input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
from typing import TYPE_CHECKING, Union, BinaryIO
from docarray.document.mixins.helper import _uri_to_blob, _to_datauri, _get_file_context
if TYPE_CHECKING: # pragma: no cover
from docarray.typing import T
class BlobDataMixin:
"""Provide helper functions for :class:`Document` to handle binary data."""
... | from typing import TYPE_CHECKING, Union, BinaryIO
from docarray.document.mixins.helper import _uri_to_blob, _to_datauri, _get_file_context
if TYPE_CHECKING:
from docarray.typing import T
class BlobDataMixin:
"""Provide helper functions for :class:`Document` to handle binary data."""
def load_uri_to_blo... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import re
from typing import Dict, List, Optional
from jina import Document, DocumentArray, Executor, requests
from jina.logging.logger import JinaLogger
class Sentencizer(Executor):
"""
:class:`Senten... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import re
from typing import Dict, List, Optional, Tuple
from jina import Document, DocumentArray, Executor, requests
from jina.logging.logger import JinaLogger
class Sentencizer(Executor):
"""
:class:... |
_base_ = 'grounding_dino_swin-t_pretrain_obj365.py'
o365v1_od_dataset = dict(
type='ODVGDataset',
data_root='data/objects365v1/',
ann_file='o365v1_train_odvg.json',
label_map_file='o365v1_label_map.json',
data_prefix=dict(img='train/'),
filter_cfg=dict(filter_empty_gt=False),
pipeline=_base... | _base_ = 'grounding_dino_swin-t_pretrain_obj365.py'
o365v1_od_dataset = dict(
type='ODVGDataset',
data_root='data/objects365v1/',
ann_file='o365v1_train_odvg.jsonl',
label_map_file='o365v1_label_map.json',
data_prefix=dict(img='train/'),
filter_cfg=dict(filter_empty_gt=False),
pipeline=_bas... |
_base_ = '../cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True,... | _base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True,... |
# 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... |
"""Argparser module for WorkerRuntime"""
from jina.parsers.helper import KVAppendAction, add_arg_group
from jina.parsers.orchestrate.runtimes.runtime import mixin_base_runtime_parser
def mixin_worker_runtime_parser(parser):
"""Mixing in arguments required by :class:`WorkerRuntime` into the given parser.
:par... | """Argparser module for WorkerRuntime"""
from jina.parsers.helper import KVAppendAction, add_arg_group
from jina.parsers.orchestrate.runtimes.runtime import mixin_base_runtime_parser
def mixin_worker_runtime_parser(parser):
"""Mixing in arguments required by :class:`WorkerRuntime` into the given parser.
:par... |
# Copyright (c) OpenMMLab. All rights reserved.
from .mask2former_track_head import Mask2FormerTrackHead
from .quasi_dense_embed_head import QuasiDenseEmbedHead
from .quasi_dense_track_head import QuasiDenseTrackHead
__all__ = [
'QuasiDenseEmbedHead', 'QuasiDenseTrackHead', 'Mask2FormerTrackHead'
]
| # Copyright (c) OpenMMLab. All rights reserved.
from .quasi_dense_embed_head import QuasiDenseEmbedHead
from .quasi_dense_track_head import QuasiDenseTrackHead
__all__ = ['QuasiDenseEmbedHead', 'QuasiDenseTrackHead']
|
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
import mmcv
from mmdet.datasets import CocoPanopticDataset
def _create_panoptic_style_json(json_name):
image1 = {
'id': 0,
'width': 640,
'height': 640,
'file_name': 'fake_name1.jpg',
}
... | import os.path as osp
import tempfile
import mmcv
from mmdet.datasets import CocoPanopticDataset
def _create_panoptic_style_json(json_name):
image1 = {
'id': 0,
'width': 640,
'height': 640,
'file_name': 'fake_name1.jpg',
}
image2 = {
'id': 1,
'width': 640... |
"""Code to help indexing data into a vectorstore.
This package contains helper logic to help deal with indexing data into
a vectorstore while avoiding duplicated content and over-writing content
if it's unchanged.
"""
from typing import TYPE_CHECKING
from langchain_core._import_utils import import_attr
if TYPE_CHEC... | """Code to help indexing data into a vectorstore.
This package contains helper logic to help deal with indexing data into
a vectorstore while avoiding duplicated content and over-writing content
if it's unchanged.
"""
from typing import TYPE_CHECKING
from langchain_core._import_utils import import_attr
if TYPE_CHEC... |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("repo_id", ["canonical_dataset_name", "org-name/dataset-name"])
@pytest.mark.parametrize("filename", ["filename.csv", "filename with blanks.csv"])
@pytest.mark.parametrize("revision", [None, "v2"])
def te... | from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("repo_id", ["canonical_dataset_name", "org-name/dataset-name"])
@pytest.mark.parametrize("path", ["filename.csv", "filename with blanks.csv"])
@pytest.mark.parametrize("revision", [None, "v2"])
def test_h... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseTranslationEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model, not mutilingual but hope to see some on the hub soon
m... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseTranslationEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model, not mutilingual but hope to see some on the hub soon
m... |
import logging
import os
from contextlib import asynccontextmanager
from uuid import uuid4
from dotenv import load_dotenv
from prisma import Prisma
from pydantic import BaseModel, Field, field_validator
from backend.util.retry import conn_retry
load_dotenv()
PRISMA_SCHEMA = os.getenv("PRISMA_SCHEMA", "schema.prisma... | import logging
import os
from contextlib import asynccontextmanager
from uuid import uuid4
from dotenv import load_dotenv
from prisma import Prisma
from pydantic import BaseModel, Field, field_validator
from backend.util.retry import conn_retry
load_dotenv()
PRISMA_SCHEMA = os.getenv("PRISMA_SCHEMA", "schema.prisma... |
from pydantic import AnyUrl as BaseAnyUrl
from docarray.document.base_node import BaseNode
from docarray.proto import NodeProto
class AnyUrl(BaseAnyUrl, BaseNode):
def _to_node_protobuf(self) -> NodeProto:
"""Convert Document into a NodeProto protobuf message. This function should
be called when ... | from pydantic import AnyUrl as BaseAnyUrl
from docarray.document.base_node import BaseNode
from docarray.proto import NodeProto
class AnyUrl(BaseAnyUrl, BaseNode):
def _to_nested_item_protobuf(self) -> 'NodeProto':
"""Convert Document into a nested item protobuf message. This function should
be c... |
from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_document import BaseDocument
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
T = TypeVar('T', bound='Text')
class Text(BaseDocument):
"""
Document for handling text.
It can contain a T... | from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_document import BaseDocument
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
T = TypeVar('T', bound='Text')
class Text(BaseDocument):
"""
Document for handling text.
It can contain a T... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.image import affine_transform as affine_transform
from keras.src.ops.image import crop_images as crop_images
from keras.src.ops.image import elastic_transform as elastic_transform... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.image import affine_transform
from keras.src.ops.image import crop_images
from keras.src.ops.image import elastic_transform
from keras.src.ops.image import extract_patches
from ke... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule
from mmcv.cnn.bricks import DropPath
from mmcv.runner import BaseModule
from .se_layer import SELayer
class InvertedResidual(BaseModule):
"""Inverted Residual Block.
Args... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from .se_layer import SELayer
class InvertedResidual(BaseModule):
"""Inverted Residual Block.
Args:
in_channels (int): The input channels of this Mod... |
import json
import aioboto3.session
import pytest
import aioboto3
from llama_index.embeddings.bedrock import BedrockEmbedding, Models
EXP_REQUEST = "foo bar baz"
EXP_RESPONSE = {
"embedding": [
0.017410278,
0.040924072,
-0.007507324,
0.09429932,
0.015304565,
]
}
class... | import json
import aioboto3.session
import pytest
import aioboto3
from llama_index.embeddings.bedrock import BedrockEmbedding, Models
EXP_REQUEST = "foo bar baz"
EXP_RESPONSE = {
"embedding": [
0.017410278,
0.040924072,
-0.007507324,
0.09429932,
0.015304565,
]
}
class... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.runner.hooks import HOOKS, Hook
@HOOKS.register_module()
class MemoryProfilerHook(Hook):
"""Memory profiler hook recording memory information including virtual
memory, swap memory, and the memory of the current process.
Args:
interval (int... | # Copyright (c) OpenMMLab. All rights reserved.
from mmcv.runner.hooks import HOOKS, Hook
@HOOKS.register_module()
class MemoryProfilerHook(Hook):
"""Memory profiler hook recording memory information: virtual memory, swap
memory and memory of current process.
Args:
interval (int): Checking interv... |
_base_ = '../mask_rcnn/mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, Tru... | _base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, Tru... |
# pylint: disable=protected-access
"""Shared typing definition."""
import ctypes
import os
from typing import (
TYPE_CHECKING,
Any,
AnyStr,
Callable,
Dict,
List,
Optional,
Sequence,
Tuple,
Type,
TypeVar,
Union,
)
# os.PathLike/string/numpy.array/scipy.sparse/pd.DataFrame... | # pylint: disable=protected-access
"""Shared typing definition."""
import ctypes
import os
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Optional,
Sequence,
Tuple,
Type,
TypeVar,
Union,
)
# os.PathLike/string/numpy.array/scipy.sparse/pd.DataFrame/dt.Frame/
#... |
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.UnitNormalization")
class UnitNormalization(Layer):
"""Unit normalization layer.
Normalize a batch of inputs so that each input in the batch has a L2 norm
equal to ... | from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.UnitNormalization")
class UnitNormalization(Layer):
"""Unit normalization layer.
Normalize a batch of inputs so that each input in the batch has a L2 norm
equal to ... |
import os
import numpy as np
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import ImageDoc
from docarray.typing import NdArray
class MyDoc(BaseDocument):
embedding: NdArray
text: str
image: ImageDoc
@pytest.mark.slow
@pytest.mark.parametrize(
'protocol', ['... | import pytest
import os
import numpy as np
from docarray import BaseDocument
from docarray.typing import NdArray
from docarray.documents import Image
from docarray import DocumentArray
class MyDoc(BaseDocument):
embedding: NdArray
text: str
image: Image
@pytest.mark.slow
@pytest.mark.parametrize(
'... |
import pytest
from docarray import DocumentArray
@pytest.fixture
def docs():
docs = DocumentArray.empty(5)
docs[0].text = 'hello'
docs[0].tags['name'] = 'hello'
docs[1].text = 'world'
docs[1].tags['name'] = 'hello'
docs[2].tags['x'] = 0.3
docs[2].tags['y'] = 0.6
docs[3].tags['x'] = 0.... | import pytest
from docarray import DocumentArray
@pytest.fixture
def docs():
docs = DocumentArray.empty(5)
docs[0].text = 'hello'
docs[0].tags['name'] = 'hello'
docs[1].text = 'world'
docs[1].tags['name'] = 'hello'
docs[2].tags['x'] = 0.3
docs[2].tags['y'] = 0.6
docs[3].tags['x'] = 0.... |
from typing import List
import numpy as np
import pytest
import torch
from jina import Document, DocumentArray
from ...audioclip_text import AudioCLIPTextEncoder
_EMBEDDING_DIM = 1024
def test_encoding_cpu():
enc = AudioCLIPTextEncoder(device='cpu')
input_data = DocumentArray([Document(text='hello world')])... | from typing import List
import numpy as np
import pytest
import torch
from jina import Document, DocumentArray
from jinahub.encoder.audioclip_text import AudioCLIPTextEncoder
_EMBEDDING_DIM = 1024
def test_encoding_cpu():
enc = AudioCLIPTextEncoder(device='cpu')
input_data = DocumentArray([Document(text='he... |
import pathlib
from typing import Any, Dict, List, Tuple, Union
from torchdata.datapipes.iter import IterDataPipe, Mapper
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import hint_sharding, hint_shuffling
from to... | import pathlib
from typing import Any, Dict, List, Tuple, Union
from torchdata.datapipes.iter import IterDataPipe, Mapper
from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import hint_sharding, hint_shuffling
from torchvision.prot... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
from typing import Dict
import numpy as np
import pytest
from image_tf_encoder import ImageTFEncoder
from jina import Document, DocumentArray, Executor
input_dim = 336
target_output_dim... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
from typing import Dict
import numpy as np
import pytest
from jina import Document, DocumentArray, Executor
from ...image_tf_encoder import ImageTFEncoder
input_dim = 336
target_output... |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
logger = datasets.utils.logging.get_logger(__name__)
@dataclass
class ParquetConfig(datasets.BuilderConfig):
"""BuilderCo... | import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
logger = datasets.utils.logging.get_logger(__name__)
@dataclass
class ParquetConfig(datasets.BuilderConfig):
"""BuilderCo... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.structures import InstanceData
from mmdet.models.dense_heads import NASFCOSHead
class TestNASFCOSHead(TestCase):
def test_nasfcos_head_loss(self):
"""Tests nasfcos head loss when truth is empty and ... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.data import InstanceData
from mmdet.models.dense_heads import NASFCOSHead
class TestNASFCOSHead(TestCase):
def test_nasfcos_head_loss(self):
"""Tests nasfcos head loss when truth is empty and non-em... |
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... |
import io
import warnings
from abc import ABC
import numpy as np
from typing_extensions import TYPE_CHECKING
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils._internal.misc import import_library, is_notebook
if TYPE_CHECKING:
from docarray.typing.bytes.image_bytes import Imag... | import io
import warnings
from abc import ABC
import numpy as np
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils._internal.misc import import_library, is_notebook
class AbstractImageTensor(AbstractTensor, ABC):
def to_bytes(self, format: str = 'PNG') -> bytes:
"""
... |
import os
import shutil
import subprocess
import numpy as np
import PIL.Image as Image
import pytest
from jina import Document, Flow
cur_dir = os.path.dirname(os.path.abspath(__file__))
def data_generator(num_docs):
for i in range(num_docs):
doc = Document(uri=os.path.join(cur_dir, '..', 'test_data', 't... | import os
import shutil
import pytest
import PIL.Image as Image
import numpy as np
from jina import Flow, Document
cur_dir = os.path.dirname(os.path.abspath(__file__))
from ...big_transfer import BigTransferEncoder
def data_generator(num_docs):
for i in range(num_docs):
doc = Document(
uri=... |
_base_ = './grid-rcnn_x101-32x4d_fpn_gn-head_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
init_cfg=dict(
type=... | _base_ = './grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
init_cfg=dict(
type=... |
"""ReAct output parser."""
import re
from typing import Tuple
from llama_index.core.agent.react.types import (
ActionReasoningStep,
BaseReasoningStep,
ResponseReasoningStep,
)
from llama_index.core.output_parsers.utils import extract_json_str
from llama_index.core.types import BaseOutputParser
def extra... | """ReAct output parser."""
import re
from typing import Tuple
from llama_index.core.agent.react.types import (
ActionReasoningStep,
BaseReasoningStep,
ResponseReasoningStep,
)
from llama_index.core.output_parsers.utils import extract_json_str
from llama_index.core.types import BaseOutputParser
def extr... |
"""Output parsers using Pydantic."""
import json
from typing import Annotated, Generic, Optional
import pydantic
from pydantic import SkipValidation
from typing_extensions import override
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import JsonOutputParser
from langc... | import json
from typing import Annotated, Generic, Optional
import pydantic
from pydantic import SkipValidation
from typing_extensions import override
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import JsonOutputParser
from langchain_core.outputs import Generation
fr... |
_base_ = ['faster-rcnn_r50_fpn_32xb2-1x_openimages.py']
model = dict(
roi_head=dict(bbox_head=dict(num_classes=500)),
test_cfg=dict(rcnn=dict(score_thr=0.01)))
# dataset settings
dataset_type = 'OpenImagesChallengeDataset'
train_dataloader = dict(
dataset=dict(
type=dataset_type,
ann_file=... | _base_ = ['faster_rcnn_r50_fpn_32x2_1x_openimages.py']
model = dict(
roi_head=dict(bbox_head=dict(num_classes=500)),
test_cfg=dict(rcnn=dict(score_thr=0.01)))
# dataset settings
dataset_type = 'OpenImagesChallengeDataset'
train_dataloader = dict(
dataset=dict(
type=dataset_type,
ann_file='... |
import sys
from typing import Any, Optional
from unittest.mock import MagicMock, patch
from langchain_community.embeddings import GPT4AllEmbeddings
_GPT4ALL_MODEL_NAME = "all-MiniLM-L6-v2.gguf2.f16.gguf"
_GPT4ALL_NTHREADS = 4
_GPT4ALL_DEVICE = "gpu"
_GPT4ALL_KWARGS = {"allow_download": False}
class MockEmbed4All(Ma... | import sys
from typing import Any, Optional
from unittest.mock import MagicMock, patch
from langchain_community.embeddings import GPT4AllEmbeddings
_GPT4ALL_MODEL_NAME = "all-MiniLM-L6-v2.gguf2.f16.gguf"
_GPT4ALL_NTHREADS = 4
_GPT4ALL_DEVICE = "gpu"
_GPT4ALL_KWARGS = {"allow_download": False}
class MockEmbed4All(Ma... |
"""Init file of LlamaIndex."""
__version__ = "0.12.32"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index.core.... | """Init file of LlamaIndex."""
__version__ = "0.12.31"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index.core.... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.models.utils.misc import get_box_tensor
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes, bbox2distance, distance2bbox
from .base_bbox_coder import BaseBBoxCoder
@TASK_UTILS.register_module()
class DistancePointBBoxCoder... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import bbox2distance, distance2bbox
from .base_bbox_coder import BaseBBoxCoder
@TASK_UTILS.register_module()
class DistancePointBBoxCoder(BaseBBoxCoder):
"""Distance Point BBox coder.
This coder e... |
_base_ = './tood_r50_fpn_ms-2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | _base_ = './tood_r50_fpn_mstrain_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True... |
from __future__ import annotations
import re
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SentenceEvaluator:
"""
Base class for all evaluators. Notably, this cl... | from __future__ import annotations
import re
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SentenceEvaluator:
"""
Base class for all evaluators. Notably, this class introduces the ``greater_is_better`` and ``primar... |
import pytest
import datasets
import datasets.config
# Import fixture modules as plugins
pytest_plugins = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def pytest_collection_modifyitems(config, items):
# Mark tests as "unit" by default if not marked as "integration" (or already marked... | import pytest
import datasets
# Import fixture modules as plugins
pytest_plugins = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def pytest_collection_modifyitems(config, items):
# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
for ite... |
from typing import Any, Dict, Union
from torchvision import tv_tensors
from torchvision.transforms.v2 import functional as F, Transform
class ConvertBoundingBoxFormat(Transform):
"""[BETA] Convert bounding box coordinates to the given ``format``, eg from "CXCYWH" to "XYXY".
.. v2betastatus:: ConvertBounding... | from typing import Any, Dict, Union
from torchvision import datapoints
from torchvision.transforms.v2 import functional as F, Transform
class ConvertBoundingBoxFormat(Transform):
"""[BETA] Convert bounding box coordinates to the given ``format``, eg from "CXCYWH" to "XYXY".
.. v2betastatus:: ConvertBounding... |
from .BinaryClassificationEvaluator import BinaryClassificationEvaluator
from .EmbeddingSimilarityEvaluator import EmbeddingSimilarityEvaluator
from .InformationRetrievalEvaluator import InformationRetrievalEvaluator
from .LabelAccuracyEvaluator import LabelAccuracyEvaluator
from .MSEEvaluator import MSEEvaluator
from ... | from .SentenceEvaluator import SentenceEvaluator
from .SimilarityFunction import SimilarityFunction
from .BinaryClassificationEvaluator import BinaryClassificationEvaluator
from .EmbeddingSimilarityEvaluator import EmbeddingSimilarityEvaluator
from .InformationRetrievalEvaluator import InformationRetrievalEvaluator
fro... |
from __future__ import annotations
from sentence_transformers.sparse_encoder.losses.CSRLoss import CSRLoss
from sentence_transformers.sparse_encoder.losses.CSRReconstructionLoss import (
CSRReconstructionLoss,
)
from sentence_transformers.sparse_encoder.losses.SparseAnglELoss import SparseAnglELoss
from sentence_t... | from __future__ import annotations
from sentence_transformers.sparse_encoder.losses.CSRLoss import CSRLoss
from sentence_transformers.sparse_encoder.losses.CSRReconstructionLoss import (
CSRReconstructionLoss,
)
from sentence_transformers.sparse_encoder.losses.SparseAnglELoss import SparseAnglELoss
from sentence_t... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_SAMPLERS
from .base_sampler import BaseSampler
@BBOX_SAMPLERS.register_module()
class RandomSampler(BaseSampler):
"""Random sampler.
Args:
num (int): Number of samples
pos_fraction (float): Fraction of po... | import torch
from ..builder import BBOX_SAMPLERS
from .base_sampler import BaseSampler
@BBOX_SAMPLERS.register_module()
class RandomSampler(BaseSampler):
"""Random sampler.
Args:
num (int): Number of samples
pos_fraction (float): Fraction of positive samples
neg_pos_up (int, optional... |
# coding=utf-8
# Copyright 2022 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 2022 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... |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class TextDatasetReader(AbstractDatasetReader):
def __init__(
self,
path_or_paths: Nest... | from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class TextDatasetReader(AbstractDatasetReader):
def __init__(
self,
path_or_paths: Nest... |
from typing import Any, Dict, Optional, Union
import numpy as np
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2.utils import is_pure_tensor
class PILToTensor(Transform):
"""[BETA] Convert a PIL Ima... | from typing import Any, Dict, Optional, Union
import numpy as np
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2.utils import is_simple_tensor
class PILToTensor(Transform):
"""[BETA] Convert a PIL I... |
"""Test self-hosted embeddings."""
from typing import Any
from langchain_community.embeddings import (
SelfHostedEmbeddings,
SelfHostedHuggingFaceEmbeddings,
SelfHostedHuggingFaceInstructEmbeddings,
)
def get_remote_instance() -> Any:
"""Get remote instance for testing."""
import runhouse as rh
... | """Test self-hosted embeddings."""
from typing import Any
from langchain_community.embeddings import (
SelfHostedEmbeddings,
SelfHostedHuggingFaceEmbeddings,
SelfHostedHuggingFaceInstructEmbeddings,
)
def get_remote_instance() -> Any:
"""Get remote instance for testing."""
import runhouse as rh
... |
from typing import TYPE_CHECKING, List
from docarray.typing.tensor.abstract_tensor import AbstractTensor
if TYPE_CHECKING:
from docarray.array import DocArrayStacked
from docarray.array.abstract_array import AnyDocArray
class DocArraySummary:
def __init__(self, da: 'AnyDocArray'):
self.da = da
... | from typing import TYPE_CHECKING, List
from docarray.typing.tensor.abstract_tensor import AbstractTensor
if TYPE_CHECKING:
from docarray.array import DocumentArrayStacked
from docarray.array.abstract_array import AnyDocumentArray
class DocumentArraySummary:
def __init__(self, da: 'AnyDocumentArray'):
... |
"""
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 2025 The HuggingFace Team, the AllenNLP library 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
#
... | # Copyright 2024 The HuggingFace Team, the AllenNLP library 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
#
... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .single_stage import SingleStageDetector
@MODELS.register_module()
class DDOD(SingleStageDetector):
"""Implementation of `DDOD <https://arxiv.org/pdf/2107.02963.... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.core import ConfigType, OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class DDOD(SingleStageDetector):
"""Implementation of `DDOD <https://arxiv.org/pdf/2107.02963.p... |
_base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/openimages_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=601)))
# Using 32 GPUS while training
optim_wrapper = dict(
type='OptimWrappe... | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/openimages_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=601)))
# Using 32 GPUS while training
optimizer = dict(type='SGD', lr=0.08, momen... |
"""
Computes embeddings
"""
from __future__ import annotations
import numpy as np
from sentence_transformers import SentenceTransformer
def test_encode_token_embeddings(paraphrase_distilroberta_base_v1_model: SentenceTransformer) -> None:
"""
Test that encode(output_value='token_embeddings') works
"""
... | """
Computes embeddings
"""
import numpy as np
from sentence_transformers import SentenceTransformer
def test_encode_token_embeddings(paraphrase_distilroberta_base_v1_model: SentenceTransformer) -> None:
"""
Test that encode(output_value='token_embeddings') works
"""
model = paraphrase_distilroberta... |
import sys
from os import path
from setuptools import find_packages
from setuptools import setup
if sys.version_info < (3, 7, 0):
raise OSError(f'DocArray requires Python >=3.7, but yours is {sys.version}')
try:
pkg_name = 'docarray'
libinfo_py = path.join(pkg_name, '__init__.py')
libinfo_content = o... | import sys
from os import path
from setuptools import find_packages
from setuptools import setup
if sys.version_info < (3, 7, 0):
raise OSError(f'DocArray requires Python >=3.7, but yours is {sys.version}')
try:
pkg_name = 'docarray'
libinfo_py = path.join(pkg_name, '__init__.py')
libinfo_content = o... |
# Copyright 2015 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 2015 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... |
_base_ = 'ssd300_voc0712.py'
input_size = 512
model = dict(
neck=dict(
out_channels=(512, 1024, 512, 256, 256, 256, 256),
level_strides=(2, 2, 2, 2, 1),
level_paddings=(1, 1, 1, 1, 1),
last_kernel_size=4),
bbox_head=dict(
in_channels=(512, 1024, 512, 256, 256, 256, 256),... | _base_ = 'ssd300_voc0712.py'
input_size = 512
model = dict(
neck=dict(
out_channels=(512, 1024, 512, 256, 256, 256, 256),
level_strides=(2, 2, 2, 2, 1),
level_paddings=(1, 1, 1, 1, 1),
last_kernel_size=4),
bbox_head=dict(
in_channels=(512, 1024, 512, 256, 256, 256, 256),... |
_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResN... | _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
out_... |
_base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco.py'
# lr steps at [0.9, 0.95, 0.975] of the maximum iterations
lr_config = dict(
warmup_iters=500, warmup_ratio=0.067, step=[81000, 85500, 87750])
# 90k iterations with batch_size 64 is roughly equivalent to 48 epochs
runner = dict(type='IterBased... | _base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_270k_coco.py'
# lr steps at [0.9, 0.95, 0.975] of the maximum iterations
lr_config = dict(
warmup_iters=500, warmup_ratio=0.067, step=[81000, 85500, 87750])
# 90k iterations with batch_size 64 is roughly equivalent to 48 epochs
runner = dict(type='IterBasedRunne... |
from __future__ import annotations
from typing import Any
from langchain_core._api import deprecated
from langchain_core.caches import BaseCache as BaseCache # For model_rebuild
from langchain_core.callbacks import Callbacks as Callbacks # For model_rebuild
from langchain_core.chat_history import BaseChatMessageHis... | from __future__ import annotations
from typing import Any
from langchain_core._api import deprecated
from langchain_core.caches import BaseCache as BaseCache # For model_rebuild
from langchain_core.callbacks import Callbacks as Callbacks # For model_rebuild
from langchain_core.chat_history import BaseChatMessageHis... |
"""Function components."""
from inspect import signature
from typing import Any, Callable, Dict, Optional, Set, Tuple
from typing_extensions import Annotated
from llama_index.core.base.query_pipeline.query import (
InputKeys,
OutputKeys,
QueryComponent,
)
from llama_index.core.bridge.pydantic import (
... | """Function components."""
from inspect import signature
from typing import Any, Callable, Dict, Optional, Set, Tuple
from typing_extensions import Annotated
from llama_index.core.base.query_pipeline.query import (
InputKeys,
OutputKeys,
QueryComponent,
)
from llama_index.core.bridge.pydantic import (
... |
import os
import asyncio
import cognee
import pytest
from llama_index.core import Document
from llama_index.graph_rag.cognee import CogneeGraphRAG
@pytest.mark.skipif(
os.getenv("OPENAI_API_KEY") is None,
reason="OPENAI_API_KEY not available to test Cognee integration",
)
@pytest.mark.asyncio()
async def tes... | import os
import asyncio
import cognee
import pytest
from llama_index.core import Document
from llama_index.graph_rag.cognee import CogneeGraphRAG
@pytest.mark.skipif(
os.getenv("OPENAI_API_KEY") is None,
reason="OPENAI_API_KEY not available to test Cognee integration",
)
@pytest.mark.asyncio()
async def tes... |
# 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.
from collections import OrderedDict
from mmengine.dist import get_dist_info
from mmengine.hooks import Hook
from torch import nn
from mmdet.registry import HOOKS
from ..utils.dist_utils import all_reduce_dict
def get_norm_states(module: nn.Module) -> OrderedDict:
... | # Copyright (c) OpenMMLab. All rights reserved.
from collections import OrderedDict
from mmcv.runner import get_dist_info
from mmcv.runner.hooks import Hook
from torch import nn
from mmdet.registry import HOOKS
from ..utils.dist_utils import all_reduce_dict
def get_norm_states(module):
async_norm_states = Order... |
# 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... | # dataset settings
dataset_type = 'OpenImagesDataset'
data_root = 'data/OpenImages/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, denorm_bbox=True),
dict(type... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmcv.runner import load_checkpoint
from .. import build_detector
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class KnowledgeDistillationSingleStageDetector(SingleStageDetector)... | import mmcv
import torch
from mmcv.runner import load_checkpoint
from .. import build_detector
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class KnowledgeDistillationSingleStageDetector(SingleStageDetector):
r"""Implementation of `Distilling the Know... |
from torchaudio._internal import module_utils as _mod_utils
from . import sox_utils
from .download import download_asset
if _mod_utils.is_sox_available():
sox_utils.set_verbosity(0)
__all__ = [
"download_asset",
"sox_utils",
]
| from torchaudio._internal import module_utils as _mod_utils
from . import sox_utils
from .download import download_asset
if _mod_utils.is_sox_available():
sox_utils.set_verbosity(1)
__all__ = [
"download_asset",
"sox_utils",
]
|
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files",
[
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.json"],
["dataset_infos... | import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"dataset_info",
[
DatasetInfo(),
DatasetInfo(
description="foo",
features=Features({"a": Value(... |
__copyright__ = 'Copyright (c) 2020-2021 Jina AI Limited. All rights reserved.'
__license__ = 'Apache-2.0'
from typing import Optional, Iterable, Any
from jina import Executor, DocumentArray, requests
from jina.excepts import BadDocType
import librosa as lr
import numpy as np
import torch
from .audio_clip.model impo... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional, Iterable, Any
from jina import Executor, DocumentArray, requests
import torch
from .audio_clip.model import AudioCLIP
from .audio_clip.utils.transforms import ToTensor1D
class Aud... |
# Copyright (c) OpenMMLab. All rights reserved.
from .base_det_dataset import BaseDetDataset
from .base_video_dataset import BaseVideoDataset
from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .coco_panoptic import CocoPanopticDataset
from .crowdhuman import CrowdHumanDataset
from .dataset_wra... | # Copyright (c) OpenMMLab. All rights reserved.
from .base_det_dataset import BaseDetDataset
from .base_video_dataset import BaseVideoDataset
from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .coco_panoptic import CocoPanopticDataset
from .crowdhuman import CrowdHumanDataset
from .dataset_wra... |
import types
from typing_extensions import TYPE_CHECKING
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.image import ImageNdArray, ImageTensor
from docarray.typing.tensor.ndarray import NdArray
from docarray... | from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.image import ImageNdArray, ImageTensor
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.tensor import AnyTensor
from docarray.typing... |
import numpy as np
import pytest
from docarray import Document, DocumentArray
@pytest.mark.parametrize('nrof_docs', [10, 100, 10_000, 10_100, 20_000, 20_100])
@pytest.mark.parametrize('columns', [[('price', 'int')], {'price': 'int'}])
def test_success_get_bulk_data(start_storage, nrof_docs, columns):
elastic_doc... | from docarray import Document, DocumentArray
import numpy as np
import pytest
@pytest.mark.parametrize('nrof_docs', [10, 100, 10_000, 10_100, 20_000, 20_100])
def test_success_get_bulk_data(start_storage, nrof_docs):
elastic_doc = DocumentArray(
storage='elasticsearch',
config={
'n_dim... |
from typing import List
from llama_index.core.base.embeddings.base import BaseEmbedding
from typing import Optional
try:
import chonkie
from chonkie import AutoEmbeddings
except ImportError:
raise ImportError(
"Could not import Autembeddings from chonkie. "
"Please install it wi... | from typing import List
from llama_index.core.base.embeddings.base import BaseEmbedding
from typing import Optional
try:
import chonkie
from chonkie import AutoEmbeddings
except ImportError:
raise ImportError(
"Could not import Autembeddings from chonkie. "
"Please install it wi... |
from typing import Optional
from llama_index.core.storage.index_store.keyval_index_store import KVIndexStore
from llama_index.storage.kvstore.firestore import FirestoreKVStore
class FirestoreIndexStore(KVIndexStore):
"""
Firestore Index store.
Args:
firestore_kvstore (FirestoreKVStore): Firestor... | from typing import Optional
from llama_index.core.storage.index_store.keyval_index_store import KVIndexStore
from llama_index.storage.kvstore.firestore import FirestoreKVStore
class FirestoreIndexStore(KVIndexStore):
"""Firestore Index store.
Args:
firestore_kvstore (FirestoreKVStore): Firestore key... |
_base_ = './vfnet_r50-mdconv-c3-c5_fpn_ms-2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_e... | _base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
n... |
from __future__ import annotations
import csv
import logging
import os
from typing import TYPE_CHECKING
import torch
from torch.utils.data import DataLoader
from sentence_transformers.evaluation.SentenceEvaluator import SentenceEvaluator
from sentence_transformers.util import batch_to_device
if TYPE_CHECKING:
f... | from __future__ import annotations
import csv
import logging
import os
from typing import TYPE_CHECKING
import torch
from torch.utils.data import DataLoader
from sentence_transformers.evaluation.SentenceEvaluator import SentenceEvaluator
from sentence_transformers.util import batch_to_device
if TYPE_CHECKING:
f... |
# Copyright (c) OpenMMLab. All rights reserved.
import sys
from unittest import TestCase
import torch.cuda
import mmengine
from mmengine.utils.dl_utils import collect_env
from mmengine.utils.dl_utils.parrots_wrapper import _get_cuda_home
class TestCollectEnv(TestCase):
def test_get_cuda_home(self):
CUD... | # Copyright (c) OpenMMLab. All rights reserved.
import sys
from unittest import TestCase
import torch.cuda
import mmengine
from mmengine.utils.dl_utils import collect_env
from mmengine.utils.dl_utils.parrots_wrapper import _get_cuda_home
class TestCollectEnv(TestCase):
def test_get_cuda_home(self):
CUD... |
import os
import shutil
import pytest
import torch
import torchaudio
class GreedyCTCDecoder(torch.nn.Module):
def __init__(self, labels, blank: int = 0):
super().__init__()
self.blank = blank
self.labels = labels
def forward(self, logits: torch.Tensor) -> str:
"""Given a sequ... | import pytest
import torch
import torchaudio
class GreedyCTCDecoder(torch.nn.Module):
def __init__(self, labels, blank: int = 0):
super().__init__()
self.blank = blank
self.labels = labels
def forward(self, logits: torch.Tensor) -> str:
"""Given a sequence logits over labels, ... |
# 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 sys
from collections.abc import Mapping
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import Formatter
class NumpyFormatter(Formatter[Mapping, np.ndarray, Mapping]):
def __init__(self, features=None, **np_array_kwargs):
supe... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.config import ConfigDict
from mmdet.registry import MODELS
from mmdet.utils import OptConfigType, OptMultiConfig
from .two_stage import TwoStageDetector
@MODELS.register_module()
class PointRend(TwoStageDetector):
"""PointRend: Image Segmentation as R... | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine.config import ConfigDict
from mmdet.core.utils import OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .two_stage import TwoStageDetector
@MODELS.register_module()
class PointRend(TwoStageDetector):
"""PointRend: Image Segmentation... |
from pathlib import Path
from typing import Any, Callable, Optional, Union
from .folder import default_loader, ImageFolder
from .utils import download_and_extract_archive, verify_str_arg
class Country211(ImageFolder):
"""`The Country211 Data Set <https://github.com/openai/CLIP/blob/main/data/country211.md>`_ fro... | from pathlib import Path
from typing import Callable, Optional, Union
from .folder import ImageFolder
from .utils import download_and_extract_archive, verify_str_arg
class Country211(ImageFolder):
"""`The Country211 Data Set <https://github.com/openai/CLIP/blob/main/data/country211.md>`_ from OpenAI.
This d... |
from typing import List
import torch
import torchaudio.prototype.transforms as T
from torch.autograd import gradcheck, gradgradcheck
from torchaudio_unittest.common_utils import get_spectrogram, get_whitenoise, TestBaseMixin
class Autograd(TestBaseMixin):
def assert_grad(
self,
transform: torch.n... | from typing import List
import torch
import torchaudio.prototype.transforms as T
from torch.autograd import gradcheck, gradgradcheck
from torchaudio_unittest.common_utils import get_spectrogram, get_whitenoise, nested_params, TestBaseMixin
class Autograd(TestBaseMixin):
def assert_grad(
self,
tra... |
import os
import urllib
import numpy as np
import PIL
import pytest
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.document.io.json import orjson_dumps
from docarray.typing import ImageUrl
CUR_DIR = os.path.dirname(os.path.abspath(__file__))
PATH_TO_IMAGE_DATA = os.path.join(CUR_DIR, '..', '..... | import os
import urllib
import numpy as np
import PIL
import pytest
from pydantic.tools import parse_obj_as
from docarray.typing import ImageUrl
CUR_DIR = os.path.dirname(os.path.abspath(__file__))
PATH_TO_IMAGE_DATA = os.path.join(CUR_DIR, '..', '..', '..', 'toydata', 'image-data')
IMAGE_PATHS = {
'png': os.pat... |
from docarray.documents.mesh.mesh_3d import Mesh3D
from docarray.documents.mesh.vertices_and_faces import VerticesAndFaces
__all__ = ['Mesh3D', 'VerticesAndFaces']
| from docarray.documents.mesh.mesh_3d import Mesh3D
__all__ = ['Mesh3D']
|
import json
from typing import Any, Type, TypeGuard, TypeVar, overload
import jsonschema
from fastapi.encoders import jsonable_encoder
from pydantic import BaseModel
from .type import type_match
def to_dict(data) -> dict:
if isinstance(data, BaseModel):
data = data.model_dump()
return jsonable_encod... | import json
from typing import Any, Type, TypeGuard, TypeVar, overload
import jsonschema
from fastapi.encoders import jsonable_encoder
from pydantic import BaseModel
from .type import type_match
def to_dict(data) -> dict:
if isinstance(data, BaseModel):
data = data.model_dump()
return jsonable_encod... |
from typing import Any, Optional, Union, cast
from langchain_core.messages import AIMessage, ToolCall
from langchain_core.messages.tool import tool_call
from langchain_core.output_parsers import BaseGenerationOutputParser
from langchain_core.outputs import ChatGeneration, Generation
from pydantic import BaseModel, Con... | from typing import Any, Optional, Union, cast
from langchain_core.messages import AIMessage, ToolCall
from langchain_core.messages.tool import tool_call
from langchain_core.output_parsers import BaseGenerationOutputParser
from langchain_core.outputs import ChatGeneration, Generation
from pydantic import BaseModel, Con... |
# Copyright 2021 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 2021 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 (c) OpenMMLab. All rights reserved.
from functools import partial
from typing import Optional
import torch
TORCH_VERSION = torch.__version__
def is_rocm_pytorch() -> bool:
"""Check whether the PyTorch is compiled on ROCm."""
is_rocm = False
if TORCH_VERSION != 'parrots':
try:
... | # Copyright (c) OpenMMLab. All rights reserved.
from functools import partial
from typing import Optional
import torch
TORCH_VERSION = torch.__version__
def is_rocm_pytorch() -> bool:
"""Check whether the PyTorch is compiled on ROCm."""
is_rocm = False
if TORCH_VERSION != 'parrots':
try:
... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# please install mmcls>=0.20.0
# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_f... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# please install mmcls>=0.20.0
# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_f... |
default_scope = 'mmdet'
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=1),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(typ... | default_scope = 'mmdet'
default_hooks = dict(
optimizer=dict(type='OptimizerHook', grad_clip=None),
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=1),
sampler_seed=di... |
"""PlaygroundsSubgraphConnectorToolSpec."""
from typing import Optional, Union
import requests
from llama_index.tools.graphql.base import GraphQLToolSpec
class PlaygroundsSubgraphConnectorToolSpec(GraphQLToolSpec):
"""
Connects to subgraphs on The Graph's decentralized network via the Playgrounds API.
... | """PlaygroundsSubgraphConnectorToolSpec."""
from typing import Optional, Union
import requests
from llama_index.tools.graphql.base import GraphQLToolSpec
class PlaygroundsSubgraphConnectorToolSpec(GraphQLToolSpec):
"""
Connects to subgraphs on The Graph's decentralized network via the Playgrounds API.
... |
_base_ = './ga-retinanet_r101-caffe_fpn_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize', scale=[(1333, 480), (1333, 960)],
keep_ratio=True),
... | _base_ = './ga_retinanet_r101_caffe_fpn_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize', scale=[(1333, 480), (1333, 960)],
keep_ratio=True),
... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet import * # noqa
from mmdet.structures import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modu... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet import * # noqa
from mmdet.structures import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modu... |
from keras.src.backend.common.name_scope import name_scope
from keras.src.backend.jax import core
from keras.src.backend.jax import distribution_lib
from keras.src.backend.jax import image
from keras.src.backend.jax import linalg
from keras.src.backend.jax import math
from keras.src.backend.jax import nn
from keras.src... | from keras.src.backend.jax import core
from keras.src.backend.jax import distribution_lib
from keras.src.backend.jax import image
from keras.src.backend.jax import linalg
from keras.src.backend.jax import math
from keras.src.backend.jax import nn
from keras.src.backend.jax import numpy
from keras.src.backend.jax import... |
import numpy as np
import pytest
from keras.src import testing
from keras.src.layers.activations import elu
class ELUTest(testing.TestCase):
def test_config(self):
elu_layer = elu.ELU()
self.run_class_serialization_test(elu_layer)
@pytest.mark.requires_trainable_backend
def test_elu(self... | import numpy as np
import pytest
from keras.src import testing
from keras.src.layers.activations import elu
class ELUTest(testing.TestCase):
def test_config(self):
elu_layer = elu.ELU()
self.run_class_serialization_test(elu_layer)
@pytest.mark.requires_trainable_backend
def test_elu(self... |
from typing import TYPE_CHECKING, Any, Type, TypeVar, Union, cast
import numpy as np
if TYPE_CHECKING:
from pydantic.fields import ModelField
from pydantic import BaseConfig
from docarray.document.base_node import BaseNode
from docarray.proto import NdArrayProto, NodeProto
T = TypeVar('T', bound='Tensor')
... | from typing import Union, TypeVar, Any, TYPE_CHECKING, Type, cast
import numpy as np
if TYPE_CHECKING:
from pydantic.fields import ModelField
from pydantic import BaseConfig
from docarray.document.base_node import BaseNode
from docarray.proto import NdArrayProto, NodeProto
T = TypeVar('T', bound='Tensor')
... |
import io
from typing import TYPE_CHECKING, Any, Tuple, Type, TypeVar
import numpy as np
from pydantic import parse_obj_as
from pydantic.validators import bytes_validator
from docarray.typing.abstract_type import AbstractType
from docarray.typing.proto_register import _register_proto
if TYPE_CHECKING:
from pydan... | import io
from typing import TYPE_CHECKING, Any, Tuple, Type, TypeVar
import numpy as np
from pydantic import parse_obj_as
from pydantic.validators import bytes_validator
from docarray.typing.abstract_type import AbstractType
from docarray.typing.proto_register import _register_proto
if TYPE_CHECKING:
from pydan... |
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Literal
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.similarity_functions import SimilarityFuncti... | from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Literal
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.similarity_functions import SimilarityFuncti... |
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