input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import argparse
from jina.enums import GatewayProtocolType
from jina.helper import parse_host_scheme
from jina.logging.predefined import default_logger
class NetworkChecker:
"""Check if a BaseDeployment is running or not."""
def __init__(self, args: 'argparse.Namespace'):
"""
Create a new :c... | import argparse
import urllib
from http import HTTPStatus
from jina.enums import GatewayProtocolType
from jina.helper import parse_host_scheme
from jina.logging.predefined import default_logger
class NetworkChecker:
"""Check if a BaseDeployment is running or not."""
def __init__(self, args: 'argparse.Namesp... |
_base_ = './freeanchor_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
# Copyright (c) OpenMMLab. All rights reserved.
from collections import OrderedDict
from mmcv.utils import print_log
from mmdet.core import eval_map, eval_recalls
from mmdet.registry import DATASETS
from .xml_style import XMLDataset
@DATASETS.register_module()
class VOCDataset(XMLDataset):
CLASSES = ('aeroplan... | # Copyright (c) OpenMMLab. All rights reserved.
from collections import OrderedDict
from mmcv.utils import print_log
from mmdet.core import eval_map, eval_recalls
from .builder import DATASETS
from .xml_style import XMLDataset
@DATASETS.register_module()
class VOCDataset(XMLDataset):
CLASSES = ('aeroplane', 'b... |
# Copyright (c) OpenMMLab. All rights reserved.
from .optimizer import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS,
AmpOptimWrapper, ApexOptimWrapper, BaseOptimWrapper,
DefaultOptimWrapperConstructor, OptimWrapper,
OptimWrapperDict, ZeroRedundancyOptim... | # Copyright (c) OpenMMLab. All rights reserved.
from .optimizer import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS,
AmpOptimWrapper, ApexOptimWrapper,
DefaultOptimWrapperConstructor, OptimWrapper,
OptimWrapperDict, ZeroRedundancyOptimizer,
... |
import warnings
from typing import List, Optional, Type
from jina.excepts import BadYAMLVersion
from jina.jaml import JAMLCompatible
from jina.jaml.parsers.base import VersionedYAMLParser
from jina.orchestrate.deployments import Deployment
from jina.serve.gateway import BaseGateway
def _get_all_parser(cls: Type['JAM... | import warnings
from typing import List, Optional, Type
from jina.excepts import BadYAMLVersion
from jina.jaml import JAMLCompatible
from jina.jaml.parsers.base import VersionedYAMLParser
from jina.serve.gateway import BaseGateway
def _get_all_parser(cls: Type['JAMLCompatible']):
"""Get all parsers and legacy pa... |
import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
| import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass
|
# 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 pathlib import Path
REPO_ROOT_DIR = Path(__file__).parent.parent.absolute()
TOYDATA_DIR = REPO_ROOT_DIR / 'tests' / 'toydata'
|
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseMSEEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
student_model = SparseEncoder("prithivida/Splade_PP_en_v1")
tea... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseMSEEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
student_model = SparseEncoder("prithivida/Splade_PP_en_v1")
tea... |
from pathlib import Path
default_exec_file = Path(__file__).absolute().parents[2] / "lightgbm"
def pytest_addoption(parser):
parser.addoption("--execfile", action="store", default=str(default_exec_file))
| from pathlib import Path
default_exec_file = Path(__file__).absolute().parents[2] / 'lightgbm'
def pytest_addoption(parser):
parser.addoption('--execfile', action='store', default=str(default_exec_file))
|
import logging
from pathlib import Path
from typing import Optional, Sequence
from llama_index.core.base.llms.types import ImageBlock
from llama_index.core.multi_modal_llms.base import ChatMessage, ImageNode
DEFAULT_OPENAI_API_TYPE = "open_ai"
DEFAULT_OPENAI_API_BASE = "https://api.openai.com/v1"
GPT4V_MODELS = {
... | import logging
from pathlib import Path
from typing import Optional, Sequence
from llama_index.core.base.llms.types import ImageBlock
from llama_index.core.multi_modal_llms.base import ChatMessage, ImageNode
DEFAULT_OPENAI_API_TYPE = "open_ai"
DEFAULT_OPENAI_API_BASE = "https://api.openai.com/v1"
GPT4V_MODELS = {
... |
_base_ = ['../common/ms_3x_coco.py', '../_base_/models/faster-rcnn_r50_fpn.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_... | _base_ = [
'../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.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'... |
_base_ = './fast-rcnn_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=dict(type='BN', requires_grad=False)... | _base_ = './fast-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='BN', requires_grad=False),
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_cfg = dict(
... |
import glob
import os
from datetime import datetime
import pytest
from jina import Document, Flow, __uptime__, __windows__
from jina.enums import LogVerbosity
from jina.helper import colored
from jina.logging.logger import JinaLogger
cur_dir = os.path.dirname(os.path.abspath(__file__))
def log(logger: JinaLogger):... | import glob
import os
from datetime import datetime
import pytest
from jina import Document, Flow, __uptime__, __windows__
from jina.enums import LogVerbosity
from jina.helper import colored
from jina.logging.logger import JinaLogger
cur_dir = os.path.dirname(os.path.abspath(__file__))
def log(logger: JinaLogger):... |
from __future__ import annotations
try:
from typing import Self
except ImportError:
from typing_extensions import Self
import torch
import transformers
from PIL import Image
from sentence_transformers.models.Router import InputModule
class CLIPModel(InputModule):
save_in_root: bool = True
def __in... | from __future__ import annotations
try:
from typing import Self
except ImportError:
from typing_extensions import Self
import torch
import transformers
from PIL import Image
from sentence_transformers.models.Asym import InputModule
class CLIPModel(InputModule):
save_in_root: bool = True
def __init... |
from backend.blocks.jina._auth import (
JinaCredentials,
JinaCredentialsField,
JinaCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request import Requests
class JinaEmbeddingBlock(Block):
cla... | from backend.blocks.jina._auth import (
JinaCredentials,
JinaCredentialsField,
JinaCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request import requests
class JinaEmbeddingBlock(Block):
cla... |
from typing import TypeVar
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.tensorflow_tensor import TensorFlowTensor, metaTensorFlow
T = TypeVar('T', bound='AudioTensorFlowTensor')
@_register_pr... | from typing import TypeVar
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.tensorflow_tensor import TensorFlowTensor, metaTensorFlow
T = TypeVar('T', bound='AudioTensorFlowTensor')
@_register_pr... |
"""
===========================================
Sparse coding with a precomputed dictionary
===========================================
Transform a signal as a sparse combination of Ricker wavelets. This example
visually compares different sparse coding methods using the
:class:`~sklearn.decomposition.SparseCoder` est... | """
===========================================
Sparse coding with a precomputed dictionary
===========================================
Transform a signal as a sparse combination of Ricker wavelets. This example
visually compares different sparse coding methods using the
:class:`~sklearn.decomposition.SparseCoder` est... |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_TestCommandArgs = namedtuple(
"_TestCommandArgs",
[
"dataset",
"name",... | import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_TestCommandArgs = namedtuple(
"_TestCommandArgs",
[
"dataset",
"name",... |
import os
import numpy as np
import keras
from keras.src import testing
from keras.src.saving.file_editor import KerasFileEditor
def get_source_model():
inputs = keras.Input((2,))
x = keras.layers.Dense(3, name="mydense")(inputs)
outputs = keras.layers.Dense(3, name="output_layer")(x)
model = keras.... | import os
import numpy as np
import keras
from keras.src import testing
from keras.src.saving.file_editor import KerasFileEditor
def get_source_model():
inputs = keras.Input((2,))
x = keras.layers.Dense(3, name="mydense")(inputs)
outputs = keras.layers.Dense(3, name="output_layer")(x)
model = keras.... |
import pytest
from llama_index.core.workflow.decorators import step
from llama_index.core.workflow.events import Event, StartEvent, StopEvent
from llama_index.core.workflow.workflow import Context, Workflow
class DummyEvent(Event):
pass
class IntermediateEvent1(Event):
value: int
class IntermediateEvent2(... | import pytest
from llama_index.core.workflow.decorators import step
from llama_index.core.workflow.events import Event, StartEvent, StopEvent
from llama_index.core.workflow.workflow import Context, Workflow
class DummyEvent(Event):
pass
class IntermediateEvent1(Event):
value: int
class IntermediateEvent2(... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseEmbeddingSimilarityEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseEmbeddingSimilarityEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser... |
from typing import Dict
from jina.helper import TYPE_CHECKING, T, deprecate_by, typename
if TYPE_CHECKING: # pragma: no cover
from jina.proto import jina_pb2
class ProtoTypeMixin:
"""The base mixin class of all Jina types.
.. note::
- All Jina types should inherit from this class.
- All... | from typing import Dict
from jina.helper import TYPE_CHECKING, T, deprecate_by, typename
if TYPE_CHECKING:
from jina.proto import jina_pb2
class ProtoTypeMixin:
"""The base mixin class of all Jina types.
.. note::
- All Jina types should inherit from this class.
- All subclass should ha... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
import numpy as np
import pytest
import torch
import torchvision.models.video as models
from jina import Document, DocumentArray, Executor
from torchvision import transforms
from video_t... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
import numpy as np
import pytest
import torch
import torchvision.models.video as models
from jina import Document, DocumentArray, Executor
from torchvision import transforms
from ...vid... |
"""
This example runs a BiLSTM after the word embedding lookup. The output of the BiLSTM is than pooled,
for example with max-pooling (which gives a system like InferSent) or with mean-pooling.
Note, you can also pass BERT embeddings to the BiLSTM.
"""
import logging
import traceback
from datetime import datetime
fr... | """
This example runs a BiLSTM after the word embedding lookup. The output of the BiLSTM is than pooled,
for example with max-pooling (which gives a system like InferSent) or with mean-pooling.
Note, you can also pass BERT embeddings to the BiLSTM.
"""
from torch.utils.data import DataLoader
import math
from sentence... |
import logging
import random
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseInformationRetrievalEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/spl... | import logging
import random
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseInformationRetrievalEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/spl... |
from __future__ import annotations
from typing import Any, Callable, List, Tuple, Type, Union
import PIL.Image
from torchvision import datapoints
from torchvision._utils import sequence_to_str
from torchvision.transforms.v2.functional import get_dimensions, get_size, is_simple_tensor
def get_bounding_boxes(flat_in... | from __future__ import annotations
from typing import Any, Callable, List, Tuple, Type, Union
import PIL.Image
from torchvision import datapoints
from torchvision._utils import sequence_to_str
from torchvision.transforms.v2.functional import get_dimensions, get_size, is_simple_tensor
def query_bounding_boxes(flat_... |
import os
from enum import Enum
from typing import Any, Dict, List, Optional
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
class SearchDepth(Enum):
"""Search depth as enumerator."""
BASIC ... | import os
from enum import Enum
from typing import Any, Dict, List, Optional
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
class SearchDepth(Enum):
"""Search depth as enumerator."""
BASIC ... |
from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import Tensor, nn
class LayerNorm(nn.Module):
def __init__(self, dimension: int):
super()... | from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import Tensor, nn
class LayerNorm(nn.Module):
def __init__(self, dimension: int):
super(L... |
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Type
if TYPE_CHECKING:
from docarray import BaseDocument
def _is_access_path_valid(doc_type: Type['BaseDocument'], access_path: str) -> bool:
"""
Check if a given access path ("__"-separated) is a valid path for a given Document class.
"""
... | from typing import TYPE_CHECKING, Any, Dict, List, Type
if TYPE_CHECKING:
from docarray import BaseDocument
def _is_access_path_valid(doc_type: Type['BaseDocument'], access_path: str) -> bool:
"""
Check if a given access path ("__"-separated) is a valid path for a given Document class.
"""
from d... |
_base_ = [
'../_base_/models/faster_rcnn_r50_caffe_c4.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.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 = [
... | _base_ = [
'../_base_/models/faster_rcnn_r50_caffe_c4.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.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 = [
... |
from __future__ import annotations
from collections.abc import Iterable
from enum import Enum
from typing import Any
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
class TripletDistanceMetric(Enum):
"""The metric for the tr... | from __future__ import annotations
from enum import Enum
from typing import Any, Iterable
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
class TripletDistanceMetric(Enum):
"""The metric for the triplet loss"""
COSINE =... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import torch
from mmdet.models.dense_heads import PAAHead, paa_head
from mmdet.models.dense_heads.paa_head import levels_to_images
def test_paa_head_loss():
"""Tests paa head loss when truth is empty and non-empty."""
class mock_... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import torch
from mmdet.models.dense_heads import PAAHead, paa_head
from mmdet.models.dense_heads.paa_head import levels_to_images
def test_paa_head_loss():
"""Tests paa head loss when truth is empty and non-empty."""
class mock_... |
import re
from typing import Dict
MISTRALAI_MODELS: Dict[str, int] = {
"mistral-tiny": 32000,
"mistral-small": 32000,
"mistral-medium": 32000,
"mistral-large": 131000,
"mistral-saba-latest": 32000,
"open-mixtral-8x7b": 32000,
"open-mistral-7b": 32000,
"open-mixtral-8x22b": 64000,
"m... | from typing import Dict
MISTRALAI_MODELS: Dict[str, int] = {
"mistral-tiny": 32000,
"mistral-small": 32000,
"mistral-medium": 32000,
"mistral-large": 131000,
"mistral-saba-latest": 32000,
"open-mixtral-8x7b": 32000,
"open-mistral-7b": 32000,
"open-mixtral-8x22b": 64000,
"mistral-sma... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine import Config
from mmengine.data import InstanceData
from mmdet import * # noqa
from mmdet.models.dense_heads import AnchorHead
class TestAnchorHead(TestCase):
def test_anchor_head_loss(self):
"""T... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine import Config
from mmengine.data import InstanceData
from mmdet import * # noqa
from mmdet.models.dense_heads import AnchorHead
class TestAnchorHead(TestCase):
def test_anchor_head_loss(self):
"""T... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.vectorstores import ElasticsearchStore
from langchain_community.vectorstores.elasticsearch import (
ApproxRetrievalStrategy,
BaseRetrievalStrategy,
ExactRetrieval... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.vectorstores import ElasticsearchStore
from langchain_community.vectorstores.elasticsearch import (
ApproxRetrievalStrategy,
BaseRetrievalStrategy,
ExactRetrieval... |
from abc import ABC, abstractmethod
from typing import Dict, Iterator, List, Optional, Type
from typing_extensions import TYPE_CHECKING
if TYPE_CHECKING:
from docarray import BaseDoc, DocList
class AbstractDocStore(ABC):
@staticmethod
@abstractmethod
def list(namespace: str, show_table: bool) -> Lis... | from abc import ABC, abstractmethod
from typing import Dict, Iterator, List, Optional, Type
from typing_extensions import TYPE_CHECKING
if TYPE_CHECKING:
from docarray import BaseDoc, DocList
class AbstractDocStore(ABC):
@staticmethod
@abstractmethod
def list(namespace: str, show_table: bool) -> Lis... |
_base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
train_cfg = dict(max_epochs=36)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=24,
by_epoch=True,
... | _base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
|
from typing import TYPE_CHECKING
from ..utils import DIFFUSERS_SLOW_IMPORT, _LazyModule, deprecate
from ..utils.import_utils import is_peft_available, is_torch_available, is_transformers_available
def text_encoder_lora_state_dict(text_encoder):
deprecate(
"text_encoder_load_state_dict in `models`",
... | from typing import TYPE_CHECKING
from ..utils import DIFFUSERS_SLOW_IMPORT, _LazyModule, deprecate
from ..utils.import_utils import is_peft_available, is_torch_available, is_transformers_available
def text_encoder_lora_state_dict(text_encoder):
deprecate(
"text_encoder_load_state_dict in `models`",
... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.runner.hooks import HOOKS
from mmcv.runner.hooks.lr_updater import (CosineAnnealingLrUpdaterHook,
annealing_cos)
@HOOKS.register_module()
class YOLOXLrUpdaterHook(CosineAnnealingLrUpdaterHook):
"""YOLOX learning ra... | from mmcv.runner.hooks import HOOKS
from mmcv.runner.hooks.lr_updater import (CosineAnnealingLrUpdaterHook,
annealing_cos)
@HOOKS.register_module()
class YOLOXLrUpdaterHook(CosineAnnealingLrUpdaterHook):
"""YOLOX learning rate scheme.
There are two main differences b... |
from __future__ import annotations
from collections.abc import Iterable
import torch.nn as nn
from torch import Tensor
from sentence_transformers.losses.CosineSimilarityLoss import CosineSimilarityLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseCosineSimilarityLoss(Cos... | from __future__ import annotations
from collections.abc import Iterable
import torch.nn as nn
from torch import Tensor
from sentence_transformers.losses.CosineSimilarityLoss import CosineSimilarityLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseCosineSimilarityLoss(Cos... |
_base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
METAINFO = {
'CLASSES':
('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
'chair', 'cow', 'dinin... | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
METAINFO = {
'CLASSES':
('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
'chair', 'cow', 'dinin... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.16.0'
short_version = __version__
def parse_version_info(version_str):
version_info = []
for x in version_str.split('.'):
if x.isdigit():
version_info.append(int(x))
elif x.find('rc') != -1:
patch_version... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.15.1'
short_version = __version__
def parse_version_info(version_str):
version_info = []
for x in version_str.split('.'):
if x.isdigit():
version_info.append(int(x))
elif x.find('rc') != -1:
patch_version... |
import warnings
from sys import platform
from typing import Optional
import torch
import torchaudio
dict_format = {
torch.uint8: "u8",
torch.int16: "s16",
torch.int32: "s32",
torch.int64: "s64",
torch.float32: "flt",
torch.float64: "dbl",
}
def play_audio(
waveform: torch.Tensor,
sam... | import warnings
from sys import platform
from typing import Optional
import torch
import torchaudio
dict_format = {
torch.uint8: "u8",
torch.int16: "s16",
torch.int32: "s32",
torch.int64: "s64",
torch.float32: "flt",
torch.float64: "dbl",
}
@torchaudio._extension.fail_if_no_ffmpeg
def play_a... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.saving.file_editor import KerasFileEditor
from keras.src.saving.object_registration import CustomObjectScope
from keras.src.saving.object_registration import (
CustomObjectScope a... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.saving.object_registration import CustomObjectScope
from keras.src.saving.object_registration import (
CustomObjectScope as custom_object_scope,
)
from keras.src.saving.object_reg... |
"""Interface for tools."""
from typing import Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool, tool
class InvalidTool(BaseTool):
"""Tool that is run when invalid tool name is encountered by agent."""
... | """Interface for tools."""
from typing import Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool, tool
class InvalidTool(BaseTool):
"""Tool that is run when invalid tool name is encountered by agent."""
... |
"""Analytics API"""
import logging
from typing import Annotated
import fastapi
import pydantic
import backend.data.analytics
from backend.server.utils import get_user_id
router = fastapi.APIRouter()
logger = logging.getLogger(__name__)
class LogRawMetricRequest(pydantic.BaseModel):
metric_name: str = pydantic... | """Analytics API"""
import logging
from typing import Annotated
import fastapi
import backend.data.analytics
from backend.server.utils import get_user_id
router = fastapi.APIRouter()
logger = logging.getLogger(__name__)
@router.post(path="/log_raw_metric")
async def log_raw_metric(
user_id: Annotated[str, fas... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import SyncBuffersHook
class TestSyncBuffersHook:
def test_sync_buffers_hook(self):
runner = Mock()
runner.model = Mock()
hook = SyncBuffersHook()
hook._after_epoch(runner)
| # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import SyncBuffersHook
class TestSyncBuffersHook:
def test_sync_buffers_hook(self):
Runner = Mock()
Runner.model = Mock()
Hook = SyncBuffersHook()
Hook._after_epoch(Runner)
|
from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_doc import BaseDoc
from docarray.typing import AnyTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils._internal.misc import import_library
if TYPE_CHECKING:
import ... | from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_doc import BaseDoc
from docarray.typing import AnyTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils._internal.misc import import_library
if TYPE_CHECKING:
import ... |
# Copyright (c) OpenMMLab. All rights reserved.
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
has_method, import_modules_from_strings, is_list_of,
is_method_overridden, is_seq_of, is_str, is_tuple_of,
iter_cast, list_cast, mmcv_full... | # Copyright (c) OpenMMLab. All rights reserved.
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
has_method, import_modules_from_strings, is_list_of,
is_method_overridden, is_seq_of, is_str, is_tuple_of,
iter_cast, list_cast, mmcv_full... |
from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layer import ( # noqa: E501
BaseImagePreprocessingLayer,
)
@keras_export("keras.layers.RandomGrayscale")
class RandomGrayscale(BaseImagePreprocessingLayer):... | from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layer import ( # noqa: E501
BaseImagePreprocessingLayer,
)
@keras_export("keras.layers.RandomGrayscale")
class RandomGrayscale(BaseImagePreprocessingLayer):... |
from typing import TYPE_CHECKING, Any, Type, TypeVar, Union
import numpy as np
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
if TYPE_CHECKING:
from pydantic import BaseConfig
from pydantic.fie... | import wave
from typing import TYPE_CHECKING, Any, Type, TypeVar, Union
import numpy as np
from pydantic import parse_obj_as
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.audio_ndarray import MAX_INT_16, AudioNdArray
from docarray.typing.url.any_url import AnyUrl
if TYP... |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
from typing import Annotated
from fastapi import FastAPI, Query
app = FastAPI()
@app.get("/items/")
async def read_items(q: Annotated[str | None, Query(min_length=3)]):
results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
if q:
results.update({"q": q})
return results
| from typing import Annotated
from fastapi import FastAPI, Query
app = FastAPI()
@app.get("/items/")
async def read_items(q: Annotated[str | None, Query(min_length=3)] = ...):
results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
if q:
results.update({"q": q})
return results
|
import random
import pytest
from jina import Document, DocumentArray
@pytest.fixture
def documents_chunk():
document_array = DocumentArray()
document = Document(tags={'query_size': 35, 'query_price': 31, 'query_brand': 1})
for i in range(0, 10):
chunk = Document()
for j in range(0, 10):
... | import random
import pytest
from jina import DocumentArray, Document
@pytest.fixture
def documents_chunk():
document_array = DocumentArray()
document = Document(tags={'query_size': 35, 'query_price': 31, 'query_brand': 1})
for i in range(0, 10):
chunk = Document()
for j in range(0, 10):
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .utils import (get_device, get_max_cuda_memory, is_cuda_available,
is_mlu_available, is_mps_available)
__all__ = [
'get_max_cuda_memory', 'get_device', 'is_cuda_available',
'is_mlu_available', 'is_mps_available'
]
| # Copyright (c) OpenMMLab. All rights reserved.
from .utils import (get_device, get_max_cuda_memory, is_cuda_available,
is_mlu_available)
__all__ = [
'get_max_cuda_memory', 'get_device', 'is_cuda_available',
'is_mlu_available'
]
|
"""Class for a VectorStore-backed memory object."""
from collections.abc import Sequence
from typing import Any, Optional, Union
from langchain_core._api import deprecated
from langchain_core.documents import Document
from langchain_core.vectorstores import VectorStoreRetriever
from pydantic import Field
from langch... | """Class for a VectorStore-backed memory object."""
from typing import Any, Dict, List, Optional, Sequence, Union
from langchain_core._api import deprecated
from langchain_core.documents import Document
from langchain_core.vectorstores import VectorStoreRetriever
from pydantic import Field
from langchain.memory.chat... |
_base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scale=[(1333, 640), (1333, 800)],
keep_ratio=... | _base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# dataset settings
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scale=[(1333, 640), (1333, 8... |
import torch
from torch import nn, Tensor
from typing import Any, Iterable, Dict
from sentence_transformers.util import fullname
from ..SentenceTransformer import SentenceTransformer
class CosineSimilarityLoss(nn.Module):
def __init__(self, model: SentenceTransformer, loss_fct=nn.MSELoss(), cos_score_transformat... | import torch
from torch import nn, Tensor
from typing import Any, Iterable, Dict
from sentence_transformers.util import fullname
from ..SentenceTransformer import SentenceTransformer
class CosineSimilarityLoss(nn.Module):
def __init__(self, model: SentenceTransformer, loss_fct=nn.MSELoss(), cos_score_transformat... |
from __future__ import annotations
import logging
import numpy as np
from torch.utils.data import IterableDataset
from sentence_transformers.readers import InputExample
logger = logging.getLogger(__name__)
class SentenceLabelDataset(IterableDataset):
"""
This dataset can be used for some specific Triplet ... | from __future__ import annotations
import logging
import numpy as np
from torch.utils.data import IterableDataset
from sentence_transformers.readers import InputExample
logger = logging.getLogger(__name__)
class SentenceLabelDataset(IterableDataset):
"""
This dataset can be used for some specific Triplet ... |
# mypy: allow-untyped-defs
import warnings
import torch
import torch.distributed.algorithms.model_averaging.averagers as averagers
class PostLocalSGDOptimizer(torch.optim.Optimizer):
r"""
Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
This... | # mypy: allow-untyped-defs
import warnings
import torch
import torch.distributed.algorithms.model_averaging.averagers as averagers
class PostLocalSGDOptimizer(torch.optim.Optimizer):
r"""
Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
This... |
import numpy as np
from docarray import BaseDoc
from docarray.array import DocArrayStacked
from docarray.array.stacked.column_storage import ColumnStorageView
from docarray.typing import AnyTensor
def test_document_view():
class MyDoc(BaseDoc):
tensor: AnyTensor
name: str
docs = [MyDoc(tenso... | import numpy as np
from docarray import BaseDocument
from docarray.array import DocumentArrayStacked
from docarray.array.stacked.column_storage import ColumnStorageView
from docarray.typing import AnyTensor
def test_document_view():
class MyDoc(BaseDocument):
tensor: AnyTensor
name: str
docs... |
from .document import DocumentArray
from .storage.qdrant import StorageMixins, QdrantConfig
__all__ = ['DocumentArrayQdrant', 'QdrantConfig']
class DocumentArrayQdrant(StorageMixins, DocumentArray):
"""
DocumentArray that stores Documents in a `Qdrant <https://weaviate.io/>`_ vector search engine.
.. no... | from .document import DocumentArray
from .storage.qdrant import StorageMixins, QdrantConfig
__all__ = ['DocumentArrayQdrant', 'QdrantConfig']
class DocumentArrayQdrant(StorageMixins, DocumentArray):
"""This is a :class:`DocumentArray` that uses Qdrant as
vector search engine and storage.
"""
def __n... |
# Copyright 2021 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 2021 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 (c) OpenMMLab. All rights reserved.
import copy
import unittest
from unittest import TestCase
import torch
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_inputs, demo_mm_proposals, get_roi_head_cfg
from mmdet.utils import register_all_modules
class TestTridentRoIHead(TestCase):
... | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import unittest
from unittest import TestCase
import torch
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_inputs, demo_mm_proposals, get_roi_head_cfg
from mmdet.utils import register_all_modules
class TestTridentRoIHead(TestCase):
... |
"""Gaussian process based regression and classification."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from . import kernels
from ._gpc import GaussianProcessClassifier
from ._gpr import GaussianProcessRegressor
__all__ = ["GaussianProcessClassifier", "GaussianProcessRegressor", "... | """Gaussian process based regression and classification."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from . import kernels
from ._gpc import GaussianProcessClassifier
from ._gpr import GaussianProcessRegressor
__all__ = ["GaussianProcessRegressor", "GaussianProcessClassifier", "... |
# Copyright (c) OpenMMLab. All rights reserved.
# flake8: noqa
from .config import *
from .dataset import *
from .fileio import *
from .registry import *
from .utils import *
| # Copyright (c) OpenMMLab. All rights reserved.
# flake8: noqa
from .config import *
from .fileio import *
from .registry import *
from .utils import *
|
# Copyright (c) OpenMMLab. All rights reserved.
from torch import Tensor
from mmdet.core import SampleList
from mmdet.core.utils import ConfigType, OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .faster_rcnn import FasterRCNN
@MODELS.register_module()
class TridentFasterRCNN(FasterRCNN):
""... | # Copyright (c) OpenMMLab. All rights reserved.
from torch import Tensor
from mmdet.core import SampleList
from mmdet.core.utils import ConfigType, OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .faster_rcnn import FasterRCNN
@MODELS.register_module()
class TridentFasterRCNN(FasterRCNN):
""... |
from dataclasses import dataclass
from typing import Callable, Optional
import datasets
@dataclass
class GeneratorConfig(datasets.BuilderConfig):
generator: Optional[Callable] = None
gen_kwargs: Optional[dict] = None
features: Optional[datasets.Features] = None
def __post_init__(self):
asser... | from dataclasses import dataclass
from typing import Callable, Optional
import datasets
@dataclass
class GeneratorConfig(datasets.BuilderConfig):
generator: Optional[Callable] = None
gen_kwargs: Optional[dict] = None
features: Optional[datasets.Features] = None
def __post_init__(self):
asser... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.registry import HOOKS
from .hook import Hook
@HOOKS.register_module()
class DistSamplerSeedHook(Hook):
"""Data-loading sampler for distributed training.
When distributed training, it is only useful in conjunction with
:obj:`EpochBasedRunner`, ... | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine.registry import HOOKS
from .hook import Hook
@HOOKS.register_module()
class DistSamplerSeedHook(Hook):
"""Data-loading sampler for distributed training.
When distributed training, it is only useful in conjunction with
:obj:`EpochBasedRunner`, ... |
# 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... |
"""
In this example we train a semantic search model to search through Wikipedia
articles about programming articles & technologies.
We use the text paragraphs from the following Wikipedia articles:
Assembly language, C , C Sharp , C++, Go , Java , JavaScript, Keras, Laravel, MATLAB, Matplotlib, MongoDB, MySQL, Natura... | """
In this example we train a semantic search model to search through Wikipedia
articles about programming articles & technologies.
We use the text paragraphs from the following Wikipedia articles:
Assembly language, C , C Sharp , C++, Go , Java , JavaScript, Keras, Laravel, MATLAB, Matplotlib, MongoDB, MySQL, Natura... |
tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.6), max_per_img=100))
img_scales = [(640, 640), (320, 320), (960, 960)]
tta_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(
type='TestTimeAug',
transforms=[
[
... | tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.6), max_per_img=100))
img_scales = [(640, 640), (320, 320), (960, 960)]
tta_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(
type='TestTimeAug',
transforms=[
[
... |
from __future__ import annotations
from .BinaryClassificationEvaluator import BinaryClassificationEvaluator
from .EmbeddingSimilarityEvaluator import EmbeddingSimilarityEvaluator
from .InformationRetrievalEvaluator import InformationRetrievalEvaluator
from .LabelAccuracyEvaluator import LabelAccuracyEvaluator
from .MS... | from .BinaryClassificationEvaluator import BinaryClassificationEvaluator
from .EmbeddingSimilarityEvaluator import EmbeddingSimilarityEvaluator
from .InformationRetrievalEvaluator import InformationRetrievalEvaluator
from .LabelAccuracyEvaluator import LabelAccuracyEvaluator
from .MSEEvaluator import MSEEvaluator
from ... |
AMI_ID = {
# Managed by XGBoost team
"linux-amd64-gpu": {
"us-west-2": "ami-0b4079c15bbbd0faf",
},
"linux-amd64-mgpu": {
"us-west-2": "ami-0b4079c15bbbd0faf",
},
"windows-gpu": {
"us-west-2": "ami-0123456bcf4cdfb82",
},
"windows-cpu": {
"us-west-2": "ami-0... | AMI_ID = {
# Managed by XGBoost team
"linux-amd64-gpu": {
"us-west-2": "ami-070080d04e81c5e39",
},
"linux-amd64-mgpu": {
"us-west-2": "ami-070080d04e81c5e39",
},
"windows-gpu": {
"us-west-2": "ami-07c14abcf529d816a",
},
"windows-cpu": {
"us-west-2": "ami-0... |
from keras.src import backend
from keras.src.layers.input_spec import InputSpec
from keras.src.layers.layer import Layer
class BaseGlobalPooling(Layer):
"""Base global pooling layer."""
def __init__(
self, pool_dimensions, data_format=None, keepdims=False, **kwargs
):
super().__init__(**k... | from keras.src import backend
from keras.src.layers.input_spec import InputSpec
from keras.src.layers.layer import Layer
class BaseGlobalPooling(Layer):
"""Base global pooling layer."""
def __init__(
self, pool_dimensions, data_format=None, keepdims=False, **kwargs
):
super().__init__(**k... |
#!/usr/bin/env python3
"""Evaluate the lightning module by loading the checkpoint, the SentencePiece model, and the global_stats.json.
Example:
python eval.py --model-type tedlium3 --checkpoint-path ./experiments/checkpoints/epoch=119-step=254999.ckpt
--dataset-path ./datasets/tedlium --sp-model-path ./spm_bpe_500... | #!/usr/bin/env python3
"""Evaluate the lightning module by loading the checkpoint, the SentencePiece model, and the global_stats.json.
Example:
python eval.py --model-type tedlium3 --checkpoint-path ./experiments/checkpoints/epoch=119-step=254999.ckpt
--dataset-path ./datasets/tedlium --sp-model-path ./spm_bpe_500... |
_base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
]
# model settings
model = dict(
type='CornerNet',
backbone=dict(
type='HourglassNet',
downsample_times=5,
num_stacks=2,
stage_channels=[256, 256, 384, 384, 384, 512],
stage_blocks=[2, ... | _base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
]
# model settings
model = dict(
type='CornerNet',
backbone=dict(
type='HourglassNet',
downsample_times=5,
num_stacks=2,
stage_channels=[256, 256, 384, 384, 384, 512],
stage_blocks=[2, ... |
import gzip
import logging
import os
from datetime import datetime
from torch.utils.data import DataLoader
from sentence_transformers import InputExample, LoggingHandler, SentenceTransformer, evaluation, losses, models, util
#### Just some code to print debug information to stdout
logging.basicConfig(
format="%(... |
from sentence_transformers import SentenceTransformer, LoggingHandler, InputExample
from sentence_transformers import models, util, datasets, evaluation, losses
import logging
import os
import gzip
from torch.utils.data import DataLoader
from datetime import datetime
#### Just some code to print debug information to... |
from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.typing import AnyEmbedding, AudioUrl
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.t... | from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.typing import AnyEmbedding, AudioUrl
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.t... |
from __future__ import annotations
from collections.abc import Iterable
import torch.nn as nn
from torch import Tensor
from sentence_transformers.losses.CosineSimilarityLoss import CosineSimilarityLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseCosineSimilarityLoss(Cos... | from __future__ import annotations
from collections.abc import Iterable
import torch.nn as nn
from torch import Tensor
from sentence_transformers.losses.CosineSimilarityLoss import CosineSimilarityLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseCosineSimilarityLoss(Cos... |
from . import ( # noqa: F401
_extension,
compliance,
datasets,
functional,
io,
kaldi_io,
models,
pipelines,
sox_effects,
transforms,
utils,
)
from .backend.common import AudioMetaData
try:
from .version import __version__, git_version # noqa: F401
except ImportError:
... | from torchaudio import ( # noqa: F401
_extension,
compliance,
datasets,
functional,
io,
kaldi_io,
models,
pipelines,
sox_effects,
transforms,
utils,
)
try:
from .version import __version__, git_version # noqa: F401
except ImportError:
pass
def _is_backend_dispatc... |
from typing import Iterator, Dict
class Offset2ID:
def __init__(self, ids=None, list_like=True):
self.ids = ids or []
self._list_like = list_like
def get_id(self, idx):
if not self._list_like:
raise ValueError(
"The offset2id is not enabled for list-like in... | from typing import Iterator, Dict
class Offset2ID:
def __init__(self, ids=None):
self.ids = ids or []
def get_id(self, idx):
return self.ids[idx]
def append(self, data):
self.ids.append(data)
def extend(self, data):
self.ids.extend(data)
def update(self, positio... |
import os
from argparse import ArgumentParser
import mmcv
import requests
import torch
from mmengine.structures import InstanceData
from mmdet.apis import inference_detector, init_detector
from mmdet.registry import VISUALIZERS
from mmdet.structures import DetDataSample
from mmdet.utils import register_all_modules
... | from argparse import ArgumentParser
import numpy as np
import requests
from mmdet.apis import inference_detector, init_detector, show_result_pyplot
from mmdet.core import bbox2result
def parse_args():
parser = ArgumentParser()
parser.add_argument('img', help='Image file')
parser.add_argument('config', h... |
from typing import Optional
import numpy as np
import pytest
from pydantic import BaseModel
from typing_extensions import TypedDict
from docarray import BaseDocument, DocumentArray
from docarray.documents import AudioDoc, ImageDoc, TextDoc
from docarray.documents.helper import create_doc, create_from_typeddict
from d... | from typing import Optional
import numpy as np
import pytest
from pydantic import BaseModel
from typing_extensions import TypedDict
from docarray import BaseDocument, DocumentArray
from docarray.documents import Audio, Image, Text
from docarray.documents.helper import create_doc, create_from_typeddict
from docarray.t... |
import os
# When using jax.experimental.enable_x64 in unit test, we want to keep the
# default dtype with 32 bits, aligning it with Keras's default.
os.environ["JAX_DEFAULT_DTYPE_BITS"] = "32"
try:
# When using torch and tensorflow, torch needs to be imported first,
# otherwise it will segfault upon import. T... | import os
# When using jax.experimental.enable_x64 in unit test, we want to keep the
# default dtype with 32 bits, aligning it with Keras's default.
os.environ["JAX_DEFAULT_DTYPE_BITS"] = "32"
try:
# When using torch and tensorflow, torch needs to be imported first,
# otherwise it will segfault upon import. T... |
import importlib
import threading
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
_has_s3fs = importlib.util.find_spec("s3fs") is not None
if _has_s3fs:
from .s3filesystem import S3FileSystem # noqa: F401
COMPRESSION_FILESYSTEMS: List[... | import importlib
from typing import List
import fsspec
from . import compression
from .hffilesystem import HfFileSystem
_has_s3fs = importlib.util.find_spec("s3fs") is not None
if _has_s3fs:
from .s3filesystem import S3FileSystem # noqa: F401
COMPRESSION_FILESYSTEMS: List[compression.BaseCompressedFileFileSy... |
"""
==================================================
Principal Component Analysis (PCA) on Iris Dataset
==================================================
This example shows a well known decomposition technique known as Principal Component
Analysis (PCA) on the
`Iris dataset <https://en.wikipedia.org/wiki/Iris_flowe... | """
=========================================================
PCA example with Iris Data-set
=========================================================
Principal Component Analysis applied to the Iris dataset.
See `here <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ for more
information on this dataset.
"""
... |
import collections
import json
import logging
import os
import string
from typing import Iterable, List
from transformers.utils.import_utils import NLTK_IMPORT_ERROR, is_nltk_available
from .WordTokenizer import ENGLISH_STOP_WORDS, WordTokenizer
logger = logging.getLogger(__name__)
class PhraseTokenizer(WordTokeni... | from typing import Union, Tuple, List, Iterable, Dict
import collections
import string
import os
import json
import logging
from .WordTokenizer import WordTokenizer, ENGLISH_STOP_WORDS
import nltk
logger = logging.getLogger(__name__)
class PhraseTokenizer(WordTokenizer):
"""Tokenizes the text with respect to exi... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
from collections import OrderedDict
import torch
def moco_convert(src, dst):
"""Convert keys in pycls pretrained moco models to mmdet style."""
# load caffe model
moco_model = torch.load(src)
blobs = moco_model['state_dict']
# conver... | import argparse
from collections import OrderedDict
import torch
def moco_convert(src, dst):
"""Convert keys in pycls pretrained moco models to mmdet style."""
# load caffe model
moco_model = torch.load(src)
blobs = moco_model['state_dict']
# convert to pytorch style
state_dict = OrderedDict(... |
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from numbers import Real
import numpy as np
from ..base import BaseEstimator, _fit_context
from ..utils._param_validation import Interval
from ..utils.sparsefuncs import mean_variance_axis, min_max_axis
from ..utils.validation import chec... | # Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from numbers import Real
import numpy as np
from ..base import BaseEstimator, _fit_context
from ..utils._param_validation import Interval
from ..utils.sparsefuncs import mean_variance_axis, min_max_axis
from ..utils.validation import chec... |
"""This module checks if the given python files can be imported without error."""
import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_mo... | """This module checks if the given python files can be imported without error."""
import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_mo... |
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 typing import Dict, 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 sentence embedding and a target sente... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='VFNet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='VFNet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... |
from .document import DocumentArray
from .storage.weaviate import StorageMixins, WeaviateConfig
__all__ = ['DocumentArrayWeaviate', 'WeaviateConfig']
class DocumentArrayWeaviate(StorageMixins, DocumentArray):
"""
DocumentArray that stores Documents in a `Weaviate <https://weaviate.io/>`_ vector search engine... | from .document import DocumentArray
from .storage.weaviate import StorageMixins, WeaviateConfig
__all__ = ['DocumentArrayWeaviate', 'WeaviateConfig']
class DocumentArrayWeaviate(StorageMixins, DocumentArray):
"""This is a :class:`DocumentArray` that uses Weaviate as
vector search engine and storage.
"""
... |
from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str]
arg3: Optional[str]
class ProcessedResponseModel(BaseModel):
... | from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.clients.request import request_generator
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py'
]
# optimizer
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(type='ResNeXt', depth=101, groups=64, base_width=4))
optim_wrapper = dict(optimizer=dict(type='SGD', lr=0.01))
| _base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py'
]
# optimizer
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(type='ResNeXt', depth=101, groups=64, base_width=4))
optimizer = dict(type='SGD', lr=0.01)
|
import itertools
from typing import (
TYPE_CHECKING,
Union,
Sequence,
overload,
Any,
List,
)
import numpy as np
from docarray import Document
from docarray.helper import typename
if TYPE_CHECKING: # pragma: no cover
from docarray.typing import (
DocumentArrayIndexType,
Do... | import itertools
from typing import (
TYPE_CHECKING,
Union,
Sequence,
overload,
Any,
List,
)
import numpy as np
from docarray import Document
from docarray.helper import typename
if TYPE_CHECKING:
from docarray.typing import (
DocumentArrayIndexType,
DocumentArraySingleton... |
import os
import pytest
from jina import Document, Flow
from jinahub.indexers.searcher.compound.FaissPostgresIndexer import FaissPostgresIndexer
cur_dir = os.path.dirname(os.path.abspath(__file__))
compose_yml = os.path.join(cur_dir, 'docker-compose.yml')
# fixes issue #208 https://github.com/jina-ai/executors/issu... | import os
import pytest
from jina import Document, Flow
from jinahub.indexers.searcher.compound.FaissPostgresSearcher import (
FaissPostgresSearcher,
)
cur_dir = os.path.dirname(os.path.abspath(__file__))
compose_yml = os.path.join(cur_dir, 'docker-compose.yml')
# fixes issue #208 https://github.com/jina-ai/exe... |
import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception:
has_failure = True
print(f... | import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception:
has_faillure = True
print(... |
# mypy: ignore-errors
"""
This module provides the TorchInductor backend integration for TorchDynamo.
TorchInductor is a compiler backend that generates optimized code for both CPU and GPU.
This module lazily imports and registers the TorchInductor compiler to avoid loading it
into memory when it is not being used. T... | # mypy: ignore-errors
"""
This module provides the TorchInductor backend integration for TorchDynamo.
TorchInductor is a compiler backend that generates optimized code for both CPU and GPU.
This module lazily imports and registers the TorchInductor compiler to avoid loading it
into memory when it is not being used. T... |
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