 1)
if writer is not None:
writer.close()
def test(self):
pass
def _eval(self):
self.model.eval()
self.eval_timer.start()
class_names = self.val_loader.dataset.class_names
eval_metric = mIoUOnline(class_names=class_names)
with torch.no_grad():
for index, (images, labels, img_paths) in enumerate(self.val_loader):
images = images.cuda()
labels = labels.cuda()
masks_pred, iou_pred = self.model(images)
predictions = torch.argmax(masks_pred, dim=1)
for batch_index in range(images.size()[0]):
pred_mask = get_numpy_from_tensor(predictions[batch_index])
gt_mask = get_numpy_from_tensor(labels[batch_index].squeeze(0))
h, w = pred_mask.shape
gt_mask = cv2.resize(gt_mask, (w, h), interpolation=cv2.INTER_NEAREST)
src_img = cv2.imread(img_paths[batch_index])
# self.visualize_segmentation(src_img,pred_mask,gt_mask,os.path.basename(img_paths[batch_index]))
self.construct_visualize_segmentation(src_img,pred_mask,gt_mask,os.path.basename(img_paths[batch_index]))
eval_metric.add(pred_mask, gt_mask)
self.model.train()
return eval_metric.get(clear=True)
def _compute_loss(self, total_loss, loss_dict, mask_pred, labels, cfg):
"""
Due to the inputs of losses are different, so if you want to add new losses,
you may need to modify the process in this function
"""
loss_cfg = cfg.losses
for index, item in enumerate(self.losses.items()):
# item -> (key: loss_name, val: loss)
real_labels = labels
if loss_cfg[item[0]].label_one_hot:
class_num = cfg.model.params.class_num
real_labels = one_hot_embedding_3d(real_labels, class_num=class_num)
tmp_loss = item[1](mask_pred, real_labels)
loss_dict[item[0]] = tmp_loss.item()
total_loss += loss_cfg[item[0]].weight * tmp_loss
def visualize_segmentation(self,image, pred_mask, gt_mask, save_path=None):
# 如果图像是 (C, H, W)，需要先变成 (H, W, C)
if image.ndim == 3 and image.shape[0] == 3 and image.shape[1] != 3:
image = np.transpose(image, (1, 2, 0)) # (C, H, W) -> (H, W, C)
# 定义用于显示 segmentation 的离散颜色映射：0=黑色, 1=绿色
cmap = ListedColormap(["black", "green"])
# 对应 0 和 1 两种类别，分界点给 [0,1,2]
norm = BoundaryNorm([0, 1, 2], cmap.N)
# 创建图像
fig, axes = plt.subplots(1, 4, figsize=(16, 4))
axes[0].imshow(image.astype(np.uint8))
axes[0].set_title("Original Image")
axes[0].axis("off")
im_pred = axes[1].imshow(pred_mask, cmap=cmap, norm=norm)
axes[1].set_title("Predicted Mask")
axes[1].axis("off")
im_gt = axes[2].imshow(gt_mask, cmap=cmap, norm=norm)
axes[2].set_title("Ground Truth Mask")
axes[2].axis("off")
legend_patches = [
mpatches.Patch(color="black", label="Background (0)"),
mpatches.Patch(color="green", label="lettuce (1)"),
]
axes[3].legend(handles=legend_patches, loc='center', fontsize=10)
axes[3].set_title("Classes Legend")
axes[3].axis("off")
# 调整布局
plt.tight_layout()
plt.savefig(os.path.join('./outputs',save_path), dpi=200, bbox_inches='tight')
plt.close()
def construct_visualize_segmentation(self, image, pred_mask, gt_mask, save_path=None):
# 如果图像是 (C, H, W)，需要先变成 (H, W, C)
if image.ndim == 3 and image.shape[0] == 3 and image.shape[1] != 3:
image = np.transpose(image, (1, 2, 0)) # (C, H, W) -> (H, W, C)
# 定义用于显示 segmentation 的离散颜色映射：0=黑色, 1=绿色, 2=红色
cmap = ListedColormap(["black", "green", "red"])
# 对应 0,1,2 三种类别，分界点给 [0,1,2,3]
norm = BoundaryNorm([0, 1, 2, 3], cmap.N)
# 创建图像
fig, axes = plt.subplots(1, 4, figsize=(16, 4))
axes[0].imshow(image.astype(np.uint8))
axes[0].set_title("Original Image")
axes[0].axis("off")
im_pred = axes[1].imshow(pred_mask, cmap=cmap, norm=norm)
axes[1].set_title("Predicted Mask")
axes[1].axis("off")
im_gt = axes[2].imshow(gt_mask, cmap=cmap, norm=norm)
axes[2].set_title("Ground Truth Mask")
axes[2].axis("off")
legend_patches = [
mpatches.Patch(color="black", label="Background (0)"),
mpatches.Patch(color="green", label="lettuce (1)"),
mpatches.Patch(color="red", label="weed (2)"),
]
axes[3].legend(handles=legend_patches, loc='center', fontsize=10)
axes[3].set_title("Classes Legend")
axes[3].axis("off")
# 调整布局
plt.tight_layout()
plt.savefig(os.path.join('./outputs',save_path), dpi=200, bbox_inches='tight')
plt.close()

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# Modified from https://github.com/KaiyangZhou/deep-person-reid/blob/master/torchreid/optim/lr_scheduler.py # noqa
# and https://github.com/JDAI-CV/fast-reid/blob/master/fastreid/solver/lr_scheduler.py
from bisect import bisect_right
from typing import List
import torch
from torch.optim.lr_scheduler import _LRScheduler
class WarmupMultiStepLR(_LRScheduler):
def __init__(
self,
optimizer: torch.optim.Optimizer,
milestones: List[int],
gamma: float = 0.1,
warmup_factor: float = 0.001,
warmup_iters: int = 1000,
warmup_method: str = "linear",
last_epoch: int = -1,
**kwargs,
):
if not list(milestones) == sorted(milestones):
raise ValueError(
"Milestones should be a list of" " increasing integers. Got {}",
milestones,
)
self.milestones = milestones
self.gamma = gamma
self.warmup_factor = warmup_factor
self.warmup_iters = warmup_iters
self.warmup_method = warmup_method
super().__init__(optimizer, last_epoch)
def get_lr(self) -> List[float]:
warmup_factor = _get_warmup_factor_at_iter(
self.warmup_method, self.last_epoch, self.warmup_iters, self.warmup_factor
)
return [
base_lr
* warmup_factor
* self.gamma ** bisect_right(self.milestones, self.last_epoch)
for base_lr in self.base_lrs
]
def _compute_values(self) -> List[float]:
# The new interface
return self.get_lr()
def _get_warmup_factor_at_iter(
method: str, iter: int, warmup_iters: int, warmup_factor: float
) -> float:
"""
Return the learning rate warmup factor at a specific iteration.
See https://arxiv.org/abs/1706.02677 for more details.
Args:
method (str): warmup method; either "constant" or "linear".
iter (int): iteration at which to calculate the warmup factor.
warmup_iters (int): the number of warmup iterations.
warmup_factor (float): the base warmup factor (the meaning changes according
to the method used).
Returns:
float: the effective warmup factor at the given iteration.
"""
if iter >= warmup_iters:
return 1.0
if method == "constant":
return warmup_factor
elif method == "linear":
alpha = iter / warmup_iters
return warmup_factor * (1 - alpha) + alpha
else:
raise ValueError("Unknown warmup method: {}".format(method))

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# modified by ziqi-jin
from .build_sam import (
build_sam,
build_sam_vit_h,
build_sam_vit_l,
build_sam_vit_b,
sam_model_registry,
)
from .modeling.sam import Sam
from .predictor import SamPredictor
from .automatic_mask_generator import SamAutomaticMaskGenerator

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torchvision.ops.boxes import batched_nms, box_area # type: ignore
from typing import Any, Dict, List, Optional, Tuple
from .modeling import Sam
from .predictor import SamPredictor
from .utils.amg import (
MaskData,
area_from_rle,
batch_iterator,
batched_mask_to_box,
box_xyxy_to_xywh,
build_all_layer_point_grids,
calculate_stability_score,
coco_encode_rle,
generate_crop_boxes,
is_box_near_crop_edge,
mask_to_rle_pytorch,
remove_small_regions,
rle_to_mask,
uncrop_boxes_xyxy,
uncrop_masks,
uncrop_points,
)
class SamAutomaticMaskGenerator:
def __init__(
self,
model: Sam,
points_per_side: Optional[int] = 32,
points_per_batch: int = 64,
pred_iou_thresh: float = 0.88,
stability_score_thresh: float = 0.95,
stability_score_offset: float = 1.0,
box_nms_thresh: float = 0.7,
crop_n_layers: int = 0,
crop_nms_thresh: float = 0.7,
crop_overlap_ratio: float = 512 / 1500,
crop_n_points_downscale_factor: int = 1,
point_grids: Optional[List[np.ndarray]] = None,
min_mask_region_area: int = 0,
output_mode: str = "binary_mask",
) -> None:
"""
Using a SAM model, generates masks for the entire image.
Generates a grid of point prompts over the image, then filters
low quality and duplicate masks. The default settings are chosen
for SAM with a ViT-H backbone.
Arguments:
model (Sam): The SAM model to use for mask prediction.
points_per_side (int or None): The number of points to be sampled
along one side of the image. The total number of points is
points_per_side**2. If None, 'point_grids' must provide explicit
point sampling.
points_per_batch (int): Sets the number of points run simultaneously
by the model. Higher numbers may be faster but use more GPU memory.
pred_iou_thresh (float): A filtering threshold in [0,1], using the
model's predicted mask quality.
stability_score_thresh (float): A filtering threshold in [0,1], using
the stability of the mask under changes to the cutoff used to binarize
the model's mask predictions.
stability_score_offset (float): The amount to shift the cutoff when
calculated the stability score.
box_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks.
crops_n_layers (int): If >0, mask prediction will be run again on
crops of the image. Sets the number of layers to run, where each
layer has 2**i_layer number of image crops.
crops_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks between different crops.
crop_overlap_ratio (float): Sets the degree to which crops overlap.
In the first crop layer, crops will overlap by this fraction of
the image length. Later layers with more crops scale down this overlap.
crop_n_points_downscale_factor (int): The number of points-per-side
sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
point_grids (list(np.ndarray) or None): A list over explicit grids
of points used for sampling, normalized to [0,1]. The nth grid in the
list is used in the nth crop layer. Exclusive with points_per_side.
min_mask_region_area (int): If >0, postprocessing will be applied
to remove disconnected regions and holes in masks with area smaller
than min_mask_region_area. Requires opencv.
output_mode (str): The form masks are returned in. Can be 'binary_mask',
'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
For large resolutions, 'binary_mask' may consume large amounts of
memory.
"""
assert (points_per_side is None) != (
point_grids is None
), "Exactly one of points_per_side or point_grid must be provided."
if points_per_side is not None:
self.point_grids = build_all_layer_point_grids(
points_per_side,
crop_n_layers,
crop_n_points_downscale_factor,
)
elif point_grids is not None:
self.point_grids = point_grids
else:
raise ValueError("Can't have both points_per_side and point_grid be None.")
assert output_mode in [
"binary_mask",
"uncompressed_rle",
"coco_rle",
], f"Unknown output_mode {output_mode}."
if output_mode == "coco_rle":
from pycocotools import mask as mask_utils # type: ignore # noqa: F401
if min_mask_region_area > 0:
import cv2 # type: ignore # noqa: F401
self.predictor = SamPredictor(model)
self.points_per_batch = points_per_batch
self.pred_iou_thresh = pred_iou_thresh
self.stability_score_thresh = stability_score_thresh
self.stability_score_offset = stability_score_offset
self.box_nms_thresh = box_nms_thresh
self.crop_n_layers = crop_n_layers
self.crop_nms_thresh = crop_nms_thresh
self.crop_overlap_ratio = crop_overlap_ratio
self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
self.min_mask_region_area = min_mask_region_area
self.output_mode = output_mode
@torch.no_grad()
def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
"""
Generates masks for the given image.
Arguments:
image (np.ndarray): The image to generate masks for, in HWC uint8 format.
Returns:
list(dict(str, any)): A list over records for masks. Each record is
a dict containing the following keys:
segmentation (dict(str, any) or np.ndarray): The mask. If
output_mode='binary_mask', is an array of shape HW. Otherwise,
is a dictionary containing the RLE.
bbox (list(float)): The box around the mask, in XYWH format.
area (int): The area in pixels of the mask.
predicted_iou (float): The model's own prediction of the mask's
quality. This is filtered by the pred_iou_thresh parameter.
point_coords (list(list(float))): The point coordinates input
to the model to generate this mask.
stability_score (float): A measure of the mask's quality. This
is filtered on using the stability_score_thresh parameter.
crop_box (list(float)): The crop of the image used to generate
the mask, given in XYWH format.
"""
# Generate masks
mask_data = self._generate_masks(image)
# Filter small disconnected regions and holes in masks
if self.min_mask_region_area > 0:
mask_data = self.postprocess_small_regions(
mask_data,
self.min_mask_region_area,
max(self.box_nms_thresh, self.crop_nms_thresh),
)
# Encode masks
if self.output_mode == "coco_rle":
mask_data["segmentations"] = [coco_encode_rle(rle) for rle in mask_data["rles"]]
elif self.output_mode == "binary_mask":
mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
else:
mask_data["segmentations"] = mask_data["rles"]
# Write mask records
curr_anns = []
for idx in range(len(mask_data["segmentations"])):
ann = {
"segmentation": mask_data["segmentations"][idx],
"area": area_from_rle(mask_data["rles"][idx]),
"bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
"predicted_iou": mask_data["iou_preds"][idx].item(),
"point_coords": [mask_data["points"][idx].tolist()],
"stability_score": mask_data["stability_score"][idx].item(),
"crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
}
curr_anns.append(ann)
return curr_anns
def _generate_masks(self, image: np.ndarray) -> MaskData:
orig_size = image.shape[:2]
crop_boxes, layer_idxs = generate_crop_boxes(
orig_size, self.crop_n_layers, self.crop_overlap_ratio
)
# Iterate over image crops
data = MaskData()
for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
data.cat(crop_data)
# Remove duplicate masks between crops
if len(crop_boxes) > 1:
# Prefer masks from smaller crops
scores = 1 / box_area(data["crop_boxes"])
scores = scores.to(data["boxes"].device)
keep_by_nms = batched_nms(
data["boxes"].float(),
scores,
torch.zeros(len(data["boxes"])), # categories
iou_threshold=self.crop_nms_thresh,
)
data.filter(keep_by_nms)
data.to_numpy()
return data
def _process_crop(
self,
image: np.ndarray,
crop_box: List[int],
crop_layer_idx: int,
orig_size: Tuple[int, ...],
) -> MaskData:
# Crop the image and calculate embeddings
x0, y0, x1, y1 = crop_box
cropped_im = image[y0:y1, x0:x1, :]
cropped_im_size = cropped_im.shape[:2]
self.predictor.set_image(cropped_im)
# Get points for this crop
points_scale = np.array(cropped_im_size)[None, ::-1]
points_for_image = self.point_grids[crop_layer_idx] * points_scale
# Generate masks for this crop in batches
data = MaskData()
for (points,) in batch_iterator(self.points_per_batch, points_for_image):
batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size)
data.cat(batch_data)
del batch_data
self.predictor.reset_image()
# Remove duplicates within this crop.
keep_by_nms = batched_nms(
data["boxes"].float(),
data["iou_preds"],
torch.zeros(len(data["boxes"])), # categories
iou_threshold=self.box_nms_thresh,
)
data.filter(keep_by_nms)
# Return to the original image frame
data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box)
data["points"] = uncrop_points(data["points"], crop_box)
data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))])
return data
def _process_batch(
self,
points: np.ndarray,
im_size: Tuple[int, ...],
crop_box: List[int],
orig_size: Tuple[int, ...],
) -> MaskData:
orig_h, orig_w = orig_size
# Run model on this batch
transformed_points = self.predictor.transform.apply_coords(points, im_size)
in_points = torch.as_tensor(transformed_points, device=self.predictor.device)
in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)
masks, iou_preds, _ = self.predictor.predict_torch(
in_points[:, None, :],
in_labels[:, None],
multimask_output=True,
return_logits=True,
)
# Serialize predictions and store in MaskData
data = MaskData(
masks=masks.flatten(0, 1),
iou_preds=iou_preds.flatten(0, 1),
points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
)
del masks
# Filter by predicted IoU
if self.pred_iou_thresh > 0.0:
keep_mask = data["iou_preds"] > self.pred_iou_thresh
data.filter(keep_mask)
# Calculate stability score
data["stability_score"] = calculate_stability_score(
data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset
)
if self.stability_score_thresh > 0.0:
keep_mask = data["stability_score"] >= self.stability_score_thresh
data.filter(keep_mask)
# Threshold masks and calculate boxes
data["masks"] = data["masks"] > self.predictor.model.mask_threshold
data["boxes"] = batched_mask_to_box(data["masks"])
# Filter boxes that touch crop boundaries
keep_mask = ~is_box_near_crop_edge(data["boxes"], crop_box, [0, 0, orig_w, orig_h])
if not torch.all(keep_mask):
data.filter(keep_mask)
# Compress to RLE
data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w)
data["rles"] = mask_to_rle_pytorch(data["masks"])
del data["masks"]
return data
@staticmethod
def postprocess_small_regions(
mask_data: MaskData, min_area: int, nms_thresh: float
) -> MaskData:
"""
Removes small disconnected regions and holes in masks, then reruns
box NMS to remove any new duplicates.
Edits mask_data in place.
Requires open-cv as a dependency.
"""
if len(mask_data["rles"]) == 0:
return mask_data
# Filter small disconnected regions and holes
new_masks = []
scores = []
for rle in mask_data["rles"]:
mask = rle_to_mask(rle)
mask, changed = remove_small_regions(mask, min_area, mode="holes")
unchanged = not changed
mask, changed = remove_small_regions(mask, min_area, mode="islands")
unchanged = unchanged and not changed
new_masks.append(torch.as_tensor(mask).unsqueeze(0))
# Give score=0 to changed masks and score=1 to unchanged masks
# so NMS will prefer ones that didn't need postprocessing
scores.append(float(unchanged))
# Recalculate boxes and remove any new duplicates
masks = torch.cat(new_masks, dim=0)
boxes = batched_mask_to_box(masks)
keep_by_nms = batched_nms(
boxes.float(),
torch.as_tensor(scores),
torch.zeros(len(boxes)), # categories
iou_threshold=nms_thresh,
)
# Only recalculate RLEs for masks that have changed
for i_mask in keep_by_nms:
if scores[i_mask] == 0.0:
mask_torch = masks[i_mask].unsqueeze(0)
mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0]
mask_data["boxes"][i_mask] = boxes[i_mask] # update res directly
mask_data.filter(keep_by_nms)
return mask_data

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# modified by ziqi-jin
import torch
from functools import partial
from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer
def build_sam_vit_h(checkpoint=None):
return _build_sam(
encoder_embed_dim=1280,
encoder_depth=32,
encoder_num_heads=16,
encoder_global_attn_indexes=[7, 15, 23, 31],
checkpoint=checkpoint,
)
build_sam = build_sam_vit_h
def build_sam_vit_l(checkpoint=None):
return _build_sam(
encoder_embed_dim=1024,
encoder_depth=24,
encoder_num_heads=16,
encoder_global_attn_indexes=[5, 11, 17, 23],
checkpoint=checkpoint,
)
def build_sam_vit_b(checkpoint=None):
return _build_sam(
encoder_embed_dim=768,
encoder_depth=12,
encoder_num_heads=12,
encoder_global_attn_indexes=[2, 5, 8, 11],
checkpoint=checkpoint,
)
sam_model_registry = {
"default": build_sam_vit_h,
"vit_h": build_sam_vit_h,
"vit_l": build_sam_vit_l,
"vit_b": build_sam_vit_b,
}
def _build_sam(
encoder_embed_dim,
encoder_depth,
encoder_num_heads,
encoder_global_attn_indexes,
checkpoint=None,
):
prompt_embed_dim = 256
image_size = 1024
vit_patch_size = 16
image_embedding_size = image_size // vit_patch_size
sam = Sam(
image_encoder=ImageEncoderViT(
depth=encoder_depth,
embed_dim=encoder_embed_dim,
img_size=image_size,
mlp_ratio=4,
norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
num_heads=encoder_num_heads,
patch_size=vit_patch_size,
qkv_bias=True,
use_rel_pos=True,
global_attn_indexes=encoder_global_attn_indexes,
window_size=14,
out_chans=prompt_embed_dim,
),
prompt_encoder=PromptEncoder(
embed_dim=prompt_embed_dim,
image_embedding_size=(image_embedding_size, image_embedding_size),
input_image_size=(image_size, image_size),
mask_in_chans=16,
),
mask_decoder=MaskDecoder(
num_multimask_outputs=3,
transformer=TwoWayTransformer(
depth=2,
embedding_dim=prompt_embed_dim,
mlp_dim=2048,
num_heads=8,
),
transformer_dim=prompt_embed_dim,
iou_head_depth=3,
iou_head_hidden_dim=256,
),
pixel_mean=[123.675, 116.28, 103.53],
pixel_std=[58.395, 57.12, 57.375],
)
if checkpoint is not None:
with open(checkpoint, "rb") as f:
state_dict = torch.load(f)
sam.load_state_dict(state_dict)
return sam

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from .sam import Sam
from .image_encoder import ImageEncoderViT
from .mask_decoder import MaskDecoder
from .prompt_encoder import PromptEncoder
from .transformer import TwoWayTransformer

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from typing import Type
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)))
# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa
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

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from .common import LayerNorm2d, MLPBlock
# 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 = 3,
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.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, 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)
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 (int or None): Input resolution for calculating the relative positional
parameter size.
"""
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
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 Attention(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 (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:
x (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 PatchEmbed(nn.Module):
"""
Image to Patch Embedding.
"""
def __init__(
self,
kernel_size: Tuple[int, int] = (16, 16),
stride: Tuple[int, int] = (16, 16),
padding: Tuple[int, int] = (0, 0),
in_chans: int = 3,
embed_dim: int = 768,
) -> None:
"""
Args:
kernel_size (Tuple): kernel size of the projection layer.
stride (Tuple): stride of the projection layer.
padding (Tuple): padding size of the projection layer.
in_chans (int): Number of input image channels.
embed_dim (int): embed_dim (int): Patch embedding dimension.
"""
super().__init__()
self.proj = nn.Conv2d(
in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.proj(x)
# B C H W -> B H W C
x = x.permute(0, 2, 3, 1)
return x

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type
from .common import LayerNorm2d
class MaskDecoder(nn.Module):
def __init__(
self,
*,
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,
) -> None:
"""
Predicts masks given an image and prompt embeddings, using a
tranformer 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)
self.iou_head_depth = iou_head_depth
self.iou_head_hidden_dim = iou_head_hidden_dim
self.output_upscaling = nn.Sequential(
nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
LayerNorm2d(transformer_dim // 4),
activation(),
nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
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
)
def forward(
self,
image_embeddings: 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
"""
masks, iou_pred = self.predict_masks(
image_embeddings=image_embeddings,
image_pe=image_pe,
sparse_prompt_embeddings=sparse_prompt_embeddings,
dense_prompt_embeddings=dense_prompt_embeddings,
)
# Select the correct mask or masks for outptu
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,
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)
# Expand per-image data in batch direction to be per-mask
src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
src = src + dense_prompt_embeddings
pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
b, c, h, w = 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)
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 = upscaled_embedding.shape
masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
# 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

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torch import nn
from typing import Any, Optional, Tuple, Type
from .common import LayerNorm2d
class PromptEncoder(nn.Module):
def __init__(
self,
embed_dim: int,
image_embedding_size: Tuple[int, int],
input_image_size: Tuple[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])
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, 2), 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, 2)
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],
) -> 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]
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],
) -> 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
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)
"""
bs = self._get_batch_size(points, boxes, masks)
sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
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 masks is not None:
dense_embeddings = self._embed_masks(masks)
else:
dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
)
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((2, 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]) -> torch.Tensor:
"""Generate positional encoding for a grid of the specified size."""
h, w = size
device: Any = self.positional_encoding_gaussian_matrix.device
grid = torch.ones((h, w), device=device, dtype=torch.float32)
y_embed = grid.cumsum(dim=0) - 0.5
x_embed = grid.cumsum(dim=1) - 0.5
y_embed = y_embed / h
x_embed = x_embed / w
pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
return pe.permute(2, 0, 1) # C x H x W
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]
return self._pe_encoding(coords.to(torch.float)) # B x N x C

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# modified by ziqi-jin
import torch
from torch import nn
from torch.nn import functional as F
from typing import Any, Dict, List, Tuple
from .image_encoder import ImageEncoderViT
from .mask_decoder import MaskDecoder
from .prompt_encoder import PromptEncoder
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
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 promts,
C is determiend 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
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

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
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 = 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 attenion 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

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from extend_sam.segment_anything_ori.modeling import Sam
from typing import Optional, Tuple
from .utils.transforms import ResizeLongestSide
class SamPredictor:
def __init__(
self,
sam_model: Sam,
) -> None:
"""
Uses SAM to calculate the image embedding for an image, and then
allow repeated, efficient mask prediction given prompts.
Arguments:
sam_model (Sam): The model to use for mask prediction.
"""
super().__init__()
self.model = sam_model
self.transform = ResizeLongestSide(sam_model.image_encoder.img_size)
self.reset_image()
def set_image(
self,
image: np.ndarray,
image_format: str = "RGB",
) -> None:
"""
Calculates the image embeddings for the provided image, allowing
masks to be predicted with the 'predict' method.
Arguments:
image (np.ndarray): The image for calculating masks. Expects an
image in HWC uint8 format, with pixel values in [0, 255].
image_format (str): The color format of the image, in ['RGB', 'BGR'].
"""
assert image_format in [
"RGB",
"BGR",
], f"image_format must be in ['RGB', 'BGR'], is {image_format}."
if image_format != self.model.image_format:
image = image[..., ::-1]
# Transform the image to the form expected by the model
input_image = self.transform.apply_image(image)
input_image_torch = torch.as_tensor(input_image, device=self.device)
input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]
self.set_torch_image(input_image_torch, image.shape[:2])
@torch.no_grad()
def set_torch_image(
self,
transformed_image: torch.Tensor,
original_image_size: Tuple[int, ...],
) -> None:
"""
Calculates the image embeddings for the provided image, allowing
masks to be predicted with the 'predict' method. Expects the input
image to be already transformed to the format expected by the model.
Arguments:
transformed_image (torch.Tensor): The input image, with shape
1x3xHxW, which has been transformed with ResizeLongestSide.
original_image_size (tuple(int, int)): The size of the image
before transformation, in (H, W) format.
"""
assert (
len(transformed_image.shape) == 4
and transformed_image.shape[1] == 3
and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size
), f"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}."
self.reset_image()
self.original_size = original_image_size
self.input_size = tuple(transformed_image.shape[-2:])
input_image = self.model.preprocess(transformed_image)
self.features = self.model.image_encoder(input_image)
self.is_image_set = True
def predict(
self,
point_coords: Optional[np.ndarray] = None,
point_labels: Optional[np.ndarray] = None,
box: Optional[np.ndarray] = None,
mask_input: Optional[np.ndarray] = None,
multimask_output: bool = True,
return_logits: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Predict masks for the given input prompts, using the currently set image.
Arguments:
point_coords (np.ndarray or None): A Nx2 array of point prompts to the
model. Each point is in (X,Y) in pixels.
point_labels (np.ndarray or None): A length N array of labels for the
point prompts. 1 indicates a foreground point and 0 indicates a
background point.
box (np.ndarray or None): A length 4 array given a box prompt to the
model, in XYXY format.
mask_input (np.ndarray): A low resolution mask input to the model, typically
coming from a previous prediction iteration. Has form 1xHxW, where
for SAM, H=W=256.
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.
return_logits (bool): If true, returns un-thresholded masks logits
instead of a binary mask.
Returns:
(np.ndarray): The output masks in CxHxW format, where C is the
number of masks, and (H, W) is the original image size.
(np.ndarray): An array of length C containing the model's
predictions for the quality of each mask.
(np.ndarray): An array of shape CxHxW, where C is the number
of masks and H=W=256. These low resolution logits can be passed to
a subsequent iteration as mask input.
"""
if not self.is_image_set:
raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
# Transform input prompts
coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None
if point_coords is not None:
assert (
point_labels is not None
), "point_labels must be supplied if point_coords is supplied."
point_coords = self.transform.apply_coords(point_coords, self.original_size)
coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device)
labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)
coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :]
if box is not None:
box = self.transform.apply_boxes(box, self.original_size)
box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device)
box_torch = box_torch[None, :]
if mask_input is not None:
mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device)
mask_input_torch = mask_input_torch[None, :, :, :]
masks, iou_predictions, low_res_masks = self.predict_torch(
coords_torch,
labels_torch,
box_torch,
mask_input_torch,
multimask_output,
return_logits=return_logits,
)
masks = masks[0].detach().cpu().numpy()
iou_predictions = iou_predictions[0].detach().cpu().numpy()
low_res_masks = low_res_masks[0].detach().cpu().numpy()
return masks, iou_predictions, low_res_masks
@torch.no_grad()
def predict_torch(
self,
point_coords: Optional[torch.Tensor],
point_labels: Optional[torch.Tensor],
boxes: Optional[torch.Tensor] = None,
mask_input: Optional[torch.Tensor] = None,
multimask_output: bool = True,
return_logits: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Predict masks for the given input prompts, using the currently set image.
Input prompts are batched torch tensors and are expected to already be
transformed to the input frame using ResizeLongestSide.
Arguments:
point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the
model. Each point is in (X,Y) in pixels.
point_labels (torch.Tensor or None): A BxN array of labels for the
point prompts. 1 indicates a foreground point and 0 indicates a
background point.
box (np.ndarray or None): A Bx4 array given a box prompt to the
model, in XYXY format.
mask_input (np.ndarray): A low resolution mask input to the model, typically
coming from a previous prediction iteration. Has form Bx1xHxW, where
for SAM, H=W=256. Masks returned by a previous iteration of the
predict method do not need further transformation.
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.
return_logits (bool): If true, returns un-thresholded masks logits
instead of a binary mask.
Returns:
(torch.Tensor): The output masks in BxCxHxW format, where C is the
number of masks, and (H, W) is the original image size.
(torch.Tensor): An array of shape BxC containing the model's
predictions for the quality of each mask.
(torch.Tensor): An array of shape BxCxHxW, where C is the number
of masks and H=W=256. These low res logits can be passed to
a subsequent iteration as mask input.
"""
if not self.is_image_set:
raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
if point_coords is not None:
points = (point_coords, point_labels)
else:
points = None
# Embed prompts
sparse_embeddings, dense_embeddings = self.model.prompt_encoder(
points=points,
boxes=boxes,
masks=mask_input,
)
# Predict masks
low_res_masks, iou_predictions = self.model.mask_decoder(
image_embeddings=self.features,
image_pe=self.model.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.model.postprocess_masks(low_res_masks, self.input_size, self.original_size)
if not return_logits:
masks = masks > self.model.mask_threshold
return masks, iou_predictions, low_res_masks
def get_image_embedding(self) -> torch.Tensor:
"""
Returns the image embeddings for the currently set image, with
shape 1xCxHxW, where C is the embedding dimension and (H,W) are
the embedding spatial dimension of SAM (typically C=256, H=W=64).
"""
if not self.is_image_set:
raise RuntimeError(
"An image must be set with .set_image(...) to generate an embedding."
)
assert self.features is not None, "Features must exist if an image has been set."
return self.features
@property
def device(self) -> torch.device:
return self.model.device
def reset_image(self) -> None:
"""Resets the currently set image."""
self.is_image_set = False
self.features = None
self.orig_h = None
self.orig_w = None
self.input_h = None
self.input_w = None

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
import math
from copy import deepcopy
from itertools import product
from typing import Any, Dict, Generator, ItemsView, List, Tuple
class MaskData:
"""
A structure for storing masks and their related data in batched format.
Implements basic filtering and concatenation.
"""
def __init__(self, **kwargs) -> None:
for v in kwargs.values():
assert isinstance(
v, (list, np.ndarray, torch.Tensor)
), "MaskData only supports list, numpy arrays, and torch tensors."
self._stats = dict(**kwargs)
def __setitem__(self, key: str, item: Any) -> None:
assert isinstance(
item, (list, np.ndarray, torch.Tensor)
), "MaskData only supports list, numpy arrays, and torch tensors."
self._stats[key] = item
def __delitem__(self, key: str) -> None:
del self._stats[key]
def __getitem__(self, key: str) -> Any:
return self._stats[key]
def items(self) -> ItemsView[str, Any]:
return self._stats.items()
def filter(self, keep: torch.Tensor) -> None:
for k, v in self._stats.items():
if v is None:
self._stats[k] = None
elif isinstance(v, torch.Tensor):
self._stats[k] = v[torch.as_tensor(keep, device=v.device)]
elif isinstance(v, np.ndarray):
self._stats[k] = v[keep.detach().cpu().numpy()]
elif isinstance(v, list) and keep.dtype == torch.bool:
self._stats[k] = [a for i, a in enumerate(v) if keep[i]]
elif isinstance(v, list):
self._stats[k] = [v[i] for i in keep]
else:
raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
def cat(self, new_stats: "MaskData") -> None:
for k, v in new_stats.items():
if k not in self._stats or self._stats[k] is None:
self._stats[k] = deepcopy(v)
elif isinstance(v, torch.Tensor):
self._stats[k] = torch.cat([self._stats[k], v], dim=0)
elif isinstance(v, np.ndarray):
self._stats[k] = np.concatenate([self._stats[k], v], axis=0)
elif isinstance(v, list):
self._stats[k] = self._stats[k] + deepcopy(v)
else:
raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
def to_numpy(self) -> None:
for k, v in self._stats.items():
if isinstance(v, torch.Tensor):
self._stats[k] = v.detach().cpu().numpy()
def is_box_near_crop_edge(
boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0
) -> torch.Tensor:
"""Filter masks at the edge of a crop, but not at the edge of the original image."""
crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)
orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)
boxes = uncrop_boxes_xyxy(boxes, crop_box).float()
near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)
near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)
near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)
return torch.any(near_crop_edge, dim=1)
def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:
box_xywh = deepcopy(box_xyxy)
box_xywh[2] = box_xywh[2] - box_xywh[0]
box_xywh[3] = box_xywh[3] - box_xywh[1]
return box_xywh
def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:
assert len(args) > 0 and all(
len(a) == len(args[0]) for a in args
), "Batched iteration must have inputs of all the same size."
n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)
for b in range(n_batches):
yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]
def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
"""
Encodes masks to an uncompressed RLE, in the format expected by
pycoco tools.
"""
# Put in fortran order and flatten h,w
b, h, w = tensor.shape
tensor = tensor.permute(0, 2, 1).flatten(1)
# Compute change indices
diff = tensor[:, 1:] ^ tensor[:, :-1]
change_indices = diff.nonzero()
# Encode run length
out = []
for i in range(b):
cur_idxs = change_indices[change_indices[:, 0] == i, 1]
cur_idxs = torch.cat(
[
torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),
cur_idxs + 1,
torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device),
]
)
btw_idxs = cur_idxs[1:] - cur_idxs[:-1]
counts = [] if tensor[i, 0] == 0 else [0]
counts.extend(btw_idxs.detach().cpu().tolist())
out.append({"size": [h, w], "counts": counts})
return out
def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
"""Compute a binary mask from an uncompressed RLE."""
h, w = rle["size"]
mask = np.empty(h * w, dtype=bool)
idx = 0
parity = False
for count in rle["counts"]:
mask[idx : idx + count] = parity
idx += count
parity ^= True
mask = mask.reshape(w, h)
return mask.transpose() # Put in C order
def area_from_rle(rle: Dict[str, Any]) -> int:
return sum(rle["counts"][1::2])
def calculate_stability_score(
masks: torch.Tensor, mask_threshold: float, threshold_offset: float
) -> torch.Tensor:
"""
Computes the stability score for a batch of masks. The stability
score is the IoU between the binary masks obtained by thresholding
the predicted mask logits at high and low values.
"""
# One mask is always contained inside the other.
# Save memory by preventing unnecesary cast to torch.int64
intersections = (
(masks > (mask_threshold + threshold_offset))
.sum(-1, dtype=torch.int16)
.sum(-1, dtype=torch.int32)
)
unions = (
(masks > (mask_threshold - threshold_offset))
.sum(-1, dtype=torch.int16)
.sum(-1, dtype=torch.int32)
)
return intersections / unions
def build_point_grid(n_per_side: int) -> np.ndarray:
"""Generates a 2D grid of points evenly spaced in [0,1]x[0,1]."""
offset = 1 / (2 * n_per_side)
points_one_side = np.linspace(offset, 1 - offset, n_per_side)
points_x = np.tile(points_one_side[None, :], (n_per_side, 1))
points_y = np.tile(points_one_side[:, None], (1, n_per_side))
points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2)
return points
def build_all_layer_point_grids(
n_per_side: int, n_layers: int, scale_per_layer: int
) -> List[np.ndarray]:
"""Generates point grids for all crop layers."""
points_by_layer = []
for i in range(n_layers + 1):
n_points = int(n_per_side / (scale_per_layer**i))
points_by_layer.append(build_point_grid(n_points))
return points_by_layer
def generate_crop_boxes(
im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float
) -> Tuple[List[List[int]], List[int]]:
"""
Generates a list of crop boxes of different sizes. Each layer
has (2**i)**2 boxes for the ith layer.
"""
crop_boxes, layer_idxs = [], []
im_h, im_w = im_size
short_side = min(im_h, im_w)
# Original image
crop_boxes.append([0, 0, im_w, im_h])
layer_idxs.append(0)
def crop_len(orig_len, n_crops, overlap):
return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))
for i_layer in range(n_layers):
n_crops_per_side = 2 ** (i_layer + 1)
overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))
crop_w = crop_len(im_w, n_crops_per_side, overlap)
crop_h = crop_len(im_h, n_crops_per_side, overlap)
crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]
crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]
# Crops in XYWH format
for x0, y0 in product(crop_box_x0, crop_box_y0):
box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]
crop_boxes.append(box)
layer_idxs.append(i_layer + 1)
return crop_boxes, layer_idxs
def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
x0, y0, _, _ = crop_box
offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)
# Check if boxes has a channel dimension
if len(boxes.shape) == 3:
offset = offset.unsqueeze(1)
return boxes + offset
def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
x0, y0, _, _ = crop_box
offset = torch.tensor([[x0, y0]], device=points.device)
# Check if points has a channel dimension
if len(points.shape) == 3:
offset = offset.unsqueeze(1)
return points + offset
def uncrop_masks(
masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int
) -> torch.Tensor:
x0, y0, x1, y1 = crop_box
if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:
return masks
# Coordinate transform masks
pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)
pad = (x0, pad_x - x0, y0, pad_y - y0)
return torch.nn.functional.pad(masks, pad, value=0)
def remove_small_regions(
mask: np.ndarray, area_thresh: float, mode: str
) -> Tuple[np.ndarray, bool]:
"""
Removes small disconnected regions and holes in a mask. Returns the
mask and an indicator of if the mask has been modified.
"""
import cv2 # type: ignore
assert mode in ["holes", "islands"]
correct_holes = mode == "holes"
working_mask = (correct_holes ^ mask).astype(np.uint8)
n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
sizes = stats[:, -1][1:] # Row 0 is background label
small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
if len(small_regions) == 0:
return mask, False
fill_labels = [0] + small_regions
if not correct_holes:
fill_labels = [i for i in range(n_labels) if i not in fill_labels]
# If every region is below threshold, keep largest
if len(fill_labels) == 0:
fill_labels = [int(np.argmax(sizes)) + 1]
mask = np.isin(regions, fill_labels)
return mask, True
def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:
from pycocotools import mask as mask_utils # type: ignore
h, w = uncompressed_rle["size"]
rle = mask_utils.frPyObjects(uncompressed_rle, h, w)
rle["counts"] = rle["counts"].decode("utf-8") # Necessary to serialize with json
return rle
def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:
"""
Calculates boxes in XYXY format around masks. Return [0,0,0,0] for
an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4.
"""
# torch.max below raises an error on empty inputs, just skip in this case
if torch.numel(masks) == 0:
return torch.zeros(*masks.shape[:-2], 4, device=masks.device)
# Normalize shape to CxHxW
shape = masks.shape
h, w = shape[-2:]
if len(shape) > 2:
masks = masks.flatten(0, -3)
else:
masks = masks.unsqueeze(0)
# Get top and bottom edges
in_height, _ = torch.max(masks, dim=-1)
in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]
bottom_edges, _ = torch.max(in_height_coords, dim=-1)
in_height_coords = in_height_coords + h * (~in_height)
top_edges, _ = torch.min(in_height_coords, dim=-1)
# Get left and right edges
in_width, _ = torch.max(masks, dim=-2)
in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]
right_edges, _ = torch.max(in_width_coords, dim=-1)
in_width_coords = in_width_coords + w * (~in_width)
left_edges, _ = torch.min(in_width_coords, dim=-1)
# If the mask is empty the right edge will be to the left of the left edge.
# Replace these boxes with [0, 0, 0, 0]
empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)
out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)
out = out * (~empty_filter).unsqueeze(-1)
# Return to original shape
if len(shape) > 2:
out = out.reshape(*shape[:-2], 4)
else:
out = out[0]
return out

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from torch.nn import functional as F
from typing import Tuple
from ..modeling import Sam
from .amg import calculate_stability_score
class SamOnnxModel(nn.Module):
"""
This model should not be called directly, but is used in ONNX export.
It combines the prompt encoder, mask decoder, and mask postprocessing of Sam,
with some functions modified to enable model tracing. Also supports extra
options controlling what information. See the ONNX export script for details.
"""
def __init__(
self,
model: Sam,
return_single_mask: bool,
use_stability_score: bool = False,
return_extra_metrics: bool = False,
) -> None:
super().__init__()
self.mask_decoder = model.mask_decoder
self.model = model
self.img_size = model.image_encoder.img_size
self.return_single_mask = return_single_mask
self.use_stability_score = use_stability_score
self.stability_score_offset = 1.0
self.return_extra_metrics = return_extra_metrics
@staticmethod
def resize_longest_image_size(
input_image_size: torch.Tensor, longest_side: int
) -> torch.Tensor:
input_image_size = input_image_size.to(torch.float32)
scale = longest_side / torch.max(input_image_size)
transformed_size = scale * input_image_size
transformed_size = torch.floor(transformed_size + 0.5).to(torch.int64)
return transformed_size
def _embed_points(self, point_coords: torch.Tensor, point_labels: torch.Tensor) -> torch.Tensor:
point_coords = point_coords + 0.5
point_coords = point_coords / self.img_size
point_embedding = self.model.prompt_encoder.pe_layer._pe_encoding(point_coords)
point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding)
point_embedding = point_embedding * (point_labels != -1)
point_embedding = point_embedding + self.model.prompt_encoder.not_a_point_embed.weight * (
point_labels == -1
)
for i in range(self.model.prompt_encoder.num_point_embeddings):
point_embedding = point_embedding + self.model.prompt_encoder.point_embeddings[
i
].weight * (point_labels == i)
return point_embedding
def _embed_masks(self, input_mask: torch.Tensor, has_mask_input: torch.Tensor) -> torch.Tensor:
mask_embedding = has_mask_input * self.model.prompt_encoder.mask_downscaling(input_mask)
mask_embedding = mask_embedding + (
1 - has_mask_input
) * self.model.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1)
return mask_embedding
def mask_postprocessing(self, masks: torch.Tensor, orig_im_size: torch.Tensor) -> torch.Tensor:
masks = F.interpolate(
masks,
size=(self.img_size, self.img_size),
mode="bilinear",
align_corners=False,
)
prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size).to(torch.int64)
masks = masks[..., : prepadded_size[0], : prepadded_size[1]]
orig_im_size = orig_im_size.to(torch.int64)
h, w = orig_im_size[0], orig_im_size[1]
masks = F.interpolate(masks, size=(h, w), mode="bilinear", align_corners=False)
return masks
def select_masks(
self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int
) -> Tuple[torch.Tensor, torch.Tensor]:
# Determine if we should return the multiclick mask or not from the number of points.
# The reweighting is used to avoid control flow.
score_reweight = torch.tensor(
[[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)]
).to(iou_preds.device)
score = iou_preds + (num_points - 2.5) * score_reweight
best_idx = torch.argmax(score, dim=1)
masks = masks[torch.arange(masks.shape[0]), best_idx, :, :].unsqueeze(1)
iou_preds = iou_preds[torch.arange(masks.shape[0]), best_idx].unsqueeze(1)
return masks, iou_preds
@torch.no_grad()
def forward(
self,
image_embeddings: torch.Tensor,
point_coords: torch.Tensor,
point_labels: torch.Tensor,
mask_input: torch.Tensor,
has_mask_input: torch.Tensor,
orig_im_size: torch.Tensor,
):
sparse_embedding = self._embed_points(point_coords, point_labels)
dense_embedding = self._embed_masks(mask_input, has_mask_input)
masks, scores = self.model.mask_decoder.predict_masks(
image_embeddings=image_embeddings,
image_pe=self.model.prompt_encoder.get_dense_pe(),
sparse_prompt_embeddings=sparse_embedding,
dense_prompt_embeddings=dense_embedding,
)
if self.use_stability_score:
scores = calculate_stability_score(
masks, self.model.mask_threshold, self.stability_score_offset
)
if self.return_single_mask:
masks, scores = self.select_masks(masks, scores, point_coords.shape[1])
upscaled_masks = self.mask_postprocessing(masks, orig_im_size)
if self.return_extra_metrics:
stability_scores = calculate_stability_score(
upscaled_masks, self.model.mask_threshold, self.stability_score_offset
)
areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1)
return upscaled_masks, scores, stability_scores, areas, masks
return upscaled_masks, scores, masks

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torch.nn import functional as F
from torchvision.transforms.functional import resize, to_pil_image # type: ignore
from copy import deepcopy
from typing import Tuple
class ResizeLongestSide:
"""
Resizes images to longest side 'target_length', as well as provides
methods for resizing coordinates and boxes. Provides methods for
transforming both numpy array and batched torch tensors.
"""
def __init__(self, target_length: int) -> None:
self.target_length = target_length
def apply_image(self, image: np.ndarray) -> np.ndarray:
"""
Expects a numpy array with shape HxWxC in uint8 format.
"""
target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
return np.array(resize(to_pil_image(image), target_size))
def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
"""
Expects a numpy array of length 2 in the final dimension. Requires the
original image size in (H, W) format.
"""
old_h, old_w = original_size
new_h, new_w = self.get_preprocess_shape(
original_size[0], original_size[1], self.target_length
)
coords = deepcopy(coords).astype(float)
coords[..., 0] = coords[..., 0] * (new_w / old_w)
coords[..., 1] = coords[..., 1] * (new_h / old_h)
return coords
def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
"""
Expects a numpy array shape Bx4. Requires the original image size
in (H, W) format.
"""
boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)
return boxes.reshape(-1, 4)
def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:
"""
Expects batched images with shape BxCxHxW and float format. This
transformation may not exactly match apply_image. apply_image is
the transformation expected by the model.
"""
# Expects an image in BCHW format. May not exactly match apply_image.
target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
return F.interpolate(
image, target_size, mode="bilinear", align_corners=False, antialias=True
)
def apply_coords_torch(
self, coords: torch.Tensor, original_size: Tuple[int, ...]
) -> torch.Tensor:
"""
Expects a torch tensor with length 2 in the last dimension. Requires the
original image size in (H, W) format.
"""
old_h, old_w = original_size
new_h, new_w = self.get_preprocess_shape(
original_size[0], original_size[1], self.target_length
)
coords = deepcopy(coords).to(torch.float)
coords[..., 0] = coords[..., 0] * (new_w / old_w)
coords[..., 1] = coords[..., 1] * (new_h / old_h)
return coords
def apply_boxes_torch(
self, boxes: torch.Tensor, original_size: Tuple[int, ...]
) -> torch.Tensor:
"""
Expects a torch tensor with shape Bx4. Requires the original image
size in (H, W) format.
"""
boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)
return boxes.reshape(-1, 4)
@staticmethod
def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]:
"""
Compute the output size given input size and target long side length.
"""
scale = long_side_length * 1.0 / max(oldh, oldw)
newh, neww = oldh * scale, oldw * scale
neww = int(neww + 0.5)
newh = int(newh + 0.5)
return (newh, neww)

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'''
@copyright ziqi-jin
'''
import time
import numpy as np
import torch
import torch.nn.functional as F
import os.path as osp
import os
def fix_params(model):
for name, param in model.named_parameters():
param.requires_grad = False
def load_params(model, params):
pass
def get_opt_pamams(model, lr_list, group_keys, wd_list):
'''
:param model: model
:param lr_list: list, contain the lr for each params group
:param wd_list: list, contain the weight decay for each params group
:param group_keys: list of list, according to the sub list to divide params to different groups
:return: list of dict
'''
assert len(lr_list) == len(group_keys), "lr_list should has the same length as group_keys"
assert len(lr_list) == len(wd_list), "lr_list should has the same length as wd_list"
params_group = [[] for _ in range(len(lr_list))]
for name, value in model.named_parameters():
for index, g_keys in enumerate(group_keys):
for g_key in g_keys:
if g_key in name:
params_group[index].append(value)
return [{'params': params_group[i], 'lr': lr_list[i], 'weight_decay': wd_list[i]} for i in range(len(lr_list))]
class Timer:
def __init__(self):
self.start_time = 0.0
self.end_time = 0.0
self.start()
def start(self):
self.start_time = time.time()
def end(self, ms=False, clear=False):
self.end_time = time.time()
if ms:
duration = int((self.end_time - self.start_time) * 1000)
else:
duration = int(self.end_time - self.start_time)
if clear:
self.start()
return duration
class Average_Meter:
def __init__(self, keys):
self.keys = keys
self.clear()
def add(self, dic):
for key, value in dic.items():
self.data_dic[key].append(value)
def get(self, keys=None, clear=False):
if keys is None:
keys = self.keys
dataset = {}
for key in keys:
dataset[key] = float(np.mean(self.data_dic[key]))
if clear:
self.clear()
return dataset
def clear(self):
self.data_dic = {key: [] for key in self.keys}
def print_and_save_log(message, path):
print(message)
with open(path, 'a+') as f:
f.write(message + '\n')
class mIoUOnline:
def __init__(self, class_names):
self.class_names = class_names
self.class_num = len(self.class_names)
self.clear()
def get_data(self, pred_mask, gt_mask):
obj_mask = gt_mask < 255
correct_mask = (pred_mask == gt_mask) * obj_mask
P_list, T_list, TP_list = [], [], []
for i in range(self.class_num):
P_list.append(np.sum((pred_mask == i) * obj_mask))
T_list.append(np.sum((gt_mask == i) * obj_mask))
TP_list.append(np.sum((gt_mask == i) * correct_mask))
return (P_list, T_list, TP_list)
def add_using_data(self, data):
P_list, T_list, TP_list = data
for i in range(self.class_num):
self.P[i] += P_list[i]
self.T[i] += T_list[i]
self.TP[i] += TP_list[i]
def add(self, pred_mask, gt_mask):
obj_mask = gt_mask < 255
correct_mask = (pred_mask == gt_mask) * obj_mask
for i in range(self.class_num):
self.P[i] += np.sum((pred_mask == i) * obj_mask)
self.T[i] += np.sum((gt_mask == i) * obj_mask)
self.TP[i] += np.sum((gt_mask == i) * correct_mask)
def get(self, detail=False, clear=True):
IoU_dic = {}
IoU_list = []
FP_list = [] # over activation
FN_list = [] # under activation
for i in range(self.class_num):
IoU = self.TP[i] / (self.T[i] + self.P[i] - self.TP[i] + 1e-10) * 100
FP = (self.P[i] - self.TP[i]) / (self.T[i] + self.P[i] - self.TP[i] + 1e-10)
FN = (self.T[i] - self.TP[i]) / (self.T[i] + self.P[i] - self.TP[i] + 1e-10)
IoU_dic[self.class_names[i]] = IoU
IoU_list.append(IoU)
FP_list.append(FP)
FN_list.append(FN)
mIoU = np.mean(np.asarray(IoU_list))
mIoU_foreground = np.mean(np.asarray(IoU_list)[1:])
FP = np.mean(np.asarray(FP_list))
FN = np.mean(np.asarray(FN_list))
if clear:
self.clear()
if detail:
return mIoU, mIoU_foreground, IoU_dic, FP, FN
else:
return mIoU, mIoU_foreground
def clear(self):
self.TP = []
self.P = []
self.T = []
for _ in range(self.class_num):
self.TP.append(0)
self.P.append(0)
self.T.append(0)
def get_numpy_from_tensor(tensor):
return tensor.cpu().detach().numpy()
def save_model(model, model_path, parallel=False, is_final=False):
if is_final:
model_path_split = model_path.split('.')
model_path = model_path_split[0] + "_final.pth"
if parallel:
torch.save(model.module.state_dict(), model_path)
else:
torch.save(model.state_dict(), model_path)
def write_log(iteration, log_path, log_data, status, writer, timer):
log_data['iteration'] = iteration
log_data['time'] = timer.end(clear=True)
message = "iteration : {val}, ".format(val=log_data['iteration'])
for key, value in log_data.items():
if key == 'iteration':
continue
message += "{key} : {val}, ".format(key=key, val=value)
message = message[:-2] # + '\n'
print_and_save_log(message, log_path)
# visualize
if writer is not None:
for key, value in log_data.items():
writer.add_scalar("{status}/{key}".format(status=status, key=key), value, iteration)
def check_folder(file_path, is_folder=False):
'''
:param file_path: the path of file, default input is a complete file name with dir path.
:param is_folder: if the input is a dir, not a file_name, is_folder should be True
:return: no return, this function will check and judge whether need to make dirs.
'''
if is_folder:
if not osp.exists(is_folder):
os.makedirs(file_path)
else:
splits = file_path.split("/")
folder_name = "/".join(splits[:-1])
if not osp.exists(folder_name):
os.makedirs(folder_name)
def one_hot_embedding_3d(labels, class_num=21):
'''
:param real_labels: B H W
:param class_num: N
:return: B N H W
'''
one_hot_labels = labels.clone()
one_hot_labels[one_hot_labels == 255] = 0 # 0 is background
return F.one_hot(one_hot_labels, num_classes=class_num).permute(0, 3, 1, 2).contiguous().float()

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import torch.nn as nn
from .losses import CustormLoss
AVAI_LOSS = {'ce': nn.CrossEntropyLoss, 'multi_label_soft_margin': nn.MultiLabelSoftMarginLoss,
'test_custom': CustormLoss, 'mse': nn.MSELoss}
def get_losses(losses):
loss_dict = {}
for name in losses:
assert name in AVAI_LOSS, print('{name} is not supported, please implement it first.'.format(name=name))
if losses[name].params is not None:
loss_dict[name] = AVAI_LOSS[name](**losses[name].params)
else:
loss_dict[name] = AVAI_LOSS[name]()
return loss_dict

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'''
@copyright ziqi-jin
You can create custom loss function in this file, then import the created loss in ./__init__.py and add the loss into AVAI_LOSS
'''
import torch.nn as nn
# example
class CustormLoss(nn.Module):
def __init__(self):
pass
def forward(self, x, y):
pass