generated from yuanbiao/python_templates
Compare commits
16 Commits
| Author | SHA1 | Date |
|---|---|---|
jixingyue
|
4f01407d2c | |
|
jixingyue
|
528e85b659 | |
|
jixingyue
|
b63a3eb007 | |
|
jixingyue
|
1ced80e461 | |
|
jixingyue
|
744a417618 | |
|
jixingyue
|
94c2d348b7 | |
|
jixingyue
|
09e9854707 | |
|
jixingyue
|
e4377404c5 | |
|
jixingyue
|
ea7d09dcc2 | |
|
jixingyue
|
3ce7a9704f | |
|
jixingyue
|
44adc2fbd3 | |
|
jixingyue
|
6062510fb4 | |
|
jixingyue
|
98c14fe93c | |
|
jixingyue
|
18e8bd48ec | |
|
jixingyue
|
8880f4ac76 | |
|
jixingyue
|
39b49ad8b5 |
79
README.md
79
README.md
|
|
@ -1,2 +1,79 @@
|
|||
# python_templates
|
||||
# 简单说明
|
||||
|
||||
1.sam_ckpt是预训练权重，由于文件过大，文件在公共资料盘02-视觉算法\laser_weeding\sam_ckpt下
|
||||
|
||||
2.训练出的不同格式的权重放在公共资料盘02-视觉算法\laser_weeding\训练出的权重
|
||||
|
||||
3.历次采集数据集放在公共资料盘02-视觉算法\laser_weeding\datasets下
|
||||
|
||||
4.为防止混乱，训练出的权重命名和历次采集数据集命名一致
|
||||
|
||||
# Introduction
|
||||
|
||||
The [Segment Anything Model (SAM)](https://github.com/facebookresearch/segment-anything) has revolutionized computer vision. Relying on fine-tuning of SAM will solve a large number of basic computer vision tasks. We are designing a **class-aware one-stage** tool for training fine-tuning models based on SAM.
|
||||
|
||||
You need to supply the datasets for your tasks and the [supported task](#Supported-Tasks) name, this tool will help you to get a finetuned model for your task. You are also allowed to design your own extend-SAM model, and FA supply the training, testing and deploy process for you.
|
||||
|
||||
<img width="640" style="display: block; margin: 0 auto;" src="https://user-images.githubusercontent.com/67993288/230864865-db8810fd-9f0c-4f3e-81b1-8753b5121d03.png">
|
||||
|
||||
## Design
|
||||
Finetune-Anything further encapsulates the three parts of the original SAM, i.e., Image Encoder Adapter, Prompt Encoder Adapter, and Mask Decoder Adatper. We will support the base extend-SAM model for each task. Users also could design your own customized modules in each adapter, use FA to design different adapters, and set whether the parameters of any module are fixed. For modules with unfixed parameters, parameters such as `lr`, `weight decay` can be set to coordinate with the fine-tuning of the model.
|
||||
check details in [How_to_use](https://github.com/ziqi-jin/finetune-anything/blob/main/how_to_use_finetune_anything.md).
|
||||
For example, MaskDecoder is encapsulated as MaskDecoderAdapter. The current MaskDecoderAdatper contains two parts, DecoderNeck and DecoderHead.
|
||||
|
||||
<img width="640" style="display: block; margin: 0 auto;" src="https://user-images.githubusercontent.com/67993288/244574810-db9a50ad-4082-4647-8b91-7a261f5aad40.svg">
|
||||
|
||||
## Supported Tasks
|
||||
- [x] Semantic Segmentation
|
||||
- [x] train
|
||||
- [x] eval
|
||||
- [ ] test
|
||||
- [ ] Matting
|
||||
- [ ] Instance Segmentation
|
||||
- [ ] Detection
|
||||
## Supported Datasets
|
||||
- [x] TorchVOCSegmentation
|
||||
- [x] BaseSemantic
|
||||
- [ ] BaseInstance
|
||||
- [ ] BaseMatting
|
||||
|
||||
## Deploy
|
||||
- [ ] Onnx export
|
||||
|
||||
## Support Plan
|
||||
FA will be updated in the following order,
|
||||
|
||||
- Mattng (task)
|
||||
- Prompt Part (structure)
|
||||
- [MobileSAM](https://github.com/ChaoningZhang/MobileSAM) (model)
|
||||
- Instance Segmentation (task)
|
||||
|
||||
# Usage
|
||||
finetune-anything(FA) supports the entire training process of SAM model fine-tuning, including the modification of the model structure, as well as the model training, verification, and testing processes. For details, check the [How_to_use](https://github.com/ziqi-jin/finetune-anything/blob/main/how_to_use_finetune_anything.md), the [Quick Start](#Quick-Start) gives an example of quickly using FA to train a custom semantic segmentation model.
|
||||
## Quick Start
|
||||
### Install
|
||||
- Step1
|
||||
```
|
||||
git clone https://git.sweetai.cn/jixingyue/laser_weeding.git
|
||||
cd laser_weeding
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
- Step2
|
||||
Download the SAM weights from [SAM repository](https://github.com/facebookresearch/segment-anything#model-checkpoints)
|
||||
|
||||
- Step3
|
||||
Modify the contents of yaml file for the specific task in **/config**, e.g., ckpt_path, model_type ...
|
||||
|
||||
### Train
|
||||
```
|
||||
CUDA_VISIBLE_DEVICES=${your GPU number} python train.py --task_name semantic_seg
|
||||
```
|
||||
|
||||
## One more thing
|
||||
|
||||
If you need to use loss, dataset, or other functions that are not supported by FA, please submit an issue, and I will help you to implement them. At the same time, developers are also welcome to develop new loss, dataset or other new functions for FA, please submit your PR (pull requests).
|
||||
|
||||
## Related Resources
|
||||
|
||||
- [Documents](https://github.com/ziqi-jin/finetune-anything/blob/main/how_to_use_finetune_anything.md)
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,95 @@
|
|||
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
|
||||
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
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
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
from torch.utils.data import Dataset
|
||||
|
||||
|
||||
class BaseDetectionDataset(Dataset):
|
||||
def __init__(self):
|
||||
assert False, print('BaseDetectionDataset is not Unimplemented.')
|
||||
|
||||
def __getitem__(self, item):
|
||||
pass
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
from torch.utils.data import Dataset
|
||||
|
||||
|
||||
class BaseInstanceDataset(Dataset):
|
||||
def __init__(self):
|
||||
assert False, print("Unimplement Dataset.")
|
||||
|
||||
def __getitem__(self, item):
|
||||
pass
|
||||
|
|
@ -0,0 +1,103 @@
|
|||
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)
|
||||
|
||||
|
|
@ -0,0 +1,265 @@
|
|||
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
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
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
|
||||
|
|
@ -0,0 +1,312 @@
|
|||
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
|
||||
iteration : 499, ce : 0.08710500337183476, total_loss : 0.04355250168591738, time : 3
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 499, mIoU : 91.15427718021175, best_valid_mIoU : 91.15427718021175, time : 57
|
||||
iteration : 519, ce : 0.10585700683295726, total_loss : 0.05292850341647863, time : 61
|
||||
iteration : 539, ce : 0.10250990018248558, total_loss : 0.05125495009124279, time : 4
|
||||
iteration : 559, ce : 0.10396505333483219, total_loss : 0.051982526667416096, time : 4
|
||||
iteration : 579, ce : 0.1050586698576808, total_loss : 0.0525293349288404, time : 3
|
||||
iteration : 599, ce : 0.10245818942785263, total_loss : 0.05122909471392632, time : 4
|
||||
iteration : 599, mIoU : 89.07530754179987, best_valid_mIoU : 91.15427718021175, time : 56
|
||||
iteration : 619, ce : 0.10242737587541342, total_loss : 0.05121368793770671, time : 60
|
||||
iteration : 639, ce : 0.11541221737861633, total_loss : 0.057706108689308165, time : 3
|
||||
iteration : 659, ce : 0.08750226031988859, total_loss : 0.04375113015994429, time : 4
|
||||
iteration : 679, ce : 0.10053982809185982, total_loss : 0.05026991404592991, time : 4
|
||||
iteration : 699, ce : 0.10735129471868277, total_loss : 0.05367564735934138, time : 4
|
||||
iteration : 699, mIoU : 90.4541198653171, best_valid_mIoU : 91.15427718021175, time : 65
|
||||
iteration : 719, ce : 0.08845936376601457, total_loss : 0.044229681883007285, time : 69
|
||||
iteration : 739, ce : 0.09841484446078538, total_loss : 0.04920742223039269, time : 4
|
||||
iteration : 19, ce : 1.6118955180048942, total_loss : 0.8059477590024471, time : 10
|
||||
iteration : 39, ce : 0.6238165870308876, total_loss : 0.3119082935154438, time : 10
|
||||
iteration : 59, ce : 0.5466279983520508, total_loss : 0.2733139991760254, time : 10
|
||||
iteration : 79, ce : 0.3118415541946888, total_loss : 0.1559207770973444, time : 10
|
||||
iteration : 99, ce : 0.22329067587852477, total_loss : 0.11164533793926239, time : 10
|
||||
iteration : 19, ce : 1.6118955209851265, total_loss : 0.8059477604925632, time : 10
|
||||
iteration : 39, ce : 0.6238171197474003, total_loss : 0.31190855987370014, time : 10
|
||||
iteration : 59, ce : 0.546618615090847, total_loss : 0.2733093075454235, time : 10
|
||||
iteration : 79, ce : 0.31183895096182823, total_loss : 0.15591947548091412, time : 10
|
||||
iteration : 99, ce : 0.22327864803373815, total_loss : 0.11163932401686907, time : 10
|
||||
iteration : 19, ce : 1.6118965715169906, total_loss : 0.8059482857584953, time : 10
|
||||
iteration : 39, ce : 0.6238209880888462, total_loss : 0.3119104940444231, time : 10
|
||||
iteration : 59, ce : 0.5466369971632957, total_loss : 0.27331849858164786, time : 10
|
||||
iteration : 79, ce : 0.31184642761945724, total_loss : 0.15592321380972862, time : 10
|
||||
iteration : 99, ce : 0.22328564003109933, total_loss : 0.11164282001554966, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 99, mIoU : 76.15534017940087, best_valid_mIoU : 76.15534017940087, time : 43
|
||||
iteration : 119, ce : 0.1746383562684059, total_loss : 0.08731917813420295, time : 53
|
||||
iteration : 139, ce : 0.1467339999973774, total_loss : 0.0733669999986887, time : 10
|
||||
iteration : 159, ce : 0.12853854857385158, total_loss : 0.06426927428692579, time : 10
|
||||
iteration : 179, ce : 0.12929687201976775, total_loss : 0.06464843600988388, time : 10
|
||||
iteration : 199, ce : 0.12117353715002536, total_loss : 0.06058676857501268, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 199, mIoU : 89.77212887720785, best_valid_mIoU : 89.77212887720785, time : 44
|
||||
iteration : 219, ce : 0.12305688261985778, total_loss : 0.06152844130992889, time : 54
|
||||
iteration : 239, ce : 0.11886226013302803, total_loss : 0.059431130066514015, time : 10
|
||||
iteration : 259, ce : 0.13031740970909594, total_loss : 0.06515870485454797, time : 10
|
||||
iteration : 279, ce : 0.1261220879852772, total_loss : 0.0630610439926386, time : 10
|
||||
iteration : 299, ce : 0.11300399377942086, total_loss : 0.05650199688971043, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 299, mIoU : 90.50859210757906, best_valid_mIoU : 90.50859210757906, time : 44
|
||||
iteration : 319, ce : 0.13202827107161283, total_loss : 0.06601413553580641, time : 54
|
||||
iteration : 339, ce : 0.10633355155587196, total_loss : 0.05316677577793598, time : 10
|
||||
iteration : 359, ce : 0.11914260871708393, total_loss : 0.059571304358541965, time : 10
|
||||
iteration : 379, ce : 0.10447845719754696, total_loss : 0.05223922859877348, time : 10
|
||||
iteration : 399, ce : 0.10292214751243592, total_loss : 0.05146107375621796, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 399, mIoU : 91.10752328387134, best_valid_mIoU : 91.10752328387134, time : 44
|
||||
iteration : 419, ce : 0.11132022961974145, total_loss : 0.05566011480987072, time : 54
|
||||
iteration : 439, ce : 0.11224379669874907, total_loss : 0.05612189834937453, time : 10
|
||||
iteration : 459, ce : 0.09896511361002922, total_loss : 0.04948255680501461, time : 10
|
||||
iteration : 479, ce : 0.09913789071142673, total_loss : 0.049568945355713365, time : 10
|
||||
iteration : 499, ce : 0.1061447437852621, total_loss : 0.05307237189263105, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 499, mIoU : 91.4555279922262, best_valid_mIoU : 91.4555279922262, time : 44
|
||||
iteration : 519, ce : 0.12007921487092972, total_loss : 0.06003960743546486, time : 54
|
||||
iteration : 539, ce : 0.10178584884852171, total_loss : 0.050892924424260855, time : 10
|
||||
iteration : 559, ce : 0.11588475815951824, total_loss : 0.05794237907975912, time : 10
|
||||
iteration : 579, ce : 0.09687731992453337, total_loss : 0.048438659962266685, time : 10
|
||||
iteration : 599, ce : 0.10488986857235431, total_loss : 0.05244493428617716, time : 10
|
||||
iteration : 599, mIoU : 91.27345932141915, best_valid_mIoU : 91.4555279922262, time : 43
|
||||
iteration : 619, ce : 0.10749562252312898, total_loss : 0.05374781126156449, time : 53
|
||||
iteration : 639, ce : 0.12049341723322868, total_loss : 0.06024670861661434, time : 10
|
||||
iteration : 659, ce : 0.1019530326128006, total_loss : 0.0509765163064003, time : 10
|
||||
iteration : 679, ce : 0.09267976079136134, total_loss : 0.04633988039568067, time : 10
|
||||
iteration : 699, ce : 0.10727790277451277, total_loss : 0.053638951387256384, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 699, mIoU : 91.86473461538597, best_valid_mIoU : 91.86473461538597, time : 44
|
||||
iteration : 719, ce : 0.08669246602803468, total_loss : 0.04334623301401734, time : 54
|
||||
iteration : 739, ce : 0.11301723029464483, total_loss : 0.056508615147322416, time : 10
|
||||
iteration : 759, ce : 0.09895374476909638, total_loss : 0.04947687238454819, time : 10
|
||||
iteration : 779, ce : 0.10607226043939591, total_loss : 0.053036130219697955, time : 10
|
||||
iteration : 799, ce : 0.09932096153497696, total_loss : 0.04966048076748848, time : 10
|
||||
iteration : 799, mIoU : 90.76968357127359, best_valid_mIoU : 91.86473461538597, time : 44
|
||||
iteration : 819, ce : 0.09316363576799631, total_loss : 0.04658181788399816, time : 54
|
||||
iteration : 839, ce : 0.10023958738893271, total_loss : 0.050119793694466355, time : 10
|
||||
iteration : 859, ce : 0.09565037228167057, total_loss : 0.04782518614083529, time : 10
|
||||
iteration : 879, ce : 0.10756326355040073, total_loss : 0.053781631775200366, time : 10
|
||||
iteration : 19, ce : 1.4597653925418854, total_loss : 0.7298826962709427, time : 10
|
||||
iteration : 39, ce : 0.7552479837089777, total_loss : 0.37762399185448886, time : 10
|
||||
iteration : 59, ce : 0.5629064556211233, total_loss : 0.28145322781056165, time : 10
|
||||
iteration : 79, ce : 0.3730143416672945, total_loss : 0.18650717083364726, time : 10
|
||||
iteration : 99, ce : 0.2633991166949272, total_loss : 0.1316995583474636, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 99, mIoU : 45.60033837143256, best_valid_mIoU : 45.60033837143256, time : 41
|
||||
iteration : 119, ce : 0.21824998827651143, total_loss : 0.10912499413825572, time : 52
|
||||
iteration : 139, ce : 0.24961501657962798, total_loss : 0.12480750828981399, time : 10
|
||||
iteration : 159, ce : 0.24858376793563366, total_loss : 0.12429188396781683, time : 10
|
||||
iteration : 179, ce : 0.19755737725645303, total_loss : 0.09877868862822652, time : 10
|
||||
iteration : 199, ce : 0.1627231553196907, total_loss : 0.08136157765984535, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 199, mIoU : 68.10482110317365, best_valid_mIoU : 68.10482110317365, time : 42
|
||||
iteration : 219, ce : 0.11887449622154236, total_loss : 0.05943724811077118, time : 52
|
||||
iteration : 239, ce : 0.08812281191349029, total_loss : 0.044061405956745146, time : 10
|
||||
iteration : 259, ce : 0.08320211656391621, total_loss : 0.041601058281958106, time : 10
|
||||
iteration : 279, ce : 0.0784364627674222, total_loss : 0.0392182313837111, time : 10
|
||||
iteration : 299, ce : 0.08906380720436573, total_loss : 0.044531903602182864, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 299, mIoU : 85.81882754923308, best_valid_mIoU : 85.81882754923308, time : 42
|
||||
iteration : 319, ce : 0.06990350810810923, total_loss : 0.034951754054054616, time : 52
|
||||
iteration : 339, ce : 0.06432983251288533, total_loss : 0.03216491625644267, time : 10
|
||||
iteration : 359, ce : 0.07002460584044456, total_loss : 0.03501230292022228, time : 10
|
||||
iteration : 379, ce : 0.0986309826374054, total_loss : 0.0493154913187027, time : 10
|
||||
iteration : 399, ce : 0.10583090535365045, total_loss : 0.05291545267682522, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 399, mIoU : 88.4803214947598, best_valid_mIoU : 88.4803214947598, time : 42
|
||||
iteration : 419, ce : 0.06240550028160215, total_loss : 0.031202750140801074, time : 52
|
||||
iteration : 439, ce : 0.08079780722036958, total_loss : 0.04039890361018479, time : 10
|
||||
iteration : 459, ce : 0.05519880400970578, total_loss : 0.02759940200485289, time : 10
|
||||
iteration : 479, ce : 0.05341993579640984, total_loss : 0.02670996789820492, time : 10
|
||||
iteration : 499, ce : 0.06429818458855152, total_loss : 0.03214909229427576, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 499, mIoU : 89.647283352165, best_valid_mIoU : 89.647283352165, time : 42
|
||||
iteration : 519, ce : 0.05206382665783167, total_loss : 0.026031913328915836, time : 53
|
||||
iteration : 539, ce : 0.07656390639021993, total_loss : 0.03828195319510996, time : 10
|
||||
iteration : 559, ce : 0.06323499651625752, total_loss : 0.03161749825812876, time : 10
|
||||
iteration : 579, ce : 0.05692016114480793, total_loss : 0.028460080572403967, time : 10
|
||||
iteration : 599, ce : 0.06588180274702608, total_loss : 0.03294090137351304, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 599, mIoU : 89.88607999691865, best_valid_mIoU : 89.88607999691865, time : 42
|
||||
iteration : 619, ce : 0.0627711565233767, total_loss : 0.03138557826168835, time : 52
|
||||
iteration : 639, ce : 0.04458166812546551, total_loss : 0.022290834062732755, time : 10
|
||||
iteration : 659, ce : 0.05658222446218133, total_loss : 0.028291112231090664, time : 10
|
||||
iteration : 679, ce : 0.04089747462421656, total_loss : 0.02044873731210828, time : 10
|
||||
iteration : 699, ce : 0.07494942974299193, total_loss : 0.037474714871495965, time : 10
|
||||
iteration : 699, mIoU : 87.9944159611403, best_valid_mIoU : 89.88607999691865, time : 42
|
||||
iteration : 719, ce : 0.06946341348811984, total_loss : 0.03473170674405992, time : 52
|
||||
iteration : 739, ce : 0.09376809406094253, total_loss : 0.04688404703047126, time : 10
|
||||
iteration : 759, ce : 0.06281587863340973, total_loss : 0.031407939316704866, time : 10
|
||||
iteration : 779, ce : 0.049504976719617844, total_loss : 0.024752488359808922, time : 10
|
||||
iteration : 799, ce : 0.06230988763272762, total_loss : 0.03115494381636381, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 799, mIoU : 90.37318020387082, best_valid_mIoU : 90.37318020387082, time : 42
|
||||
iteration : 819, ce : 0.06486173206940293, total_loss : 0.03243086603470147, time : 53
|
||||
iteration : 839, ce : 0.05320575626101345, total_loss : 0.026602878130506723, time : 10
|
||||
iteration : 859, ce : 0.05594585915096104, total_loss : 0.02797292957548052, time : 10
|
||||
iteration : 879, ce : 0.04406192186288536, total_loss : 0.02203096093144268, time : 10
|
||||
iteration : 899, ce : 0.05902999769896269, total_loss : 0.029514998849481344, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 899, mIoU : 90.77366247407701, best_valid_mIoU : 90.77366247407701, time : 42
|
||||
iteration : 919, ce : 0.05046119377948344, total_loss : 0.02523059688974172, time : 53
|
||||
iteration : 939, ce : 0.055241983570158484, total_loss : 0.027620991785079242, time : 10
|
||||
iteration : 959, ce : 0.06541968509554863, total_loss : 0.032709842547774315, time : 10
|
||||
iteration : 979, ce : 0.056352639896795155, total_loss : 0.028176319948397578, time : 10
|
||||
iteration : 999, ce : 0.04117121635936201, total_loss : 0.020585608179681004, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 999, mIoU : 90.95911907670035, best_valid_mIoU : 90.95911907670035, time : 43
|
||||
iteration : 1019, ce : 0.04268322917632759, total_loss : 0.021341614588163794, time : 53
|
||||
iteration : 1039, ce : 0.07534589348360896, total_loss : 0.03767294674180448, time : 10
|
||||
iteration : 1059, ce : 0.06266294419765472, total_loss : 0.03133147209882736, time : 10
|
||||
iteration : 1079, ce : 0.040896324906498194, total_loss : 0.020448162453249097, time : 10
|
||||
iteration : 1099, ce : 0.05627818256616592, total_loss : 0.02813909128308296, time : 10
|
||||
iteration : 1099, mIoU : 90.65854459999014, best_valid_mIoU : 90.95911907670035, time : 42
|
||||
iteration : 1119, ce : 0.05832021026872099, total_loss : 0.029160105134360494, time : 52
|
||||
iteration : 1139, ce : 0.05653570280410349, total_loss : 0.028267851402051746, time : 10
|
||||
iteration : 1159, ce : 0.0540118848439306, total_loss : 0.0270059424219653, time : 10
|
||||
iteration : 1179, ce : 0.059156589978374544, total_loss : 0.029578294989187272, time : 10
|
||||
iteration : 1199, ce : 0.0586970953270793, total_loss : 0.02934854766353965, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 1199, mIoU : 91.30907206844861, best_valid_mIoU : 91.30907206844861, time : 42
|
||||
iteration : 1219, ce : 0.046731388312764466, total_loss : 0.023365694156382233, time : 52
|
||||
iteration : 1239, ce : 0.04605461307801306, total_loss : 0.02302730653900653, time : 10
|
||||
iteration : 1259, ce : 0.05333511717617512, total_loss : 0.02666755858808756, time : 10
|
||||
iteration : 1279, ce : 0.058591234497725964, total_loss : 0.029295617248862982, time : 10
|
||||
iteration : 1299, ce : 0.044012406887486574, total_loss : 0.022006203443743287, time : 10
|
||||
iteration : 1299, mIoU : 90.44798195565556, best_valid_mIoU : 91.30907206844861, time : 42
|
||||
iteration : 1319, ce : 0.03853592430241406, total_loss : 0.01926796215120703, time : 52
|
||||
iteration : 1339, ce : 0.04643560294061899, total_loss : 0.023217801470309496, time : 10
|
||||
iteration : 1359, ce : 0.05803217897191644, total_loss : 0.02901608948595822, time : 10
|
||||
iteration : 1379, ce : 0.06334102130495012, total_loss : 0.03167051065247506, time : 10
|
||||
iteration : 1399, ce : 0.08214310212060809, total_loss : 0.041071551060304044, time : 10
|
||||
iteration : 1399, mIoU : 90.18570528496528, best_valid_mIoU : 91.30907206844861, time : 42
|
||||
iteration : 1419, ce : 0.043989807507023214, total_loss : 0.021994903753511607, time : 52
|
||||
iteration : 1439, ce : 0.05312715098261833, total_loss : 0.026563575491309166, time : 10
|
||||
iteration : 1459, ce : 0.05344270861241966, total_loss : 0.02672135430620983, time : 10
|
||||
iteration : 1479, ce : 0.04879952352494001, total_loss : 0.024399761762470006, time : 10
|
||||
iteration : 1499, ce : 0.05729071167297661, total_loss : 0.028645355836488307, time : 10
|
||||
iteration : 1499, mIoU : 91.04520387661324, best_valid_mIoU : 91.30907206844861, time : 41
|
||||
iteration : 1519, ce : 0.03750903834588826, total_loss : 0.01875451917294413, time : 52
|
||||
iteration : 1539, ce : 0.04227787498384714, total_loss : 0.02113893749192357, time : 10
|
||||
iteration : 1559, ce : 0.043323819525539875, total_loss : 0.021661909762769938, time : 10
|
||||
iteration : 1579, ce : 0.039240577118471266, total_loss : 0.019620288559235633, time : 10
|
||||
iteration : 1599, ce : 0.05065902634523809, total_loss : 0.025329513172619045, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 1599, mIoU : 91.3858830174658, best_valid_mIoU : 91.3858830174658, time : 42
|
||||
iteration : 1619, ce : 0.042945701791904864, total_loss : 0.021472850895952432, time : 53
|
||||
iteration : 19, ce : 0.8456825375556946, total_loss : 0.4228412687778473, time : 10
|
||||
iteration : 39, ce : 0.4564362831413746, total_loss : 0.2282181415706873, time : 10
|
||||
iteration : 59, ce : 0.5016216538846493, total_loss : 0.25081082694232465, time : 10
|
||||
iteration : 79, ce : 0.1747898418456316, total_loss : 0.0873949209228158, time : 10
|
||||
iteration : 99, ce : 0.17478084242902697, total_loss : 0.08739042121451349, time : 10
|
||||
iteration : 19, ce : 0.8456787191331386, total_loss : 0.4228393595665693, time : 10
|
||||
iteration : 39, ce : 0.45643005296587946, total_loss : 0.22821502648293973, time : 10
|
||||
iteration : 59, ce : 0.5015822313725948, total_loss : 0.2507911156862974, time : 10
|
||||
iteration : 79, ce : 0.1747533490881324, total_loss : 0.0873766745440662, time : 10
|
||||
iteration : 99, ce : 0.17461899896152316, total_loss : 0.08730949948076158, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 99, mIoU : 70.67741422260869, best_valid_mIoU : 70.67741422260869, time : 48
|
||||
iteration : 119, ce : 0.17073935624212028, total_loss : 0.08536967812106014, time : 58
|
||||
iteration : 139, ce : 0.13068198719993235, total_loss : 0.06534099359996617, time : 10
|
||||
iteration : 159, ce : 0.08914234582334757, total_loss : 0.044571172911673784, time : 10
|
||||
iteration : 179, ce : 0.1461833517998457, total_loss : 0.07309167589992285, time : 10
|
||||
iteration : 199, ce : 0.11983967162668704, total_loss : 0.05991983581334352, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 199, mIoU : 71.61286606894889, best_valid_mIoU : 71.61286606894889, time : 48
|
||||
iteration : 219, ce : 0.11703812740743161, total_loss : 0.058519063703715804, time : 58
|
||||
iteration : 239, ce : 0.126152902841568, total_loss : 0.063076451420784, time : 10
|
||||
iteration : 259, ce : 0.11562294252216816, total_loss : 0.05781147126108408, time : 10
|
||||
iteration : 279, ce : 0.09897459410130978, total_loss : 0.04948729705065489, time : 10
|
||||
iteration : 299, ce : 0.12509905751794576, total_loss : 0.06254952875897288, time : 10
|
||||
saved model in ./experiment/model/semantic_sam/model.pth
|
||||
iteration : 299, mIoU : 80.60803678405016, best_valid_mIoU : 80.60803678405016, time : 49
|
||||
iteration : 319, ce : 0.09828957775607705, total_loss : 0.049144788878038526, time : 59
|
||||
iteration : 339, ce : 0.08943888759240508, total_loss : 0.04471944379620254, time : 10
|
||||
iteration : 359, ce : 0.06585264699533581, total_loss : 0.03292632349766791, time : 10
|
||||
iteration : 379, ce : 0.09908102322369813, total_loss : 0.04954051161184907, time : 10
|
||||
iteration : 399, ce : 0.06755148817319423, total_loss : 0.033775744086597115, time : 10
|
||||
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
|
||||
|
|
@ -0,0 +1,106 @@
|
|||
import argparse
|
||||
import time
|
||||
from omegaconf import OmegaConf
|
||||
import torch
|
||||
from extend_sam import get_model
|
||||
import onnxruntime
|
||||
import numpy as np
|
||||
import os
|
||||
import glob
|
||||
import cv2
|
||||
|
||||
supported_tasks = ['detection', 'semantic_seg', 'instance_seg']
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--task_name', default='semantic_seg', type=str)
|
||||
parser.add_argument('--cfg', default=None, type=str)
|
||||
|
||||
if __name__ == '__main__':
|
||||
args = parser.parse_args()
|
||||
task_name = args.task_name
|
||||
if args.cfg is not None:
|
||||
config = OmegaConf.load(args.cfg)
|
||||
else:
|
||||
assert task_name in supported_tasks, "Please input the supported task name."
|
||||
config = OmegaConf.load("./config/{task_name}.yaml".format(task_name=args.task_name))
|
||||
|
||||
train_cfg = config.train
|
||||
model = get_model(model_name=train_cfg.model.sam_name, **train_cfg.model.params)
|
||||
|
||||
# 加载模型权重
|
||||
model.load_state_dict(torch.load('experiment/model/semantic_sam/model.pth', map_location='cpu'))
|
||||
model.eval()
|
||||
|
||||
# 准备示例输入
|
||||
dummy_input = torch.randn(1, 3, 1024, 1024)
|
||||
|
||||
# 确保模型和输入在同一设备上
|
||||
device = torch.device('cpu')
|
||||
model = model.to(device)
|
||||
dummy_input = dummy_input.to(device)
|
||||
|
||||
# 导出ONNX模型
|
||||
torch.onnx.export(
|
||||
model, # 要转换的模型
|
||||
dummy_input, # 模型的输入
|
||||
# "semantic_sam.onnx", # 导出的ONNX文件名
|
||||
"best_multi.onnx", # 导出的ONNX文件名
|
||||
export_params=True, # 存储训练好的参数权重
|
||||
opset_version=13, # ONNX算子集版本
|
||||
do_constant_folding=True, # 是否执行常量折叠优化
|
||||
input_names=['input'], # 输入节点的名称
|
||||
output_names=['output'], # 输出节点的名称
|
||||
dynamic_axes={ # 动态尺寸
|
||||
'input': {0: 'batch_size'},
|
||||
'output': {0: 'batch_size'}
|
||||
}
|
||||
)
|
||||
|
||||
# print("ONNX model exported successfully to semantic_sam.onnx")
|
||||
print("ONNX model exported successfully to best_multi.onnx")
|
||||
|
||||
# 加载ONNX模型进行推理
|
||||
# ort_session = onnxruntime.InferenceSession("semantic_sam.onnx")
|
||||
ort_session = onnxruntime.InferenceSession("./best_multi.onnx")
|
||||
|
||||
# 设置输入输出路径
|
||||
test_data_path = '../test_data'
|
||||
output_dir = './result2'
|
||||
|
||||
# 遍历测试图片进行推理
|
||||
for img_path in glob.glob(os.path.join(test_data_path,'*.jpg')):
|
||||
print(img_path)
|
||||
# 读取图片并转换为RGB
|
||||
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
|
||||
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
||||
|
||||
# 预处理图片
|
||||
img = cv2.resize(img, (1024, 1024))
|
||||
input_tensor = img.transpose(2, 0, 1).astype(np.float32)
|
||||
input_tensor = np.expand_dims(input_tensor, axis=0)
|
||||
|
||||
# ONNX推理
|
||||
start_time = time.time() # Start time measurement
|
||||
ort_inputs = {ort_session.get_inputs()[0].name: input_tensor}
|
||||
output = ort_session.run(None, ort_inputs)[0]
|
||||
end_time = time.time() # End time measurement
|
||||
|
||||
# 计算推理时间
|
||||
inference_time = end_time - start_time
|
||||
print(f"Inference time for {img_path}: {inference_time:.4f} seconds")
|
||||
|
||||
# 后处理
|
||||
pred = np.argmax(output, axis=1)
|
||||
pred = pred.squeeze(0)
|
||||
|
||||
# 保存结果
|
||||
img_name = os.path.splitext(os.path.basename(img_path))[0]
|
||||
sub_output_dir = os.path.join(output_dir, img_name)
|
||||
os.makedirs(sub_output_dir, exist_ok=True)
|
||||
|
||||
unique_labels = np.unique(pred)
|
||||
for label in unique_labels:
|
||||
binary_mask = (pred == label).astype(np.uint8) * 255
|
||||
mask_filename = os.path.join(sub_output_dir, f'class_{label}.png')
|
||||
cv2.imwrite(mask_filename, binary_mask)
|
||||
|
||||
print(f"Processed {img_path}, saved masks to {sub_output_dir}")
|
||||
|
|
@ -0,0 +1,127 @@
|
|||
# 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
|
||||
|
|
@ -0,0 +1,48 @@
|
|||
# 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)
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
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
|
||||
|
|
@ -0,0 +1,97 @@
|
|||
# @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
|
||||
|
|
@ -0,0 +1,228 @@
|
|||
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
|
||||
|
|
@ -0,0 +1,99 @@
|
|||
# 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
|
||||
|
|
@ -0,0 +1,19 @@
|
|||
# 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
|
||||
|
|
@ -0,0 +1,227 @@
|
|||
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()
|
||||
|
|
@ -0,0 +1,75 @@
|
|||
# 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))
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
# 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
|
||||
|
|
@ -0,0 +1,372 @@
|
|||
# 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
|
||||
|
|
@ -0,0 +1,108 @@
|
|||
# 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
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
# 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
|
||||
|
|
@ -0,0 +1,43 @@
|
|||
# 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
|
||||
|
|
@ -0,0 +1,395 @@
|
|||
# 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
|
||||
|
|
@ -0,0 +1,177 @@
|
|||
# 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
|
||||
|
|
@ -0,0 +1,214 @@
|
|||
# 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
|
||||
|
|
@ -0,0 +1,175 @@
|
|||
# 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
|
||||
|
|
@ -0,0 +1,240 @@
|
|||
# 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
|
||||
|
|
@ -0,0 +1,269 @@
|
|||
# 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
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
# 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.
|
||||
|
|
@ -0,0 +1,346 @@
|
|||
# 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
|
||||
|
|
@ -0,0 +1,144 @@
|
|||
# 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
|
||||
|
|
@ -0,0 +1,102 @@
|
|||
# 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)
|
||||
|
|
@ -0,0 +1,234 @@
|
|||
'''
|
||||
@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()
|
||||
|
|
@ -0,0 +1,64 @@
|
|||
import argparse
|
||||
from omegaconf import OmegaConf
|
||||
from torch.utils.data import DataLoader
|
||||
from datasets import get_dataset,get_lettuce_dataset
|
||||
from losses import get_losses
|
||||
from extend_sam import get_model, get_optimizer, get_scheduler, get_opt_pamams, get_runner
|
||||
import random
|
||||
import numpy as np
|
||||
import torch
|
||||
import os
|
||||
import glob
|
||||
from torchvision import transforms
|
||||
from PIL import Image
|
||||
import cv2
|
||||
supported_tasks = ['detection', 'semantic_seg', 'instance_seg']
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--task_name', default='semantic_seg', type=str)
|
||||
parser.add_argument('--cfg', default=None, type=str)
|
||||
|
||||
if __name__ == '__main__':
|
||||
args = parser.parse_args()
|
||||
task_name = args.task_name
|
||||
if args.cfg is not None:
|
||||
config = OmegaConf.load(args.cfg)
|
||||
else:
|
||||
assert task_name in supported_tasks, "Please input the supported task name."
|
||||
config = OmegaConf.load("./config/{task_name}.yaml".format(task_name=args.task_name))
|
||||
test_cfg = config.test
|
||||
train_cfg = config.train
|
||||
model = get_model(model_name=train_cfg.model.sam_name, **train_cfg.model.params)
|
||||
#我以./weed_data下的图片为例
|
||||
test_data_path = '../test_data'
|
||||
output_dir = './result'
|
||||
model.load_state_dict(torch.load('experiment/model/semantic_sam/lam_vit_b_01ec64.pth'))
|
||||
model.eval()
|
||||
model.cuda()
|
||||
preprocess = transforms.Compose([
|
||||
transforms.Resize((1024, 1024)),
|
||||
transforms.ToTensor(),
|
||||
])
|
||||
for img_path in glob.glob(os.path.join(test_data_path,'*.jpg')):
|
||||
print(img_path)
|
||||
# 读取图片并转换为 RGB
|
||||
#img = Image.open(img_path).convert("RGB")
|
||||
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
|
||||
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
||||
# 对图片进行预处理，并增加 batch 维度
|
||||
#input_tensor = preprocess(img).unsqueeze(0).cuda()
|
||||
img = cv2.resize(img, (1024, 1024))
|
||||
input_tensor = torch.from_numpy(img.transpose(2, 0, 1)).float().cuda().unsqueeze(0)
|
||||
with torch.no_grad():
|
||||
output,_ = model(input_tensor)
|
||||
pred = torch.argmax(output, dim=1)
|
||||
pred = pred.squeeze(0).cpu().numpy()
|
||||
img_name = os.path.splitext(os.path.basename(img_path))[0]
|
||||
sub_output_dir = os.path.join(output_dir, img_name)
|
||||
os.makedirs(sub_output_dir, exist_ok=True)
|
||||
unique_labels = np.unique(pred)
|
||||
for label in unique_labels:
|
||||
# 生成二值 mask：像素属于该类别则为 255，否则为 0
|
||||
binary_mask = (pred == label).astype(np.uint8) * 255
|
||||
mask_filename = os.path.join(sub_output_dir, f'class_{label}.png')
|
||||
cv2.imwrite(mask_filename, binary_mask)
|
||||
print(f"Processed {img_path}, saved masks to {sub_output_dir}")
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
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
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
'''
|
||||
@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
|
||||
|
|
@ -0,0 +1,502 @@
|
|||
import os
|
||||
import math
|
||||
import serial.tools.list_ports
|
||||
import serial
|
||||
import minimalmodbus
|
||||
import time
|
||||
import numpy as np
|
||||
import cv2
|
||||
|
||||
|
||||
class monocular_camera:
|
||||
def __init__(self, camera_id=0):
|
||||
self.cap = cv2.VideoCapture(camera_id, cv2.CAP_V4L2)
|
||||
self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640) # 设置分辨率宽
|
||||
self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480) # 设置分辨率高
|
||||
self.cap.set(cv2.CAP_PROP_FPS, 30) # 设置帧率
|
||||
self.num = 0
|
||||
if not self.cap.isOpened():
|
||||
print("Cannot open camera")
|
||||
exit()
|
||||
|
||||
def grab_imgs(self):
|
||||
ret, frame = self.cap.read()
|
||||
if not ret:
|
||||
print("Can't receive frame (stream end?). Exiting ...")
|
||||
return None
|
||||
# 存储图片
|
||||
# savePath = os.path.join("./path", "Camera_{:0>3d}.png".format(self.num))
|
||||
# cv2.imwrite(savePath, frame)
|
||||
# self.num += 1
|
||||
# 显示帧
|
||||
# cv2.imshow("frame", frame)
|
||||
# cv2.waitKey(1000)
|
||||
return frame
|
||||
|
||||
|
||||
def capture_point(calib, region_idx):
|
||||
|
||||
print("拍照")
|
||||
picture = cam3.grab_imgs()
|
||||
# weed
|
||||
img_weed = cv2.GaussianBlur(picture, (5, 5), 0)
|
||||
hsv = cv2.cvtColor(img_weed, cv2.COLOR_BGR2HSV)
|
||||
low_hsv = np.array([33, 43, 46])
|
||||
high_hsv = np.array([99, 255, 255])
|
||||
mask_green = cv2.inRange(hsv, lowerb=low_hsv, upperb=high_hsv)
|
||||
contours, _ = cv2.findContours(mask_green.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||||
contours_l = []
|
||||
weed_list = []
|
||||
center_xy_list = []
|
||||
|
||||
|
||||
for i in range(len(contours)):
|
||||
area = cv2.contourArea(contours[i])
|
||||
if area > 10:
|
||||
contours_l.append(contours[i])
|
||||
cnt = contours[i]
|
||||
x_weed, y_weed, w_weed, h_weed = cv2.boundingRect(cnt)
|
||||
center_x, center_y = (x_weed + w_weed / 2), (y_weed + h_weed / 2)
|
||||
weed_list.append([int(center_x), int(center_y)])
|
||||
p_laser_x = (calib[0][0] * center_x) + (calib[0][1] * center_y) + calib[0][2]
|
||||
p_laser_y = (calib[1][0] * center_x) + (calib[1][1] * center_y) + calib[1][2]
|
||||
encoder_position_x = int(p_laser_x) * 91
|
||||
encoder_position_y = int(p_laser_y) * 91
|
||||
if region_idx == 1:
|
||||
if encoder_position_x > -10920 and encoder_position_x < 10920 and encoder_position_y > - 10920 and encoder_position_y < 10920:
|
||||
center_xy_list.append([encoder_position_x, encoder_position_y - 455]) # -455是车辆前进的补偿
|
||||
if region_idx == 2:
|
||||
if encoder_position_x > -10920 and encoder_position_x < 10920 and encoder_position_y > - 10920 and encoder_position_y < 10920:
|
||||
center_xy_list.append([encoder_position_x, encoder_position_y - 455]) # -455是车辆前进的补偿
|
||||
if region_idx == 3:
|
||||
if encoder_position_x > -10920 and encoder_position_x < 10920 and encoder_position_y > - 10920 and encoder_position_y < 10920:
|
||||
center_xy_list.append([encoder_position_x, encoder_position_y - 455]) # -455是车辆前进的补偿
|
||||
if region_idx == 4:
|
||||
if encoder_position_x > -10920 and encoder_position_x < 10920 and encoder_position_y > - 10920 and encoder_position_y < 10920:
|
||||
center_xy_list.append([encoder_position_x, encoder_position_y - 455]) # -455是车辆前进的补偿
|
||||
else:
|
||||
continue
|
||||
center_xy_list = sorted(center_xy_list, key=lambda x: x[1], reverse=True) # 按y轴从大到小排序
|
||||
print("center_xy_list", center_xy_list)
|
||||
|
||||
return center_xy_list
|
||||
|
||||
def capture_point_data(idx):
|
||||
camera_list = [Red_Control_board_1.A_Dev.camera, Red_Control_board_2.A_Dev.camera, Red_Control_board_3.A_Dev.camera, Red_Control_board_4.A_Dev.camera]
|
||||
calib = camera_list[idx-1].Calibration_Parameter
|
||||
region_idx = camera_list[idx-1].Camera_Region
|
||||
camera_list[idx-1].coordinate_all = capture_point(calib, region_idx)
|
||||
if len(camera_list[0].coordinate_all) > 0:
|
||||
camera_list[0].coordinate_all = [camera_list[0].coordinate_all[0]]
|
||||
camera_list[0].coordinate_all.append([0, 0])
|
||||
camera_list[idx-1].coordinate_all_empty = False
|
||||
|
||||
# camera_list = [Red_Control_board_3.A_Dev.camera]
|
||||
# calib = camera_list[0].Calibration_Parameter
|
||||
# region_idx = camera_list[0].Camera_Region
|
||||
# camera_list[0].coordinate_all = capture_point(calib, region_idx)
|
||||
# if len(camera_list[0].coordinate_all) > 0:
|
||||
# camera_list[0].coordinate_all = [camera_list[0].coordinate_all[0]]
|
||||
# camera_list[0].coordinate_all.append([0, 0])
|
||||
# camera_list[0].coordinate_all_empty = False
|
||||
|
||||
def convert_to_modbus_format(value):
|
||||
# 如果值为负数，转换为 Modbus 无符号整数形式
|
||||
if value < 0:
|
||||
value += 65536 # 或者 value = value & 0xFFFF，这种方法也可用于确保值在 0 到 65535 之间
|
||||
return value
|
||||
|
||||
def is_timeout(start_time, timeout):
|
||||
return time.time() - start_time >= timeout
|
||||
|
||||
class camera:
|
||||
def __init__(self,Calibration_Parameter,Camera_Regio):
|
||||
self.Calibration_Parameter = Calibration_Parameter
|
||||
self.Camera_Region = Camera_Regio
|
||||
self.coordinate_all = []
|
||||
self.coordinate_all_empty = True
|
||||
self.coordinate_list = []
|
||||
self.num_x = - 9100
|
||||
self.num_y = - 9100
|
||||
|
||||
def get_coordinate(self):
|
||||
print("现在是区间序号为" + str(self.Camera_Region) + "标定参数为" + str(self.Calibration_Parameter) + "来取值了")
|
||||
self.coordinate_list.clear()
|
||||
|
||||
|
||||
if len(self.coordinate_all) > 0:
|
||||
self.num_x = self.coordinate_all[0][0]
|
||||
self.num_y = self.coordinate_all[0][1]
|
||||
self.coordinate_all.pop(0)
|
||||
self.coordinate_list.append([self.num_x, self.num_y])
|
||||
self.coordinate_list = self.coordinate_list[0] # 降维
|
||||
else:
|
||||
# 没有坐标了 设置标志位通知外面拍照 直接结束函数
|
||||
self.coordinate_all_empty = True # 空
|
||||
self.coordinate_list.clear()
|
||||
|
||||
print("get_coordinate", self.coordinate_list)
|
||||
return self.coordinate_list
|
||||
|
||||
|
||||
class Laser :
|
||||
def __init__(self,Modbus_dev,Calibration_Parameter_this,Camera_Regio_this):
|
||||
self.camera = camera(Calibration_Parameter_this,Camera_Regio_this)
|
||||
self.Modbus_dev = Modbus_dev
|
||||
self.start_time = time.time()
|
||||
self.Coordinate_buf = []
|
||||
#寄存器定义
|
||||
self.Laser_100x_voltage_reg = 33
|
||||
self.Laser_100x_current_reg = 34
|
||||
self.Laser_time_reg = 35
|
||||
self.Laser_switch_reg = 36
|
||||
self.Motor_X_mode_reg = 37
|
||||
self.Motor_Y_mode_reg = 38
|
||||
self.Motor_X_acceleration_reg = 39
|
||||
self.Motor_Y_acceleration_reg = 40
|
||||
self.Motor_X_speed_reg = 41
|
||||
self.Motor_Y_speed_reg = 42
|
||||
self.X_reg = 43
|
||||
self.Y_reg = 44
|
||||
self.Start_reg = 45
|
||||
self.Servo_finish_reg = 46
|
||||
self.Laser_finish_reg = 47
|
||||
#状态机定义
|
||||
self.state = 'Init_State'
|
||||
self.transitions = {
|
||||
'Init_State' : self.Init_State_Func,
|
||||
'IDLE_State' : self.IDLE_State_Func,
|
||||
'Set_XY_State': self.Set_XY_Func ,
|
||||
'Start_State' : self.Start_Func ,
|
||||
'Wait_State' : self.Wait_State_Func,
|
||||
'Err_State' : self.Err_State_Func ,
|
||||
}
|
||||
|
||||
def process(self):
|
||||
self.transitions[self.state]()
|
||||
|
||||
def Updata_State(self, new_state):
|
||||
if new_state in self.transitions:
|
||||
self.state = new_state
|
||||
else:
|
||||
print(f"无法转换到未知状态: {new_state}")
|
||||
pass
|
||||
|
||||
def Init_State_Func(self):
|
||||
print("Init_State_Func")
|
||||
Noused_Current_TIMEms_OFF_XM_YM_XA_YA_XV_YV = [0,10,300,0,1,1,2000,2000,2000,2000] # 能量范围[0-100]
|
||||
try:
|
||||
read_finish_register = self.Modbus_dev.read_registers(self.Servo_finish_reg, 1)
|
||||
|
||||
except Exception as e:
|
||||
self.Updata_State('Init_State')
|
||||
print("Wait_State_err")
|
||||
else:
|
||||
#读寄存器错成功 如果初次上电,激光为忙碌,中间断掉再启动激光为空闲,所以这里不判断激光
|
||||
if read_finish_register[0] == 1:
|
||||
#表示上电成功
|
||||
try:
|
||||
self.Modbus_dev.write_registers(self.Laser_100x_voltage_reg, Noused_Current_TIMEms_OFF_XM_YM_XA_YA_XV_YV)#由于是连续的，所以写一次
|
||||
self.start_time = time.time()
|
||||
except Exception as e:
|
||||
if is_timeout(self.start_time, 3):
|
||||
#写入寄存器错误已经超时
|
||||
self.Updata_State('Init_State')
|
||||
print("Init_State_err")
|
||||
else:
|
||||
#写入寄存器错误但未超时
|
||||
self.Updata_State('Init_State')
|
||||
print("Init_State")
|
||||
|
||||
else:
|
||||
#写入寄存器错成功
|
||||
# print("laser power write succeed")
|
||||
read_power_register = self.Modbus_dev.read_registers(self.Laser_100x_voltage_reg, 10)
|
||||
read_power_register = list(read_power_register)
|
||||
print(read_power_register)
|
||||
if read_power_register == Noused_Current_TIMEms_OFF_XM_YM_XA_YA_XV_YV:
|
||||
self.Updata_State('IDLE_State')
|
||||
print("Init_State_over")
|
||||
else:
|
||||
self.Updata_State('Init_State')
|
||||
print("Init_State")
|
||||
else:
|
||||
#表示上电还没成功
|
||||
self.Updata_State('Init_State')
|
||||
|
||||
def Open_State_Func(self):
|
||||
print("Open_State_Func")
|
||||
Noused_Current_TIMEms_OFF_XM_YM_XA_YA_XV_YV = [0,10,300,0,1,1,2000,2000,2000,2000] # 能量范围[0-100]
|
||||
try:
|
||||
read_finish_register = self.Modbus_dev.read_registers(self.Servo_finish_reg, 1)
|
||||
|
||||
except Exception as e:
|
||||
# self.Updata_State('Close_State')
|
||||
print("Close_State_err")
|
||||
else:
|
||||
#读寄存器错成功 如果初次上电,激光为忙碌,中间断掉再启动激光为空闲,所以这里不判断激光
|
||||
if read_finish_register[0] == 1:
|
||||
#表示上电成功
|
||||
try:
|
||||
self.Modbus_dev.write_registers(self.Laser_100x_voltage_reg, Noused_Current_TIMEms_OFF_XM_YM_XA_YA_XV_YV)#由于是连续的，所以写一次
|
||||
self.start_time = time.time()
|
||||
except Exception as e:
|
||||
if is_timeout(self.start_time, 3):
|
||||
#写入寄存器错误已经超时
|
||||
# self.Updata_State('Close_State')
|
||||
print("Close_State_err")
|
||||
else:
|
||||
#写入寄存器错误但未超时
|
||||
# self.Updata_State('Close_State')
|
||||
print("Close_State")
|
||||
|
||||
else:
|
||||
#写入寄存器错成功
|
||||
# print("laser power write succeed")
|
||||
read_power_register = self.Modbus_dev.read_registers(self.Laser_100x_voltage_reg, 10)
|
||||
read_power_register = list(read_power_register)
|
||||
print(read_power_register)
|
||||
if read_power_register == Noused_Current_TIMEms_OFF_XM_YM_XA_YA_XV_YV:
|
||||
# self.Updata_State('Mid_State')
|
||||
print("Init_State_over")
|
||||
else:
|
||||
# self.Updata_State('Close_State')
|
||||
print("Init_State")
|
||||
else:
|
||||
#表示上电还没成功
|
||||
# self.Updata_State('close_State')
|
||||
pass
|
||||
|
||||
def Close_State_Func(self):
|
||||
print("Close_State_Func")
|
||||
Noused_Current_TIMEms_OFF_XM_YM_XA_YA_XV_YV = [0,0,100,0,1,1,2000,2000,2000,2000] # 时间表示在中心带停留的时间
|
||||
try:
|
||||
read_finish_register = self.Modbus_dev.read_registers(self.Servo_finish_reg, 1)
|
||||
|
||||
except Exception as e:
|
||||
# self.Updata_State('Close_State')
|
||||
print("Close_State_err")
|
||||
else:
|
||||
#读寄存器错成功 如果初次上电,激光为忙碌,中间断掉再启动激光为空闲,所以这里不判断激光
|
||||
if read_finish_register[0] == 1:
|
||||
#表示上电成功
|
||||
try:
|
||||
self.Modbus_dev.write_registers(self.Laser_100x_voltage_reg, Noused_Current_TIMEms_OFF_XM_YM_XA_YA_XV_YV)#由于是连续的，所以写一次
|
||||
self.start_time = time.time()
|
||||
except Exception as e:
|
||||
if is_timeout(self.start_time, 3):
|
||||
#写入寄存器错误已经超时
|
||||
# self.Updata_State('Close_State')
|
||||
print("Close_State_err")
|
||||
else:
|
||||
#写入寄存器错误但未超时
|
||||
# self.Updata_State('Close_State')
|
||||
print("Close_State")
|
||||
|
||||
else:
|
||||
#写入寄存器错成功
|
||||
# print("laser power write succeed")
|
||||
read_power_register = self.Modbus_dev.read_registers(self.Laser_100x_voltage_reg, 10)
|
||||
read_power_register = list(read_power_register)
|
||||
print(read_power_register)
|
||||
if read_power_register == Noused_Current_TIMEms_OFF_XM_YM_XA_YA_XV_YV:
|
||||
# self.Updata_State('Mid_State')
|
||||
print("Init_State_over")
|
||||
else:
|
||||
# self.Updata_State('Close_State')
|
||||
print("Init_State")
|
||||
else:
|
||||
#表示上电还没成功
|
||||
# self.Updata_State('close_State')
|
||||
pass
|
||||
|
||||
def IDLE_State_Func(self):
|
||||
print("IDLE_State_Func",self.Coordinate_buf)
|
||||
if not self.Coordinate_buf:
|
||||
# 表示没有值可以打
|
||||
# 拍照获取值
|
||||
self.Coordinate_buf = [] # 这里为什么要清空一次列表？？
|
||||
self.Coordinate_buf.append(camera.get_coordinate(self.camera))
|
||||
self.Coordinate_buf = self.Coordinate_buf[0]
|
||||
self.Updata_State('IDLE_State')
|
||||
print("get coordinate")
|
||||
else:
|
||||
# 表示列表里还有值 那就去打
|
||||
print("准备去打")
|
||||
if len(self.Coordinate_buf ) == 2:
|
||||
if self.Coordinate_buf[0] == 0 and self.Coordinate_buf[1] == 0:
|
||||
self.Close_State_Func()
|
||||
else:
|
||||
self.Open_State_Func()
|
||||
self.start_time = time.time()
|
||||
self.Updata_State('Set_XY_State')
|
||||
print("IDLE_to_Set_XY")
|
||||
else:
|
||||
self.Coordinate_buf.clear()
|
||||
self.Updata_State('IDLE_State')
|
||||
|
||||
|
||||
|
||||
def Set_XY_Func(self):
|
||||
# print("Set_XY")
|
||||
try:
|
||||
converted_values = [convert_to_modbus_format(value) for value in self.Coordinate_buf]
|
||||
self.Modbus_dev.write_registers(self.X_reg, converted_values)#由于是连续的，所以写一次
|
||||
except Exception as e:
|
||||
if is_timeout(self.start_time, 3):
|
||||
#写入寄存器错误已经超时
|
||||
self.Updata_State('Err_State')
|
||||
print("Set_XY_err")
|
||||
self.Coordinate_buf.clear()
|
||||
|
||||
else:
|
||||
#写入寄存器错误但未超时
|
||||
self.Updata_State('Set_XY_State')
|
||||
# print("Set_XY_run")
|
||||
else:
|
||||
#写入寄存器错成功
|
||||
self.start_time = time.time()
|
||||
self.Updata_State('Start_State')
|
||||
print("Set_XY_OK")
|
||||
self.Coordinate_buf.clear()
|
||||
|
||||
def Start_Func(self):
|
||||
print("Start_Func")
|
||||
try:
|
||||
self.Modbus_dev.write_registers(self.Start_reg, [1])
|
||||
except Exception as e:
|
||||
if is_timeout(self.start_time, 3):
|
||||
#写入寄存器错误已经超时
|
||||
self.Updata_State('Err_State')
|
||||
print("Start_State_err")
|
||||
else:
|
||||
#写入寄存器错误但未超时
|
||||
self.Updata_State('Start_State')
|
||||
print("Start_State_run")
|
||||
|
||||
else:
|
||||
#写入寄存器错成功
|
||||
self.start_time = time.time()
|
||||
self.Updata_State('Wait_State')
|
||||
print("Start_State_ok")
|
||||
|
||||
def Wait_State_Func(self):
|
||||
print("Wait_State_Func")
|
||||
try:
|
||||
read_laser_register = self.Modbus_dev.read_registers(self.Servo_finish_reg, 2)
|
||||
print(read_laser_register)
|
||||
except Exception as e:
|
||||
if is_timeout(self.start_time, 3):
|
||||
#读寄存器错误已经超时
|
||||
self.Updata_State('Err_State')
|
||||
print("Wait_State_err")
|
||||
|
||||
else:
|
||||
#读寄存器错误但未超时
|
||||
self.Updata_State('Wait_State')
|
||||
print("Wait_State_run")
|
||||
else:
|
||||
#读寄存器错成功
|
||||
if (read_laser_register[0] == 1 and read_laser_register[1] == 1):
|
||||
#表示激光器完成
|
||||
self.Updata_State('IDLE_State') # 注释
|
||||
print("Wait_State_ok")
|
||||
else:
|
||||
#表示没完成
|
||||
if is_timeout(self.start_time, 90):#电机最长运行30s 激光最长1min
|
||||
#没完成并且超时
|
||||
self.Updata_State('Err_State')
|
||||
print("Wait_StateErr")
|
||||
else:
|
||||
#没完成但未超时
|
||||
self.Updata_State('Wait_State')
|
||||
print("Wait_State_loop")
|
||||
|
||||
|
||||
|
||||
def Err_State_Func(self):
|
||||
print("Err_State_Func")
|
||||
print("卡死 : 人工检查设备错误")
|
||||
print("准备恢复为初始状态")
|
||||
self.Updata_State('Init_State')
|
||||
|
||||
|
||||
|
||||
class Control_board :
|
||||
def __init__(self,COM,Slave_address,Calibration_Parameter,Camera_Regio):
|
||||
self.modbus = minimalmodbus.Instrument(COM, Slave_address) # 端口名, 从站地址
|
||||
self.modbus.serial.baudrate = 115200 # 波特率
|
||||
self.modbus.serial.bytesize = 8
|
||||
self.modbus.serial.parity = serial.PARITY_NONE
|
||||
self.modbus.serial.stopbits = 1
|
||||
self.modbus.serial.timeout = 1 # seconds
|
||||
self.A_Dev = Laser(self.modbus,Calibration_Parameter,Camera_Regio)
|
||||
|
||||
def process(self):
|
||||
self.A_Dev.process()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
transform1 = [[ 9.78925834e-01, 1.96192472e-02, -2.97419867e+02],
|
||||
[ 3.01586932e-02, -9.63219883e-01, 3.61695282e+02]]
|
||||
|
||||
transform2 = [[ 9.78925834e-01, 1.96192472e-02, -2.97419867e+02],
|
||||
[ 3.01586932e-02, -9.63219883e-01, 3.61695282e+02]]
|
||||
|
||||
transform3 = [[ 1.55162446e+00, -4.99468032e-03, -5.35657043e+02],
|
||||
[-3.34157095e-03, -1.54830655e+00, 4.28145094e+02]]
|
||||
|
||||
transform4 = [[ 1.55162446e+00, -4.99468032e-03, -5.35657043e+02],
|
||||
[-3.34157095e-03, -1.54830655e+00, 4.28145094e+02]]
|
||||
|
||||
print("开始初始化相机")
|
||||
cam1 = monocular_camera(camera_id=1)
|
||||
cam2 = monocular_camera(camera_id=1)
|
||||
cam3 = monocular_camera(camera_id=0)
|
||||
cam4 = monocular_camera(camera_id=1)
|
||||
cam_list = [cam1, cam2, cam3, cam4]
|
||||
print("相机初始化完毕")
|
||||
|
||||
# 设备注册
|
||||
Red_Control_board_1 = Control_board("COM3",1,transform1,1)
|
||||
Red_Control_board_2 = Control_board("COM3",2,transform2,2)
|
||||
Red_Control_board_3 = Control_board("COM3",3,transform3,3)
|
||||
Red_Control_board_4 = Control_board("COM3",4,transform4,4)
|
||||
|
||||
while True:
|
||||
Red_Control_board_1.process()
|
||||
Red_Control_board_2.process()
|
||||
Red_Control_board_3.process()
|
||||
Red_Control_board_4.process()
|
||||
# 如果4个列表都为空
|
||||
if Red_Control_board_1.A_Dev.camera.coordinate_all_empty :
|
||||
print("1号设备空了")
|
||||
if Red_Control_board_1.A_Dev.state == "IDLE_State" :
|
||||
print("1号设备都空了，且在IDLE_State")
|
||||
capture_point_data(idx=1)
|
||||
else:
|
||||
continue
|
||||
if Red_Control_board_1.A_Dev.camera.coordinate_all_empty :
|
||||
print("2号设备空了")
|
||||
if Red_Control_board_1.A_Dev.state == "IDLE_State" :
|
||||
print("2号设备都空了，且在IDLE_State")
|
||||
capture_point_data(idx=2)
|
||||
else:
|
||||
continue
|
||||
if Red_Control_board_1.A_Dev.camera.coordinate_all_empty :
|
||||
print("3号设备空了")
|
||||
if Red_Control_board_1.A_Dev.state == "IDLE_State" :
|
||||
print("3号设备都空了，且在IDLE_State")
|
||||
capture_point_data(idx=3)
|
||||
else:
|
||||
continue
|
||||
if Red_Control_board_1.A_Dev.camera.coordinate_all_empty :
|
||||
print("4号设备空了")
|
||||
if Red_Control_board_1.A_Dev.state == "IDLE_State" :
|
||||
print("4号设备都空了，且在IDLE_State")
|
||||
capture_point_data(idx=4)
|
||||
else:
|
||||
continue
|
||||
else:
|
||||
continue
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 185 KiB |
|
|
@ -0,0 +1,7 @@
|
|||
numpy==1.24.2
|
||||
omegaconf==2.3.0
|
||||
opencv_python==4.7.0.72
|
||||
pandas==2.0.1
|
||||
Pillow==9.5.0
|
||||
torch==1.7.1
|
||||
torchvision==0.8.2
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 1.7 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 1.6 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 1.7 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 1.6 KiB |
|
|
@ -0,0 +1 @@
|
|||
公共资料区-02视觉算法\laser_weeding\sam_ckpt
|
||||
|
|
@ -0,0 +1,63 @@
|
|||
'''
|
||||
@copyright ziqi-jin
|
||||
'''
|
||||
import argparse
|
||||
from omegaconf import OmegaConf
|
||||
from torch.utils.data import DataLoader
|
||||
from datasets import get_dataset,get_lettuce_dataset
|
||||
from losses import get_losses
|
||||
from extend_sam import get_model, get_optimizer, get_scheduler, get_opt_pamams, get_runner
|
||||
import random
|
||||
import numpy as np
|
||||
import torch
|
||||
def set_seed(seed: int):
|
||||
"""
|
||||
固定训练过程中的随机种子，保证结果相对可复现。
|
||||
"""
|
||||
random.seed(seed) # Python 内置的 random
|
||||
np.random.seed(seed) # NumPy
|
||||
torch.manual_seed(seed) # PyTorch CPU
|
||||
torch.cuda.manual_seed(seed) # PyTorch当前 GPU
|
||||
torch.cuda.manual_seed_all(seed) # PyTorch所有 GPU
|
||||
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.backends.cudnn.benchmark = False
|
||||
set_seed(42)
|
||||
|
||||
supported_tasks = ['detection', 'semantic_seg', 'instance_seg']
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--task_name', default='semantic_seg', type=str)
|
||||
parser.add_argument('--cfg', default=None, type=str)
|
||||
|
||||
if __name__ == '__main__':
|
||||
args = parser.parse_args()
|
||||
task_name = args.task_name
|
||||
if args.cfg is not None:
|
||||
config = OmegaConf.load(args.cfg)
|
||||
else:
|
||||
assert task_name in supported_tasks, "Please input the supported task name."
|
||||
config = OmegaConf.load("./config/{task_name}.yaml".format(task_name=args.task_name))
|
||||
|
||||
train_cfg = config.train
|
||||
val_cfg = config.val
|
||||
test_cfg = config.test
|
||||
|
||||
train_dataset,val_dataset = get_lettuce_dataset()
|
||||
train_loader = DataLoader(train_dataset, batch_size=train_cfg.bs, shuffle=True, num_workers=train_cfg.num_workers,
|
||||
drop_last=train_cfg.drop_last)
|
||||
val_loader = DataLoader(val_dataset, batch_size=val_cfg.bs, shuffle=False, num_workers=val_cfg.num_workers,
|
||||
drop_last=val_cfg.drop_last)
|
||||
losses = get_losses(losses=train_cfg.losses)
|
||||
# according the model name to get the adapted model
|
||||
model = get_model(model_name=train_cfg.model.sam_name, **train_cfg.model.params)
|
||||
opt_params = get_opt_pamams(model, lr_list=train_cfg.opt_params.lr_list, group_keys=train_cfg.opt_params.group_keys,
|
||||
wd_list=train_cfg.opt_params.wd_list)
|
||||
optimizer = get_optimizer(opt_name=train_cfg.opt_name, params=opt_params, lr=train_cfg.opt_params.lr_default,
|
||||
momentum=train_cfg.opt_params.momentum, weight_decay=train_cfg.opt_params.wd_default)
|
||||
scheduler = get_scheduler(optimizer=optimizer, lr_scheduler=train_cfg.scheduler_name)
|
||||
runner = get_runner(train_cfg.runner_name)(model, optimizer, losses, train_loader, val_loader, scheduler)
|
||||
# train_step
|
||||
runner.train(train_cfg)
|
||||
if test_cfg.need_test:
|
||||
runner.test(test_cfg)
|
||||
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 59 KiB |
Loading…
Reference in New Issue