input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... |
_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),
... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.chat_loaders.utils import (
map_ai_messages,
map_ai_messages_in_session,
merge_chat_runs,
merge_chat_runs_in_session,
)
# Create a way to dynamically loo... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.chat_loaders.utils import (
map_ai_messages,
map_ai_messages_in_session,
merge_chat_runs,
merge_chat_runs_in_session,
)
# Create a way to dynamically loo... |
from typing import Dict, List, Tuple
import pytest
from opentelemetry.metrics import Meter
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import (
HistogramDataPoint,
InMemoryMetricReader,
Metric,
)
from jina.serve.networking.instrumentation import _NetworkingHis... | from typing import Dict, List, Tuple
import pytest
from opentelemetry.metrics import Meter
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import (
HistogramDataPoint,
InMemoryMetricReader,
Metric,
)
from jina.serve.networking import _NetworkingHistograms
@pytes... |
"""
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
:return:
"""
model = paraphrase_... | """
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
:return:
"""
model = paraphrase... |
"""Download llama-pack as template."""
import logging
import os
import subprocess
import sys
from importlib import util
from pathlib import Path
from typing import Any, Optional, Union
import requests
from llama_index.core.download.utils import (
ChangeDirectory,
get_file_content,
initialize_directory,
... | """Download llama-pack as template."""
import logging
import os
import subprocess
import sys
from importlib import util
from pathlib import Path
from typing import Any, Optional, Union
import requests
from llama_index.core.download.utils import (
ChangeDirectory,
get_file_content,
initialize_directory,
... |
# 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... | # 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... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... |
import logging
from typing import Any
from backend.data.block import (
Block,
BlockCategory,
BlockInput,
BlockOutput,
BlockSchema,
BlockType,
get_block,
)
from backend.data.execution import ExecutionStatus
from backend.data.model import SchemaField
from backend.util import json
logger = lo... | import logging
from typing import Any
from backend.data.block import (
Block,
BlockCategory,
BlockInput,
BlockOutput,
BlockSchema,
BlockType,
get_block,
)
from backend.data.execution import ExecutionStatus
from backend.data.model import SchemaField
from backend.util import json
logger = lo... |
import pathlib
from typing import Any, Callable, Optional, Union
from .folder import default_loader
from .utils import verify_str_arg
from .vision import VisionDataset
class StanfordCars(VisionDataset):
"""Stanford Cars Dataset
The Cars dataset contains 16,185 images of 196 classes of cars. The data is
... | import pathlib
from typing import Any, Callable, Optional, Tuple, Union
from .folder import default_loader
from .utils import verify_str_arg
from .vision import VisionDataset
class StanfordCars(VisionDataset):
"""Stanford Cars Dataset
The Cars dataset contains 16,185 images of 196 classes of cars. The dat... |
from typing import Any, Optional
from typing_extensions import override
from langchain_core.caches import RETURN_VAL_TYPE, BaseCache
from langchain_core.globals import set_llm_cache
from langchain_core.language_models import FakeListLLM
class InMemoryCache(BaseCache):
"""In-memory cache used for testing purpose... | from typing import Any, Optional
from langchain_core.caches import RETURN_VAL_TYPE, BaseCache
from langchain_core.globals import set_llm_cache
from langchain_core.language_models import FakeListLLM
class InMemoryCache(BaseCache):
"""In-memory cache used for testing purposes."""
def __init__(self) -> None:
... |
import numpy as np
import pytest
from pydantic import parse_obj_as
from docarray.base_document.document import BaseDocument
from docarray.documents import Mesh3D
from tests import TOYDATA_DIR
LOCAL_OBJ_FILE = str(TOYDATA_DIR / 'tetrahedron.obj')
REMOTE_OBJ_FILE = 'https://people.sc.fsu.edu/~jburkardt/data/obj/al.obj'... | import numpy as np
import pytest
from pydantic import parse_obj_as
from docarray import BaseDocument
from docarray.documents import Mesh3D
from tests import TOYDATA_DIR
LOCAL_OBJ_FILE = str(TOYDATA_DIR / 'tetrahedron.obj')
REMOTE_OBJ_FILE = 'https://people.sc.fsu.edu/~jburkardt/data/obj/al.obj'
@pytest.mark.slow
@p... |
from __future__ import annotations
from typing import Union, Sequence, Literal
import torch
import torch.fft
from torch.fft import * # noqa: F403
from ._typing import Array
# Several torch fft functions do not map axes to dim
def fftn(
x: Array,
/,
*,
s: Sequence[int] = None,
axes: Sequence[int... | from __future__ import annotations
from typing import TYPE_CHECKING
if TYPE_CHECKING:
import torch
array = torch.Tensor
from typing import Union, Sequence, Literal
from torch.fft import * # noqa: F403
import torch.fft
# Several torch fft functions do not map axes to dim
def fftn(
x: array,
/,
... |
import functools
import os
import os.path
import pathlib
from typing import Any, BinaryIO, Collection, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import FileLister, FileOpener, Filter, IterDataPipe, Mapper
from torchvision.prototype.datapoints import Label
from torchvision.prototype.datasets.util... | import functools
import os
import os.path
import pathlib
from typing import Any, BinaryIO, Collection, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import FileLister, FileOpener, Filter, IterDataPipe, Mapper
from torchvision.prototype.datasets.utils import EncodedData, EncodedImage
from torchvision... |
#!/usr/bin/env python
import functools as func
import glob
import os.path as osp
import re
import numpy as np
url_prefix = 'https://github.com/open-mmlab/mmdetection/blob/main/'
files = sorted(glob.glob('../configs/*/README.md'))
stats = []
titles = []
num_ckpts = 0
for f in files:
url = osp.dirname(f.replace(... | #!/usr/bin/env python
import functools as func
import glob
import os.path as osp
import re
import numpy as np
url_prefix = 'https://github.com/open-mmlab/mmdetection/blob/3.x/'
files = sorted(glob.glob('../configs/*/README.md'))
stats = []
titles = []
num_ckpts = 0
for f in files:
url = osp.dirname(f.replace('... |
_base_ = './sparse-rcnn_r50_fpn_ms-480-800-3x_coco.py'
num_proposals = 300
model = dict(
rpn_head=dict(num_proposals=num_proposals),
test_cfg=dict(
_delete_=True, rpn=None, rcnn=dict(max_per_img=num_proposals)))
# augmentation strategy originates from DETR.
train_pipeline = [
dict(
type='Lo... | _base_ = './sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py'
num_proposals = 300
model = dict(
rpn_head=dict(num_proposals=num_proposals),
test_cfg=dict(
_delete_=True, rpn=None, rcnn=dict(max_per_img=num_proposals)))
# augmentation strategy originates from DETR.
train_pipeline = [
dict(
typ... |
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
Extend this class and implement __call__ for custom evaluators.
... | 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
Extend this class and implement __call__ for custom evaluators.
... |
import pytest
from backend.util.request import validate_url
@pytest.mark.parametrize(
"url, trusted_origins, expected_value, should_raise",
[
# Rejected IP ranges
("localhost", [], None, True),
("192.168.1.1", [], None, True),
("127.0.0.1", [], None, True),
("0.0.0.0",... | import pytest
from backend.util.request import validate_url
def test_validate_url():
# Rejected IP ranges
with pytest.raises(ValueError):
validate_url("localhost", [])
with pytest.raises(ValueError):
validate_url("192.168.1.1", [])
with pytest.raises(ValueError):
validate_ur... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Tuple, Dict, List
import numpy as np
from jina import Executor, requests, DocumentArray, Document
from jina_commons import get_logger
from jina_commons.indexers.dump import import_vectors
class ... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Tuple, Dict, List
import numpy as np
from jina import Executor, requests, DocumentArray, Document
from jina_commons import get_logger
from jina_commons.indexers.dump import import_vectors
class ... |
from __future__ import annotations
import logging
from dataclasses import dataclass
from sentence_transformers.data_collator import SentenceTransformerDataCollator
logger = logging.getLogger(__name__)
@dataclass
class SparseEncoderDataCollator(SentenceTransformerDataCollator):
"""Collator for a SparseEncoder m... | from __future__ import annotations
import logging
from dataclasses import dataclass
from sentence_transformers.data_collator import SentenceTransformerDataCollator
logger = logging.getLogger(__name__)
@dataclass
class SparseEncoderDataCollator(SentenceTransformerDataCollator):
"""Collator for a SparseEncoder m... |
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class MaskScoringRCNN(TwoStageDetector):
"""Mask Scoring RCNN.
https://arxiv.org/abs/1903.00241
"""
def __init__(self,
backbone,
... | from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class MaskScoringRCNN(TwoStageDetector):
"""Mask Scoring RCNN.
https://arxiv.org/abs/1903.00241
"""
def __init__(self,
backbone,
rpn_head,
roi_head,... |
"""
LexRank implementation
Source: https://github.com/crabcamp/lexrank/tree/dev
"""
import logging
import numpy as np
from scipy.sparse.csgraph import connected_components
from scipy.special import softmax
logger = logging.getLogger(__name__)
def degree_centrality_scores(
similarity_matrix,
threshold=None,... | """
LexRank implementation
Source: https://github.com/crabcamp/lexrank/tree/dev
"""
import numpy as np
from scipy.sparse.csgraph import connected_components
from scipy.special import softmax
import logging
logger = logging.getLogger(__name__)
def degree_centrality_scores(
similarity_matrix,
threshold=None,
... |
# dataset settings
dataset_type = 'DeepFashionDataset'
data_root = 'data/DeepFashion/In-shop/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/... | # dataset settings
dataset_type = 'DeepFashionDataset'
data_root = 'data/DeepFashion/In-shop/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dic... |
# Copyright (c) OpenMMLab. All rights reserved.
import asyncio
import contextlib
import logging
import os
import time
from typing import List
import torch
logger = logging.getLogger(__name__)
DEBUG_COMPLETED_TIME = bool(os.environ.get('DEBUG_COMPLETED_TIME', False))
@contextlib.asynccontextmanager
async def comple... | import asyncio
import contextlib
import logging
import os
import time
from typing import List
import torch
logger = logging.getLogger(__name__)
DEBUG_COMPLETED_TIME = bool(os.environ.get('DEBUG_COMPLETED_TIME', False))
@contextlib.asynccontextmanager
async def completed(trace_name='',
name='',
... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... |
# 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 __future__ import annotations
from typing import Any, Dict, Optional
from docarray import BaseDoc, DocList
from docarray.typing import AnyEmbedding, AnyTensor
class LegacyDocument(BaseDoc):
"""
This Document is the LegacyDocument. It follows the same schema as in DocArray <=0.21.
It can be useful t... |
from docarray.array.document import DocumentArray
from docarray.array.storage.annlite import StorageMixins, AnnliteConfig
__all__ = ['AnnliteConfig', 'DocumentArrayAnnlite']
class DocumentArrayAnnlite(StorageMixins, DocumentArray):
"""
DocumentArray that stores Documents in `ANNLite <https://github.com/jina-... | from .document import DocumentArray
from .storage.annlite import StorageMixins, AnnliteConfig
__all__ = ['AnnliteConfig', 'DocumentArrayAnnlite']
class DocumentArrayAnnlite(StorageMixins, DocumentArray):
"""
DocumentArray that stores Documents in `ANNLite <https://github.com/jina-ai/annlite>`_.
.. note:... |
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
# MMEngine support the following two ways, users can choose
# according to convenience
# optim_wrapper = di... | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
fp16 = dict(loss_scale=512.)
|
from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDocument
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import AudioTorchTensor, AudioUrl
from docarray.utils.misc import is_tf_avail... | from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDocument
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import AudioTorchTensor, AudioUrl
from tests import TOYDATA_DIR
AUDIO_FILES ... |
# Copyright (c) OpenMMLab. All rights reserved.
import asyncio
from argparse import ArgumentParser
import mmcv
from mmdet.apis import (async_inference_detector, inference_detector,
init_detector)
from mmdet.registry import VISUALIZERS
def parse_args():
parser = ArgumentParser()
parse... | # Copyright (c) OpenMMLab. All rights reserved.
import asyncio
from argparse import ArgumentParser
import mmcv
from mmdet.apis import (async_inference_detector, inference_detector,
init_detector)
from mmdet.registry import VISUALIZERS
from mmdet.utils import register_all_modules
def parse_ar... |
from langchain_core.prompts.chat import (
ChatPromptTemplate,
)
from langchain_core.prompts.prompt import PromptTemplate
from langchain.chains.prompt_selector import ConditionalPromptSelector, is_chat_model
DEFAULT_REFINE_PROMPT_TMPL = (
"The original question is as follows: {question}\n"
"We have provide... | # flake8: noqa
from langchain.chains.prompt_selector import ConditionalPromptSelector, is_chat_model
from langchain_core.prompts.chat import (
AIMessagePromptTemplate,
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain_core.prompts.prompt import PromptTemplate
... |
"""Query Understanding agent pack."""
from typing import Any, Dict, List, Optional
from llama_index.core.agent import AgentRunner
from llama_index.core.callbacks import CallbackManager
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.llms.llm import LLM
from llama_index.core.tools.type... | """Query Understanding agent pack."""
from typing import Any, Dict, List, Optional
from llama_index.core.agent import AgentRunner
from llama_index.core.callbacks import CallbackManager
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.llms.llm import LLM
from llama_index.core.tools.type... |
import pytest
from langchain.evaluation import ExactMatchStringEvaluator
@pytest.fixture
def exact_match_string_evaluator() -> ExactMatchStringEvaluator:
"""Create an ExactMatchStringEvaluator with default configuration."""
return ExactMatchStringEvaluator()
@pytest.fixture
def exact_match_string_evaluator... | import pytest
from langchain.evaluation import ExactMatchStringEvaluator
@pytest.fixture
def exact_match_string_evaluator() -> ExactMatchStringEvaluator:
"""Create an ExactMatchStringEvaluator with default configuration."""
return ExactMatchStringEvaluator()
@pytest.fixture
def exact_match_string_evaluator... |
import grpc
import pytest
from jina import Flow
from jina.clients import Client
from jina.serve.helper import get_server_side_grpc_options
from jina.serve.runtimes.gateway.grpc import GRPCGateway
from tests import random_docs
@pytest.fixture(scope='function')
def flow_with_grpc():
class AuthInterceptor(grpc.aio.... | import grpc
import pytest
from jina import Flow
from jina.clients import Client
from jina.serve.helper import get_server_side_grpc_options
from jina.serve.runtimes.gateway.grpc import GRPCGateway
from tests import random_docs
@pytest.fixture(scope='function')
def flow_with_grpc(monkeypatch):
class AuthIntercepto... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import (
GoogleApiClient,
GoogleApiYoutubeLoader,
YoutubeLoader,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolida... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import (
GoogleApiClient,
GoogleApiYoutubeLoader,
YoutubeLoader,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolida... |
from typing import Optional
import numpy as np
import torch
from docarray import DocumentArray
from docarray.base_document import BaseDocument
from docarray.typing import NdArray, TorchTensor
def test_proto_simple():
class CustomDoc(BaseDocument):
text: str
doc = CustomDoc(text='hello')
Custom... | from typing import Optional
import numpy as np
import torch
from docarray import DocumentArray
from docarray.document import BaseDocument
from docarray.typing import NdArray, TorchTensor
def test_proto_simple():
class CustomDoc(BaseDocument):
text: str
doc = CustomDoc(text='hello')
CustomDoc.f... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders.pyspark_dataframe import (
PySparkDataFrameLoader,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecat... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders.pyspark_dataframe import (
PySparkDataFrameLoader,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecat... |
"""Checks the bundled license is installed with the wheel."""
import platform
import site
from itertools import chain
from pathlib import Path
site_packages = site.getsitepackages()
site_packages_path = (Path(p) for p in site_packages)
try:
distinfo_path = next(
chain(
s
for site... | """Checks the bundled license is installed with the wheel."""
import platform
import site
from itertools import chain
from pathlib import Path
site_packages = site.getsitepackages()
site_packages_path = (Path(p) for p in site_packages)
try:
distinfo_path = next(
chain(
s
for site... |
from typing import Any, Literal, Optional, Union
from exa_py import Exa # type: ignore[untyped-import]
from exa_py.api import (
HighlightsContentsOptions, # type: ignore[untyped-import]
TextContentsOptions, # type: ignore[untyped-import]
)
from langchain_core.callbacks import CallbackManagerForRetrieverRun
... | from typing import Any, Dict, List, Literal, Optional, Union
from exa_py import Exa # type: ignore[untyped-import]
from exa_py.api import (
HighlightsContentsOptions, # type: ignore[untyped-import]
TextContentsOptions, # type: ignore[untyped-import]
)
from langchain_core.callbacks import CallbackManagerForR... |
_base_ = './decoupled_solo_r50_fpn_3x_coco.py'
# model settings
model = dict(
mask_head=dict(
type='DecoupledSOLOLightHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 8, 16, 32, 32],
scale_ranges=((1, 64), (32, 128), (64... | _base_ = './decoupled_solo_r50_fpn_3x_coco.py'
# model settings
model = dict(
mask_head=dict(
type='DecoupledSOLOLightHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 8, 16, 32, 32],
scale_ranges=((1, 64), (32, 128), (64... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... |
# 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... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from mmengine.data import InstanceData
from parameterized import parameterized
from mmdet.models.roi_heads import StandardRoIHead # noqa
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_input... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from mmengine.data import InstanceData
from parameterized import parameterized
from mmdet.models.roi_heads import StandardRoIHead # noqa
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_input... |
#!/usr/bin/env python3
"""Generate feature statistics for training set.
Example:
python global_stats.py --model-type librispeech --dataset-path /home/librispeech
"""
import json
import logging
import pathlib
from argparse import ArgumentParser, RawTextHelpFormatter
import torch
import torchaudio
from common import (... | #!/usr/bin/env python3
"""Generate feature statistics for training set.
Example:
python global_stats.py --model-type librispeech --dataset-path /home/librispeech
"""
import json
import logging
import pathlib
from argparse import ArgumentParser, RawTextHelpFormatter
import torch
import torchaudio
from common import (... |
# mypy: allow-untyped-defs
r"""Autograd anomaly mode."""
import warnings
import torch
__all__ = ["detect_anomaly", "set_detect_anomaly"]
class detect_anomaly:
r"""Context-manager that enable anomaly detection for the autograd engine.
This does two things:
- Running the forward pass with detection en... | # mypy: allow-untyped-defs
r"""Autograd anomaly mode."""
import warnings
import torch
__all__ = ["detect_anomaly", "set_detect_anomaly"]
class detect_anomaly:
r"""Context-manager that enable anomaly detection for the autograd engine.
This does two things:
- Running the forward pass with detection ena... |
import numpy as np
import pytest
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
class RescalingTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_rescaling_basics(self):
self.run_layer_test(
... | import numpy as np
import pytest
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
class RescalingTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_rescaling_basics(self):
self.run_layer_test(
... |
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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... |
from __future__ import annotations
import json
from json import JSONDecodeError
from typing import Annotated, Any, Optional, TypeVar, Union
import jsonpatch # type: ignore[import]
import pydantic
from pydantic import SkipValidation
from langchain_core.exceptions import OutputParserException
from langchain_core.outp... | from __future__ import annotations
import json
from json import JSONDecodeError
from typing import Annotated, Any, Optional, TypeVar, Union
import jsonpatch # type: ignore[import]
import pydantic
from pydantic import SkipValidation
from langchain_core.exceptions import OutputParserException
from langchain_core.outp... |
from jina import Client
from docarray import DocList
from docarray.documents import TextDoc
if __name__ == '__main__':
c = Client(host='grpc://0.0.0.0:54321')
da = c.post(
'/', DocList[TextDoc]([TextDoc(), TextDoc()]), return_type=DocList[TextDoc]
)
print(da.text)
| from jina import Client
from docarray import DocList
from docarray.documents import TextDoc
if __name__ == '__main__':
c = Client(host='grpc://0.0.0.0:54321')
da = c.post('/', DocList[TextDoc]([TextDoc(), TextDoc()]), return_type=DocList[TextDoc])
print(da.text)
|
import json
import logging
from enum import Enum
from typing import Any
from requests.exceptions import HTTPError, RequestException
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request import requests
logger = logging.getLo... | import json
import logging
from enum import Enum
from typing import Any
from requests.exceptions import HTTPError, RequestException
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request import requests
logger = logging.getLo... |
# Copyright (c) OpenMMLab. All rights reserved.
import math
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16
from mmdet.models.builder import NECKS
@NECKS.register_module()
class CTResNetNeck(BaseModule):
"""The neck used in `CenterNet <https://arxiv.org/abs/19... | import math
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16
from mmdet.models.builder import NECKS
@NECKS.register_module()
class CTResNetNeck(BaseModule):
"""The neck used in `CenterNet <https://arxiv.org/abs/1904.07850>`_ for
object classification and bo... |
"""This module contains the core type definitions and protocols used throughout Dynamo.
The types defined here fall into several categories:
- Guard related types (GuardFn, GuardFail, GuardedCode): Used for tracking and managing guards that protect compiled code
- Frame and cache types (FrameState, CacheEntry): Used f... | """This module contains the core type definitions and protocols used throughout Dynamo.
The types defined here fall into several categories:
- Guard related types (GuardFn, GuardFail, GuardedCode): Used for tracking and managing guards that protect compiled code
- Frame and cache types (FrameState, CacheEntry): Used f... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import subprocess
import numpy as np
import pytest
from jina import Document, DocumentArray, Flow
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_video_torch_encoder():
model_state... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import numpy as np
from jina import Document, Flow, DocumentArray
from ...custom_image_torch_encoder import CustomImageTorchEncoder
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_vi... |
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.sparse_encoder import (
MLMTransformer,
SparseEncoder,
SparseTranslationEvaluator,
SpladePooling,
)
logging.basicConfig(format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO)
# Initialize the SP... |
# Copyright (c) OpenMMLab. All rights reserved.
from .builder import build_match_cost
from .match_cost import BBoxL1Cost, ClassificationCost, FocalLossCost, IoUCost
__all__ = [
'build_match_cost', 'ClassificationCost', 'BBoxL1Cost', 'IoUCost',
'FocalLossCost'
]
| from .builder import build_match_cost
from .match_cost import BBoxL1Cost, ClassificationCost, FocalLossCost, IoUCost
__all__ = [
'build_match_cost', 'ClassificationCost', 'BBoxL1Cost', 'IoUCost',
'FocalLossCost'
]
|
_base_ = './mask-rcnn_hrnetv2p-w18-1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
b... | _base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
b... |
import functools
import importlib
import os
import re
from pathlib import Path
from typing import TYPE_CHECKING, TypeVar
if TYPE_CHECKING:
from backend.data.block import Block
T = TypeVar("T")
@functools.cache
def load_all_blocks() -> dict[str, type["Block"]]:
from backend.data.block import Block
# Dyn... | import functools
import importlib
import os
import re
from pathlib import Path
from typing import TYPE_CHECKING, TypeVar
if TYPE_CHECKING:
from backend.data.block import Block
T = TypeVar("T")
@functools.cache
def load_all_blocks() -> dict[str, type["Block"]]:
from backend.data.block import Block
# Dyn... |
import asyncio
import os
import random
import string
import tempfile
import time
import pytest
from jina import helper
@pytest.fixture(scope='function')
def random_workspace_name():
"""Generate a random workspace name with digits and letters."""
rand = ''.join(random.choices(string.ascii_uppercase + string.... | import asyncio
import os
import random
import string
import tempfile
import time
import pytest
from jina import helper
@pytest.fixture(scope='function')
def random_workspace_name():
"""Generate a random workspace name with digits and letters."""
rand = ''.join(random.choices(string.ascii_uppercase + string.... |
from typing import Iterable, Type
from docarray.array.abstract_array import AbstractDocumentArray
from docarray.array.mixins import GetAttributeArrayMixin, ProtoArrayMixin
from docarray.document import AnyDocument, BaseDocument, BaseNode
class DocumentArray(
list,
ProtoArrayMixin,
GetAttributeArrayMixin,... | from typing import Iterable, Type
from docarray.array.abstract_array import AbstractDocumentArray
from docarray.array.mixins import GetAttributeArrayMixin, ProtoArrayMixin
from docarray.document import AnyDocument, BaseDocument, BaseNode
from docarray.document.abstract_document import AbstractDocument
class Document... |
from __future__ import annotations
import os
import pytest
from sentence_transformers import SentenceTransformer
from sentence_transformers.models import Pooling, Transformer
from sentence_transformers.util import is_datasets_available
from tests.utils import SafeTemporaryDirectory
if is_datasets_available():
f... | from __future__ import annotations
import os
import pytest
from sentence_transformers import CrossEncoder, SentenceTransformer
from sentence_transformers.models import Pooling, Transformer
from sentence_transformers.util import is_datasets_available
from tests.utils import SafeTemporaryDirectory
if is_datasets_avai... |
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`",
... |
_base_ = 'mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py' # noqa
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(type='AmpOptimWrapper')
| _base_ = 'mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py'
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(type='AmpOptimWrapper')
|
# pants requires this import to recognize the dep
import pytest_asyncio # noqa: F401
import pytest
import os
from llama_index.embeddings.nvidia import NVIDIAEmbedding as Interface
from llama_index.embeddings.nvidia.base import DEFAULT_MODEL
from typing import Generator
# this fixture is used to mask the NVIDIA_AP... | import pytest
import os
from llama_index.embeddings.nvidia import NVIDIAEmbedding as Interface
from llama_index.embeddings.nvidia.base import DEFAULT_MODEL
from typing import Generator
# this fixture is used to mask the NVIDIA_API_KEY environment variable and restore it
# after the test. it also returns the value o... |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | # coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... |
import collections.abc
import dataclasses
from typing import Optional, Sequence
import pytest
import torch
from torch.nn.functional import one_hot
from torchvision.prototype import datapoints
from transforms_v2_legacy_utils import combinations_grid, DEFAULT_EXTRA_DIMS, from_loader, from_loaders, TensorLoader
@data... | import collections.abc
import dataclasses
from typing import Optional, Sequence
import pytest
import torch
from common_utils import combinations_grid, DEFAULT_EXTRA_DIMS, from_loader, from_loaders, TensorLoader
from torch.nn.functional import one_hot
from torchvision.prototype import datapoints
@dataclasses.datacl... |
import types
from typing_extensions import TYPE_CHECKING
from docarray.typing.tensor.image.image_ndarray import ImageNdArray
from docarray.typing.tensor.image.image_tensor import ImageTensor
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING:
... | import types
from typing_extensions import TYPE_CHECKING
from docarray.typing.tensor.image.image_ndarray import ImageNdArray
from docarray.typing.tensor.image.image_tensor import ImageTensor
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING:
... |
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 __future__ import annotations
import torch
import transformers
from PIL import Image
from torch import nn
class CLIPModel(nn.Module):
save_in_root: bool = True
def __init__(self, model_name: str = "openai/clip-vit-base-patch32", processor_name=None) -> None:
super().__init__()
if proce... |
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
from sentence_transformers.util import pairwise_cos_sim, pairwise... | 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
from sentence_transformers.util import pairwise_cos_sim, pairwise... |
"""LLMResult class."""
from __future__ import annotations
from copy import deepcopy
from typing import Literal, Optional, Union
from pydantic import BaseModel
from langchain_core.outputs.chat_generation import ChatGeneration, ChatGenerationChunk
from langchain_core.outputs.generation import Generation, GenerationCh... | """LLMResult class."""
from __future__ import annotations
from copy import deepcopy
from typing import Literal, Optional, Union
from pydantic import BaseModel
from langchain_core.outputs.chat_generation import ChatGeneration, ChatGenerationChunk
from langchain_core.outputs.generation import Generation, GenerationCh... |
# Copyright (c) OpenMMLab. All rights reserved.
from .visualizer import Visualizer
from .writer import (BaseWriter, ComposedWriter, LocalWriter,
TensorboardWriter, WandbWriter)
__all__ = [
'Visualizer', 'BaseWriter', 'LocalWriter', 'WandbWriter',
'TensorboardWriter', 'ComposedWriter'
]
| # Copyright (c) OpenMMLab. All rights reserved.
from .visualizer import Visualizer
__all__ = ['Visualizer']
|
"""
This examples measures the inference speed of a certain model
Usage:
python evaluation_inference_speed.py
OR
python evaluation_inference_speed.py model_name
"""
import sys
import time
import torch
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
# Limit torch to 4 threads... | """
This examples measures the inference speed of a certain model
Usage:
python evaluation_inference_speed.py
OR
python evaluation_inference_speed.py model_name
"""
from sentence_transformers import SentenceTransformer
import sys
import time
import torch
from datasets import load_dataset
# Limit torch to 4 threads
t... |
from typing import Iterable, Dict
from ..base.getsetdel import BaseGetSetDelMixin
from ..base.helper import Offset2ID
from .... import Document
class GetSetDelMixin(BaseGetSetDelMixin):
"""Provide concrete implementation for ``__getitem__``, ``__setitem__``,
and ``__delitem__`` for ``DocumentArrayElastic``""... | from typing import Iterable, Dict
from ..base.getsetdel import BaseGetSetDelMixin
from ..base.helper import Offset2ID
from .... import Document
class GetSetDelMixin(BaseGetSetDelMixin):
"""Provide concrete implementation for ``__getitem__``, ``__setitem__``,
and ``__delitem__`` for ``DocumentArrayElastic``""... |
from docarray.array.any_array import AnyDocArray
from docarray.array.doc_list.doc_list import DocList
from docarray.array.doc_vec.doc_vec import DocVec
__all__ = ['DocList', 'DocVec', 'AnyDocArray']
| from docarray.array.array.array import DocArray
from docarray.array.stacked.array_stacked import DocArrayStacked
__all__ = ['DocArray', 'DocArrayStacked']
|
import logging
import time
from datetime import datetime
from apscheduler.schedulers.background import BackgroundScheduler
from apscheduler.triggers.cron import CronTrigger
from autogpt_libs.utils.cache import thread_cached
from backend.data.block import BlockInput
from backend.data.schedule import (
ExecutionSch... | import logging
import time
from datetime import datetime
from apscheduler.schedulers.background import BackgroundScheduler
from apscheduler.triggers.cron import CronTrigger
from autogpt_libs.utils.cache import thread_cached_property
from backend.data.block import BlockInput
from backend.data.schedule import (
Exe... |
# Copyright (c) OpenMMLab. All rights reserved.
import os
import platform
import warnings
import cv2
import torch.multiprocessing as mp
from mmengine import DefaultScope
def setup_multi_processes(cfg):
"""Setup multi-processing environment variables."""
# set multi-process start method as `fork` to speed up ... | # Copyright (c) OpenMMLab. All rights reserved.
import os
import platform
import warnings
import cv2
import torch.multiprocessing as mp
def setup_multi_processes(cfg):
"""Setup multi-processing environment variables."""
# set multi-process start method as `fork` to speed up the training
if platform.syste... |
from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional, utils # usort: skip
from ._transform import Transform # usort: skip
from ._augment import Cutmix, Mixup, RandomErasing
from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
fro... | from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional, utils # usort: skip
from ._transform import Transform # usort: skip
from ._augment import Cutmix, Mixup, RandomErasing
from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
fro... |
import numpy as np
from docarray import BaseDocument
from docarray.typing import NdArray
def test_set_tensor():
class MyDocument(BaseDocument):
tensor: NdArray
d = MyDocument(tensor=np.zeros((3, 224, 224)))
assert isinstance(d.tensor, NdArray)
assert isinstance(d.tensor, np.ndarray)
ass... | import numpy as np
from docarray import Document
from docarray.typing import NdArray
def test_set_tensor():
class MyDocument(Document):
tensor: NdArray
d = MyDocument(tensor=np.zeros((3, 224, 224)))
assert isinstance(d.tensor, NdArray)
assert isinstance(d.tensor, np.ndarray)
assert (d.t... |
import os
from pathlib import Path
from typing import List, Optional, Tuple, Union
import torch
import torchaudio
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.utils import extract_archive
_URL = "https://zenodo.org/record/3338373/files/musdb18hq.zip"
_CHECKS... | import os
from pathlib import Path
from typing import List, Optional, Tuple, Union
import torch
import torchaudio
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.utils import extract_archive
_URL = "https://zenodo.org/record/3338373/files/musdb18hq.zip"
_CHECKS... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import bbox_overlaps, get_box_tensor
def cast_tensor_type(x, scale=1., dtype=None):
if dtype == 'fp16':
# scale is for preventing overflows
x = (x / scale).half()
retu... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import bbox_overlaps, get_box_tensor
def cast_tensor_type(x, scale=1., dtype=None):
if dtype == 'fp16':
# scale is for preventing overflows
x = (x / scale).half()
retu... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from torch.optim import SGD
from mmengine.evaluator import BaseMetric
from mmengine.model import BaseModel
from mmengine.runner import Runner
class MMResNet5... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from torch.optim import SGD
from torch.utils.data import DataLoader
from mmengine.evaluator import BaseMetric
from mmengine.model import BaseModel
from mmengin... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='PAA',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=di... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='PAA',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=di... |
__version__ = '0.16.2'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
| __version__ = '0.16.1'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
|
from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._tv_tensor import TVTensor
class Image(TVTensor):
""":class:`torch.Tensor` subclass for images with shape ``[..., C, H, W]``.
.. note::
In the :ref:`transforms <transforms>`, ``Image`` i... | from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._tv_tensor import TVTensor
class Image(TVTensor):
""":class:`torch.Tensor` subclass for images.
.. note::
In the :ref:`transforms <transforms>`, ``Image`` instances are largely
i... |
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
file_client_args = dict(backend='disk')
# comment out the code below to use different file client
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# ... | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
file_client_args = dict(backend='disk')
# comment out the code below to use different file client
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# ... |
_base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='SingleStageDetector',
backbone=dict(
type='MobileNetV2',
out_indices=(4, 7),
norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
init_cfg=dict(type='TruncNormal', layer='C... | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='SingleStageDetector',
backbone=dict(
type='MobileNetV2',
out_indices=(4, 7),
norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
init_cfg=dict(type='TruncNormal', layer='C... |
"""**Text Splitters** are classes for splitting text.
**Class hierarchy:**
.. code-block::
BaseDocumentTransformer --> TextSplitter --> <name>TextSplitter # Example: CharacterTextSplitter
RecursiveCharacterTextSplitter --> <name>TextSplitter
Note: **MarkdownHea... | """**Text Splitters** are classes for splitting text.
**Class hierarchy:**
.. code-block::
BaseDocumentTransformer --> TextSplitter --> <name>TextSplitter # Example: CharacterTextSplitter
RecursiveCharacterTextSplitter --> <name>TextSplitter
Note: **MarkdownHea... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.regularizers import deserialize as deserialize
from keras.src.regularizers import get as get
from keras.src.regularizers import serialize as serialize
from keras.src.regularizers.regu... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.regularizers import deserialize
from keras.src.regularizers import get
from keras.src.regularizers import serialize
from keras.src.regularizers.regularizers import L1
from keras.src.r... |
from PIL import Image
from sentence_transformers import SentenceTransformer, models, util
###########
image = Image.open("two_dogs_in_snow.jpg")
from transformers import CLIPModel, CLIPProcessor
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip... | from PIL import Image
from sentence_transformers import SentenceTransformer, models, util
###########
image = Image.open("two_dogs_in_snow.jpg")
from transformers import CLIPModel, CLIPProcessor
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.datasets.california_housing import load_data as load_data
| """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.datasets.california_housing import load_data
|
from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers.losses.TripletLoss import TripletDistanceMetric, TripletLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseTripletLoss(TripletLoss):
def __init_... | from __future__ import annotations
from sentence_transformers.losses.TripletLoss import TripletDistanceMetric, TripletLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseTripletLoss(TripletLoss):
def __init__(
self, model: SparseEncoder, distance_metric=TripletDi... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
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 TestGr... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
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 TestGr... |
from docarray.typing.bytes import ImageBytes
from docarray.typing.id import ID
from docarray.typing.tensor import ImageNdArray, ImageTensor
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.ndarray impo... | from docarray.typing.bytes import ImageBytes
from docarray.typing.id import ID
from docarray.typing.tensor import ImageNdArray, ImageTensor
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.ndarray impo... |
from langchain_core.output_parsers.json import (
SimpleJsonOutputParser,
)
from langchain_core.utils.json import (
parse_and_check_json_markdown,
parse_json_markdown,
parse_partial_json,
)
__all__ = [
"SimpleJsonOutputParser",
"parse_and_check_json_markdown",
"parse_json_markdown",
"par... | from langchain_core.output_parsers.json import (
SimpleJsonOutputParser,
)
from langchain_core.utils.json import (
parse_and_check_json_markdown,
parse_json_markdown,
parse_partial_json,
)
__all__ = [
"SimpleJsonOutputParser",
"parse_partial_json",
"parse_json_markdown",
"parse_and_chec... |
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_additional_imports = {}
_import_structure = {"pipeline_output": ["ChromaPi... | from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_additional_imports = {}
_import_structure = {"pipeline_output": ["ChromaPi... |
"""
Mbox parser.
Contains simple parser for mbox files.
"""
import logging
from pathlib import Path
from typing import Any, Dict, List, Optional
from fsspec import AbstractFileSystem
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
logger = logging.getLogger(__name_... | """Mbox parser.
Contains simple parser for mbox files.
"""
import logging
from pathlib import Path
from typing import Any, Dict, List, Optional
from fsspec import AbstractFileSystem
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
logger = logging.getLogger(__name__... |
import torch
from torch import nn
from typing import List
import os
import json
class CNN(nn.Module):
"""CNN-layer with multiple kernel-sizes over the word embeddings"""
def __init__(
self,
in_word_embedding_dimension: int,
out_channels: int = 256,
kernel_sizes: List[int] = [1... | import torch
from torch import nn
from typing import List
import os
import json
class CNN(nn.Module):
"""CNN-layer with multiple kernel-sizes over the word embeddings"""
def __init__(
self,
in_word_embedding_dimension: int,
out_channels: int = 256,
kernel_sizes: List[int] = [1... |
from typing import Any, Optional
from typing_extensions import get_origin
from typing_inspect import get_args, is_typevar, is_union_type
from docarray.typing.id import ID
from docarray.typing.tensor.abstract_tensor import AbstractTensor
def is_type_tensor(type_: Any) -> bool:
"""Return True if type is a type Te... | from typing import Any, Optional
from typing_extensions import get_origin
from typing_inspect import get_args, is_typevar, is_union_type
from docarray.typing.tensor.abstract_tensor import AbstractTensor
def is_type_tensor(type_: Any) -> bool:
"""Return True if type is a type Tensor or an Optional Tensor type.""... |
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import TorchEmbedding, TorchTensor
def test_proto_tensor():
tensor = parse_obj_as(TorchTensor, torch.zeros(3, 224, 224))
tensor._to_node_protobuf()... | import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import TorchEmbedding, TorchTensor
def test_proto_tensor():
tensor = parse_obj_as(TorchTensor, torch.zeros(3, 224, 224))
tensor._to_node_protobuf()... |
import asyncio
import numpy as np
from typing import Any, List, Literal, Optional
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.bridge.pydantic import Field, PrivateAttr, ConfigDict
from fastembed import TextEmbedding
class FastEmbedEmbedding(BaseEmbedding):
"""
Qdrant... | from typing import Any, List, Literal, Optional
import numpy as np
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from fastembed import TextEmbedding
class FastEmbedEmbedding(BaseEmbedding):
"""
Qdrant FastEmbedding models.
... |
import io
import logging
from enum import Enum
import replicate
import replicate.exceptions
import requests
from replicate.helpers import FileOutput
from backend.data.graph import Graph
from backend.util.settings import Settings
logger = logging.getLogger(__name__)
class ImageSize(str, Enum):
LANDSCAPE = "1024... | import io
import logging
from enum import Enum
import replicate
import replicate.exceptions
import requests
from replicate.helpers import FileOutput
from backend.data.graph import Graph
from backend.util.settings import Settings
logger = logging.getLogger(__name__)
class ImageSize(str, Enum):
LANDSCAPE = "1024... |
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