repo stringlengths 7 90 | file_url stringlengths 81 315 | file_path stringlengths 4 228 | content stringlengths 0 32.8k | language stringclasses 1
value | license stringclasses 7
values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 14:38:15 2026-01-05 02:33:18 | truncated bool 2
classes |
|---|---|---|---|---|---|---|---|---|
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/industrialEI/pose-estimation-llio/testenv/trajectory_consistency.py | examples/industrialEI/pose-estimation-llio/testenv/trajectory_consistency.py | # Copyright 2022 The KubeEdge Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/industrialEI/pose-estimation-llio/testalgorithms/llio_fusion/utils.py | examples/industrialEI/pose-estimation-llio/testalgorithms/llio_fusion/utils.py | import os
import datetime
import numpy as np
import copy
import yaml
from functools import wraps
import time
from core.common.log import LOGGER
MATPLOTLIB_AVAILABLE = False
OPEN3D_AVAILABLE = False
def timeit(func):
@wraps(func)
def timeit_wrapper(*args, **kwargs):
start_time = time.perf_counter()
... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/industrialEI/pose-estimation-llio/testalgorithms/llio_fusion/__init__.py | examples/industrialEI/pose-estimation-llio/testalgorithms/llio_fusion/__init__.py | """
LLIO Fusion Algorithm Package for Ianvs Benchmarking.
This package provides LiDAR-Inertial-Lidar-Odometry (LLIO) implementation
for pose estimation benchmarking on KITTI dataset.
"""
__version__ = "1.0.0"
__author__ = "LLIO Team"
# Core algorithm imports
from .basemodel import BaseModel
from .llio_estimator impo... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/industrialEI/pose-estimation-llio/testalgorithms/llio_fusion/llio_estimator.py | examples/industrialEI/pose-estimation-llio/testalgorithms/llio_fusion/llio_estimator.py | """
LLIO Estimator for Ianvs Framework
This module implements the core LLIO (LiDAR-Inertial-Lidar-Odometry) algorithm
for use within the Ianvs benchmarking framework.
"""
import os
# Set environment variables before any imports to avoid CUDA issues
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import copy
import numpy as ... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/industrialEI/pose-estimation-llio/testalgorithms/llio_fusion/basemodel.py | examples/industrialEI/pose-estimation-llio/testalgorithms/llio_fusion/basemodel.py | # Copyright 2022 The KubeEdge Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | true |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/industrialEI/pose-estimation-llio/testalgorithms/llio_fusion/kitti/__init__.py | examples/industrialEI/pose-estimation-llio/testalgorithms/llio_fusion/kitti/__init__.py | # KITTI data loading module for LLIO | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/industrialEI/pose-estimation-llio/testalgorithms/llio_fusion/kitti/dataloader.py | examples/industrialEI/pose-estimation-llio/testalgorithms/llio_fusion/kitti/dataloader.py | from datetime import datetime
import numpy as np
from core.common.log import LOGGER
# Minimal stand-in for Dataset using NumPy only
class Data:
class Dataset:
def __init__(self):
pass
def __len__(self):
return 0
def __getitem__(self, i):
return {}
impor... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/industrialEI/pose-estimation-llio/testalgorithms/llio_fusion/kitti/calib.py | examples/industrialEI/pose-estimation-llio/testalgorithms/llio_fusion/kitti/calib.py | import numpy as np
from core.common.log import LOGGER
class KittiCalib:
def __init__(self):
# Default calibration values for KITTI dataset
# These are typical values, but should be loaded from calib files
self.velo2imu = np.array([
[0.0, -1.0, 0.0, 0.0],
[0.0, 0.0, ... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/industrialEI/single_task_learning_bench/deformable_component_manipulation/testenv/acc.py | examples/industrialEI/single_task_learning_bench/deformable_component_manipulation/testenv/acc.py | """
Enhanced Assembly Accuracy Metric with Compact and Clear Layout - FIXED VERSION
================================================================================
"""
import numpy as np
import logging
from pathlib import Path
from typing import Dict, List, Tuple, Optional
from sedna.common.class_factory import Class... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | true |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/industrialEI/single_task_learning_bench/deformable_component_manipulation/testalgorithms/components/manipulation.py | examples/industrialEI/single_task_learning_bench/deformable_component_manipulation/testalgorithms/components/manipulation.py | """
Manipulation Module for Deformable Assembly
Complete robot control + force/torque sensing for deformable components
Utilizes dataset:
- Force/Torque sensor data (sensor_data/)
- Robot trajectories (trajectory/)
- Ground truth poses (labels/)
- Episode annotations (annotations/)
"""
import os
import json
import lo... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/industrialEI/single_task_learning_bench/deformable_component_manipulation/testalgorithms/components/__init__.py | examples/industrialEI/single_task_learning_bench/deformable_component_manipulation/testalgorithms/components/__init__.py | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false | |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/industrialEI/single_task_learning_bench/deformable_component_manipulation/testalgorithms/components/perception.py | examples/industrialEI/single_task_learning_bench/deformable_component_manipulation/testalgorithms/components/perception.py | """
Perception Module for Deformable Assembly
Complete pipeline: YOLO + 6D Pose + Deformation Estimation
Utilizes full dataset:
- RGB images (YOLO detection)
- Depth images (3D pose estimation)
- Segmentation masks (deformation analysis)
- Labels/annotations (ground truth)
- Metadata (camera intrinsics)
"""
import o... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/industrialEI/single_task_learning_bench/deformable_component_manipulation/testalgorithms/components/naive_assembly_process.py | examples/industrialEI/single_task_learning_bench/deformable_component_manipulation/testalgorithms/components/naive_assembly_process.py | """
Naive Assembly Process - Complete End-to-End Orchestration
Integrates Perception + Manipulation for Deformable Component Assembly
This module serves as the main algorithm for Ianvs benchmarking.
It coordinates the complete pipeline:
1. Perception: Detect + Localize + Analyze Deformation
2. Manipulation: Plan + Exe... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/GovDoc2Poster/singletask_learning_bench/testenv/acc.py | examples/GovDoc2Poster/singletask_learning_bench/testenv/acc.py | # Copyright 2024 New Government Agent Project
# New government agent test environment implementation
import os
import json
import logging
from typing import Dict, List, Any, Optional, Tuple
import time
from sedna.common.class_factory import ClassType, ClassFactory
def _calculate_vlm_score(predictions: List[Dict[str, ... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/GovDoc2Poster/singletask_learning_bench/testalgorithms/gen/gov_painter.py | examples/GovDoc2Poster/singletask_learning_bench/testalgorithms/gen/gov_painter.py | # Copyright 2024 New Government Agent Project
# New Government Poster Painter
import os
import json
import logging
import time
from typing import Dict, List, Any, Optional, Tuple
from pathlib import Path
# Comment out local image processing dependencies, use API calls
# import PIL.Image
# import PIL.ImageDraw
# import... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/GovDoc2Poster/singletask_learning_bench/testalgorithms/gen/gov_evaluator.py | examples/GovDoc2Poster/singletask_learning_bench/testalgorithms/gen/gov_evaluator.py | # Copyright 2024 New Government Agent Project
# New Government Poster VLM Evaluator
import base64
import os
import logging
from typing import Dict, Any, Optional, Tuple, List
import time
from pathlib import Path
import base64
import hashlib
# Import LLM related libraries
from openai import OpenAI
# Import image proce... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | true |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/GovDoc2Poster/singletask_learning_bench/testalgorithms/gen/gov_parser.py | examples/GovDoc2Poster/singletask_learning_bench/testalgorithms/gen/gov_parser.py | # Copyright 2024 New Government Agent Project
# New Government Document Parser - Based on PyPDF2 and LLM
import os
import json
import re
import logging
from typing import Dict, Any
# Import LLM related libraries
from openai import OpenAI
class NewGovernmentDocumentParser:
"""
New Government Document Parser
... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/GovDoc2Poster/singletask_learning_bench/testalgorithms/gen/gov_planner.py | examples/GovDoc2Poster/singletask_learning_bench/testalgorithms/gen/gov_planner.py | # Copyright 2024 New Government Agent Project
# New Government Poster Planner
import json
import logging
from typing import Dict, Any, Optional
# Import LLM related libraries
from openai import OpenAI
class NewGovernmentPosterPlanner:
"""
New Government Poster Planner
Responsible for planning poster... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/GovDoc2Poster/singletask_learning_bench/testalgorithms/gen/basemodel.py | examples/GovDoc2Poster/singletask_learning_bench/testalgorithms/gen/basemodel.py | # Copyright 2024 New Government Agent Project
# New Government Agent Poster Generation Base Model
import os
import json
import time
import logging
import asyncio
from typing import Dict, List, Any, Optional, Tuple
from pathlib import Path
import re
import random
from concurrent.futures import ThreadPoolExecutor, as_co... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | true |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/__init__.py | examples/yaoba/__init__.py | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false | |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_yolox_tta/resource/yolox_s_8x8_300e_yaoba.py | examples/yaoba/singletask_learning_yolox_tta/resource/yolox_s_8x8_300e_yaoba.py | optimizer = dict(
type='SGD',
lr=0.01,
momentum=0.9,
weight_decay=0.0005,
nesterov=True,
paramwise_cfg=dict(norm_decay_mult=0.0, bias_decay_mult=0.0))
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='YOLOX',
warmup='exp',
by_epoch=False,
warmup_by_epoch=True,
... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_yolox_tta/resource/utils/general_TTA_v5.py | examples/yaoba/singletask_learning_yolox_tta/resource/utils/general_TTA_v5.py | import time
from multiprocessing import Pool
import warnings
warnings.filterwarnings("ignore")
import copy
from mmdet.datasets import build_dataset
import json
import os
from mmdet.core.post_processing.bbox_nms import batched_nms
import torch
from mmdet.core import bbox2result
from tqdm import tqdm
from mmdet.apis imp... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_yolox_tta/resource/utils/general_TTA_v4.py | examples/yaoba/singletask_learning_yolox_tta/resource/utils/general_TTA_v4.py | import time
from multiprocessing import Pool
import warnings
warnings.filterwarnings("ignore")
import copy
from mmdet.datasets import build_dataset
import json
import os
from mmdet.core.post_processing.bbox_nms import batched_nms
import torch
from mmdet.core import bbox2result
from tqdm import tqdm
from mmdet.apis imp... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_yolox_tta/resource/utils/TTA_augs_xyxy_cv2.py | examples/yaoba/singletask_learning_yolox_tta/resource/utils/TTA_augs_xyxy_cv2.py | import cv2
import PIL
import mmcv
import numpy as np
from PIL import Image
import PIL.ImageOps, PIL.ImageDraw, PIL.ImageEnhance
from collections import OrderedDict
FILL_COLOR = (0, 0, 0)
def xyxy_to_xywh(boxes):
width = boxes[2] - boxes[0]
height = boxes[3] - boxes[1]
return [boxes[0], boxes[1], width, h... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_yolox_tta/resource/utils/demo.py | examples/yaoba/singletask_learning_yolox_tta/resource/utils/demo.py | import torch
import sys
sys.path.append('/home/wjj/wjj/Public/code/ianvs')
from mmdet.apis import init_detector
from custom_ianvs.test_time_aug.TTA_augs_xyxy_cv2 import TTA_Aug_List, TTA_Aug_Space
from custom_ianvs.test_time_aug.TTA_strategy import TTA_Strategy
from custom_ianvs.test_time_aug.general_TTA_v5 import mo... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_yolox_tta/resource/utils/__init__.py | examples/yaoba/singletask_learning_yolox_tta/resource/utils/__init__.py | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false | |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_yolox_tta/resource/utils/TTA_strategy.py | examples/yaoba/singletask_learning_yolox_tta/resource/utils/TTA_strategy.py | import os.path
import warnings
warnings.filterwarnings("ignore")
from .general_TTA_v5 import *
from itertools import zip_longest, combinations, permutations
import sys
sys.path.append("/home/wjj/wjj/Public/code/huawei")
augment_list = TTA_Aug_List()
class TTA_Strategy(object):
def __init__(self, model, val_ima... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_yolox_tta/testenv/map.py | examples/yaoba/singletask_learning_yolox_tta/testenv/map.py | import json
from mmdet.datasets import build_dataset
from sedna.common.class_factory import ClassType, ClassFactory
__all__ = ["map"]
@ClassFactory.register(ClassType.GENERAL, alias="map")
def map(y_true, y_pred):
img_prefix = y_true[0]
ann_file = y_true[1]
fp = open(ann_file, mode="r", encoding="utf-8")... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_yolox_tta/testalgorithms/basemodel.py | examples/yaoba/singletask_learning_yolox_tta/testalgorithms/basemodel.py | from __future__ import absolute_import, division, print_function
import json
from sedna.common.class_factory import ClassType, ClassFactory
import copy
import os
import os.path as osp
import time
import mmcv
import torch
from mmcv import Config
from mmcv.utils import get_git_hash
from multiprocessing import Pool
from m... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_boost/resource/train.py | examples/yaoba/singletask_learning_boost/resource/train.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import copy
import os
import os.path as osp
import time
import warnings
import mmcv
import torch
import torch.distributed as dist
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from mmcv.utils import get_git_hash
fro... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_boost/resource/base_config.py | examples/yaoba/singletask_learning_boost/resource/base_config.py | model = dict(
type='FasterRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained'... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_boost/resource/__init__.py | examples/yaoba/singletask_learning_boost/resource/__init__.py | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false | |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_boost/resource/FPN_model_config.py | examples/yaoba/singletask_learning_boost/resource/FPN_model_config.py | custom_imports = dict(
imports=[
'examples.yaoba.singletask_learning_boost.resource.fpn_component.weighted_standard_roi_head',
'examples.yaoba.singletask_learning_boost.resource.fpn_component.convfc_bbox_head_weightv4',
'examples.yaoba.singletask_learning_boost.resource.fpn_component.tradboo... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_boost/resource/fpn_component/tradboost_pipe.py | examples/yaoba/singletask_learning_boost/resource/fpn_component/tradboost_pipe.py | from mmdet.datasets.builder import PIPELINES
from mmdet.datasets.pipelines.loading import LoadAnnotations
@PIPELINES.register_module()
class TRLoadAnnotations(LoadAnnotations):
def __init__(self, with_weight=False, **kwargs):
super().__init__(**kwargs)
self.with_weight = with_weight
def _load... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_boost/resource/fpn_component/tradboost_coco.py | examples/yaoba/singletask_learning_boost/resource/fpn_component/tradboost_coco.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmdet.datasets.builder import DATASETS
from mmdet.datasets.coco import CocoDataset
@DATASETS.register_module()
class TRCOCO(CocoDataset):
def _parse_ann_info(self, img_info, ann_info):
"""Parse bbox and mask annotation.
e... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_boost/resource/fpn_component/weighted_standard_roi_head.py | examples/yaoba/singletask_learning_boost/resource/fpn_component/weighted_standard_roi_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import json
import numpy as np
import torch
from mmdet.core import bbox2roi
from mmdet.core.bbox.iou_calculators import build_iou_calculator
from mmdet.models.builder import HEADS
from mmdet.models.roi_heads.standard_roi_head import StandardRoIHead
@HEADS.register_modul... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_boost/resource/fpn_component/__init__.py | examples/yaoba/singletask_learning_boost/resource/fpn_component/__init__.py | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false | |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_boost/resource/fpn_component/convfc_bbox_head_weightv4.py | examples/yaoba/singletask_learning_boost/resource/fpn_component/convfc_bbox_head_weightv4.py | import numpy as np
from mmdet.core.bbox.iou_calculators import build_iou_calculator
from mmdet.models.builder import HEADS
from mmdet.models.roi_heads.bbox_heads.convfc_bbox_head import Shared2FCBBoxHead
from mmcv.runner import force_fp32
import torch
from mmdet.models.losses import accuracy
from mmdet.core import mult... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_boost/resource/utils/txt2json_yaoba_change_id.py | examples/yaoba/singletask_learning_boost/resource/utils/txt2json_yaoba_change_id.py | import json
from tqdm import tqdm
import os
import random
category_list = ['yanse', 'huahen', 'mosun']
def xyxy_to_xywh(boxes):
width = abs(boxes[2] - boxes[0])
height = abs(boxes[3] - boxes[1])
if boxes[0] < boxes[2]:
top_left_x = boxes[0]
else:
top_left_x = boxes[2]
if boxes[1] ... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_boost/resource/utils/transform_unkonwn.py | examples/yaoba/singletask_learning_boost/resource/utils/transform_unkonwn.py | import json
import shutil
import os.path
import copy
from tqdm import tqdm
from .TTA_augs_xyxy_cv2 import *
def topleftxywh_to_xyxy(boxes):
"""
args:
boxes:list of topleft_x,topleft_y,width,height,
return:
boxes:list of x,y,x,y,cooresponding to top left and bottom right
"""
x_top_... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_boost/resource/utils/infer_and_error.py | examples/yaoba/singletask_learning_boost/resource/utils/infer_and_error.py | import json
import os
import cv2
from mmdet.apis import inference_detector, init_detector
from custom_code.instance_based.utils.transform_unkonwn import xyxy_to_xywh
from tqdm import tqdm
from custom_code.test_time_aug.general_TTA_v4 import topleftxywh_to_xyxy
CATEGORY = [{"id": 0, "name": "yanse"},
{"id":... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_boost/resource/utils/TTA_augs_xyxy_cv2.py | examples/yaoba/singletask_learning_boost/resource/utils/TTA_augs_xyxy_cv2.py | import cv2
import PIL
import mmcv
import numpy as np
from PIL import Image
import PIL.ImageOps, PIL.ImageDraw, PIL.ImageEnhance
FILL_COLOR = (0, 0, 0)
def xyxy_to_xywh(boxes):
width = boxes[2] - boxes[0]
height = boxes[3] - boxes[1]
return [boxes[0], boxes[1], width, height]
def xywh_to_xyxy(boxes):
... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_boost/resource/utils/random_select.py | examples/yaoba/singletask_learning_boost/resource/utils/random_select.py | import os
import random
from random import sample
def random_select(src_txt, num, out_dir, select_name, rest_name):
fp = open(src_txt, mode="r", encoding='utf-8')
results = fp.readlines()
fp_select = open(os.path.join(out_dir, select_name), mode="a+", encoding='utf-8')
fp_rest = open(os.path.join(out_... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_boost/resource/utils/F1-score.py | examples/yaoba/singletask_learning_boost/resource/utils/F1-score.py | import json
import os
import cv2
import numpy as np
from tqdm import tqdm
from mmdet.apis import init_detector, inference_detector
from utils.c1c.cal_json_file_MAP import topleftxywh_to_xyxy
"""
接口参考map计算接口，评估一个模型在某json文件下的F1-score
"""
THR = 0.5
SCORE_THR = 0.9
def _single_F1score(predict_result, labels, thr=THR):
... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_boost/testenv/map.py | examples/yaoba/singletask_learning_boost/testenv/map.py | import json
from mmdet.datasets import build_dataset
from sedna.common.class_factory import ClassType, ClassFactory
__all__ = ["map"]
@ClassFactory.register(ClassType.GENERAL, alias="map")
def map(y_true, y_pred):
img_prefix = y_true[0]
ann_file = y_true[1]
fp = open(ann_file, mode="r", encoding="utf-8")... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/yaoba/singletask_learning_boost/testalgorithms/basemodel.py | examples/yaoba/singletask_learning_boost/testalgorithms/basemodel.py | from __future__ import absolute_import, division, print_function
import json
from sedna.common.class_factory import ClassType, ClassFactory
from sedna.datasources import TxtDataParse, JSONDataParse
from pycocotools.coco import COCO
import copy
import os
import os.path as osp
import time
import mmcv
import torch
from mm... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testenv/accuracy.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testenv/accuracy.py | # Copyright 2022 The KubeEdge Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/task_definition_by_domain.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/task_definition_by_domain.py | from typing import List, Any, Tuple
from sedna.datasources import BaseDataSource
from sedna.common.class_factory import ClassType, ClassFactory
from sedna.algorithms.seen_task_learning.artifact import Task
__all__ = ('TaskDefinitionByDomain',)
@ClassFactory.register(ClassType.STP, alias="TaskDefinitionByDomain")
cl... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/task_allocation_by_domain.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/task_allocation_by_domain.py | from sedna.datasources import BaseDataSource
from sedna.common.class_factory import ClassFactory, ClassType
__all__ = ('TaskAllocationByDomain',)
@ClassFactory.register(ClassType.STP, alias="TaskAllocationByDomain")
class TaskAllocationByOrigin:
"""
Corresponding to `TaskDefinitionByOrigin`
Parameters
... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/basemodel.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/basemodel.py | import os
import gc
import numpy as np
import torch
from torch.utils.data import DataLoader
from sedna.common.class_factory import ClassType, ClassFactory
from sedna.common.config import Context
from sedna.common.file_ops import FileOps
from sedna.common.log import LOGGER
from PIL import Image
from torchvision import t... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/accuracy.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/accuracy.py | from basemodel import val_args
from utils.metrics import Evaluator
from tqdm import tqdm
from dataloaders import make_data_loader
from sedna.common.class_factory import ClassType, ClassFactory
__all__ = ('accuracy')
@ClassFactory.register(ClassType.GENERAL)
def accuracy(y_true, y_pred, **kwargs):
args = val_args... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/train.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/train.py | import argparse
import os
import numpy as np
from tqdm import tqdm
import torch
import copy
from mypath import Path
from dataloaders import make_data_loader
from models.erfnet_RA_parallel import Net as Net_RAP
from utils.loss import SegmentationLosses
from models.replicate import patch_replication_callback
from util... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/run_server.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/run_server.py | # Copyright 2021 The KubeEdge Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/eval.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/eval.py | import argparse
import os
import numpy as np
from tqdm import tqdm
import time
import torch
from torchvision.transforms import ToPILImage
from PIL import Image
from dataloaders import make_data_loader
from dataloaders.utils import decode_seg_map_sequence, Colorize
from utils.metrics import Evaluator
from models.erfnet... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/sedna_evaluate.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/sedna_evaluate.py | import os
os.environ['BACKEND_TYPE'] = 'PYTORCH'
from sedna.core.lifelong_learning import LifelongLearning
from sedna.datasources import IndexDataParse
from sedna.common.config import Context
from accuracy import accuracy
from basemodel import Model
def _load_txt_dataset(dataset_url):
# use original dataset url
... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/sedna_predict.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/sedna_predict.py | import os
os.environ['BACKEND_TYPE'] = 'PYTORCH'
os.environ["TEST_DATASET_URL"] = "./data_txt/door_test.txt"
os.environ["EDGE_OUTPUT_URL"] = "./edge_kb"
os.environ["ORIGINAL_DATASET_URL"] = "/tmp"
import torch
import numpy as np
from PIL import Image
import base64
import tempfile
from io import BytesIO
from torchvisi... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/mypath.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/mypath.py | class Path(object):
@staticmethod
def db_root_dir(dataset):
if dataset == 'cityscapes':
return './ianvs/project/RFNet-master/Data/cityscapes/' # folder that contains leftImg8bit/
elif dataset == 'citylostfound':
return './ianvs/project/RFNet-master/Data/cityscapesand... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/test.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/test.py | import numpy as np
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
CPA_results = np.load("./cpa_results.npy").T
ratios = [0.3, 0.5, 0.6, 0.7, 0.8, 0.9]
ratio_counts = np.zeros((len(CPA_results), len(ratios)), dtype=float)
for i in range(len(CPA_results)):
for j in range(len(ratios)):
... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/sedna_train.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/sedna_train.py | import os
os.environ['BACKEND_TYPE'] = 'PYTORCH'
os.environ["OUTPUT_URL"] = "./cloud_kb/"
# os.environ['CLOUD_KB_INDEX'] = "./cloud_kb/index.pkl"
os.environ["TRAIN_DATASET_URL"] = "./data_txt/sedna_data.txt"
os.environ["KB_SERVER"] = "http://0.0.0.0:9020"
os.environ["HAS_COMPLETED_INITIAL_TRAINING"] = "false"
from sed... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/predict.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/predict.py | import os
os.environ['BACKEND_TYPE'] = 'PYTORCH'
# set at yaml
# os.environ["PREDICT_RESULT_DIR"] = "./inference_results"
# os.environ["EDGE_OUTPUT_URL"] = "./edge_kb"
# os.environ["video_url"] = "./video/radio.mp4"
# os.environ["MODEL_URLS"] = "./cloud_next_kb/index.pkl"
import cv2
cv2.setNumThreads(0)
cv2.ocl.setUs... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/basemodel.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/basemodel.py | import os
import numpy as np
import torch
from PIL import Image
import argparse
from train import Trainer
from eval import Validator
from tqdm import tqdm
from eval import load_my_state_dict
from utils.metrics import Evaluator
from dataloaders import make_data_loader
from dataloaders import custom_transforms as tr
from... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/models/util.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/models/util.py | import torch
import torch.nn as nn
import torch.nn.functional as F
upsample = lambda x, size: F.interpolate(x, size, mode='bilinear', align_corners=False)
batchnorm_momentum = 0.01 / 2
def get_n_params(parameters):
pp = 0
for p in parameters:
nn = 1
for s in list(p.size()):
nn = n... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/models/erfnet.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/models/erfnet.py | # ERFNet full model definition for Pytorch
# Sept 2017
# Eduardo Romera
#######################
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
class DownsamplerBlock (nn.Module):
def __init__(self, ninput, noutput, nb_tasks=1):
super().__init__()
... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/models/replicate.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/models/replicate.py | # -*- coding: utf-8 -*-
# File : replicate.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import functools
from torch.nn.parallel.dat... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/utils/metrics.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/utils/metrics.py | import numpy as np
class Evaluator(object):
def __init__(self, num_class):
self.num_class = num_class
self.confusion_matrix = np.zeros((self.num_class,)*2) # shape:(num_class, num_class)
def Pixel_Accuracy(self):
Acc = np.diag(self.confusion_matrix).sum() / self.confusion_matrix.sum(... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/utils/summaries.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/utils/summaries.py | import os
import torch
from torchvision.utils import make_grid
# from tensorboardX import SummaryWriter
from torch.utils.tensorboard import SummaryWriter
from dataloaders.utils import decode_seg_map_sequence
class TensorboardSummary(object):
def __init__(self, directory):
self.directory = directory
de... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/utils/saver.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/utils/saver.py | import os
import time
import shutil
import tempfile
import torch
from collections import OrderedDict
import glob
class Saver(object):
def __init__(self, args):
self.args = args
self.directory = os.path.join('/tmp', args.dataset, args.checkname)
self.runs = sorted(glob.glob(os.path.join(sel... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/utils/iouEval.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/utils/iouEval.py | import torch
class iouEval:
def __init__(self, nClasses, ignoreIndex=20):
self.nClasses = nClasses
self.ignoreIndex = ignoreIndex if nClasses > ignoreIndex else -1 # if ignoreIndex is larger than nClasses, consider no ignoreIndex
self.reset()
def reset(self):
classes = self.... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/utils/args.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/utils/args.py | class TrainArgs:
def __init__(self, **kwargs):
self.depth = False
self.dataset = 'cityscapes'
self.workers = 4
self.base_size = 1024
self.crop_size = 768
self.loss_type = 'ce'
self.epochs = kwargs.get("epochs", 1)
self.start_epoch = 0
self.num... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/utils/loss.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/utils/loss.py | import torch
import torch.nn as nn
class SegmentationLosses(object):
def __init__(self, weight=None, size_average=True, batch_average=True, ignore_index=255, cuda=False, gpu_ids=0): # ignore_index=255
self.ignore_index = ignore_index
self.weight = weight
self.size_average = size_average
... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/utils/__init__.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/utils/__init__.py | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false | |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/utils/calculate_weights.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/utils/calculate_weights.py | import os
from tqdm import tqdm
import numpy as np
from mypath import Path
def calculate_weigths_labels(dataset, dataloader, num_classes):
# Create an instance from the data loader
z = np.zeros((num_classes,))
# Initialize tqdm
tqdm_batch = tqdm(dataloader)
print('Calculating classes weights')
... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/utils/lr_scheduler.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/utils/lr_scheduler.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## ECE Department, Rutgers University
## Email: zhang.hang@rutgers.edu
## Copyright (c) 2017
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this sou... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/custom_transforms.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/custom_transforms.py | import torch
import random
import numpy as np
from PIL import Image, ImageOps, ImageFilter
class Normalize(object):
"""Normalize a tensor image with mean and standard deviation.
Args:
mean (tuple): means for each channel.
std (tuple): standard deviations for each channel.
"""
def __ini... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/utils.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/utils.py | import matplotlib.pyplot as plt
import numpy as np
import torch
def decode_seg_map_sequence(label_masks, dataset='pascal'):
rgb_masks = []
for label_mask in label_masks:
rgb_mask = decode_segmap(label_mask, dataset)
rgb_masks.append(rgb_mask)
rgb_masks = torch.from_numpy(np.array(rgb_masks)... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/custom_transforms_rgb.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/custom_transforms_rgb.py | import torch
import random
import numpy as np
from PIL import Image, ImageOps, ImageFilter
class Normalize(object):
"""Normalize a tensor image with mean and standard deviation.
Args:
mean (tuple): means for each channel.
std (tuple): standard deviations for each channel.
"""
def __ini... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/__init__.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/__init__.py | from dataloaders.datasets import cityscapes, citylostfound, cityrand, target, xrlab, e1, mapillary
from torch.utils.data import DataLoader
def make_data_loader(args, train_data=None, valid_data=None, test_data=None, **kwargs):
if args.dataset == 'cityscapes':
if train_data is not None:
train_s... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/datasets/mapillary.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/datasets/mapillary.py | import os
import numpy as np
import scipy.misc as m
from PIL import Image
from torch.utils import data
from mypath import Path
from torchvision import transforms
from dataloaders import custom_transforms as tr
class CityscapesSegmentation(data.Dataset):
NUM_CLASSES = 24
def __init__(self, args, root=Path.db_r... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/datasets/citylostfound.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/datasets/citylostfound.py | import os
import numpy as np
from PIL import Image
from torch.utils import data
from mypath import Path
from torchvision import transforms
from dataloaders import custom_transforms as tr
from dataloaders import custom_transforms_rgb as tr_rgb
class CitylostfoundSegmentation(data.Dataset):
NUM_CLASSES = 20
def... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/datasets/xrlab.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/datasets/xrlab.py | import os
import numpy as np
import scipy.misc as m
from PIL import Image
from torch.utils import data
from mypath import Path
from torchvision import transforms
from dataloaders import custom_transforms as tr
class CityscapesSegmentation(data.Dataset):
NUM_CLASSES = 25
def __init__(self, args, root=Path.db_r... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/datasets/cityrand.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/datasets/cityrand.py | import os
import numpy as np
import scipy.misc as m
from PIL import Image
from torch.utils import data
from mypath import Path
from torchvision import transforms
from dataloaders import custom_transforms as tr
class CityscapesSegmentation(data.Dataset):
NUM_CLASSES = 19
def __init__(self, args, root=Path.db_r... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/datasets/cityscapes.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/datasets/cityscapes.py | import os
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
import numpy as np
from PIL import Image
from torch.utils import data
from mypath import Path
from torchvision import transforms
from dataloaders import custom_transforms as tr
class CityscapesSegmentation(data.Dataset):
NUM_CLASSES... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/datasets/__init__.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/datasets/__init__.py | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false | |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/datasets/target.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/datasets/target.py | import os
import numpy as np
import scipy.misc as m
from PIL import Image
from torch.utils import data
from mypath import Path
from torchvision import transforms
from dataloaders import custom_transforms as tr
class CityscapesSegmentation(data.Dataset):
NUM_CLASSES = 24
def __init__(self, args, root=Path.db_r... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/datasets/e1.py | examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/erfnet/ERFNet/dataloaders/datasets/e1.py | import os
import numpy as np
import scipy.misc as m
from PIL import Image
from torch.utils import data
from mypath import Path
from torchvision import transforms
from dataloaders import custom_transforms as tr
class CityscapesSegmentation(data.Dataset):
NUM_CLASSES = 24
def __init__(self, args, root=Path.db_r... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/resources/algorithms/common.py | examples/resources/algorithms/common.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Common modules
"""
import json
import math
import platform
import warnings
from collections import OrderedDict, namedtuple
from copy import copy
from pathlib import Path
import cv2
import numpy as np
import pandas as pd
import requests
import torch
import torch.nn as nn... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | true |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/pcb-aoi/singletask_learning_bench/fault_detection/testenv/f1_score.py | examples/pcb-aoi/singletask_learning_bench/fault_detection/testenv/f1_score.py | # Copyright 2022 The KubeEdge Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/pcb-aoi/singletask_learning_bench/fault_detection/testalgorithms/fpn/basemodel.py | examples/pcb-aoi/singletask_learning_bench/fault_detection/testalgorithms/fpn/basemodel.py | # Copyright 2022 The KubeEdge Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/pcb-aoi/incremental_learning_bench/fault_detection/testenv/f1_score.py | examples/pcb-aoi/incremental_learning_bench/fault_detection/testenv/f1_score.py | # Copyright 2022 The KubeEdge Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/pcb-aoi/incremental_learning_bench/fault_detection/testalgorithms/fpn/hard_example_mining.py | examples/pcb-aoi/incremental_learning_bench/fault_detection/testalgorithms/fpn/hard_example_mining.py | # Copyright 2022 The KubeEdge Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/pcb-aoi/incremental_learning_bench/fault_detection/testalgorithms/fpn/basemodel.py | examples/pcb-aoi/incremental_learning_bench/fault_detection/testalgorithms/fpn/basemodel.py | # Copyright 2022 The KubeEdge Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/bdd/lifelong_learning_bench/curb-detection/testenv/map.py | examples/bdd/lifelong_learning_bench/curb-detection/testenv/map.py | # Copyright 2022 The KubeEdge Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/bdd/lifelong_learning_bench/curb-detection/testalgorithms/yolo/task_remodeling.py | examples/bdd/lifelong_learning_bench/curb-detection/testalgorithms/yolo/task_remodeling.py | # Copyright 2021 The KubeEdge Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/bdd/lifelong_learning_bench/curb-detection/testalgorithms/yolo/task_allocation_by_origin.py | examples/bdd/lifelong_learning_bench/curb-detection/testalgorithms/yolo/task_allocation_by_origin.py | from sedna.datasources import BaseDataSource
from sedna.common.class_factory import ClassFactory, ClassType
from mmcls.apis import init_model
from mmcv.parallel import collate, scatter
from mmcls.datasets.pipelines import Compose
from mmcls.apis import set_random_seed
set_random_seed(0, deterministic=True)
import torch... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/bdd/lifelong_learning_bench/curb-detection/testalgorithms/yolo/inference_integrate.py | examples/bdd/lifelong_learning_bench/curb-detection/testalgorithms/yolo/inference_integrate.py | # Copyright 2021 The KubeEdge Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/bdd/lifelong_learning_bench/curb-detection/testalgorithms/yolo/basemodel.py | examples/bdd/lifelong_learning_bench/curb-detection/testalgorithms/yolo/basemodel.py | import os
import numpy as np
import torch
from sedna.common.class_factory import ClassType, ClassFactory
# set backend
os.environ['BACKEND_TYPE'] = 'PYTORCH'
yolo_hub_path = '/home/shifan/.cache/torch/hub/ultralytics_yolov5_master'
@ClassFactory.register(ClassType.GENERAL, alias="BaseModel")
class BaseModel:
def... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/bdd/lifelong_learning_bench/curb-detection/testalgorithms/yolo/model_selector/choose_net_b64.py | examples/bdd/lifelong_learning_bench/curb-detection/testalgorithms/yolo/model_selector/choose_net_b64.py | model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=18,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='MultiLabelLinearClsHead',
num_classes=20,
in_channels=... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/Cloud_Robotics/cloud-edge-collaborative-inference_bench/perception-reasoning/testenv/acc.py | examples/Cloud_Robotics/cloud-edge-collaborative-inference_bench/perception-reasoning/testenv/acc.py | # Copyright 2024 The KubeEdge Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/Cloud_Robotics/cloud-edge-collaborative-inference_bench/perception-reasoning/testalgorithms/cloud_edge_dispacher.py | examples/Cloud_Robotics/cloud-edge-collaborative-inference_bench/perception-reasoning/testalgorithms/cloud_edge_dispacher.py | # Copyright 2024 The KubeEdge Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/Cloud_Robotics/cloud-edge-collaborative-inference_bench/perception-reasoning/testalgorithms/cloud_model.py | examples/Cloud_Robotics/cloud-edge-collaborative-inference_bench/perception-reasoning/testalgorithms/cloud_model.py | # Copyright 2024 The KubeEdge Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
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