input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | from typing import Dict, List, Optional, Set, Tuple, Union
import pytest
from docarray.typing import NdArray, TorchTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils._internal._typing import (
is_tensor_union,
is_type_tensor,
safe_issubclass,
)
from docarray.utils... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import logging
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.logging import print_log
from mmengine.registry import RUNNERS
from mmengine.runner import Runner
def parse_args():
parser = argparse.Argumen... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import logging
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.logging import print_log
from mmengine.registry import RUNNERS
from mmengine.runner import Runner
from mmdet.utils import register_all_modules
d... |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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... | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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... |
# model settings
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32)
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
preprocess_cfg=preprocess_cfg,
type='FasterRCNN',
backbone=dict(
type='ResNet',
dept... | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
norm_cfg=norm_cfg,
... |
import os
from pathlib import Path
from typing import List, Tuple, Union
import torch
from torch.utils.data import Dataset
from torchaudio.datasets.utils import _load_waveform
_TASKS_TO_MIXTURE = {
"sep_clean": "mix_clean",
"enh_single": "mix_single",
"enh_both": "mix_both",
"sep_noisy": "mix_both",
}... | from pathlib import Path
from typing import List, Tuple, Union
import torch
import torchaudio
from torch.utils.data import Dataset
SampleType = Tuple[int, torch.Tensor, List[torch.Tensor]]
_TASKS_TO_MIXTURE = {
"sep_clean": "mix_clean",
"enh_single": "mix_single",
"enh_both": "mix_both",
"sep_noisy":... |
# coding: utf-8
"""Helper script for checking versions in the dynamic symbol table.
This script checks that LightGBM library is linked to the appropriate symbol versions.
"""
import re
import sys
from pathlib import Path
def check_dependicies(objdump_string: str) -> None:
"""Check the dynamic symbol versions.
... | # coding: utf-8
"""Helper script for checking versions in the dynamic symbol table.
This script checks that LightGBM library is linked to the appropriate symbol versions.
"""
import re
import sys
from pathlib import Path
def check_dependicies(objdump_string: str) -> None:
"""Check the dynamic symbol versions.
... |
import logging
import pathlib
from postmarker.core import PostmarkClient
from postmarker.models.emails import EmailManager
from prisma.enums import NotificationType
from pydantic import BaseModel
from backend.data.notifications import (
NotificationEventModel,
NotificationTypeOverride,
T_co,
)
from backen... | import logging
import pathlib
from postmarker.core import PostmarkClient
from postmarker.models.emails import EmailManager
from prisma.enums import NotificationType
from pydantic import BaseModel
from backend.data.notifications import (
NotificationEventModel,
NotificationTypeOverride,
T_co,
)
from backen... |
import collections
import itertools
import numpy as np
from absl.testing import parameterized
from torch.utils.data import Dataset as TorchDataset
from keras.src.testing import test_case
from keras.src.testing.test_utils import named_product
from keras.src.utils.dataset_utils import split_dataset
from keras.src.utils... | import itertools
import numpy as np
from absl.testing import parameterized
from torch.utils.data import Dataset as TorchDataset
from keras.src.testing import test_case
from keras.src.testing.test_utils import named_product
from keras.src.utils.dataset_utils import split_dataset
from keras.src.utils.module_utils impor... |
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(pad_size_divisor=64),
neck=dict(
type='FPN_CARAFE',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
start_level=0,
end_level=-1,
norm_cfg=None,
... | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
neck=dict(
type='FPN_CARAFE',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
start_level=0,
end_level=-1,
norm_cfg=None,
act_cfg=None,
order=('conv', 'norm', ... |
from textwrap import dedent
from types import SimpleNamespace
from unittest.mock import patch
from urllib.parse import quote
import pytest
from huggingface_hub import CommitOperationAdd, CommitOperationDelete
import datasets
from datasets.config import METADATA_CONFIGS_FIELD
from datasets.hub import delete_from_hub
f... | from textwrap import dedent
from types import SimpleNamespace
from unittest.mock import patch
from urllib.parse import quote
import pytest
from huggingface_hub import CommitOperationAdd, CommitOperationDelete
import datasets
from datasets.config import METADATA_CONFIGS_FIELD
from datasets.hub import delete_from_hub
f... |
"""Example of parametrized tests for analytics endpoints."""
import json
from unittest.mock import AsyncMock, Mock
import fastapi
import fastapi.testclient
import pytest
import pytest_mock
from pytest_snapshot.plugin import Snapshot
import backend.server.routers.analytics as analytics_routes
from backend.server.conf... | """Example of parametrized tests for analytics endpoints."""
import json
from unittest.mock import AsyncMock, Mock
import fastapi
import fastapi.testclient
import pytest
import pytest_mock
from pytest_snapshot.plugin import Snapshot
import backend.server.routers.analytics as analytics_routes
from backend.server.conf... |
from __future__ import annotations
from typing import Any, cast, List, Optional, Tuple, Union
import torch
from torchvision.transforms import InterpolationMode
from ._feature import _Feature, FillTypeJIT
class Mask(_Feature):
@classmethod
def _wrap(cls, tensor: torch.Tensor) -> Mask:
return tensor.... | from __future__ import annotations
from typing import Any, cast, List, Optional, Tuple, Union
import torch
from torchvision.transforms import InterpolationMode
from ._feature import _Feature, FillTypeJIT
class Mask(_Feature):
@classmethod
def _wrap(cls, tensor: torch.Tensor) -> Mask:
return tensor.... |
import warnings
from typing import Any, Dict, List, Union
import numpy as np
import PIL.Image
import torch
from torchvision.prototype import features
from torchvision.prototype.transforms import Transform
from torchvision.transforms import functional as _F
from typing_extensions import Literal
from ._transform impor... | import warnings
from typing import Any, Dict, Union
import numpy as np
import PIL.Image
import torch
from torchvision.prototype import features
from torchvision.prototype.transforms import Transform
from torchvision.transforms import functional as _F
from typing_extensions import Literal
from ._transform import _Ran... |
# Copyright (c) OpenMMLab. All rights reserved.
import importlib
import os.path as osp
from mmengine.config import Config
from mmengine.config.utils import (_get_cfg_metainfo,
_get_external_cfg_base_path,
_get_package_and_cfg_path)
from mmengine.reg... | # Copyright (c) OpenMMLab. All rights reserved.
import importlib
import os.path as osp
from mmengine.config import Config
from mmengine.config.utils import (_get_cfg_metainfo,
_get_external_cfg_base_path,
_get_package_and_cfg_path)
from mmengine.reg... |
"""
This scripts demonstrates how to train a Sparse Encoder model for Information Retrieval.
As dataset, we use sentence-transformers/msmarco-bm25, where we have triplets versions of MSMARCO mined thanks to BM25.
As loss function, we use MultipleNegativesRankingLoss in the SpladeLoss.
"""
import logging
import trac... | """
This scripts demonstrates how to train a Sparse Encoder model for Information Retrieval.
As dataset, we use sentence-transformers/msmarco-bm25, where we have triplets versions of MSMARCO mined thanks to BM25.
As loss function, we use MultipleNegativesRankingLoss in the SpladeLoss.
"""
import logging
import trac... |
from functools import partial
from huggingface_hub import hf_hub_url
from huggingface_hub.utils import get_session, hf_raise_for_status
hf_dataset_url = partial(hf_hub_url, repo_type="dataset")
def check_auth(hf_api, repo_id, token=None):
headers = hf_api._build_hf_headers(token=token)
path = f"{hf_api.end... | from functools import partial
from huggingface_hub import hf_hub_url
hf_dataset_url = partial(hf_hub_url, repo_type="dataset")
|
import numpy as np
import pytest
from keras.src import backend
from keras.src import layers
from keras.src.testing import test_case
class SpatialDropoutTest(test_case.TestCase):
@pytest.mark.requires_trainable_backend
def test_spatial_dropout_1d(self):
self.run_layer_test(
layers.SpatialD... | import numpy as np
import pytest
from keras.src import backend
from keras.src import layers
from keras.src.testing import test_case
class SpatialDropoutTest(test_case.TestCase):
@pytest.mark.requires_trainable_backend
def test_spatial_dropout_1d(self):
self.run_layer_test(
layers.SpatialD... |
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
file_client_args = dict(backend='disk')
# comment out the code below to use different file client
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# ... | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
image_size = (1024, 1024)
file_client_args = dict(backend='disk')
# comment out the code below to use diffe... |
from ._conformer_wav2vec2 import (
conformer_wav2vec2_base,
conformer_wav2vec2_model,
conformer_wav2vec2_pretrain_base,
conformer_wav2vec2_pretrain_large,
conformer_wav2vec2_pretrain_model,
ConformerWav2Vec2PretrainModel,
)
from ._emformer_hubert import emformer_hubert_base, emformer_hubert_mode... | from ._conformer_wav2vec2 import (
conformer_wav2vec2_base,
conformer_wav2vec2_model,
conformer_wav2vec2_pretrain_base,
conformer_wav2vec2_pretrain_large,
conformer_wav2vec2_pretrain_model,
ConformerWav2Vec2PretrainModel,
)
from ._emformer_hubert import emformer_hubert_base, emformer_hubert_mode... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
import torch
from mmengine.structures import InstanceData
from mmengine.testing import assert_allclose
from mmdet.models.task_modules.assigners import PointAssigner
class TestPointAssigner(unittest.TestCase):
def test_point_assigner(self):
... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
import torch
from mmengine.data import InstanceData
from mmengine.testing import assert_allclose
from mmdet.models.task_modules.assigners import PointAssigner
class TestPointAssigner(unittest.TestCase):
def test_point_assigner(self):
assig... |
# coding: utf-8
import pytest
import lightgbm as lgb
from .utils import SERIALIZERS, pickle_and_unpickle_object
def reset_feature_fraction(boosting_round):
return 0.6 if boosting_round < 15 else 0.8
@pytest.mark.parametrize('serializer', SERIALIZERS)
def test_early_stopping_callback_is_picklable(serializer):
... | # coding: utf-8
import pytest
import lightgbm as lgb
from .utils import SERIALIZERS, pickle_and_unpickle_object, pickle_obj, unpickle_obj
def reset_feature_fraction(boosting_round):
return 0.6 if boosting_round < 15 else 0.8
@pytest.mark.parametrize('serializer', SERIALIZERS)
def test_early_stopping_callback_... |
"""Loads rich text files."""
from pathlib import Path
from typing import Any, List, Union
from langchain_community.document_loaders.unstructured import (
UnstructuredFileLoader,
validate_unstructured_version,
)
class UnstructuredRTFLoader(UnstructuredFileLoader):
"""Load `RTF` files using `Unstructured`... | """Loads rich text files."""
from pathlib import Path
from typing import Any, List, Union
from langchain_community.document_loaders.unstructured import (
UnstructuredFileLoader,
validate_unstructured_version,
)
class UnstructuredRTFLoader(UnstructuredFileLoader):
"""Load `RTF` files using `Unstructured`... |
import re
from typing import Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents.agent import AgentOutputParser
from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS
class ConvoOutputParser(AgentOutputPar... | import re
from typing import Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents.agent import AgentOutputParser
from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS
class ConvoOutputParser(AgentOutputPar... |
from typing import Any, Dict, Iterator
import torch
from ..utils import _log_api_usage_once
try:
from ._load_gpu_decoder import _HAS_GPU_VIDEO_DECODER
except ModuleNotFoundError:
_HAS_GPU_VIDEO_DECODER = False
from ._video_opt import (
_HAS_VIDEO_OPT,
_probe_video_from_file,
_probe_video_from_me... | from typing import Any, Dict, Iterator
import torch
from ..utils import _log_api_usage_once
from ._video_opt import (
_HAS_VIDEO_OPT,
_probe_video_from_file,
_probe_video_from_memory,
_read_video_from_file,
_read_video_from_memory,
_read_video_timestamps_from_file,
_read_video_timestamps_... |
import abc
import argparse
import functools
import inspect
from typing import TYPE_CHECKING, Callable, Optional
from jina.helper import convert_tuple_to_list
from jina.jaml import JAMLCompatible
from jina.logging.logger import JinaLogger
from jina.serve.helper import store_init_kwargs, wrap_func
from jina.serve.stream... | import abc
import argparse
from typing import TYPE_CHECKING, Optional
from jina.jaml import JAMLCompatible
from jina.logging.logger import JinaLogger
from jina.serve.streamer import GatewayStreamer
__all__ = ['BaseGateway']
if TYPE_CHECKING:
from prometheus_client import CollectorRegistry
class BaseGateway(JAM... |
"""Tool for the Serper.dev Google Search API."""
from typing import Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool
from pydantic import Field
from langchain_community.utilities.google_serper import GoogleS... | """Tool for the Serper.dev Google Search API."""
from typing import Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool
from pydantic import Field
from langchain_community.utilities.google_serper import GoogleS... |
from docarray.predefined_document.audio import Audio
from docarray.predefined_document.image import Image
from docarray.predefined_document.mesh import Mesh3D
from docarray.predefined_document.point_cloud import PointCloud3D
from docarray.predefined_document.text import Text
__all__ = ['Text', 'Image', 'Audio', 'Mesh3... | from docarray.predefined_document.image import Image
from docarray.predefined_document.mesh import Mesh3D
from docarray.predefined_document.point_cloud import PointCloud3D
from docarray.predefined_document.text import Text
__all__ = ['Text', 'Image', 'Mesh3D', 'PointCloud3D']
|
"""
Integration Tests of llama-index-vector-stores-mongodb
with MongoDB Atlas Vector Datastore and OPENAI Embedding model.
As described in docs/providers/mongodb/setup.md, to run this, one must
have a running MongoDB Atlas Cluster, and
provide a valid OPENAI_API_KEY.
"""
import os
from time import sleep
from typing i... | """Integration Tests of llama-index-vector-stores-mongodb
with MongoDB Atlas Vector Datastore and OPENAI Embedding model.
As described in docs/providers/mongodb/setup.md, to run this, one must
have a running MongoDB Atlas Cluster, and
provide a valid OPENAI_API_KEY.
"""
import os
from time import sleep
from typing im... |
# Copyright (c) OpenMMLab. All rights reserved.
import tempfile
from unittest import TestCase
from unittest.mock import Mock
import torch
import torch.nn as nn
from mmengine.evaluator import Evaluator
from mmengine.model import BaseModel
from mmengine.optim import OptimWrapper
from mmengine.runner import Runner
from t... | # Copyright (c) OpenMMLab. All rights reserved.
import tempfile
from unittest import TestCase
from unittest.mock import Mock
import torch
import torch.nn as nn
from mmengine.evaluator import Evaluator
from mmengine.model import BaseModel
from mmengine.optim import OptimWrapper
from mmengine.runner import Runner
from t... |
import json
import os
import requests
import sys
import time
from typing import Dict, List, Tuple
CHECK_INTERVAL = 30
def get_environment_variables() -> Tuple[str, str, str, str, str]:
"""Retrieve and return necessary environment variables."""
try:
with open(os.environ["GITHUB_EVENT_PATH"]) as f:
... | import json
import os
import requests
import sys
import time
from typing import Dict, List, Tuple
CHECK_INTERVAL = 30
def get_environment_variables() -> Tuple[str, str, str, str, str]:
"""Retrieve and return necessary environment variables."""
try:
with open(os.environ["GITHUB_EVENT_PATH"]) as f:
... |
# pyre-strict
# mypy: allow-untyped-defs
import os
from concurrent.futures import Future, ThreadPoolExecutor
from typing import Optional, Union
import torch.distributed as dist
from torch.distributed.checkpoint._async_executor import _AsyncCheckpointExecutor
from torch.distributed.checkpoint.metadata import STATE_DICT... | # pyre-strict
# mypy: allow-untyped-defs
import os
from concurrent.futures import Future, ThreadPoolExecutor
from typing import Optional, Union
import torch.distributed as dist
from torch.distributed.checkpoint._async_executor import _AsyncCheckpointExecutor
from torch.distributed.checkpoint.metadata import STATE_DICT... |
from typing import Dict
from jina import Client, Document, DocumentArray, Executor, Flow, requests
ORIGINAL_PARAMS = {'param1': 50, 'param2': 60, 'exec_name': {'param1': 'changed'}}
OVERRIDEN_EXECUTOR1_PARAMS = {
'param1': 'changed',
'param2': 60,
'exec_name': {'param1': 'changed'},
}
class DummyOverrid... | from typing import Dict
from jina import Flow, DocumentArray, Document, Executor, Client, requests
ORIGINAL_PARAMS = {'param1': 50, 'param2': 60, 'exec_name': {'param1': 'changed'}}
OVERRIDEN_EXECUTOR1_PARAMS = {
'param1': 'changed',
'param2': 60,
'exec_name': {'param1': 'changed'},
}
class DummyOverrid... |
"""Functionality for loading agents."""
import json
import logging
from pathlib import Path
from typing import Any, Optional, Union
import yaml
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.tools import Tool
from langchain.agents.agent imp... | """Functionality for loading agents."""
import json
import logging
from pathlib import Path
from typing import Any, List, Optional, Union
import yaml
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.tools import Tool
from langchain.agents.age... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import TencentCOSDirectoryLoader
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optio... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import TencentCOSDirectoryLoader
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optio... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras import _tf_keras as _tf_keras
from keras import activations as activations
from keras import applications as applications
from keras import backend as backend
from keras import callbacks ... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api import _tf_keras
from keras.api import activations
from keras.api import applications
from keras.api import backend
from keras.api import callbacks
from keras.api import config
from k... |
_base_ = '../retinanet/retinanet_x101-32x4d_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
type='PISARetinaHead',
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
| _base_ = '../retinanet/retinanet_x101_32x4d_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
type='PISARetinaHead',
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
|
# Copyright 2019-present, the HuggingFace Inc. team.
#
# 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 o... | # Copyright 2019-present, the HuggingFace Inc. team.
#
# 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 o... |
from __future__ import annotations
from torch import Tensor, nn
from sentence_transformers.cross_encoder.CrossEncoder import CrossEncoder
from sentence_transformers.util import fullname
class MSELoss(nn.Module):
def __init__(self, model: CrossEncoder, activation_fct: nn.Module = nn.Identity(), **kwargs) -> None... | from __future__ import annotations
from torch import Tensor, nn
from sentence_transformers.cross_encoder import CrossEncoder
from sentence_transformers.util import fullname
class MSELoss(nn.Module):
def __init__(self, model: CrossEncoder, **kwargs) -> None:
super().__init__()
self.model = model
... |
import torch
from torch import nn, Tensor
from typing import Iterable, Dict
from ..SentenceTransformer import SentenceTransformer
from .. import util
class CoSENTLoss(nn.Module):
def __init__(self, model: SentenceTransformer, scale: float = 20.0, similarity_fct=util.pairwise_cos_sim):
"""
This cla... | import torch
from torch import nn, Tensor
from typing import Iterable, Dict
from ..SentenceTransformer import SentenceTransformer
from .. import util
class CoSENTLoss(nn.Module):
def __init__(self, model: SentenceTransformer, scale: float = 20.0, similarity_fct=util.pairwise_cos_sim):
"""
This cla... |
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 = ... |
# Copyright 2024 The TensorFlow Authors. 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 required by applica... | # Copyright 2024 The TensorFlow Authors. 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 required by applica... |
# dataset settings
dataset_type = 'OpenImagesDataset'
data_root = 'data/OpenImages/'
# 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/detection/coco/'
# ... | # dataset settings
dataset_type = 'OpenImagesDataset'
data_root = 'data/OpenImages/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend... |
# Copyright 2024 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 required by applicabl... | # Copyright 2024 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 required by applicabl... |
from abc import abstractmethod
from typing import (
TYPE_CHECKING,
TypeVar,
Sequence,
List,
Union,
Optional,
Dict,
)
from qdrant_client.http.models.models import Distance
from docarray import Document, DocumentArray
from docarray.math import ndarray
from docarray.score import NamedScore
if... | from abc import abstractmethod
from typing import (
TYPE_CHECKING,
TypeVar,
Sequence,
List,
Dict,
Optional,
)
from qdrant_client.http.models.models import Distance
from docarray import Document, DocumentArray
from docarray.math import ndarray
from docarray.score import NamedScore
if TYPE_CHEC... |
from typing import Optional
import torch
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen,... | from typing import Optional, Tuple
import torch
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, ... |
from abc import ABC
import pytest
from docarray import DocumentArray
from docarray.array.storage.memory import GetSetDelMixin, SequenceLikeMixin
from docarray.array.storage.redis.backend import BackendMixin, RedisConfig
class StorageMixins(BackendMixin, GetSetDelMixin, SequenceLikeMixin, ABC):
...
class Docume... | from abc import ABC
import pytest
from docarray import DocumentArray
from docarray.array.storage.memory import GetSetDelMixin, SequenceLikeMixin
from docarray.array.storage.redis.backend import BackendMixin, RedisConfig
class StorageMixins(BackendMixin, GetSetDelMixin, SequenceLikeMixin, ABC):
...
class Docume... |
"""Test GooseAI"""
import pytest
from pydantic import SecretStr
from pytest import MonkeyPatch
from langchain_community.llms.gooseai import GooseAI
from langchain_community.utils.openai import is_openai_v1
def _openai_v1_installed() -> bool:
try:
return is_openai_v1()
except Exception as _:
... | """Test GooseAI"""
import pytest
from pydantic import SecretStr
from pytest import MonkeyPatch
from langchain_community.llms.gooseai import GooseAI
from langchain_community.utils.openai import is_openai_v1
def _openai_v1_installed() -> bool:
try:
return is_openai_v1()
except Exception as _:
... |
_base_ = './decoupled-solo_r50_fpn_3x_coco.py'
# model settings
model = dict(
mask_head=dict(
type='DecoupledSOLOLightHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 8, 16, 32, 32],
scale_ranges=((1, 64), (32, 128), (64... | _base_ = './decoupled-solo_r50_fpn_3x_coco.py'
# model settings
model = dict(
mask_head=dict(
type='DecoupledSOLOLightHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 8, 16, 32, 32],
scale_ranges=((1, 64), (32, 128), (64... |
"""Base class for Slack tools."""
from __future__ import annotations
from typing import TYPE_CHECKING
from langchain_core.tools import BaseTool
from pydantic import Field
from langchain_community.tools.slack.utils import login
if TYPE_CHECKING:
# This is for linting and IDE typehints
from slack_sdk import ... | """Base class for Slack tools."""
from __future__ import annotations
from typing import TYPE_CHECKING
from langchain_core.tools import BaseTool
from pydantic import Field
from langchain_community.tools.slack.utils import login
if TYPE_CHECKING:
# This is for linting and IDE typehints
from slack_sdk import ... |
from __future__ import annotations
from .splade_callbacks import SchedulerType, SpladeRegularizerWeightSchedulerCallback
__all__ = ["SpladeRegularizerWeightSchedulerCallback", "SchedulerType"]
| from __future__ import annotations
from .splade_callbacks import SchedulerType, SpladeLambdaSchedulerCallback
__all__ = ["SpladeLambdaSchedulerCallback", "SchedulerType"]
|
import csv
import gzip
import logging
import os
from datetime import datetime
import torch
from sentence_transformers import InputExample, LoggingHandler, SentenceTransformer, losses, models, util
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
#### Just some code to print debug information... | import torch
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
from sentence_transformers import SentenceTransformer, LoggingHandler, models, util, InputExample
from sentence_transformers import losses
import os
import gzip
import csv
from datetime import datetime
import logging
#### Just some ... |
_base_ = 'yolact_r50_1x8_coco.py'
optimizer = dict(type='SGD', lr=8e-3, momentum=0.9, weight_decay=5e-4)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1000,
warmup_ratio=0.1,
step=[20, 42, 49, 52])
... | _base_ = 'yolact_r50_1x8_coco.py'
optimizer = dict(type='SGD', lr=8e-3, momentum=0.9, weight_decay=5e-4)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1000,
warmup_ratio=0.1,
step=[20, 42, 49, 52])
|
from backend.data.credit import get_user_credit_model
from backend.data.execution import (
ExecutionResult,
NodeExecutionEntry,
RedisExecutionEventBus,
create_graph_execution,
get_execution_results,
get_incomplete_executions,
get_latest_execution,
update_execution_status,
update_grap... | from backend.data.credit import get_user_credit_model
from backend.data.execution import (
ExecutionResult,
NodeExecutionEntry,
RedisExecutionEventBus,
create_graph_execution,
get_execution_results,
get_incomplete_executions,
get_latest_execution,
update_execution_status,
update_grap... |
# flake8: noqa
import numpy as np
from keras.src import backend
from keras.src import ops
from keras.src import testing
from keras.src.optimizers.adamw import AdamW
class AdamWTest(testing.TestCase):
def test_config(self):
optimizer = AdamW(
learning_rate=0.5,
weight_decay=0.008... | # flake8: noqa
import numpy as np
from keras.src import backend
from keras.src import ops
from keras.src import testing
from keras.src.optimizers.adamw import AdamW
class AdamWTest(testing.TestCase):
def test_config(self):
optimizer = AdamW(
learning_rate=0.5,
weight_decay=0.008... |
_base_ = './faster-rcnn_r50-caffe_c4-1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768),
... | _base_ = './faster-rcnn_r50-caffe_c4-1x_coco.py'
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import AINRuleOps
from langchain_community.tools.ainetwork.rule import RuleSchema
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import AINRuleOps
from langchain_community.tools.ainetwork.rule import RuleSchema
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising... |
"""Official evaluation script for v1.1 of the SQuAD dataset."""
import argparse
import json
import re
import string
import sys
from collections import Counter
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
return re.sub(r"\... | """ Official evaluation script for v1.1 of the SQuAD dataset. """
import argparse
import json
import re
import string
import sys
from collections import Counter
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
return re.sub(r... |
import os
from typing import Any, Optional
from llama_index.llms.openai_like import OpenAILike
class Groq(OpenAILike):
"""
Groq LLM.
Examples:
`pip install llama-index-llms-groq`
```python
from llama_index.llms.groq import Groq
# Set up the Groq class with the required ... | import os
from typing import Any, Optional
from llama_index.llms.openai_like import OpenAILike
class Groq(OpenAILike):
"""
Groq LLM.
Examples:
`pip install llama-index-llms-groq`
```python
from llama_index.llms.groq import Groq
# Set up the Groq class with the required ... |
import copy
from dataclasses import dataclass, asdict, field
from typing import (
Union,
Dict,
Optional,
TYPE_CHECKING,
Iterable,
List,
Tuple,
)
import numpy as np
from docarray.array.storage.base.backend import BaseBackendMixin, TypeMap
from docarray.helper import dataclass_from_dict, fil... | import copy
from dataclasses import dataclass, asdict, field
from typing import (
Union,
Dict,
Optional,
TYPE_CHECKING,
Iterable,
List,
Tuple,
)
import numpy as np
from docarray.array.storage.base.backend import BaseBackendMixin, TypeMap
from docarray.helper import dataclass_from_dict, fil... |
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.monotone_constraints import is_correctly_constrained, training_dset
rng = np.random.RandomState(1994)
def non_decreasing(L: np.ndarray) -> bool:
return all((x - y) < 0.001 for x, y in zip(L, L[1:]))
d... | import sys
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
sys.path.append("tests/python")
import test_monotone_constraints as tmc
rng = np.random.RandomState(1994)
def non_decreasing(L):
return all((x - y) < 0.001 for x, y in zip(L, L[1:]))
def non_increasing(L):
... |
# Copyright (c) OpenMMLab. All rights reserved.
import functools
from typing import Callable, Optional
import torch
import torch.nn.functional as F
from torch import Tensor
def reduce_loss(loss: Tensor, reduction: str) -> Tensor:
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss ten... | # Copyright (c) OpenMMLab. All rights reserved.
import functools
import torch
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
... |
from typing import List, Optional
import torchaudio
from torchaudio._internal.module_utils import deprecated
# TODO: Once legacy global backend is removed, move this to torchaudio.__init__
def _init_backend():
from . import utils
torchaudio.info = utils.get_info_func()
torchaudio.load = utils.get_load_f... | from typing import List, Optional
import torchaudio
from torchaudio._internal.module_utils import deprecated
from . import utils
# TODO: Once legacy global backend is removed, move this to torchaudio.__init__
def _init_backend():
torchaudio.info = utils.get_info_func()
torchaudio.load = utils.get_load_func(... |
import os
import urllib
import pytest
from pydantic import parse_obj_as, schema_json_of
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import TextUrl
from tests import TOYDATA_DIR
REMOTE_TEXT_FILE = 'https://de.wikipedia.org/wiki/Brixen'
CUR_DIR = os.path.dirname(os.path.abspath(__file__))
L... | import os
import urllib
import pytest
from pydantic import parse_obj_as, schema_json_of
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import TextUrl
from tests import TOYDATA_DIR
REMOTE_TEXT_FILE = 'https://de.wikipedia.org/wiki/Brixen'
CUR_DIR = os.path.dirname(os.path.abspath(__file__))
L... |
from __future__ import annotations
from .splade_callbacks import SchedulerType, SpladeRegularizerWeightSchedulerCallback
__all__ = ["SpladeRegularizerWeightSchedulerCallback", "SchedulerType"]
| from __future__ import annotations
from .splade_callbacks import SchedulerType, SpladeWeightRegulizerSchedulerCallback
__all__ = ["SpladeWeightRegulizerSchedulerCallback", "SchedulerType"]
|
"""Embeddings."""
from importlib import import_module
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from langchain_core.embeddings.embeddings import Embeddings
from langchain_core.embeddings.fake import (
DeterministicFakeEmbedding,
FakeEmbeddings,
)
__all__ = ["DeterministicFakeEmbe... | """Embeddings."""
from langchain_core.embeddings.embeddings import Embeddings
from langchain_core.embeddings.fake import DeterministicFakeEmbedding, FakeEmbeddings
__all__ = ["DeterministicFakeEmbedding", "Embeddings", "FakeEmbeddings"]
|
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from examples/modular-transformers/modular_add_function.py.
# Do NOT edit this file manually as any edits will be overwritten by the generatio... | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from examples/modular-transformers/modular_add_function.py.
# Do NOT edit this file manually as any edits will be overwritten by the generatio... |
import pathlib
from typing import Any, BinaryIO, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import Demultiplexer, Filter, IterDataPipe, IterKeyZipper, JsonParser, Mapper, UnBatcher
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvisio... | import pathlib
from typing import Any, BinaryIO, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import Demultiplexer, Filter, IterDataPipe, IterKeyZipper, JsonParser, Mapper, UnBatcher
from torchvision.prototype.datapoints import Label
from torchvision.prototype.datasets.utils import Dataset, Encoded... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import glob
import os.path as osp
import mmcv
from mmcv import Config
def parse_args():
parser = argparse.ArgumentParser(
description='Gather benchmarked models metric')
parser.add_argument('config', help='test config file path')
par... | import argparse
import glob
import os.path as osp
import mmcv
from mmcv import Config
def parse_args():
parser = argparse.ArgumentParser(
description='Gather benchmarked models metric')
parser.add_argument('config', help='test config file path')
parser.add_argument(
'root',
type=s... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.losses.losses import binary_crossentropy
from keras.src.losses.losses import binary_focal_crossentropy
from keras.src.losses.losses import categorical_crossentropy
from keras.src.loss... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.losses.losses import binary_crossentropy
from keras.src.losses.losses import binary_focal_crossentropy
from keras.src.losses.losses import categorical_crossentropy
from keras.src.loss... |
from typing import List
import numpy as np
from torch.utils.data import Dataset
from transformers.utils.import_utils import NLTK_IMPORT_ERROR, is_nltk_available
from sentence_transformers.readers.InputExample import InputExample
class DenoisingAutoEncoderDataset(Dataset):
"""
The DenoisingAutoEncoderDataset... | from typing import List
import numpy as np
from torch.utils.data import Dataset
from transformers.utils.import_utils import NLTK_IMPORT_ERROR, is_nltk_available
from sentence_transformers.readers.InputExample import InputExample
class DenoisingAutoEncoderDataset(Dataset):
"""
The DenoisingAutoEncoderDataset... |
from typing import TYPE_CHECKING, Dict, Iterable
from sentence_transformers.evaluation.SentenceEvaluator import SentenceEvaluator
if TYPE_CHECKING:
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SequentialEvaluator(SentenceEvaluator):
"""
This evaluator allows that multi... | from sentence_transformers import SentenceTransformer
from . import SentenceEvaluator
from typing import Dict, Iterable
class SequentialEvaluator(SentenceEvaluator):
"""
This evaluator allows that multiple sub-evaluators are passed. When the model is evaluated,
the data is passed sequentially to all sub-e... |
import pytest
from jina import Flow
from jina.enums import GatewayProtocolType
from tests import random_docs
@pytest.mark.slow
@pytest.mark.parametrize('protocol', ['http', 'websocket', 'grpc'])
@pytest.mark.parametrize('changeto_protocol', ['grpc', 'http', 'websocket'])
def test_change_gateway(protocol, changeto_pr... | import pytest
from jina import Flow
from jina.enums import GatewayProtocolType
from tests import random_docs
@pytest.mark.slow
@pytest.mark.parametrize('protocol', ['http', 'websocket', 'grpc'])
@pytest.mark.parametrize('changeto_protocol', ['grpc', 'http', 'websocket'])
def test_change_gateway(protocol, changeto_pr... |
# flake8: noqa
# 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/LI... | # flake8: noqa
# 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/LI... |
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from argparse import Namespace
def deployment(args: 'Namespace'):
"""
Start a Deployment
:param args: arguments coming from the CLI.
"""
from jina.orchestrate.deployments import Deployment
try:
with Deployment(args) as d:
... | from typing import TYPE_CHECKING
if TYPE_CHECKING:
from argparse import Namespace
def deployment(args: 'Namespace'):
"""
Start a Deployment
:param args: arguments coming from the CLI.
"""
from jina.orchestrate.deployments import Deployment
try:
with Deployment(args) as d:
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .base_sampler import BaseSampler
from .combined_sampler import CombinedSampler
from .instance_balanced_pos_sampler import InstanceBalancedPosSampler
from .iou_balanced_neg_sampler import IoUBalancedNegSampler
from .mask_pseudo_sampler import MaskPseudoSampler
from .m... | # Copyright (c) OpenMMLab. All rights reserved.
from .base_sampler import BaseSampler
from .combined_sampler import CombinedSampler
from .instance_balanced_pos_sampler import InstanceBalancedPosSampler
from .iou_balanced_neg_sampler import IoUBalancedNegSampler
from .ohem_sampler import OHEMSampler
from .pseudo_sampler... |
# Copyright (c) OpenMMLab. All rights reserved.
from .checkloss_hook import CheckInvalidLossHook
from .mean_teacher_hook import MeanTeacherHook
from .memory_profiler_hook import MemoryProfilerHook
from .num_class_check_hook import NumClassCheckHook
from .pipeline_switch_hook import PipelineSwitchHook
from .set_epoch_in... | # Copyright (c) OpenMMLab. All rights reserved.
from .checkloss_hook import CheckInvalidLossHook
from .mean_teacher_hook import MeanTeacherHook
from .memory_profiler_hook import MemoryProfilerHook
from .num_class_check_hook import NumClassCheckHook
from .set_epoch_info_hook import SetEpochInfoHook
from .sync_norm_hook ... |
import urllib.request
from typing import List
from defusedxml.ElementTree import fromstring
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.readers.web.async_web.base import AsyncWebPageReader
class SitemapReader(BaseReader):
"""
Asynchronous... | import urllib.request
import xml.etree.ElementTree as ET
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.readers.web.async_web.base import AsyncWebPageReader
class SitemapReader(BaseReader):
"""Asynchronous sitemap reader ... |
import jax
from jax import numpy as jnp
from keras.src.optimizers import base_optimizer
class JaxOptimizer(base_optimizer.BaseOptimizer):
"""A class for JAX specific optimizer logic.
Its purpose is to route around statelessness
requirements in cond ops used for EMA handling
and gradient accumulation... | import jax
from jax import numpy as jnp
from keras.src.optimizers import base_optimizer
class JaxOptimizer(base_optimizer.BaseOptimizer):
"""A class for JAX specific optimizer logic.
Its purpose is to route around statelessness
requirements in cond ops used for EMA handling
and gradient accumulation... |
# 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 required by applicabl... | # Copyright 2024 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 required by applicabl... |
# model settings
model = dict(
type='RPN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_stage... | # model settings
preprocess_cfg = dict(
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True,
pad_size_divisor=32)
model = dict(
type='RPN',
preprocess_cfg=preprocess_cfg,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(... |
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
... | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
... |
import random
from collections import defaultdict
import torch
from torch.utils.data.sampler import Sampler
def create_groups(groups, k):
"""Bins sample indices with respect to groups, remove bins with less than k samples
Args:
groups (list[int]): where ith index stores ith sample's group id
Re... | import random
from collections import defaultdict
import torch
from torch.utils.data.sampler import Sampler
def create_groups(groups, k):
"""Bins sample indices with respect to groups, remove bins with less than k samples
Args:
groups (list[int]): where ith index stores ith sample's group id
Re... |
"""Feedly Rss Reader."""
import json
from pathlib import Path
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class FeedlyRssReader(BaseReader):
"""
Feedly Rss Reader.
Get entries from Feedly Rss Reader
Uses Feedly Official python-api-client: https... | """Feedly Rss Reader."""
import json
from pathlib import Path
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class FeedlyRssReader(BaseReader):
"""Feedly Rss Reader.
Get entries from Feedly Rss Reader
Uses Feedly Official python-api-client: https://gi... |
from typing import Optional
from docarray.document import BaseDocument
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import Embedding
class Text(BaseDocument):
"""
Document for handling text.
It can contain a TextUrl (`Text.url`), a str (`Text.text`),
and an Embedding (`Te... | from typing import Optional
from docarray.document import BaseDocument
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import Embedding
class Text(BaseDocument):
"""
Document for handling text.
It can contain a TextUrl (`Text.url`), a str (`Text.text`),
and an Embedding (`Te... |
import os
from functools import lru_cache
from typing import Union
import ffmpeg
import numpy as np
import torch
import torch.nn.functional as F
from .utils import exact_div
# hard-coded audio hyperparameters
SAMPLE_RATE = 16000
N_FFT = 400
N_MELS = 80
HOP_LENGTH = 160
CHUNK_LENGTH = 30
N_SAMPLES = CHUNK_LENGTH * SA... | import os
from functools import lru_cache
from typing import Union
import ffmpeg
import numpy as np
import torch
import torch.nn.functional as F
from .utils import exact_div
# hard-coded audio hyperparameters
SAMPLE_RATE = 16000
N_FFT = 400
N_MELS = 80
HOP_LENGTH = 160
CHUNK_LENGTH = 30
N_SAMPLES = CHUNK_LENGTH * SA... |
_base_ = './rtmdet-ins_s_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' # noqa
model = dict(
backbone=dict(
deepen_factor=0.167,
widen_factor=0.375,
init_cfg=dict(
type='Pretrained',... | _base_ = './rtmdet-ins_s_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' # noqa
model = dict(
backbone=dict(
deepen_factor=0.167,
widen_factor=0.375,
init_cfg=dict(
type='Pretrained',... |
_base_ = './solov2_r50_fpn_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 800), (1333, 768), (1333, 73... | _base_ = 'solov2_r50_fpn_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 800), (1333, 768), (1333, 736)... |
import numpy as np
import pytest
from tensorflow import data as tf_data
import keras
from keras.src import backend
from keras.src import layers
from keras.src import testing
class RandomHueTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_layer(self):
self.run_layer_test(
... | import numpy as np
import pytest
from tensorflow import data as tf_data
import keras
from keras.src import backend
from keras.src import layers
from keras.src import testing
class RandomHueTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_layer(self):
self.run_layer_test(
... |
# Owner(s): ["oncall: distributed checkpointing"]
import unittest.mock as mock
from torch.distributed.checkpoint._experimental.barriers import TCPStoreBarrier
from torch.distributed.checkpoint._experimental.types import RankInfo
from torch.testing._internal.common_utils import run_tests, TestCase
class TestBarriers... | # Owner(s): ["oncall: distributed checkpointing"]
import unittest.mock as mock
from torch.distributed.checkpoint._experimental.barriers import TCPStoreBarrier
from torch.testing._internal.common_utils import run_tests, TestCase
class TestBarriers(TestCase):
@mock.patch("torch.distributed.TCPStore")
@mock.pa... |
from __future__ import annotations
from typing import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import SentenceTransformer
class MSELoss(nn.Module):
def __init__(self, model: SentenceTransformer) -> None:
"""
Computes the MSE loss between the computed sentenc... | from __future__ import annotations
from typing import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import SentenceTransformer
class MSELoss(nn.Module):
def __init__(self, model: SentenceTransformer) -> None:
"""
Computes the MSE loss between the computed sentenc... |
import csv
import os
from pathlib import Path
from typing import Union
import torchaudio
from torch.utils.data import Dataset
class FluentSpeechCommands(Dataset):
"""Create *Fluent Speech Commands* [:footcite:`fluent`] Dataset
Args:
root (str of Path): Path to the directory where the dataset is foun... | import csv
import os
from pathlib import Path
from typing import Union
import torchaudio
from torch.utils.data import Dataset
class FluentSpeechCommands(Dataset):
"""Create *Fluent Speech Commands* [:footcite:`fluent`] Dataset
Args:
root (str of Path): Path to the directory where the dataset is foun... |
_base_ = '../fast_rcnn/fast-rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
neck=[
dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
dict(
type='BFP',
in_channels=256,
num_l... | _base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
neck=[
dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
dict(
type='BFP',
in_channels=256,
num_l... |
import functools
import torch
import torch._custom_ops
import torch.library
# Ensure that torch.ops.torchvision is visible
import torchvision.extension # noqa: F401
@functools.lru_cache(None)
def get_meta_lib():
return torch.library.Library("torchvision", "IMPL", "Meta")
def register_meta(op_name, overload_n... | import functools
import torch
import torch.library
# Ensure that torch.ops.torchvision is visible
import torchvision.extension # noqa: F401
@functools.lru_cache(None)
def get_meta_lib():
return torch.library.Library("torchvision", "IMPL", "Meta")
def register_meta(op_name, overload_name="default"):
def w... |
__version__ = '0.13.34'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
| __version__ = '0.13.34'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
|
from __future__ import annotations
from enum import Enum
from typing import Callable
from numpy import ndarray
from torch import Tensor
from .util import (
cos_sim,
dot_score,
euclidean_sim,
manhattan_sim,
pairwise_cos_sim,
pairwise_dot_score,
pairwise_euclidean_sim,
pairwise_manhatta... | from enum import Enum
from typing import Callable, List, Union
from numpy import ndarray
from torch import Tensor
from .util import (
cos_sim,
dot_score,
euclidean_sim,
manhattan_sim,
pairwise_cos_sim,
pairwise_dot_score,
pairwise_euclidean_sim,
pairwise_manhattan_sim,
)
class Simila... |
_base_ = ['./mask2former_r50_lsj_8x2_50e_coco.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
depths = [2, 2, 6, 2]
model = dict(
type='Mask2Former',
backbone=dict(
_delete_=True,
type='SwinTransformer',
emb... | _base_ = ['./mask2former_r50_lsj_8x2_50e_coco.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
depths = [2, 2, 6, 2]
model = dict(
type='Mask2Former',
backbone=dict(
_delete_=True,
type='SwinTransformer',
em... |
# flake8: noqa
import torchaudio
from torchaudio._backend.utils import get_info_func, get_load_func, get_save_func
from . import utils
from .utils import _is_backend_dispatcher_enabled, get_audio_backend, list_audio_backends, set_audio_backend
if _is_backend_dispatcher_enabled():
torchaudio.info = get_info_func(... | # flake8: noqa
from . import utils
from .utils import get_audio_backend, list_audio_backends, set_audio_backend
utils._init_audio_backend()
|
_base_ = './faster-rcnn_hrnetv2p-w18-1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... | _base_ = './faster_rcnn_hrnetv2p_w18_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... |
import numpy as np
from docarray import BaseDoc, DocList
from docarray.typing import NdArray
from pydantic import Field, BaseModel
from typing import Optional, Literal
from jina import Executor, requests
class TextDoc(BaseDoc):
text: str = Field(description="The text of the document", default="")
class Embeddi... | import numpy as np
from docarray import BaseDoc, DocList
from docarray.typing import NdArray
from pydantic import Field, BaseModel
from jina import Executor, requests
class TextDoc(BaseDoc):
text: str = Field(description="The text of the document", default="")
class EmbeddingResponseModel(TextDoc):
embeddi... |
import torch
from ._bounding_boxes import BoundingBoxes, BoundingBoxFormat, is_rotated_bounding_format
from ._image import Image
from ._keypoints import KeyPoints
from ._mask import Mask
from ._torch_function_helpers import set_return_type
from ._tv_tensor import TVTensor
from ._video import Video
# TODO: Fix this. ... | import torch
from ._bounding_boxes import BoundingBoxes, BoundingBoxFormat, is_rotated_bounding_format
from ._image import Image
from ._keypoints import KeyPoints
from ._mask import Mask
from ._torch_function_helpers import set_return_type
from ._tv_tensor import TVTensor
from ._video import Video
# TODO: Fix this. ... |
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