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/imagenet/multiedge_inference_bench/testenv/accuracy.py | examples/imagenet/multiedge_inference_bench/testenv/accuracy.py | from sedna.common.class_factory import ClassType, ClassFactory
__all__ = ('accuracy')
@ClassFactory.register(ClassType.GENERAL, alias="accuracy")
def accuracy(y_true, y_pred, **kwargs):
y_pred = y_pred.get("pred")
total = len(y_pred)
y_true_ = [int(y_true[i].split('/')[-1]) for (_, i) in y_pred]
y_pre... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/imagenet/multiedge_inference_bench/testenv/peak_power.py | examples/imagenet/multiedge_inference_bench/testenv/peak_power.py | import sys
import os
from sedna.common.class_factory import ClassType, ClassFactory
import matplotlib.pyplot as plt
__all__ = ('peak_power')
@ClassFactory.register(ClassType.GENERAL, alias="peak_power")
def peak_power(y_true, y_pred, **kwargs):
power_usage_per_device = y_pred.get("power_usage_per_device")
p... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/imagenet/multiedge_inference_bench/testenv/fps.py | examples/imagenet/multiedge_inference_bench/testenv/fps.py | import sys
import os
from sedna.common.class_factory import ClassType, ClassFactory
import matplotlib.pyplot as plt
__all__ = ('fps')
@ClassFactory.register(ClassType.GENERAL, alias="fps")
def fps(y_true, y_pred, **kwargs):
total = len(y_pred.get("pred"))
inference_time_per_device = y_pred.get("inference_ti... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/imagenet/multiedge_inference_bench/testenv/peak_memory.py | examples/imagenet/multiedge_inference_bench/testenv/peak_memory.py | import sys
import os
from sedna.common.class_factory import ClassType, ClassFactory
import matplotlib.pyplot as plt
__all__ = ('peak_memory')
@ClassFactory.register(ClassType.GENERAL, alias="peak_memory")
def peak_power(y_true, y_pred, **kwargs):
mem_usage_per_device = y_pred.get("mem_usage_per_device")
plt... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/model_cfg.py | examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/model_cfg.py | """Model configurations and default parameters."""
import logging
from typing import Any, Callable, List, Optional, Tuple
from transformers import AutoConfig
from models import ModuleShard, ModuleShardConfig
from models.transformers import bert, deit, vit
import devices
_logger = logging.getLogger(__name__)
_MODEL_CO... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/profiler.py | examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/profiler.py | """Module shard profiler."""
import argparse
import gc
import os
import time
import numpy as np
import psutil
import torch
import torch.multiprocessing as mp
import yaml
from transformers import BertTokenizer
import devices
import model_cfg
def get_shapes(tensors):
"""Get the tensor shapes, excluding the outer di... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/dataset.py | examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/dataset.py | import logging
import random
from typing import Callable, Optional, Sequence
import os
from torch.utils.data import DataLoader, Dataset, Subset
from transformers import ViTFeatureExtractor
from torchvision.datasets import ImageNet
def load_dataset_imagenet(feature_extractor: Callable, root: str, split: str='train') ... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/devices.py | examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/devices.py | """Common device configuration."""
from typing import Tuple, Union
import torch
# The torch.device to use for computation
DEVICE = None
def forward_pre_hook_to_device(_module, inputs) \
-> Union[Tuple[torch.tensor], Tuple[Tuple[torch.Tensor]]]:
"""Move tensors to the compute device (e.g., GPU), if needed."""
... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/basemodel.py | examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/basemodel.py | # Modified 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 agr... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/models/__init__.py | examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/models/__init__.py | """Models module."""
from typing import Any, Tuple, Type, Union
from torch import nn, Tensor
ModuleShardData: Type = Union[Tensor, Tuple[Tensor, ...]]
"""A module shard input/output type."""
class ModuleShardConfig:
"""Base class for shard configurations (distinct from model configurations)."""
# pylint: dis... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/models/transformers/bert.py | examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/models/transformers/bert.py | """BERT transformers."""
from collections.abc import Mapping
import logging
import math
from typing import Union
import numpy as np
import torch
from torch import nn
from transformers import BertConfig, BertForSequenceClassification, BertModel
from transformers.models.bert.modeling_bert import (
BertEmbeddings, Ber... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/models/transformers/vit.py | examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/models/transformers/vit.py | """ViT Transformers."""
from collections.abc import Mapping
import logging
import math
import os
from typing import Optional, Union
import numpy as np
import requests
import torch
from torch import nn
from transformers import ViTConfig
from transformers.models.vit.modeling_vit import (
ViTEmbeddings, ViTIntermediat... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/models/transformers/deit.py | examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/models/transformers/deit.py | """DeiT Transformers."""
from collections.abc import Mapping
import logging
import math
from typing import Optional, Union
import numpy as np
import torch
from torch import nn
from transformers import DeiTConfig
from transformers.models.deit.modeling_deit import DeiTEmbeddings
from transformers.models.vit.modeling_vit ... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/models/transformers/__init__.py | examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/models/transformers/__init__.py | """Transformers module."""
from typing import Tuple, Type, Union
from torch import Tensor
TransformerShardData: Type = Union[Tensor, Tuple[Tensor, Tensor]]
"""A transformer shard input/output type."""
| python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/utils/yaml_files.py | examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/utils/yaml_files.py | """Manage YAML files."""
import os
import yaml
def _yaml_load_map(file):
if os.path.exists(file):
with open(file, 'r', encoding='utf-8') as yfile:
yml = yaml.safe_load(yfile)
else:
yml = {}
return yml
def yaml_models_load(file) -> dict:
"""Load a YAML models file."""
... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/utils/yaml_types.py | examples/imagenet/multiedge_inference_bench/testalgorithms/automatic/utils/yaml_types.py | """YAML types."""
from typing import List, Optional, Union
def _assert_list_type(lst, dtype):
assert isinstance(lst, list)
for var in lst:
assert isinstance(var, dtype)
def yaml_model(num_layers: int, parameters_in: int, parameters_out: List[int],
mem_MB: Union[List[int], List[float]]... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/imagenet/multiedge_inference_bench/testalgorithms/manual/dataset.py | examples/imagenet/multiedge_inference_bench/testalgorithms/manual/dataset.py | import logging
import random
from typing import Callable, Optional, Sequence
import os
from torch.utils.data import DataLoader, Dataset, Subset
from transformers import ViTFeatureExtractor
from torchvision.datasets import ImageNet
def load_dataset_imagenet(feature_extractor: Callable, root: str, split: str='train') ... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/imagenet/multiedge_inference_bench/testalgorithms/manual/basemodel.py | examples/imagenet/multiedge_inference_bench/testalgorithms/manual/basemodel.py | # Modified 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 agr... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/llm-agent/singletask_learning_bench/testenv/rouge.py | examples/llm-agent/singletask_learning_bench/testenv/rouge.py | import evaluate
import numpy as np
from sedna.common.class_factory import ClassType, ClassFactory
from transformers import AutoTokenizer,AutoModelForCausalLM
import logging
@ClassFactory.register(ClassType.GENERAL, alias="rouge1")
def rouge1(y_true, y_pred, **kwargs):
rouge=evaluate.load('./examples/LLM-Agent-Benc... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/llm-agent/singletask_learning_bench/testalgorithms/basemodel.py | examples/llm-agent/singletask_learning_bench/testalgorithms/basemodel.py | import os
import zipfile
import logging
from transformers import AutoTokenizer,AutoModelForCausalLM
import torch
from peft import LoraConfig,get_peft_model,TaskType,PeftModel
from transformers import AutoModelForCausalLM,TrainingArguments,Trainer,pipeline,AutoTokenizer,DataCollatorForSeq2Seq
from sedna.common.class_fac... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cloud_VLA_finetune/singletask_learning_bench/testenv/test.py | examples/cloud_VLA_finetune/singletask_learning_bench/testenv/test.py | import json
from sedna.common.class_factory import ClassType, ClassFactory
__all__ = ["accuracy"]
def read_jsonl(file_path):
data = []
with open(file_path, 'r', encoding='utf-8') as file:
for line in file:
data.append(json.loads(line.strip()))
return data
@ClassFactory.register(ClassTy... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cloud_VLA_finetune/singletask_learning_bench/testalgorithms/vla_dataselect/finetuning.py | examples/cloud_VLA_finetune/singletask_learning_bench/testalgorithms/vla_dataselect/finetuning.py | # using_finetune.py
import os
import socket
from datetime import timedelta
from pathlib import Path
from typing import Optional, Mapping
import torch
import torch.distributed as dist
from torch.multiprocessing import spawn
from vla_component.vlaScripts.finetune import FinetuneConfig, finetune
__all__ = ["build_cfg",... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cloud_VLA_finetune/singletask_learning_bench/testalgorithms/vla_dataselect/basemodel.py | examples/cloud_VLA_finetune/singletask_learning_bench/testalgorithms/vla_dataselect/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/cloud_VLA_finetune/singletask_learning_bench/testalgorithms/vla_dataselect/generation.py | examples/cloud_VLA_finetune/singletask_learning_bench/testalgorithms/vla_dataselect/generation.py | from typing import Optional
from vla_component.experiments.robot.libero.run_libero_eval import GenerateConfig, eval_libero
def build_generation_cfg(
model_family: str = "openvla", # "openvla"
pretrained_checkpoint: str = "/inspire/hdd/global_user/chaimingxu-240108540141/jwq-test/model/models--openvla--openvla... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/smart_coding/smart_coding_learning_bench/comment/testenv/llm_judgement.py | examples/smart_coding/smart_coding_learning_bench/comment/testenv/llm_judgement.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/smart_coding/smart_coding_learning_bench/comment/testalgorithms/gen/basemodel.py | examples/smart_coding/smart_coding_learning_bench/comment/testalgorithms/gen/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/smart_coding/smart_coding_learning_bench/issue/testenv/llm_judgement.py | examples/smart_coding/smart_coding_learning_bench/issue/testenv/llm_judgement.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/smart_coding/smart_coding_learning_bench/issue/testalgorithms/gen/basemodel.py | examples/smart_coding/smart_coding_learning_bench/issue/testalgorithms/gen/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/llm_simple_qa/testenv/acc.py | examples/llm_simple_qa/testenv/acc.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/llm_simple_qa/testalgorithms/gen/op_eval.py | examples/llm_simple_qa/testalgorithms/gen/op_eval.py | from mmengine.config import read_base
from opencompass.models import HuggingFacewithChatTemplate
# import sys
# sys.path.append('/home/icyfeather/project/ianvs')
with read_base():
from core.op_extra.datasets.cmmlu.cmmlu_gen import cmmlu_datasets
datasets = [*cmmlu_datasets]
models = [
dict(
type=... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/llm_simple_qa/testalgorithms/gen/basemodel.py | examples/llm_simple_qa/testalgorithms/gen/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/TAB/cloud_edge_collaborative_inference_bench/data.py | examples/TAB/cloud_edge_collaborative_inference_bench/data.py | import json
import os
import numpy as np
from typing import List, Dict
from sedna.datasources import BaseDataSource
from sedna.common.class_factory import ClassFactory, ClassType
import logging
logging.basicConfig(level=logging.INFO)
@ClassFactory.register(ClassType.GENERAL, alias="ECHRDataProcessor")
class ECHRDataP... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/TAB/cloud_edge_collaborative_inference_bench/test_env/performance_metrics.py | examples/TAB/cloud_edge_collaborative_inference_bench/test_env/performance_metrics.py |
import time
from sedna.common.class_factory import ClassFactory, ClassType
from sentence_transformers import SentenceTransformer, util
import logging
import json
import os
def _extract_annotations_from_text(text: str):
if not isinstance(text, str):
return {}
marker = "\n\nANNOTATIONS_JSON="
if ma... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/TAB/cloud_edge_collaborative_inference_bench/test_env/privacy_metrics.py | examples/TAB/cloud_edge_collaborative_inference_bench/test_env/privacy_metrics.py |
import os
import sys
from sedna.common.class_factory import ClassFactory, ClassType
import logging
import json
try:
from ..test_algorithms.privacy_desensitization.privacy_evaluator import PrivacyEvaluator
except (ImportError, ValueError):
current_dir = os.path.dirname(os.path.abspath(__file__))
bench_... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/TAB/cloud_edge_collaborative_inference_bench/test_env/visualization_tools.py | examples/TAB/cloud_edge_collaborative_inference_bench/test_env/visualization_tools.py | import os
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from prettytable import from_csv
__all__ = ["generate_report"]
class VisualizationTools:
def __init__(self, output_dir="./results"):
self.output_dir = output_dir
self.rank_dir = os.path.join(output_dir, "rank")
... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/TAB/cloud_edge_collaborative_inference_bench/test_algorithms/privacy_desensitization/differential_privacy.py | examples/TAB/cloud_edge_collaborative_inference_bench/test_algorithms/privacy_desensitization/differential_privacy.py | import numpy as np
import time
from diffprivlib.mechanisms import Laplace
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
import os
class DifferentialPrivacy:
"""Implementing differential privacy protection methods"""
def __init__(self, epsilon=1.0, model_path: str | N... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/TAB/cloud_edge_collaborative_inference_bench/test_algorithms/privacy_desensitization/ner_masking.py | examples/TAB/cloud_edge_collaborative_inference_bench/test_algorithms/privacy_desensitization/ner_masking.py | import spacy
import time
import subprocess
class NERMasking:
"""Privacy entity masking using named entity recognition (adapted to ECHR entity types)"""
def __init__(self):
try:
self.nlp = spacy.load("en_core_web_lg")
except OSError:
subprocess.run(["python", "-m", "... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/TAB/cloud_edge_collaborative_inference_bench/test_algorithms/privacy_desensitization/regex_pseudonymization.py | examples/TAB/cloud_edge_collaborative_inference_bench/test_algorithms/privacy_desensitization/regex_pseudonymization.py | import re
import time
class RegexPseudonymization:
"""Use regular expressions for privacy data anonymization"""
def __init__(self):
self.patterns = {
'email': r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
'phone': r'\b(?:\+\d{1,3}\s?)?\(?\d{3}\)?[\s.-]?\d{3}[\s.-]... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/TAB/cloud_edge_collaborative_inference_bench/test_algorithms/privacy_desensitization/__init__.py | examples/TAB/cloud_edge_collaborative_inference_bench/test_algorithms/privacy_desensitization/__init__.py | from .regex_pseudonymization import RegexPseudonymization
from .ner_masking import NERMasking
from .differential_privacy import DifferentialPrivacy
def desensitize(text, methods=None):
methods = methods or ["regex"]
current_text = text
regex_processor = RegexPseudonymization()
ner_processor = NER... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/TAB/cloud_edge_collaborative_inference_bench/test_algorithms/privacy_desensitization/privacy_evaluator.py | examples/TAB/cloud_edge_collaborative_inference_bench/test_algorithms/privacy_desensitization/privacy_evaluator.py | import numpy as np
import spacy
from sentence_transformers import SentenceTransformer, util
import os
from sedna.datasources import BaseDataSource
import json
class PrivacyEvaluator:
def __init__(self, dataset: BaseDataSource = None):
# Force offline mode for transformers/sentence-transformers to avoi... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/TAB/cloud_edge_collaborative_inference_bench/test_algorithms/hard_sample_mining/hard_sample_mining.py | examples/TAB/cloud_edge_collaborative_inference_bench/test_algorithms/hard_sample_mining/hard_sample_mining.py | # test_algorithms/hard_sample_mining/hard_sample_mining.py
"""
Full process inference scheduler (cloud/edge/privacy offloading), compatible with Sedna/Anvs model dependency automatic injection.
Implementation strategy:
1. YAML registration for "type: edgemodel/cloudmodel", the framework will be instantiated and automat... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/TAB/cloud_edge_collaborative_inference_bench/test_algorithms/edge_model/edge_model.py | examples/TAB/cloud_edge_collaborative_inference_bench/test_algorithms/edge_model/edge_model.py | # edge_model.py
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import time
import json
import os
from sedna.common.class_factory import ClassFactory, ClassType
class HuggingfaceLLM:
def __init__(self,** kwargs):
self.model_name = kwargs.get("model")
self.tokenizer = AutoToke... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/TAB/cloud_edge_collaborative_inference_bench/test_algorithms/cloud_model/cloud_model.py | examples/TAB/cloud_edge_collaborative_inference_bench/test_algorithms/cloud_model/cloud_model.py | # cloud_model.py
import time
import json
import os
import sys
import requests
from dotenv import load_dotenv
from sedna.common.class_factory import ClassFactory, ClassType
import logging
current_dir = os.path.dirname(os.path.abspath(__file__))
try:
from ..privacy_desensitization import (
RegexPseudonymi... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testenv/metrics/semantic_conformance.py | examples/phys_scene_gen/singletask_learning_bench/testenv/metrics/semantic_conformance.py | # Copyright 2025 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/phys_scene_gen/singletask_learning_bench/testenv/metrics/navigation_validity.py | examples/phys_scene_gen/singletask_learning_bench/testenv/metrics/navigation_validity.py | # Copyright 2025 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/phys_scene_gen/singletask_learning_bench/testenv/metrics/__init__.py | examples/phys_scene_gen/singletask_learning_bench/testenv/metrics/__init__.py | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false | |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/holodeck_generator.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/holodeck_generator.py | # Copyright 2024 Holodeck.
#
# 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 writing, so... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/constants.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/constants.py | # Copyright 2024 Holodeck.
#
# 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 writing, so... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/phys_scene_generator.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/phys_scene_generator.py | # Copyright 2025 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/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/__init__.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/__init__.py | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false | |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/prompts.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/prompts.py | # Copyright 2024 Holodeck, 2025 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 ... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/objaverse_retriever.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/objaverse_retriever.py | # Copyright 2024 Holodeck.
#
# 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 writing, so... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/rooms.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/rooms.py | # Copyright 2024 Holodeck.
#
# 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 writing, so... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/object_selector.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/object_selector.py | # Copyright 2024 Holodeck, 2025 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 ... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | true |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/ceiling_objects.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/ceiling_objects.py | # Copyright 2024 Holodeck, 2025 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 ... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/unified_retriever.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/unified_retriever.py | # Copyright 2025 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/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/windows.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/windows.py | # Copyright 2024 Holodeck.
#
# 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 writing, so... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/skybox.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/skybox.py | # Copyright 2024 Holodeck.
#
# 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 writing, so... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/utils.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/utils.py | # Copyright 2024 Holodeck, 2025 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 ... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/milp_utils.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/milp_utils.py | # Copyright 2024 Holodeck.
#
# 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 writing, so... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/__init__.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/__init__.py | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false | |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/walls.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/walls.py | # Copyright 2024 Holodeck.
#
# 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 writing, so... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/wall_objects.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/wall_objects.py | # Copyright 2024 Holodeck, 2025 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 ... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/lights.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/lights.py | # Copyright 2024 Holodeck.
#
# 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 writing, so... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/doors.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/doors.py | # Copyright 2024 Holodeck.
#
# 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 writing, so... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/layers.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/layers.py | # Copyright 2024 Holodeck.
#
# 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 writing, so... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/holodeck.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/holodeck.py | # Copyright 2024 Holodeck, 2025 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 ... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/small_objects.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/small_objects.py | # Copyright 2024 Holodeck, 2025 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 ... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/floor_objects.py | examples/phys_scene_gen/singletask_learning_bench/testalgorithms/ai2holodeck/generation/floor_objects.py | # Copyright 2024 Holodeck, 2025 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 ... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | true |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/singletask_learning_bench/semantic-segmentation/testenv/map.py | examples/cityscapes/singletask_learning_bench/semantic-segmentation/testenv/map.py | import torch
from tqdm import tqdm
from sedna.common.class_factory import ClassType, ClassFactory
from RFNet.dataloaders import make_data_loader
import RFNet.eval_config as valid_cfgs
from RFNet.utils.metrics import Evaluator
__all__ = ('accuracy')
@ClassFactory.register(ClassType.GENERAL, alias="map")
def accuracy... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/singletask_learning_bench/semantic-segmentation/testalgorithms/rfnet/basemodel.py | examples/cityscapes/singletask_learning_bench/semantic-segmentation/testalgorithms/rfnet/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/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/util.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/util.py | import yaml
def load_yaml(path):
with open(path) as f:
data = yaml.load(f, Loader=yaml.FullLoader)
# print(data)
return data
if __name__ == '__main__':
load_yaml('config.yaml')
| python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/__init__.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/__init__.py | from . import deeplabv3
from . import selftaughtlearning
| python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/train.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/train.py | from sedna.common.log import LOGGER
from sedna.algorithms.unseen_task_processing.GANwithSelfTaughtLearning.util import load_yaml
import matplotlib.pyplot as plt
import os
from sedna.algorithms.unseen_task_processing.GANwithSelfTaughtLearning.deeplabv3.datasets import DatasetTrain, DatasetVal
from sedna.algorithms.unse... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/datasets.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/datasets.py | import torch
import torch.utils.data
import numpy as np
import cv2
import os
train_dirs = ["jena/", "zurich/", "weimar/", "ulm/", "tubingen/", "stuttgart/",
"strasbourg/", "monchengladbach/", "krefeld/", "hanover/",
"hamburg/", "erfurt/", "dusseldorf/", "darmstadt/", "cologne/",
... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/__init__.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/__init__.py | from . import train
from . import datasets
from . import model
| python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/model/deeplabv3.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/model/deeplabv3.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import sys
from sedna.algorithms.unseen_task_processing.GANwithSelfTaughtLearning.deeplabv3.model.resnet import ResNet18_OS16, ResNet34_OS16, ResNet50_OS16, ResNet101_OS16, ResNet152_OS16, ResNet18_OS8, ResNet34_OS8
from sedna.algorithms.unse... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/model/resnet.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/model/resnet.py | # camera-ready
# NOTE! OS: output stride, the ratio of input image resolution to final output resolution (OS16: output size is (img_h/16, img_w/16)) (OS8: output size is (img_h/8, img_w/8))
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
def make_layer(block, in... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/model/__init__.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/model/__init__.py | from . import aspp
from . import deeplabv3
from . import resnet | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/model/aspp.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/model/aspp.py | # camera-ready
import torch
import torch.nn as nn
import torch.nn.functional as F
class ASPP(nn.Module):
def __init__(self, num_classes):
super(ASPP, self).__init__()
self.conv_1x1_1 = nn.Conv2d(512, 256, kernel_size=1)
self.bn_conv_1x1_1 = nn.BatchNorm2d(256)
self.conv_3x3_1 = n... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/utils/preprocess_data.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/utils/preprocess_data.py | # camera-ready
import pickle
import numpy as np
import cv2
import os
from collections import namedtuple
# (NOTE! this is taken from the official Cityscapes scripts:)
Label = namedtuple( 'Label' , [
'name' , # The identifier of this label, e.g. 'car', 'person', ... .
# We use them to un... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/utils/utils.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/utils/utils.py | # camera-ready
import torch
import torch.nn as nn
import numpy as np
def add_weight_decay(net, l2_value, skip_list=()):
# https://raberrytv.wordpress.com/2017/10/29/pytorch-weight-decay-made-easy/
decay, no_decay = [], []
for name, param in net.named_parameters():
if not param.requires_grad:
... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/utils/__init__.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/utils/__init__.py | from . import utils | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/utils/random_code.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/deeplabv3/utils/random_code.py | # camera-ready
# this file contains code snippets which I have found (more or less) useful at
# some point during the project. Probably nothing interesting to see here.
import pickle
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
model_id = "13_2_2_2"
with open("/home/fr... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/train.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/train.py | from util import load_yaml
import sys
import lpips
import torch
from torch import nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data.dataloader import DataLoader
from torchvision import transforms
from torchvision import utils as vutils
import argparse
import random
from tqdm import t... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/diffaug.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/diffaug.py | # Differentiable Augmentation for Data-Efficient GAN Training
# Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han
# https://arxiv.org/pdf/2006.10738
import torch
import torch.nn.functional as F
def DiffAugment(x, policy='', channels_first=True):
if policy:
if not channels_first:
x ... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/models.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/models.py | from random import randint
import torch
import torch.nn as nn
from torch.nn.utils import spectral_norm
import torch.nn.functional as F
import random
seq = nn.Sequential
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
try:
m.weight.data.normal_(0.0, ... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/generate_fake_imgs.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/generate_fake_imgs.py | import torch
from models import Generator, weights_init
import matplotlib.pyplot as plt
import os
from collections import OrderedDict
import numpy as np
from skimage import io
device = 'cuda'
ngf = 64
nz = 256
im_size = 1024
netG = Generator(ngf=ngf, nz=nz, im_size=im_size).to(device)
weights_init(netG)
weight... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/__init__.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/__init__.py | from . import train | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/operation.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/operation.py | import lmdb
from io import BytesIO
import os
import numpy as np
import torch
import torch.utils.data as data
from torch.utils.data import Dataset
from PIL import Image
from copy import deepcopy
import shutil
import json
def InfiniteSampler(n):
"""Data sampler"""
i = n - 1
order = np.random.permutation(n)
... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/lpips/base_model.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/lpips/base_model.py | import os
import torch
from torch.autograd import Variable
from pdb import set_trace as st
from IPython import embed
class BaseModel():
def __init__(self):
pass;
def name(self):
return 'BaseModel'
def initialize(self, use_gpu=True, gpu_ids=[0]):
self.use_gpu = use_gpu
... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/lpips/dist_model.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/lpips/dist_model.py |
from __future__ import absolute_import
import sys
import numpy as np
import torch
from torch import nn
import os
from collections import OrderedDict
from torch.autograd import Variable
import itertools
from .base_model import BaseModel
from scipy.ndimage import zoom
import fractions
import functools
import skimage.tr... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/lpips/networks_basic.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/lpips/networks_basic.py |
from __future__ import absolute_import
import sys
import torch
import torch.nn as nn
import torch.nn.init as init
from torch.autograd import Variable
import numpy as np
from pdb import set_trace as st
from skimage import color
from IPython import embed
from . import pretrained_networks as pn
import lpips as util
de... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/lpips/__init__.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/lpips/__init__.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import skimage
import torch
from torch.autograd import Variable
from lpips import dist_model
from skimage.metrics import structural_similarity as compare_ssim
class PerceptualLoss(torch... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/lpips/pretrained_networks.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/GAN/lpips/pretrained_networks.py | from collections import namedtuple
import torch
from torchvision import models as tv
from IPython import embed
class squeezenet(torch.nn.Module):
def __init__(self, requires_grad=False, pretrained=True):
super(squeezenet, self).__init__()
pretrained_features = tv.squeezenet1_1(pretrained=pretrained... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/selftaughtlearning/train.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/selftaughtlearning/train.py | from torchvision.utils import save_image
from models import Autoencoder
import torch
import cv2
import numpy as np
from torch.utils.data import DataLoader
import os
import torch.nn as nn
import torch.optim as optim
import csv
import time
import sys
from util import load_yaml
class DatasetAutoEncoder(torch.utils.data.... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/selftaughtlearning/models.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/selftaughtlearning/models.py | import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
def __init__(self) -> None:
super(Encoder, self).__init__()
self.enc1 = nn.Conv2d(
in_channels=3, out_channels=8, kernel_size=3, stride=2, padding=1
)
self.enc2 = nn.Conv2d(
in_c... | python | Apache-2.0 | 645c83695c14b75f0ba37f53f7b1842b95b09f6f | 2026-01-05T07:07:55.342322Z | false |
kubeedge/ianvs | https://github.com/kubeedge/ianvs/blob/645c83695c14b75f0ba37f53f7b1842b95b09f6f/examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/selftaughtlearning/__init__.py | examples/cityscapes/lifelong_learning_bench/unseen_task_processing-GANwithSelfTaughtLearning/selftaughtlearning/__init__.py | from . import train | 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/testenv/orientation_error.py | examples/industrialEI/pose-estimation-llio/testenv/orientation_error.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/testenv/position_error.py | examples/industrialEI/pose-estimation-llio/testenv/position_error.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 |
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