input stringlengths 33 5k | output stringlengths 32 5k |
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# Copyright (c) OpenMMLab. All rights reserved.
from .det_inferencer import DetInferencer
from .inference import (async_inference_detector, inference_detector,
inference_mot, init_detector, init_track_model)
__all__ = [
'init_detector', 'async_inference_detector', 'inference_detector',
... | # Copyright (c) OpenMMLab. All rights reserved.
from .det_inferencer import DetInferencer
from .inference import (async_inference_detector, inference_detector,
init_detector)
__all__ = [
'init_detector', 'async_inference_detector', 'inference_detector',
'DetInferencer'
]
|
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.models.utils.misc import get_box_tensor
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import bbox_overlaps
def cast_tensor_type(x, scale=1., dtype=None):
if dtype == 'fp16':
# scale is for preventing overflows
... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import bbox_overlaps
def cast_tensor_type(x, scale=1., dtype=None):
if dtype == 'fp16':
# scale is for preventing overflows
x = (x / scale).half()
return x
@TASK_UTI... |
from __future__ import annotations
from collections.abc import Iterable
from typing import Any
import torch
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import fullname
class CosineSimilarityLoss(nn.Module):
def __init__(... | from __future__ import annotations
from collections.abc import Iterable
from typing import Any
import torch
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import fullname
class CosineSimilarityLoss(nn.Module):
def __init__(... |
# Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .compat_config import compat_cfg
from .dist_utils import (all_reduce_dict, allreduce_grads, reduce_mean,
sync_random_seed)
from .logger import get_caller_name, log_img_scale
from .memory import AvoidCUDAOO... | # Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .compat_config import compat_cfg
from .dist_utils import (all_reduce_dict, allreduce_grads, reduce_mean,
sync_random_seed)
from .logger import get_caller_name, log_img_scale
from .memory import AvoidCUDAOO... |
import importlib
import shutil
import warnings
from typing import List
import fsspec
import fsspec.asyn
from fsspec.implementations.local import LocalFileSystem
from ..utils.deprecation_utils import deprecated
from . import compression
_has_s3fs = importlib.util.find_spec("s3fs") is not None
if _has_s3fs:
from... | import importlib
import shutil
import warnings
from typing import List
import fsspec
import fsspec.asyn
from fsspec.implementations.local import LocalFileSystem
from ..utils.deprecation_utils import deprecated
from . import compression
_has_s3fs = importlib.util.find_spec("s3fs") is not None
if _has_s3fs:
from... |
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets 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
#
# U... | # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets 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
#
# U... |
# Copyright (c) OpenMMLab. All rights reserved.
import re
from mmengine.config import Config
def replace_cfg_vals(ori_cfg):
"""Replace the string "${key}" with the corresponding value.
Replace the "${key}" with the value of ori_cfg.key in the config. And
support replacing the chained ${key}. Such as, re... | # Copyright (c) OpenMMLab. All rights reserved.
import re
from mmcv.utils import Config
def replace_cfg_vals(ori_cfg):
"""Replace the string "${key}" with the corresponding value.
Replace the "${key}" with the value of ori_cfg.key in the config. And
support replacing the chained ${key}. Such as, replace... |
"""
Empty index.
An index that doesn't contain any documents. Can only be used for
pure LLM calls.
"""
from typing import Any, Dict, Optional, Sequence
from llama_index.core.base.base_query_engine import BaseQueryEngine
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.data_struct... | """Empty index.
An index that doesn't contain any documents. Can only be used for
pure LLM calls.
"""
from typing import Any, Dict, Optional, Sequence
from llama_index.core.base.base_query_engine import BaseQueryEngine
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.data_structs... |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import torch.nn as nn
from mmcv.cnn import ConvModule, Scale
from mmdet.models.dense_heads.fcos_head import FCOSHead
from mmdet.registry import MODELS
@MODELS.register_module()
class NASFCOSHead(FCOSHead):
"""Anchor-free head used in `NASFCOS <https://... | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import torch.nn as nn
from mmcv.cnn import ConvModule, Scale
from mmdet.models.dense_heads.fcos_head import FCOSHead
from ..builder import HEADS
@HEADS.register_module()
class NASFCOSHead(FCOSHead):
"""Anchor-free head used in `NASFCOS <https://arxiv.o... |
import random
import pytest
from jina import Document, DocumentArray
from lightgbm_ranker import LightGBMRanker
NUM_DOCS = 1000
NUM_MATCHES = 5
@pytest.fixture
def ranker():
return LightGBMRanker(
query_features=['brand_query', 'price_query'],
match_features=['brand_match', 'price_match'],
... | import random
import pytest
from jina import Document, DocumentArray
from ..lightgbm_ranker import LightGBMRanker
NUM_DOCS = 1000
NUM_MATCHES = 5
@pytest.fixture
def ranker():
return LightGBMRanker(
query_features=['brand_query', 'price_query'],
match_features=['brand_match', 'price_match'],
... |
_base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/cityscapes_detection.py',
'../_base_/default_runtime.py'
]
model = dict(
backbone=dict(init_cfg=None),
roi_head=dict(
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_ou... | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/cityscapes_detection.py',
'../_base_/default_runtime.py'
]
model = dict(
backbone=dict(init_cfg=None),
roi_head=dict(
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_ou... |
"""
This examples trains a CrossEncoder for the NLI task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it learns to predict the labels: "contradiction": 0, "entailment": 1, "neutral": 2.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:
python trai... | """
This examples trains a CrossEncoder for the NLI task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it learns to predict the labels: "contradiction": 0, "entailment": 1, "neutral": 2.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:
python trai... |
import os
from pathlib import Path
from torchaudio.datasets import gtzan
from torchaudio_unittest.common_utils import get_whitenoise, normalize_wav, save_wav, TempDirMixin, TorchaudioTestCase
def get_mock_dataset(root_dir):
"""
root_dir: directory to the mocked dataset
"""
mocked_samples = []
moc... | import os
from pathlib import Path
from torchaudio.datasets import gtzan
from torchaudio_unittest.common_utils import (
get_whitenoise,
normalize_wav,
save_wav,
TempDirMixin,
TorchaudioTestCase,
)
def get_mock_dataset(root_dir):
"""
root_dir: directory to the mocked dataset
"""
mo... |
# Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .cornernet import CornerNet
from .deformable_detr import DeformableDETR
from .detr import DETR
from .fast_r... | from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .cornernet import CornerNet
from .deformable_detr import DeformableDETR
from .detr import DETR
from .fast_rcnn import FastRCNN
from .faster_rcnn import Fas... |
from __future__ import annotations
try:
from typing import Self
except ImportError:
from typing_extensions import Self
from torch import Tensor, nn
from sentence_transformers.models.Module import Module
class LayerNorm(Module):
config_keys: list[str] = ["dimension"]
def __init__(self, dimension: i... | from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import Tensor, nn
class LayerNorm(nn.Module):
def __init__(self, dimension: int):
super()... |
from typing import List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
import asana
class AsanaReader(BaseReader):
"""
Asana reader. Reads data from an Asana workspace.
Args:
asana_token (str): Asana token.
"""
def __init__(... | from typing import List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
import asana
class AsanaReader(BaseReader):
"""Asana reader. Reads data from an Asana workspace.
Args:
asana_token (str): Asana token.
"""
def __init__(self, ... |
from typing import Any, Dict
from torchvision import datapoints
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2.utils import is_simple_tensor
class UniformTemporalSubsample(Transform):
_transformed_types = (is_simple_tensor, datapoints.Video)
def __init__(sel... | from typing import Any, Dict
from torchvision import datapoints
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2.utils import is_simple_tensor
class UniformTemporalSubsample(Transform):
_transformed_types = (is_simple_tensor, datapoints.Video)
def __init__(sel... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.initializers import deserialize as deserialize
from keras.src.initializers import get as get
from keras.src.initializers import serialize as serialize
from keras.src.initializers.cons... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.initializers import deserialize
from keras.src.initializers import get
from keras.src.initializers import serialize
from keras.src.initializers.constant_initializers import STFT
from ... |
_base_ = ['./ld_r18-gflv1-r101_fpn_1x_coco.py']
model = dict(
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_c... | _base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py']
model = dict(
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_c... |
from typing import TYPE_CHECKING, Any, Dict, Type, TypeVar
from docarray.document.abstract_document import AbstractDocument
from docarray.document.base_node import BaseNode
if TYPE_CHECKING:
from docarray.proto import DocumentProto, NodeProto
try:
import torch # noqa: F401
except ImportError:
torch_imp... | from typing import TYPE_CHECKING, Any, Dict, Type, TypeVar
from docarray.document.abstract_document import AbstractDocument
from docarray.document.base_node import BaseNode
if TYPE_CHECKING:
from docarray.proto import DocumentProto, NodeProto
try:
import torch # noqa: F401
except ImportError:
torch_imp... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Optional, Sequence, Tuple, Union
from mmengine.data import BaseDataElement
from .base import BaseEvaluator
class ComposedEvaluator:
"""Wrapper class to compose multiple :class:`BaseEvaluator` instances.
Args:
evaluators (Sequenc... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Optional, Sequence, Tuple, Union
from mmengine.data import BaseDataSample
from .base import BaseEvaluator
class ComposedEvaluator:
"""Wrapper class to compose multiple :class:`BaseEvaluator` instances.
Args:
evaluators (Sequence... |
from langchain.chains.router.base import MultiRouteChain, RouterChain
from langchain.chains.router.llm_router import LLMRouterChain
from langchain.chains.router.multi_prompt import MultiPromptChain
from langchain.chains.router.multi_retrieval_qa import MultiRetrievalQAChain
__all__ = [
"LLMRouterChain",
"Multi... | from langchain.chains.router.base import MultiRouteChain, RouterChain
from langchain.chains.router.llm_router import LLMRouterChain
from langchain.chains.router.multi_prompt import MultiPromptChain
from langchain.chains.router.multi_retrieval_qa import MultiRetrievalQAChain
__all__ = [
"RouterChain",
"MultiRou... |
import os
import time
import pytest
from jina import Flow, Executor
class SlowExecutor(Executor):
def close(self) -> None:
with open(os.path.join(self.metas.workspace, 'test'), 'w', encoding='utf-8') as f:
time.sleep(10)
f.write('x')
@pytest.mark.slow
def test_slow_executor_clo... | import os
import time
import pytest
from jina import Flow, Executor
class SlowExecutor(Executor):
def close(self) -> None:
with open(os.path.join(self.metas.workspace, 'test'), 'w') as f:
time.sleep(10)
f.write('x')
@pytest.mark.slow
def test_slow_executor_close(tmpdir):
wi... |
"""Standard LangChain interface tests"""
import os
from typing import Type
from langchain_core.language_models import BaseChatModel
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_openai import AzureChatOpenAI
OPENAI_API_VERSION = os.environ.get("AZURE_OPENAI_API_VERSION", "")... | """Standard LangChain interface tests"""
import os
from typing import Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_openai import AzureChatOpenAI
OPENAI_API_VERSION = os.environ.get("AZURE_OPENAI_API... |
import math
from keras.src import backend
from keras.src import layers
from keras.src import ops
from keras.src.api_export import keras_export
@keras_export("keras.layers.GaussianDropout")
class GaussianDropout(layers.Layer):
"""Apply multiplicative 1-centered Gaussian noise.
As it is a regularization layer... | import math
from keras.src import backend
from keras.src import layers
from keras.src import ops
from keras.src.api_export import keras_export
@keras_export("keras.layers.GaussianDropout")
class GaussianDropout(layers.Layer):
"""Apply multiplicative 1-centered Gaussian noise.
As it is a regularization layer... |
import os
from typing import Dict, Tuple
import numpy as np
from jina import Document, DocumentArray, Executor, requests
from jina.logging.logger import JinaLogger
class CrudIndexer(Executor):
"""Simple indexer class"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.logger = ... | import os
from typing import Dict, Tuple
import numpy as np
from jina import Document, DocumentArray, Executor, requests
from jina.logging.logger import JinaLogger
class CrudIndexer(Executor):
"""Simple indexer class"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.logger = ... |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.backbones import ResNeSt
from mmdet.models.backbones.resnest import Bottleneck as BottleneckS
def test_resnest_bottleneck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
Bot... | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.backbones import ResNeSt
from mmdet.models.backbones.resnest import Bottleneck as BottleneckS
def test_resnest_bottleneck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
Bot... |
import os
from pathlib import Path
from torchaudio.datasets import cmuarctic
from torchaudio_unittest.common_utils import get_whitenoise, normalize_wav, save_wav, TempDirMixin, TorchaudioTestCase
def get_mock_dataset(root_dir):
"""
root_dir: directory to the mocked dataset
"""
mocked_data = []
sa... | import os
from pathlib import Path
from torchaudio.datasets import cmuarctic
from torchaudio_unittest.common_utils import (
get_whitenoise,
normalize_wav,
save_wav,
TempDirMixin,
TorchaudioTestCase,
)
def get_mock_dataset(root_dir):
"""
root_dir: directory to the mocked dataset
"""
... |
from keras.src import backend
from keras.src import tree
from keras.src.api_export import keras_export
from keras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layer import ( # noqa: E501
BaseImagePreprocessingLayer,
)
@keras_export("keras.layers.RandomGrayscale")
class RandomGrayscale(Ba... | from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layer import ( # noqa: E501
BaseImagePreprocessingLayer,
)
@keras_export("keras.layers.RandomGrayscale")
class RandomGrayscale(BaseImagePreprocessingLayer):... |
_base_ = '../cascade_rcnn/cascade-rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| _base_ = '../cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
|
from abc import abstractmethod
from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, TypeVar, Union
from docarray import Document, DocumentArray
from docarray.math import ndarray
from docarray.score import NamedScore
from qdrant_client.http import models
from qdrant_client.http.models.models import Distanc... | from abc import abstractmethod
from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, TypeVar, Union
from docarray import Document, DocumentArray
from docarray.math import ndarray
from docarray.score import NamedScore
from qdrant_client.http import models
from qdrant_client.http.models.models import Distanc... |
from __future__ import annotations
from sentence_transformers.losses.MSELoss import MSELoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseMSELoss(MSELoss):
def __init__(self, model: SparseEncoder) -> None:
"""
# TODO: Update as it's mentionned trainings ... | from __future__ import annotations
from sentence_transformers.losses.MSELoss import MSELoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseMSELoss(MSELoss):
def __init__(self, model: SparseEncoder) -> None:
return super().__init__(model)
|
from keras.src.api_export import keras_export
# Unique source of truth for the version number.
__version__ = "3.9.0"
@keras_export("keras.version")
def version():
return __version__
| from keras.src.api_export import keras_export
# Unique source of truth for the version number.
__version__ = "3.8.0"
@keras_export("keras.version")
def version():
return __version__
|
# Copyright (c) OpenMMLab. All rights reserved.
from .augment_wrappers import AutoAugment, RandAugment
from .colorspace import (AutoContrast, Brightness, Color, ColorTransform,
Contrast, Equalize, Invert, Posterize, Sharpness,
Solarize, SolarizeAdd)
from .compose import... | # Copyright (c) OpenMMLab. All rights reserved.
from .auto_augment import (AutoAugment, BrightnessTransform, ColorTransform,
ContrastTransform, EqualizeTransform, Rotate, Shear,
Translate)
from .compose import Compose
from .formatting import (ImageToTensor, PackDetI... |
from typing import List, Optional
from docarray.base_doc.doc import BaseDoc
def test_base_document_init():
doc = BaseDoc()
assert doc.id is not None
def test_update():
class MyDocument(BaseDoc):
content: str
title: Optional[str] = None
tags_: List
doc1 = MyDocument(
... | from typing import List, Optional
from docarray.base_doc.doc import BaseDoc
def test_base_document_init():
doc = BaseDoc()
assert doc.id is not None
def test_update():
class MyDocument(BaseDoc):
content: str
title: Optional[str] = None
tags_: List
doc1 = MyDocument(
... |
# coding: utf-8
"""Script for generating files with NuGet package metadata."""
import datetime
import sys
from pathlib import Path
from shutil import copyfile
if __name__ == "__main__":
source = Path(sys.argv[1])
nuget_dir = Path(__file__).absolute().parent / "nuget"
linux_folder_path = nuget_dir / "runti... | # coding: utf-8
"""Script for generating files with NuGet package metadata."""
import datetime
import sys
from pathlib import Path
from shutil import copyfile
if __name__ == "__main__":
source = Path(sys.argv[1])
current_dir = Path(__file__).absolute().parent
linux_folder_path = current_dir / "runtimes" /... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict
import torch.nn as nn
from torch import Tensor
from mmdet.registry import MODELS
from ..layers import (ConditionalDetrTransformerDecoder,
DetrTransformerEncoder, SinePositionalEncoding)
from .detr import DETR
@MODELS.regis... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict
import torch.nn as nn
from torch import Tensor
from mmdet.registry import MODELS
from ..layers import (ConditionalDetrTransformerDecoder,
DetrTransformerEncoder, SinePositionalEncoding)
from .detr import DETR
@MODELS.regis... |
import warnings
from typing import Optional, TypeVar
from docarray.typing.bytes.video_bytes import VideoBytes, VideoLoadResult
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.utils._internal.misc import is_notebook
T = TypeVar('T', bound='VideoUr... | import warnings
from typing import Optional, TypeVar
from docarray.typing.bytes.video_bytes import VideoBytes, VideoLoadResult
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.utils._internal.misc import is_notebook
T = TypeVar('T', bound='VideoUr... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.tree.tree_api import MAP_TO_NONE
from keras.src.tree.tree_api import assert_same_paths
from keras.src.tree.tree_api import assert_same_structure
from keras.src.tree.tree_api import fl... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.tree.tree_api import assert_same_paths
from keras.src.tree.tree_api import assert_same_structure
from keras.src.tree.tree_api import flatten
from keras.src.tree.tree_api import flatte... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.quantizers import deserialize
from keras.src.quantizers import get
from keras.src.quantizers import serialize
from keras.src.quantizers.quantizers import AbsMaxQuantizer
from keras.sr... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.quantizers import deserialize
from keras.src.quantizers import get
from keras.src.quantizers import serialize
from keras.src.quantizers.quantizers import AbsMaxQuantizer
from keras.sr... |
"""Callback Handler that writes to a file."""
from __future__ import annotations
from pathlib import Path
from typing import TYPE_CHECKING, Any, Optional, TextIO, cast
from typing_extensions import override
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.utils.input import print_text
i... | """Callback Handler that writes to a file."""
from __future__ import annotations
from pathlib import Path
from typing import TYPE_CHECKING, Any, Optional, TextIO, cast
from typing_extensions import override
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.utils.input import print_text
i... |
from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._datapoint import Datapoint
class Image(Datapoint):
"""[BETA] :class:`torch.Tensor` subclass for images.
Args:
data (tensor-like, PIL.Image.Image): Any data that can be turned into a tens... | from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._datapoint import Datapoint
class Image(Datapoint):
"""[BETA] :class:`torch.Tensor` subclass for images.
Args:
data (tensor-like, PIL.Image.Image): Any data that can be turned into a tens... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
from mmdet.registry import MODELS
from .fcn_mask_head import FCNMaskHead
@MODELS.register_module()
class SCNetMaskHead(FCNMaskHead):
"""Mask head for `SCNet <https://arxiv.org/abs/2012.10150>`_.
Args... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
from mmdet.registry import MODELS
from .fcn_mask_head import FCNMaskHead
@MODELS.register_module()
class SCNetMaskHead(FCNMaskHead):
"""Mask head for `SCNet <https://arxiv.org/abs/2012.10150>`_.
Args... |
import torch
from torch import Tensor
from torch import nn
from typing import List, Dict
import os
import json
import logging
import numpy as np
from .tokenizer import WhitespaceTokenizer
logger = logging.getLogger(__name__)
class BoW(nn.Module):
"""Implements a Bag-of-Words (BoW) model to derive sentence embed... | import torch
from torch import Tensor
from torch import nn
from typing import List, Dict
import os
import json
import logging
import numpy as np
from .tokenizer import WhitespaceTokenizer
logger = logging.getLogger(__name__)
class BoW(nn.Module):
"""Implements a Bag-of-Words (BoW) model to derive sentence embed... |
import argparse
import logging
from typing import Optional
import torch
import torchaudio
from torchaudio.prototype.ctc_decoder import lexicon_decoder
logger = logging.getLogger(__name__)
def _download_files(lexicon_file, kenlm_file):
torch.hub.download_url_to_file(
"https://pytorch.s3.amazonaws.com/to... | import argparse
import logging
from typing import Optional
import torch
import torchaudio
from torchaudio.prototype.ctc_decoder import lexicon_decoder
logger = logging.getLogger(__name__)
def _download_files(lexicon_file, kenlm_file):
torch.hub.download_url_to_file(
"https://pytorch.s3.amazonaws.com/to... |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... | # coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... |
# Copyright (c) OpenMMLab. All rights reserved.
from .default_scope import DefaultScope
from .registry import Registry, build_from_cfg
from .root import (DATA_SAMPLERS, DATASETS, EVALUATORS, HOOKS, LOOPS,
MODEL_WRAPPERS, MODELS, OPTIMIZER_CONSTRUCTORS, OPTIMIZERS,
PARAM_SCHEDULERS,... | # Copyright (c) OpenMMLab. All rights reserved.
from .registry import Registry, build_from_cfg
from .root import (DATA_SAMPLERS, DATASETS, EVALUATORS, HOOKS, LOOPS,
MODEL_WRAPPERS, MODELS, OPTIMIZER_CONSTRUCTORS, OPTIMIZERS,
PARAM_SCHEDULERS, RUNNER_CONSTRUCTORS, RUNNERS, TASK_UTIL... |
"""
This script contains an example how to perform semantic search with Seismic.
For more information, please refer to the documentation:
https://github.com/TusKANNy/seismic/blob/main/docs/Guidelines.md
All you need is installing the `pyseismic-lsr` package:
```
pip install pyseismic-lsr
```
"""
import time
from dat... | """
This script contains an example how to perform semantic search with Seismic.
For more information, please refer to the documentation:
https://github.com/TusKANNy/seismic/blob/main/docs/Guidelines.md
All you need is installing the `pyseismic-lsr` package:
```
pip install pyseismic-lsr
```
"""
import time
from dat... |
from __future__ import annotations
import collections
import json
import logging
import os
import string
from typing import Iterable
from transformers.utils.import_utils import NLTK_IMPORT_ERROR, is_nltk_available
from .WordTokenizer import ENGLISH_STOP_WORDS, WordTokenizer
logger = logging.getLogger(__name__)
cl... | import collections
import json
import logging
import os
import string
from typing import Iterable, List
from transformers.utils.import_utils import NLTK_IMPORT_ERROR, is_nltk_available
from .WordTokenizer import ENGLISH_STOP_WORDS, WordTokenizer
logger = logging.getLogger(__name__)
class PhraseTokenizer(WordTokeni... |
from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional, utils # usort: skip
from ._transform import Transform # usort: skip
from ._augment import CutMix, MixUp, RandomErasing
from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
fro... | from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional, utils # usort: skip
from ._transform import Transform # usort: skip
from ._augment import CutMix, MixUp, RandomErasing
from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
fro... |
# Copyright 2020 The HuggingFace 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... | # Copyright 2020 The HuggingFace 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... |
import importlib.util
import warnings
from functools import wraps
from typing import Optional
import torch
def is_module_available(*modules: str) -> bool:
r"""Returns if a top-level module with :attr:`name` exists *without**
importing it. This is generally safer than try-catch block around a
`import X`. ... | import importlib.util
import warnings
from functools import wraps
from typing import Optional
import torch
def is_module_available(*modules: str) -> bool:
r"""Returns if a top-level module with :attr:`name` exists *without**
importing it. This is generally safer than try-catch block around a
`import X`. ... |
#!/usr/bin/env python
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# 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... | #!/usr/bin/env python
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# 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... |
import types
from typing import TYPE_CHECKING
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING:
from docarray.index.backends.elastic import ElasticDocIndex # noqa: F401
from docarray.index.backends.elasticv7 import ElasticV7DocIndex #... | import types
from typing import TYPE_CHECKING
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING:
from docarray.index.backends.elastic import ElasticDocIndex # noqa: F401
from docarray.index.backends.elasticv7 import ElasticV7DocIndex #... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from .torchscript_consistency_test_impl import TorchScriptConsistencyCPUOnlyTestImpl, TorchScriptConsistencyTestImpl
class TorchScriptConsistencyCPUFloat32Test(TorchScriptConsistencyTestImpl, PytorchTestCase):
dtype = torch.float32
dev... | import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from .torchscript_consistency_test_impl import TorchScriptConsistencyTestImpl
class TorchScriptConsistencyCPUFloat32Test(TorchScriptConsistencyTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cpu")
class Torch... |
"""Test program utils."""
import pytest
from typing import List, Optional
from llama_index.core.bridge.pydantic import BaseModel, Field
from llama_index.core.base.llms.types import ChatMessage, ChatResponse, MessageRole
from llama_index.core.program.utils import (
_repair_incomplete_json,
process_streaming_obj... | """Test program utils."""
import pytest
from typing import List, Optional
from llama_index.core.bridge.pydantic import BaseModel, Field
from llama_index.core.base.llms.types import ChatMessage, ChatResponse, MessageRole
from llama_index.core.program.utils import (
_repair_incomplete_json,
process_streaming_obje... |
"""Hypothetical Document Embeddings.
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
import logging
from typing import Any, Dict, List, Optional
from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.embeddings import Embeddings
from langchain_core.language_model... | """Hypothetical Document Embeddings.
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
import numpy as np
from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.embeddings import Embeddings
from langchain_core.language_m... |
import datetime
import prisma.fields
import prisma.models
import pytest
import backend.server.v2.library.model as library_model
@pytest.mark.asyncio
async def test_agent_preset_from_db():
# Create mock DB agent
db_agent = prisma.models.AgentPreset(
id="test-agent-123",
createdAt=datetime.dat... | import datetime
import prisma.fields
import prisma.models
import pytest
import backend.server.v2.library.model as library_model
@pytest.mark.asyncio
async def test_agent_preset_from_db():
# Create mock DB agent
db_agent = prisma.models.AgentPreset(
id="test-agent-123",
createdAt=datetime.dat... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import bbox_overlaps, get_box_tensor
def cast_tensor_type(x, scale=1., dtype=None):
if dtype == 'fp16':
# scale is for preventing overflows
x = (x / scale).half()
retu... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.models.utils.misc import get_box_tensor
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import bbox_overlaps
def cast_tensor_type(x, scale=1., dtype=None):
if dtype == 'fp16':
# scale is for preventing overflows
... |
# pants requires this import to recognize the dep
import pytest_asyncio # noqa: F401
import pytest
import os
from llama_index.multi_modal_llms.nvidia import NVIDIAMultiModal as Interface
from llama_index.multi_modal_llms.nvidia.utils import DEFAULT_MODEL
from typing import Generator
# this fixture is used to mask... | import pytest
import os
from llama_index.multi_modal_llms.nvidia import NVIDIAMultiModal as Interface
from llama_index.multi_modal_llms.nvidia.utils import DEFAULT_MODEL
from typing import Generator
# this fixture is used to mask the NVIDIA_API_KEY environment variable and restore it
# after the test. it also retur... |
import os
import tempfile
import httpx
import pytest
from PIL import Image
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.embeddings.cohere import CohereEmbedding
from llama_index.embeddings.cohere.base import VALID_MODEL_INPUT_TYPES
def test_embedding_class():
emb = CohereEmbed... | import os
import tempfile
import httpx
import pytest
from PIL import Image
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.embeddings.cohere import CohereEmbedding
from llama_index.embeddings.cohere.base import VALID_MODEL_INPUT_TYPES
def test_embedding_class():
emb = CohereEmbed... |
# dataset settings
dataset_type = 'CocoPanopticDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='dis... | # dataset settings
dataset_type = 'CocoPanopticDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='dis... |
from langchain_core.callbacks import __all__
EXPECTED_ALL = [
"RetrieverManagerMixin",
"LLMManagerMixin",
"ChainManagerMixin",
"ToolManagerMixin",
"Callbacks",
"CallbackManagerMixin",
"RunManagerMixin",
"BaseCallbackHandler",
"AsyncCallbackHandler",
"BaseCallbackManager",
"B... | from langchain_core.callbacks import __all__
EXPECTED_ALL = [
"RetrieverManagerMixin",
"LLMManagerMixin",
"ChainManagerMixin",
"ToolManagerMixin",
"Callbacks",
"CallbackManagerMixin",
"RunManagerMixin",
"BaseCallbackHandler",
"AsyncCallbackHandler",
"BaseCallbackManager",
"B... |
import json
import logging
import os
from typing import Dict, List
import torch
from torch import Tensor, nn
logger = logging.getLogger(__name__)
class WordWeights(nn.Module):
"""This model can weight word embeddings, for example, with idf-values."""
def __init__(self, vocab: List[str], word_weights: Dict[... | import torch
from torch import Tensor
from torch import nn
from typing import List, Dict
import os
import json
import logging
logger = logging.getLogger(__name__)
class WordWeights(nn.Module):
"""This model can weight word embeddings, for example, with idf-values."""
def __init__(self, vocab: List[str], wo... |
import pytest
from backend.data import db
from backend.executor.scheduler import SchedulerClient
from backend.server.model import CreateGraph
from backend.usecases.sample import create_test_graph, create_test_user
from backend.util.service import get_service_client
from backend.util.test import SpinTestServer
@pytes... | import pytest
from backend.data import db
from backend.executor import Scheduler
from backend.server.model import CreateGraph
from backend.usecases.sample import create_test_graph, create_test_user
from backend.util.service import get_service_client
from backend.util.test import SpinTestServer
@pytest.mark.asyncio(l... |
import types
from typing_extensions import TYPE_CHECKING
from docarray.typing.tensor.embedding.embedding import AnyEmbedding
from docarray.typing.tensor.embedding.ndarray import NdArrayEmbedding
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING... | import types
from typing_extensions import TYPE_CHECKING
from docarray.typing.tensor.embedding.embedding import AnyEmbedding
from docarray.typing.tensor.embedding.ndarray import NdArrayEmbedding
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING... |
"""
Quantile Regression
===================
.. versionadded:: 2.0.0
The script is inspired by this awesome example in sklearn:
https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html
.. note::
The feature is only supported using the Python, R, and C packages. In addition,... | """
Quantile Regression
===================
.. versionadded:: 2.0.0
The script is inspired by this awesome example in sklearn:
https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html
.. note::
The feature is only supported using the Python, R, and C packages. In addition,... |
import numpy as np
from sentence_transformers.sparse_encoder import SparseEncoder
from sentence_transformers.sparse_encoder.models import MLMTransformer, SpladePooling
def main():
# Initialize the SPLADE model
model_name = "naver/splade-cocondenser-ensembledistil" # "opensearch-project/opensearch-neural-spa... | import numpy as np
from sentence_transformers.sparse_encoder import SparseEncoder
from sentence_transformers.sparse_encoder.models import MLMTransformer, SpladePooling
def main():
# Initialize the SPLADE model
model_name = "opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill" # "prithivida/S... |
import importlib.machinery
import os
from torch.hub import _get_torch_home
_HOME = os.path.join(_get_torch_home(), "datasets", "vision")
_USE_SHARDED_DATASETS = False
IN_FBCODE = False
def _download_file_from_remote_location(fpath: str, url: str) -> None:
pass
def _is_remote_location_available() -> bool:
... | import importlib.machinery
import os
from torch.hub import _get_torch_home
_HOME = os.path.join(_get_torch_home(), "datasets", "vision")
_USE_SHARDED_DATASETS = False
def _download_file_from_remote_location(fpath: str, url: str) -> None:
pass
def _is_remote_location_available() -> bool:
return False
tr... |
import numpy as np
from docarray import BaseDoc
from docarray.array import DocVec
from docarray.array.doc_vec.column_storage import ColumnStorageView
from docarray.typing import AnyTensor
def test_column_storage_init():
class InnerDoc(BaseDoc):
price: int
class MyDoc(BaseDoc):
tensor: AnyTen... | import numpy as np
from docarray import BaseDoc
from docarray.array import DocVec
from docarray.array.doc_vec.column_storage import ColumnStorageView
from docarray.typing import AnyTensor
def test_column_storage_init():
class InnerDoc(BaseDoc):
price: int
class MyDoc(BaseDoc):
tensor: AnyTen... |
import os
import sys
import pytest
import torch
import torchaudio
from torchaudio.prototype.pipelines import CONVTASNET_BASE_LIBRI2MIX, HDEMUCS_HIGH_MUSDB_PLUS
sys.path.append(os.path.join(os.path.dirname(__file__), "..", "..", "examples"))
from source_separation.utils.metrics import sdr
@pytest.mark.parametrize(
... | import os
import sys
import torch
import torchaudio
from torchaudio.prototype.pipelines import CONVTASNET_BASE_LIBRI2MIX
sys.path.append(os.path.join(os.path.dirname(__file__), "..", "..", "examples"))
from source_separation.utils.metrics import PIT, sdr
def test_source_separation_models(mixture_source, clean_sour... |
_base_ = './queryinst_r50_fpn_ms-480-800-3x_coco.py'
num_proposals = 300
model = dict(
rpn_head=dict(num_proposals=num_proposals),
test_cfg=dict(
_delete_=True,
rpn=None,
rcnn=dict(max_per_img=num_proposals, mask_thr_binary=0.5)))
# augmentation strategy originates from DETR.
train_pipe... | _base_ = './queryinst_r50_fpn_ms-480-800-3x_coco.py'
num_proposals = 300
model = dict(
rpn_head=dict(num_proposals=num_proposals),
test_cfg=dict(
_delete_=True,
rpn=None,
rcnn=dict(max_per_img=num_proposals, mask_thr_binary=0.5)))
# augmentation strategy originates from DETR.
train_pipe... |
"""Custom query engine."""
from abc import abstractmethod
from typing import Union
from llama_index.core.base.base_query_engine import BaseQueryEngine
from llama_index.core.base.response.schema import RESPONSE_TYPE, Response
from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict
from llama_index.co... | """Custom query engine."""
from abc import abstractmethod
from typing import Union
from llama_index.core.base.base_query_engine import BaseQueryEngine
from llama_index.core.base.response.schema import RESPONSE_TYPE, Response
from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict
from llama_index.co... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, Optional, Union
from mmengine.optim import _ParamScheduler
from mmengine.registry import HOOKS
from mmengine.utils import is_list_of
from .hook import Hook
DATA_BATCH = Optional[Union[dict, tuple, list]]
@HOOKS.register_module()
class ParamSch... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Union
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Union[dict, tuple, list]]
@HOOKS.register_module()
class ParamSchedulerHook(Hook):
"""A hook to update some hyper-parameters in optimizer, e.g., lea... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda
from torchaudio_unittest.models.emformer.emformer_test_impl import EmformerTestImpl
@skipIfNoCuda
class EmformerFloat32GPUTest(EmformerTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cuda")
@skip... | import torch
from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase
from torchaudio_unittest.models.emformer.emformer_test_impl import EmformerTestImpl
@skipIfNoCuda
class EmformerFloat32GPUTest(EmformerTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cuda")
@skip... |
_base_ = './retinanet_r50-caffe_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
ke... | _base_ = './retinanet_r50-caffe_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768),
(1333, 800)],
keep... |
"""Unit tests for verifying event dispatching.
Much of this code is indirectly tested already through many end-to-end tests
that generate traces based on the callbacks. The traces are all verified
via snapshot testing (e.g., see unit tests for runnables).
"""
import contextvars
from contextlib import asynccontextmana... | """Unit tests for verifying event dispatching.
Much of this code is indirectly tested already through many end-to-end tests
that generate traces based on the callbacks. The traces are all verified
via snapshot testing (e.g., see unit tests for runnables).
"""
import contextvars
from contextlib import asynccontextmana... |
# Owner(s): ["module: inductor"]
import sys
import unittest
from torch.testing._internal.common_utils import IS_CI, IS_WINDOWS, skipIfXpu
from torch.testing._internal.inductor_utils import GPU_TYPE, HAS_GPU, requires_gpu
if IS_WINDOWS and IS_CI:
sys.stderr.write(
"Windows CI does not have necessary depe... | # Owner(s): ["module: inductor"]
import sys
import unittest
from torch.testing._internal.common_utils import IS_CI, IS_WINDOWS, skipIfXpu
from torch.testing._internal.inductor_utils import GPU_TYPE, HAS_GPU, requires_gpu
if IS_WINDOWS and IS_CI:
sys.stderr.write(
"Windows CI does not have necessary depe... |
_base_ = '../_base_/default_runtime.py'
# model settings
model = dict(
type='YOLOV3',
backbone=dict(
type='MobileNetV2',
out_indices=(2, 4, 6),
act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://mmdet/mobilen... | _base_ = '../_base_/default_runtime.py'
# model settings
model = dict(
type='YOLOV3',
backbone=dict(
type='MobileNetV2',
out_indices=(2, 4, 6),
act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://mmdet/mobilen... |
"""**OutputParser** classes parse the output of an LLM call.
**Class hierarchy:**
.. code-block::
BaseLLMOutputParser --> BaseOutputParser --> <name>OutputParser # ListOutputParser, PydanticOutputParser
**Main helpers:**
.. code-block::
Serializable, Generation, PromptValue
""" # noqa: E501
from typing... | """**OutputParser** classes parse the output of an LLM call.
**Class hierarchy:**
.. code-block::
BaseLLMOutputParser --> BaseOutputParser --> <name>OutputParser # ListOutputParser, PydanticOutputParser
**Main helpers:**
.. code-block::
Serializable, Generation, PromptValue
""" # noqa: E501
from import... |
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any
from sentence_transformers.evaluation import TranslationEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
logger = ... | from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any
from sentence_transformers.evaluation import TranslationEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
logger = ... |
"""Module containing the base parser for arguments of Jina."""
import argparse
from jina.parsers.helper import _chf
def set_base_parser():
"""Set the base parser
:return: the parser
"""
from jina import __version__
from jina.helper import colored, format_full_version_info, get_full_version
... | """Module containing the base parser for arguments of Jina."""
import argparse
from jina.parsers.helper import _chf
def set_base_parser():
"""Set the base parser
:return: the parser
"""
from jina import __version__
from jina.helper import colored, format_full_version_info, get_full_version
... |
# Copyright (c) OpenMMLab. All rights reserved.
import os
import unittest
from unittest.mock import MagicMock, patch
import pytest
from mmdet.datasets import DATASETS
@patch('mmdet.datasets.CocoDataset.load_annotations', MagicMock())
@patch('mmdet.datasets.CustomDataset.load_annotations', MagicMock())
@patch('mmdet... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import MagicMock, patch
import pytest
from mmdet.datasets import DATASETS
@patch('mmdet.datasets.CocoDataset.load_annotations', MagicMock())
@patch('mmdet.datasets.CustomDataset.load_annotations', MagicMock())
@patch('mmdet.datasets.XMLDataset.load_... |
import json
import logging
from typing import List
from langchain_core._api.deprecation import deprecated
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.messages import (
BaseMessage,
message_to_dict,
messages_from_dict,
)
logger = logging.getLogger(__name__)
DEFAULT_D... | import json
import logging
from typing import List
from langchain_core._api.deprecation import deprecated
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.messages import (
BaseMessage,
message_to_dict,
messages_from_dict,
)
logger = logging.getLogger(__name__)
DEFAULT_D... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '3.0.0rc1'
short_version = __version__
def parse_version_info(version_str):
"""Parse a version string into a tuple.
Args:
version_str (str): The version string.
Returns:
tuple[int | str]: The version info, e.g., "1.3.0" is par... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '3.0.0rc0'
short_version = __version__
def parse_version_info(version_str):
"""Parse a version string into a tuple.
Args:
version_str (str): The version string.
Returns:
tuple[int | str]: The version info, e.g., "1.3.0" is par... |
from typing import Any, Dict, List, Optional, Sequence, Type, Union
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.prototype.datapoints import Label, OneHotLabel
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import _FillType, ... | from typing import Any, Dict, List, Optional, Sequence, Type, Union
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.prototype.datapoints import Label, OneHotLabel
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import _FillType, ... |
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(
type='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
action_prob=(1, 0, 0),
scale=(0.8, 1.2),
dx=15,
... | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(
type='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
action_prob=(1, 0, 0),
scale=(0.8, 1... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_module()
class ParamSchedulerHook(Hook):
"""A hook to update some hyper-parameters in optimizer, e.g., learning r... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Optional, Sequence, Tuple
from mmengine.data import BaseDataElement
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataElement]]]
@HOOKS.register_module()
class ParamSchedulerHook(Hook):... |
from typing import Any, Dict
import torch
from torchvision.transforms.v2 import functional as F, Transform
class UniformTemporalSubsample(Transform):
"""[BETA] Uniformly subsample ``num_samples`` indices from the temporal dimension of the video.
.. v2betastatus:: UniformTemporalSubsample transform
Vide... | from typing import Any, Dict
import torch
from torchvision import datapoints
from torchvision.transforms.v2 import functional as F, Transform
class UniformTemporalSubsample(Transform):
"""[BETA] Uniformly subsample ``num_samples`` indices from the temporal dimension of the video.
.. v2betastatus:: UniformTe... |
import asyncio
from typing import Any, Dict, Generator, List, Union
import pytest
from llama_index.core.schema import (
BaseNode,
IndexNode,
TextNode,
)
from llama_index.core.vector_stores.types import (
VectorStoreQuery,
)
from llama_index.vector_stores.lantern import LanternVectorStore
# for testing... | import asyncio
from typing import Any, Dict, Generator, List, Union
import pytest
from llama_index.core.schema import (
BaseNode,
IndexNode,
TextNode,
)
from llama_index.core.vector_stores.types import (
VectorStoreQuery,
)
from llama_index.vector_stores.lantern import LanternVectorStore
# for testing... |
_base_ = '../grid_rcnn/grid-rcnn_r50_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=25... | _base_ = '../grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=25... |
import numpy as np
import orjson
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.document.io.json import orjson_dumps
from docarray.typing import NdArray
def test_proto_tensor():
tensor = parse_obj_as(NdArray, np.zeros((3, 224, 224)))
tensor._to_node_protobuf()
def test_from_list():... | import numpy as np
import orjson
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.document.io.json import orjson_dumps
from docarray.typing import Tensor
def test_proto_tensor():
tensor = parse_obj_as(Tensor, np.zeros((3, 224, 224)))
tensor._to_node_protobuf()
def test_from_list():
... |
from __future__ import annotations
import logging
from typing import Literal
import torch
from torch import Tensor
from sentence_transformers.models.InputModule import InputModule
from .tokenizer import WhitespaceTokenizer
logger = logging.getLogger(__name__)
class BoW(InputModule):
"""Implements a Bag-of-Wo... | from __future__ import annotations
import json
import logging
import os
from typing import Literal
import torch
from torch import Tensor, nn
from .tokenizer import WhitespaceTokenizer
logger = logging.getLogger(__name__)
class BoW(nn.Module):
"""Implements a Bag-of-Words (BoW) model to derive sentence embeddi... |
"""
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch.
It uses MatryoshkaLoss with the powerful CoSENTLoss to train models that perform well at output dimensions [768, 512, 256, 128, 64].
It generates sentence embeddings that can be compared using... | """
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch.
It uses MatryoshkaLoss with the powerful CoSENTLoss to train models that perform well at output dimensions [768, 512, 256, 128, 64].
It generates sentence embeddings that can be compared using... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
from collections import OrderedDict
import torch
def convert_stem(model_key, model_weight, state_dict, converted_names):
new_key = model_key.replace('stem.conv', 'conv1')
new_key = new_key.replace('stem.bn', 'bn1')
state_dict[new_key] = mode... | import argparse
from collections import OrderedDict
import torch
def convert_stem(model_key, model_weight, state_dict, converted_names):
new_key = model_key.replace('stem.conv', 'conv1')
new_key = new_key.replace('stem.bn', 'bn1')
state_dict[new_key] = model_weight
converted_names.add(model_key)
... |
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class MaskRCNN(TwoStageDetector):
"""Implementation of `Mask R-CNN <https://arxiv.org/abs/1703.06870>`_"""
def __init__(self,
backbone,
... | from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class MaskRCNN(TwoStageDetector):
"""Implementation of `Mask R-CNN <https://arxiv.org/abs/1703.06870>`_"""
def __init__(self,
backbone,
rpn_head,
roi_head,
... |
from __future__ import annotations
from collections.abc import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import SentenceTransformer, util
class DistillKLDivLoss(nn.Module):
# TODO
def __init__(self, model: SentenceTransformer, similarity_fct=util.pairwise_dot_score) -> ... | from __future__ import annotations
from collections.abc import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import SentenceTransformer, util
class DistillKLDivLoss(nn.Module):
# TODO
def __init__(self, model: SentenceTransformer, similarity_fct=util.pairwise_dot_score) -> ... |
# dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/segmentation/citysca... | # dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize',
scale=[(2048, 800), (2048, 1024)],
keep_ratio=True),
dict(type='Random... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.registry import MODELS
from .anchor_head import AnchorHead
@MODELS.register_module()
class RetinaHead(AnchorHead):
r"""An anchor-based head used in `RetinaNet
<https://arxiv.org/pdf/1708.02002.pdf... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.registry import MODELS
from .anchor_head import AnchorHead
@MODELS.register_module()
class RetinaHead(AnchorHead):
r"""An anchor-based head used in `RetinaNet
<https://arxiv.org/pdf/1708.02002.pdf... |
import torch
from keras.src.backend import config
from keras.src.backend import standardize_dtype
from keras.src.backend.common import dtypes
from keras.src.backend.torch.core import cast
from keras.src.backend.torch.core import convert_to_tensor
def cholesky(x):
return torch.linalg.cholesky(x)
def det(x):
... | import torch
from keras.src.backend import config
from keras.src.backend import standardize_dtype
from keras.src.backend.common import dtypes
from keras.src.backend.torch.core import cast
from keras.src.backend.torch.core import convert_to_tensor
def cholesky(x):
return torch.linalg.cholesky(x)
def det(x):
... |
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