input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import prisma
AGENT_NODE_INCLUDE: prisma.types.AgentNodeInclude = {
"Input": True,
"Output": True,
"Webhook": True,
"AgentBlock": True,
}
AGENT_GRAPH_INCLUDE: prisma.types.AgentGraphInclude = {
"AgentNodes": {"include": AGENT_NODE_INCLUDE} # type: ignore
}
EXECUTION_RESULT_INCLUDE: prisma.types.... | import prisma
AGENT_NODE_INCLUDE: prisma.types.AgentNodeInclude = {
"Input": True,
"Output": True,
"AgentBlock": True,
}
AGENT_GRAPH_INCLUDE: prisma.types.AgentGraphInclude = {
"AgentNodes": {"include": AGENT_NODE_INCLUDE} # type: ignore
}
EXECUTION_RESULT_INCLUDE: prisma.types.AgentNodeExecutionInc... |
import pytest
from llama_index.core.readers.base import BaseReader
from llama_index.readers.hive.base import InvalidSqlError, _validate_sql_query
from llama_index.readers.hive import HiveReader
def test_class():
assert issubclass(HiveReader, BaseReader)
def test_validation():
with pytest.raises(InvalidSqlE... | from llama_index.core.readers.base import BaseReader
from llama_index.readers.hive import HiveReader
def test_class():
names_of_base_classes = [b.__name__ for b in HiveReader.__mro__]
assert BaseReader.__name__ in names_of_base_classes
|
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | # coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... |
# Copyright (c) OpenMMLab. All rights reserved.
import base64
import os
import mmcv
import torch
from ts.torch_handler.base_handler import BaseHandler
from mmdet.apis import inference_detector, init_detector
class MMdetHandler(BaseHandler):
threshold = 0.5
def initialize(self, context):
properties ... | # Copyright (c) OpenMMLab. All rights reserved.
import base64
import os
import mmcv
import torch
from ts.torch_handler.base_handler import BaseHandler
from mmdet.apis import inference_detector, init_detector
class MMdetHandler(BaseHandler):
threshold = 0.5
def initialize(self, context):
properties ... |
from typing import Any
from langchain_core.agents import AgentAction
from langchain_core.prompts.chat import ChatPromptTemplate
class AgentScratchPadChatPromptTemplate(ChatPromptTemplate):
"""Chat prompt template for the agent scratchpad."""
@classmethod
def is_lc_serializable(cls) -> bool:
retu... | from typing import Any, Dict, List, Tuple
from langchain_core.agents import AgentAction
from langchain_core.prompts.chat import ChatPromptTemplate
class AgentScratchPadChatPromptTemplate(ChatPromptTemplate):
"""Chat prompt template for the agent scratchpad."""
@classmethod
def is_lc_serializable(cls) ->... |
from typing import Any, Dict, Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import util
from sentence_transformers.SentenceTransformer import SentenceTransformer
class MultipleNegativesSymmetricRankingLoss(nn.Module):
def __init__(self, model: SentenceTransformer, scale: float = ... | from typing import Dict, Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import util
from sentence_transformers.SentenceTransformer import SentenceTransformer
class MultipleNegativesSymmetricRankingLoss(nn.Module):
def __init__(self, model: SentenceTransformer, scale: float = 20.0,... |
import functools
import pytest
from jina.helper import iscoroutinefunction
from jina.serve.executors import get_executor_taboo
from jina.serve.executors.decorators import dynamic_batching, requests
from jina.serve.helper import store_init_kwargs
def test_store_init_kwargs():
store_init_kwargs_decorator = functo... | import functools
import pytest
from jina.helper import iscoroutinefunction
from jina.serve.executors import get_executor_taboo
from jina.serve.executors.decorators import requests
from jina.serve.helper import store_init_kwargs
def test_store_init_kwargs():
store_init_kwargs_decorator = functools.partial(
... |
class DataAdapter:
"""Base class for input data adapters.
The purpose of a DataAdapter is to provide a unified interface to
iterate over input data provided in a variety of formats -- such as
NumPy arrays, tf.Tensors, tf.data.Datasets, Keras PyDatasets, etc.
"""
def get_numpy_iterator(self):
... | class DataAdapter:
"""Base class for input data adapters.
The purpose of a DataAdapter is to provide a unfied interface to
iterate over input data provided in a variety of formats -- such as
NumPy arrays, tf.Tensors, tf.data.Datasets, Keras PyDatasets, etc.
"""
def get_numpy_iterator(self):
... |
_base_ = './mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py'
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' # noqa
model = dict(
backbone=dict(
depths=[2, 2, 18, 2],
init_cfg=dict(type='Pretrained', checkpoint=pretrained)))
| _base_ = './mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py'
pretrained = 'https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219-7f9d988b.pth' # noqa
model = dict(
backbone=dict(depths=[2, 2, 18, 2]),
init_cfg=dict(type='Pretrained', checkpoi... |
from pydantic import BaseModel
from backend.data.block import (
Block,
BlockCategory,
BlockManualWebhookConfig,
BlockOutput,
BlockSchema,
)
from backend.data.model import SchemaField
from backend.integrations.providers import ProviderName
from backend.integrations.webhooks.compass import CompassWeb... | from pydantic import BaseModel
from backend.data.block import (
Block,
BlockCategory,
BlockManualWebhookConfig,
BlockOutput,
BlockSchema,
)
from backend.data.model import SchemaField
from backend.integrations.providers import ProviderName
from backend.integrations.webhooks.compass import CompassWeb... |
_base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
train_p... | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
train_p... |
from docarray.typing.url.any_url import AnyUrl
from docarray.typing.url.audio_url import AudioUrl
from docarray.typing.url.image_url import ImageUrl
from docarray.typing.url.text_url import TextUrl
from docarray.typing.url.url_3d.mesh_url import Mesh3DUrl
from docarray.typing.url.url_3d.point_cloud_url import PointClou... | from docarray.typing.url.any_url import AnyUrl
from docarray.typing.url.image_url import ImageUrl
from docarray.typing.url.text_url import TextUrl
from docarray.typing.url.url_3d.mesh_url import Mesh3DUrl
from docarray.typing.url.url_3d.point_cloud_url import PointCloud3DUrl
__all__ = ['ImageUrl', 'AnyUrl', 'TextUrl',... |
"""
This script contains an example how to perform semantic search with Elasticsearch.
You need Elasticsearch up and running locally:
https://www.elastic.co/guide/en/elasticsearch/reference/current/run-elasticsearch-locally.html
Further, you need the Python Elasticsearch Client installed: https://elasticsearch-py.rea... | """
This script contains an example how to perform semantic search with Elasticsearch.
You need Elasticsearch up and running locally:
https://www.elastic.co/guide/en/elasticsearch/reference/current/run-elasticsearch-locally.html
Further, you need the Python Elasticsearch Client installed: https://elasticsearch-py.rea... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import tempfile
from collections import OrderedDict
import torch
from mmengine import Config
from mmengine.utils import digit_version
def parse_config(config_strings):
temp_file = tempfile.NamedTemporaryFile()
config_path = f'{temp_file.name}.py... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import tempfile
from collections import OrderedDict
import torch
from mmengine import Config
def parse_config(config_strings):
temp_file = tempfile.NamedTemporaryFile()
config_path = f'{temp_file.name}.py'
with open(config_path, 'w') as f:
... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence
from mmengine.hooks import Hook
from mmengine.model import is_model_wrapper
from mmdet.registry import HOOKS
@HOOKS.register_module()
class YOLOXModeSwitchHook(Hook):
"""Switch the mode of YOLOX during training.
This hook turns off... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence
from mmengine.hooks import Hook
from mmengine.model import is_model_wrapper
from mmdet.registry import HOOKS
@HOOKS.register_module()
class YOLOXModeSwitchHook(Hook):
"""Switch the mode of YOLOX during training.
This hook turns off... |
_base_ = '../fast_rcnn/fast-rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
neck=[
dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
dict(
type='BFP',
in_channels=256,
num_l... | _base_ = '../fast_rcnn/fast-rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
neck=[
dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
dict(
type='BFP',
in_channels=256,
num_l... |
import functools
import pytest
from jina.helper import iscoroutinefunction
from jina.serve.executors import get_executor_taboo
from jina.serve.executors.decorators import requests
from jina.serve.helper import store_init_kwargs
def test_store_init_kwargs():
store_init_kwargs_decorator = functools.partial(
... | import functools
import pytest
from jina.helper import iscoroutinefunction
from jina.serve.executors import get_default_metas, get_executor_taboo
from jina.serve.executors.decorators import requests
from jina.serve.helper import store_init_kwargs, wrap_func
def test_store_init_kwargs():
store_init_kwargs_decora... |
"""Init file."""
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.llama_pack.download import download_llama_pack
__all__ = [
"BaseLlamaPack",
"download_llama_pack",
]
| """Init file."""
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.llama_pack.download import download_llama_pack
__all__ = [
"BaseLlamaPack",
"download_llama_pack",
]
|
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResNeSt',
... | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResNeSt',
... |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
from mmengine.logging import HistoryBuffer
array_method = [np.array, lambda x: x]
try:
import torch
except ImportError:
pass
else:
array_method.append(torch.tensor)
class TestLoggerBuffer:
def test_init(self):
... | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmengine import HistoryBuffer
class TestLoggerBuffer:
def test_init(self):
log_buffer = HistoryBuffer()
assert log_buffer.max_length == 1000000
log_history, counts = log_buffer.data
... |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.utils import (LearnedPositionalEncoding,
SinePositionalEncoding)
def test_sine_positional_encoding(num_feats=16, batch_size=2):
# test invalid type of scale
with pytest.raises(Assertio... | import pytest
import torch
from mmdet.models.utils import (LearnedPositionalEncoding,
SinePositionalEncoding)
def test_sine_positional_encoding(num_feats=16, batch_size=2):
# test invalid type of scale
with pytest.raises(AssertionError):
module = SinePositionalEncoding... |
"""
This examples trains a CrossEncoder for the STSbenchmark task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it output a continuous labels 0...1 to indicate the similarity between the input pair.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:... | """
This examples trains a CrossEncoder for the STSbenchmark task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it output a continuous labels 0...1 to indicate the similarity between the input pair.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:... |
import sys
from os import path
from setuptools import find_packages
from setuptools import setup
if sys.version_info < (3, 7, 0):
raise OSError(f'DocArray requires Python >=3.7, but yours is {sys.version}')
try:
pkg_name = 'docarray'
libinfo_py = path.join(pkg_name, '__init__.py')
libinfo_content = o... | import sys
from os import path
from setuptools import find_packages
from setuptools import setup
if sys.version_info < (3, 7, 0):
raise OSError(f'DocArray requires Python >=3.7, but yours is {sys.version}')
try:
pkg_name = 'docarray'
libinfo_py = path.join(pkg_name, '__init__.py')
libinfo_content = o... |
from typing import Any, Collection, List, Optional, Tuple, Union
from llama_index.core.tools.types import AsyncBaseTool
from pydantic import BaseModel
class LLMCompilerParseResult(BaseModel):
"""LLMCompiler parser result."""
thought: str
idx: int
tool_name: str
args: str
class JoinerOutput(Bas... | from typing import Any, Collection, List, Optional, Tuple, Union
from llama_index.core.tools.types import AsyncBaseTool
from pydantic import BaseModel
class LLMCompilerParseResult(BaseModel):
"""LLMCompiler parser result."""
thought: str
idx: int
tool_name: str
args: str
class JoinerOutput(Bas... |
from llama_index.core.base.llms.types import (
LLMMetadata,
)
from llama_index.core.bridge.pydantic import Field
from llama_index.llms.openai_like import OpenAILike
class LlamaAPI(OpenAILike):
"""LlamaAPI LLM.
Examples:
`pip install llama-index-llms-llama-api`
```python
from llam... | from typing import Any, Callable, Dict, Optional, Sequence
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseGen,
CompletionResponse,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.c... |
from __future__ import annotations
from sentence_transformers.losses.GISTEmbedLoss import GISTEmbedLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseGISTEmbedLoss(GISTEmbedLoss):
def __init__(
self,
model: SparseEncoder,
guide: SparseEncoder,
... | from __future__ import annotations
from sentence_transformers.losses.GISTEmbedLoss import GISTEmbedLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseGISTEmbedLoss(GISTEmbedLoss):
def __init__(
self,
model: SparseEncoder,
guide: SparseEncoder,
... |
import os
import urllib
import pytest
from pydantic import parse_obj_as, schema_json_of
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import TextUrl
from tests import TOYDATA_DIR
REMOTE_TEXT_FILE = 'https://de.wikipedia.org/wiki/Brixen'
CUR_DIR = os.path.dirname(os.path.abspath(__file__))
L... | import os
import urllib
import pytest
from pydantic import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import TextUrl
from tests import TOYDATA_DIR
REMOTE_TEXT_FILE = 'https://de.wikipedia.org/wiki/Brixen'
CUR_DIR = os.path.dirname(os.path.abspath(__file... |
import os
import pytest
import yaml
from jina import Gateway
from jina.jaml import JAML
from jina.serve.executors import BaseExecutor
class MyDummyGateway(Gateway):
async def setup_server(self):
self.server = 'dummy server'
async def run_server(self):
self.logger.info(self.server)
asyn... | import os
import yaml
from jina import Gateway
from jina.jaml import JAML
from jina.serve.executors import BaseExecutor
class MyDummyGateway(Gateway):
async def setup_server(self):
self.server = 'dummy server'
async def run_server(self):
self.logger.info(self.server)
async def shutdown... |
from groq._utils._utils import quote
from backend.blocks.jina._auth import (
TEST_CREDENTIALS,
TEST_CREDENTIALS_INPUT,
JinaCredentials,
JinaCredentialsField,
JinaCredentialsInput,
)
from backend.blocks.search import GetRequest
from backend.data.block import Block, BlockCategory, BlockOutput, BlockS... | from groq._utils._utils import quote
from backend.blocks.jina._auth import (
TEST_CREDENTIALS,
TEST_CREDENTIALS_INPUT,
JinaCredentials,
JinaCredentialsField,
JinaCredentialsInput,
)
from backend.blocks.search import GetRequest
from backend.data.block import Block, BlockCategory, BlockOutput, BlockS... |
import multiprocessing
import random
import time
from functools import partial
import pytest
from jina import Client, Document, DocumentArray, Executor, Flow, requests
from jina.types.request.data import Response
NUM_REQUESTS = 5
class MyExecutor(Executor):
@requests(on='/ping')
def ping(self, **kwargs):
... | import multiprocessing
import random
import time
from functools import partial
import pytest
from jina import Client, Document, DocumentArray, Executor, Flow, requests
from jina.types.request.data import Response
NUM_REQUESTS = 5
class MyExecutor(Executor):
@requests(on='/ping')
def ping(self, **kwargs):
... |
"""Gemini embeddings file."""
import deprecated
from typing import Any, List, Optional
from llama_index.core.base.embeddings.base import (
DEFAULT_EMBED_BATCH_SIZE,
BaseEmbedding,
)
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.callbacks.base import CallbackManager
imp... | """Gemini embeddings file."""
import deprecated
from typing import Any, List, Optional
from llama_index.core.base.embeddings.base import (
DEFAULT_EMBED_BATCH_SIZE,
BaseEmbedding,
)
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.callbacks.base import CallbackManager
imp... |
import subprocess
import pytest
from jina import Document, DocumentArray, Flow
from ...flair_text import FlairTextEncoder
_EMBEDDING_DIM = 100
@pytest.mark.parametrize('request_size', [1, 10, 50, 100])
def test_integration(request_size: int):
docs = DocumentArray(
[Document(text='just some random text ... | import subprocess
import pytest
from jina import Document, DocumentArray, Flow
from ...flair_text import FlairTextEncoder
_EMBEDDING_DIM = 100
@pytest.mark.parametrize('request_size', [1, 10, 50, 100])
def test_integration(request_size: int):
docs = DocumentArray(
[Document(text='just some random text ... |
from .AdaptiveLayerLoss import AdaptiveLayerLoss
from .CosineSimilarityLoss import CosineSimilarityLoss
from .SoftmaxLoss import SoftmaxLoss
from .MultipleNegativesRankingLoss import MultipleNegativesRankingLoss
from .MultipleNegativesSymmetricRankingLoss import MultipleNegativesSymmetricRankingLoss
from .TripletLoss i... | from .CosineSimilarityLoss import CosineSimilarityLoss
from .SoftmaxLoss import SoftmaxLoss
from .MultipleNegativesRankingLoss import MultipleNegativesRankingLoss
from .MultipleNegativesSymmetricRankingLoss import MultipleNegativesSymmetricRankingLoss
from .TripletLoss import TripletDistanceMetric, TripletLoss
from .Ma... |
from typing import List
from pydantic import BaseModel
from backend.blocks.exa._auth import (
ExaCredentials,
ExaCredentialsField,
ExaCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request impor... | from typing import List
from pydantic import BaseModel
from backend.blocks.exa._auth import (
ExaCredentials,
ExaCredentialsField,
ExaCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request impor... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.config import read_base
with read_base():
from mmdet.configs.retinanet.retinanet_r50_caffe_fpn_1x_coco import *
from mmdet.configs.retinanet.retinanet_r101_caffe_fpn_1x_coco import \
model as r101
model = r101
| # Copyright (c) OpenMMLab. All rights reserved.
if '_base_':
from mmdet.configs.retinanet.retinanet_r50_caffe_fpn_1x_coco import *
from mmdet.configs.retinanet.retinanet_r101_caffe_fpn_1x_coco import \
model as r101
model = r101
|
"""Init file of LlamaIndex."""
__version__ = "0.12.24.post1"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index... | """Init file of LlamaIndex."""
__version__ = "0.12.24"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index.core.... |
from dataclasses import dataclass
from functools import partial
from typing import Callable
import torch
import torchaudio
from torchaudio.models import conv_tasnet_base, hdemucs_high
@dataclass
class SourceSeparationBundle:
"""Dataclass that bundles components for performing source separation.
Example
... | from dataclasses import dataclass
from functools import partial
from typing import Callable
import torch
import torchaudio
from torchaudio.models import conv_tasnet_base, hdemucs_high
@dataclass
class SourceSeparationBundle:
"""Dataclass that bundles components for performing source separation.
Example
... |
"""
===================================================
Recursive feature elimination with cross-validation
===================================================
A Recursive Feature Elimination (RFE) example with automatic tuning of the
number of features selected with cross-validation.
"""
# Authors: The scikit-learn... | """
===================================================
Recursive feature elimination with cross-validation
===================================================
A Recursive Feature Elimination (RFE) example with automatic tuning of the
number of features selected with cross-validation.
"""
# Authors: The scikit-learn... |
_base_ = './cascade-mask-rcnn_r50_fpn_instaboost-4x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
_base_ = '../glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365.py'
lang_model_name = 'bert-base-uncased'
model = dict(bbox_head=dict(early_fuse=True))
dataset_type = 'Flickr30kDataset'
data_root = 'data/flickr30k_entities/'
test_pipeline = [
dict(
type='LoadImageFromFile', backend_args=None,
imdecod... | _base_ = '../glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365.py'
lang_model_name = 'bert-base-uncased'
model = dict(bbox_head=dict(early_fuse=True), )
dataset_type = 'Flickr30kDataset'
data_root = 'data/flickr30k/'
test_pipeline = [
dict(
type='LoadImageFromFile', backend_args=None,
imdecode_backe... |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
def bbox_overlaps(bboxes1,
bboxes2,
mode='iou',
eps=1e-6,
use_legacy_coordinate=False):
"""Calculate the ious between each bbox of bboxes1 and bboxes2.
Args:
bbox... | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
def bbox_overlaps(bboxes1, bboxes2, mode='iou', eps=1e-6):
"""Calculate the ious between each bbox of bboxes1 and bboxes2.
Args:
bboxes1(ndarray): shape (n, 4)
bboxes2(ndarray): shape (k, 4)
mode(str): iou (intersectio... |
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.torch_tensor import TorchTensor, metaTorchAndNode
@_register_proto(proto_type_name='audio_torch_tensor')
class AudioTorchTensor(AbstractAudioTensor,... | from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.torch_tensor import TorchTensor, metaTorchAndNode
@_register_proto(proto_type_name='audio_torch_tensor')
class AudioTorchTensor(AbstractAudioTensor,... |
# Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .base_detr import DetectionTransformer
from .boxinst import BoxInst
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .condinst import CondInst
from .co... | # Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .boxinst import BoxInst
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .condinst import CondInst
from .cornernet import CornerNet
from .crowddet impo... |
# Copyright (c) OpenMMLab. All rights reserved.
import glob
import os
import os.path as osp
import urllib
import warnings
from typing import Union
import torch
from mmengine.config import Config, ConfigDict
from mmengine.logging import print_log
from mmengine.utils import scandir
IMG_EXTENSIONS = ('.jpg', '.jpeg', '.... | # Copyright (c) OpenMMLab. All rights reserved.
import glob
import os
import os.path as osp
import warnings
from typing import Union
from mmengine.config import Config, ConfigDict
from mmengine.logging import print_log
def find_latest_checkpoint(path, suffix='pth'):
"""Find the latest checkpoint from the working... |
"""
This is a simple application for sentence embeddings: semantic search
We have a corpus with various sentences. Then, for a given query sentence,
we want to find the most similar sentence in this corpus.
This script outputs for various queries the top 5 most similar sentences in the corpus.
"""
import torch
from... | """
This is a simple application for sentence embeddings: semantic search
We have a corpus with various sentences. Then, for a given query sentence,
we want to find the most similar sentence in this corpus.
This script outputs for various queries the top 5 most similar sentences in the corpus.
"""
import torch
from... |
# Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .boxinst_head import BoxInstBboxHead, BoxInstMaskHead
from .cascade_rpn_head import CascadeRPNHead, StageCasca... | # Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
from .centernet_head import CenterNetHead
from .c... |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.backbones.hourglass import HourglassNet
def test_hourglass_backbone():
with pytest.raises(AssertionError):
# HourglassNet's num_stacks should larger than 0
HourglassNet(num_stacks=0)
with pytest.rais... | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.backbones.hourglass import HourglassNet
def test_hourglass_backbone():
with pytest.raises(AssertionError):
# HourglassNet's num_stacks should larger than 0
HourglassNet(num_stacks=0)
with pytest.rais... |
from ...utils import is_flax_available, is_torch_available
if is_torch_available():
from .controlnet import ControlNetModel, ControlNetOutput
from .controlnet_flux import FluxControlNetModel, FluxControlNetOutput, FluxMultiControlNetModel
from .controlnet_hunyuan import (
HunyuanControlNetOutput,
... | from ...utils import is_flax_available, is_torch_available
if is_torch_available():
from .controlnet import ControlNetModel, ControlNetOutput
from .controlnet_flux import FluxControlNetModel, FluxControlNetOutput, FluxMultiControlNetModel
from .controlnet_hunyuan import (
HunyuanControlNetOutput,
... |
# Copyright (c) OpenMMLab. All rights reserved.
import glob
import os
import os.path as osp
import warnings
from mmengine.config import Config, ConfigDict
from mmengine.logging import print_log
def find_latest_checkpoint(path, suffix='pth'):
"""Find the latest checkpoint from the working directory.
Args:
... | # Copyright (c) OpenMMLab. All rights reserved.
import glob
import os
import os.path as osp
import warnings
from mmengine.config import Config, ConfigDict
from mmengine.logging import print_log
def find_latest_checkpoint(path, suffix='pth'):
"""Find the latest checkpoint from the working directory.
Args:
... |
import pathlib
from typing import Any, BinaryIO, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import Demultiplexer, Filter, IterDataPipe, IterKeyZipper, JsonParser, Mapper, UnBatcher
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvisio... | import pathlib
from typing import Any, BinaryIO, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import Demultiplexer, Filter, IterDataPipe, IterKeyZipper, JsonParser, Mapper, UnBatcher
from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
from torchvision.prototype.da... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet import * # noqa
from mmdet.core import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
class TestSingleStageInstanceSegmentor(TestCa... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet import * # noqa
from mmdet.core import DetDataSample
from .utils import demo_mm_inputs, get_detector_cfg
class TestSingleStageInstanceSegmentor(TestCase):
... |
from __future__ import annotations
from .PhraseTokenizer import PhraseTokenizer
from .WhitespaceTokenizer import WhitespaceTokenizer
from .WordTokenizer import ENGLISH_STOP_WORDS, TransformersTokenizerWrapper, WordTokenizer
__all__ = [
"WordTokenizer",
"WhitespaceTokenizer",
"PhraseTokenizer",
"ENGLIS... | from __future__ import annotations
from .PhraseTokenizer import PhraseTokenizer
from .WhitespaceTokenizer import WhitespaceTokenizer
from .WordTokenizer import ENGLISH_STOP_WORDS, WordTokenizer
__all__ = ["WordTokenizer", "WhitespaceTokenizer", "PhraseTokenizer", "ENGLISH_STOP_WORDS"]
|
_base_ = ['../_base_/models/retinanet_r50_fpn.py', '../common/ms_3x_coco.py']
# optimizer
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
optim_wrapper... | _base_ = ['../_base_/models/retinanet_r50_fpn.py', '../common/ms_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))
|
from typing import Any, Optional, Type, TypeVar, Union
from pydantic import Field
from docarray.base_doc import BaseDoc
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
T = TypeVar('T', bound='TextDoc')
class TextDoc(BaseDoc):
"""
Document for handling text.
... | from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_doc import BaseDoc
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
T = TypeVar('T', bound='TextDoc')
class TextDoc(BaseDoc):
"""
Document for handling text.
It can contain:
- a [... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from pathlib import Path
import pytest
@pytest.fixture(scope='session')
def docker_image_name() -> str:
return Path(__file__).parents[1].stem.lower()
@pytest.fixture(scope='session')
def bui... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import pytest
from jina import Document, DocumentArray
@pytest.fixture()
def test_dir() -> str:
return os.path.dirname(os.path.abspath(__file__))
@pytest.fixture()
def data_generator(test_dir: str):
... |
AMI_ID = {
# Managed by XGBoost team
"linux-amd64-gpu": {
"us-west-2": "ami-08c3bc1dd5ec8bc5c",
},
"linux-amd64-mgpu": {
"us-west-2": "ami-08c3bc1dd5ec8bc5c",
},
"windows-gpu": {
"us-west-2": "ami-03c7f2156f93b22a7",
},
"windows-cpu": {
"us-west-2": "ami-0... | AMI_ID = {
# Managed by XGBoost team
"linux-amd64-gpu": {
"us-west-2": "ami-094271bed4788ddb5",
},
"linux-amd64-mgpu": {
"us-west-2": "ami-094271bed4788ddb5",
},
"windows-gpu": {
"us-west-2": "ami-0839681594a1d7627",
},
"windows-cpu": {
"us-west-2": "ami-0... |
import builtins
import json
from enum import Enum
from typing import List, Optional, Type, Union
from langchain_core.callbacks import AsyncCallbackManagerForToolRun
from pydantic import BaseModel, Field
from langchain_community.tools.ainetwork.base import AINBaseTool
class AppOperationType(str, Enum):
"""Type o... | import builtins
import json
from enum import Enum
from typing import List, Optional, Type, Union
from langchain_core.callbacks import AsyncCallbackManagerForToolRun
from pydantic import BaseModel, Field
from langchain_community.tools.ainetwork.base import AINBaseTool
class AppOperationType(str, Enum):
"""Type o... |
_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.)
| _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.)
|
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from typing import Literal
from pydantic import BaseModel, ConfigDict, SecretStr
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import (
CredentialsField,
C... | import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from typing import Literal
from pydantic import BaseModel, ConfigDict, SecretStr
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import (
CredentialsField,
C... |
_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(
... | _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(
... |
_base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
output_img=True),
neck=d... | _base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
output_img=True),
neck=d... |
# Copyright (c) OpenMMLab. All rights reserved.
_base_ = [
'mmdet::_base_/models/faster-rcnn_r50_fpn.py',
'mmdet::_base_/datasets/coco_detection.py',
'mmdet::_base_/schedules/schedule_1x.py',
'mmdet::_base_/default_runtime.py'
]
| # Copyright (c) OpenMMLab. All rights reserved.
_base_ = [
'mmdet::_base_/models/faster_rcnn_r50_fpn.py',
'mmdet::_base_/datasets/coco_detection.py',
'mmdet::_base_/schedules/schedule_1x.py',
'mmdet::_base_/default_runtime.py'
]
|
import math
import torch
import torchaudio.prototype.functional as F
from parameterized import parameterized
from torch.autograd import gradcheck
from torchaudio_unittest.common_utils import TestBaseMixin
class AutogradTestImpl(TestBaseMixin):
@parameterized.expand(
[
(8000, (2, 3, 5, 7)),
... | import torch
import torchaudio.prototype.functional as F
from parameterized import parameterized
from torch.autograd import gradcheck
from torchaudio_unittest.common_utils import TestBaseMixin
class AutogradTestImpl(TestBaseMixin):
@parameterized.expand(
[
(8000, (2, 3, 5, 7)),
(80... |
#!/usr/bin/env python3
# Write the available versions page (--rst) and the version switcher JSON (--json).
# Version switcher see:
# https://pydata-sphinx-theme.readthedocs.io/en/stable/user_guide/version-dropdown.html
# https://pydata-sphinx-theme.readthedocs.io/en/stable/user_guide/announcements.html#announcement-ba... | #!/usr/bin/env python3
# Write the available versions page (--rst) and the version switcher JSON (--json).
# Version switcher see:
# https://pydata-sphinx-theme.readthedocs.io/en/stable/user_guide/version-dropdown.html
# https://pydata-sphinx-theme.readthedocs.io/en/stable/user_guide/announcements.html#announcement-ba... |
"""
===================================
Visualizations with Display Objects
===================================
.. currentmodule:: sklearn.metrics
In this example, we will construct display objects,
:class:`ConfusionMatrixDisplay`, :class:`RocCurveDisplay`, and
:class:`PrecisionRecallDisplay` directly from their resp... | """
===================================
Visualizations with Display Objects
===================================
.. currentmodule:: sklearn.metrics
In this example, we will construct display objects,
:class:`ConfusionMatrixDisplay`, :class:`RocCurveDisplay`, and
:class:`PrecisionRecallDisplay` directly from their resp... |
from __future__ import annotations
import json
import os
from typing import Any
import torch
from torch import nn
class SpladePooling(nn.Module):
"""SPLADE pooling layer that aggregates MLM logits using max or sum pooling.
This pooling layer takes MLM logits (shape: batch_size, seq_length, vocab_size)
... | from __future__ import annotations
import json
import os
from typing import Any
import torch
from torch import nn
class SpladePooling(nn.Module):
"""SPLADE pooling layer that aggregates MLM logits using max or sum pooling.
This pooling layer takes MLM logits (shape: batch_size, seq_length, vocab_size)
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .dropblock import DropBlock
from .msdeformattn_pixel_decoder import MSDeformAttnPixelDecoder
from .pixel_decoder import PixelDecoder, TransformerEncoderPixelDecoder
__all__ = [
'DropBlock', 'PixelDecoder', 'TransformerEncoderPixelDecoder',
'MSDeformAttnPixel... | # Copyright (c) OpenMMLab. All rights reserved.
from .dropblock import DropBlock
from .pixel_decoder import PixelDecoder, TransformerEncoderPixelDecoder
__all__ = ['DropBlock', 'PixelDecoder', 'TransformerEncoderPixelDecoder']
|
r"""
AgentSearch reader.
Example as of 1/8/2024:
```python
AgentSearch = download_loader("AgentSearch")
document = reader.load_data(
query="latest news",
search_provider="bing"
)[0]
print(f'Document:\n{document} ')
```
```plaintext
Document:
Doc ID: 67a57dfe-8bd6-4c69-af9d-683e76177119
Text: The latest new... | r"""AgentSearch reader.
Example as of 1/8/2024:
```python
AgentSearch = download_loader("AgentSearch")
document = reader.load_data(
query="latest news",
search_provider="bing"
)[0]
print(f'Document:\n{document} ')
```
```plaintext
Document:
Doc ID: 67a57dfe-8bd6-4c69-af9d-683e76177119
Text: The latest news... |
"""
=====================================
How to write your own Datapoint class
=====================================
This guide is intended for advanced users and downstream library maintainers. We explain how to
write your own datapoint class, and how to make it compatible with the built-in
Torchvision v2 transforms... | """
=====================================
How to write your own Datapoint class
=====================================
This guide is intended for downstream library maintainers. We explain how to
write your own datapoint class, and how to make it compatible with the built-in
Torchvision v2 transforms. Before continuing... |
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... | 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... |
"""Callback Handler that prints to std out."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Optional
from typing_extensions import override
from langchain_core.callbacks.base import BaseCallbackHandler
from langchain_core.utils import print_text
if TYPE_CHECKING:
from langchain_cor... | """Callback Handler that prints to std out."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Optional
from langchain_core.callbacks.base import BaseCallbackHandler
from langchain_core.utils import print_text
if TYPE_CHECKING:
from langchain_core.agents import AgentAction, AgentFinish... |
"""General node utils."""
import logging
import uuid
from typing import List, Optional, Protocol, runtime_checkable
from llama_index.core.schema import (
BaseNode,
Document,
ImageDocument,
ImageNode,
NodeRelationship,
TextNode,
)
from llama_index.core.utils import truncate_text
logger = loggi... | """General node utils."""
import logging
import uuid
from typing import List, Optional, Protocol, runtime_checkable
from llama_index.core.schema import (
BaseNode,
Document,
ImageDocument,
ImageNode,
NodeRelationship,
TextNode,
)
from llama_index.core.utils import truncate_text
logger = loggi... |
# 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... |
_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({
# ... |
from typing import List, Optional, Literal
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
class SpiderWebReader(BasePydanticReader):
"""
Scrapes a URL for data and returns llm-ready data with `Spider.cloud`.
Must have the Python package `spider-... | from typing import List, Optional, Literal
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
class SpiderWebReader(BasePydanticReader):
"""
Scrapes a URL for data and returns llm-ready data with `Spider.cloud`.
Must have the Python package `spider-... |
import os
import time
import pytest
from jina import Client, Document, DocumentArray, Flow
cur_dir = os.path.dirname(os.path.abspath(__file__))
img_name = 'jina/replica-exec'
@pytest.fixture(scope='function')
def docker_image_built():
import docker
client = docker.from_env()
client.images.build(path=... | import os
import time
import pytest
from jina import Client, Document, DocumentArray, Flow
cur_dir = os.path.dirname(os.path.abspath(__file__))
img_name = 'jina/replica-exec'
exposed_port = 12345
@pytest.fixture(scope='function')
def docker_image_built():
import docker
client = docker.from_env()
clie... |
import logging
import time
from abc import ABC, abstractmethod
from typing import ClassVar, Optional
from backend.data.model import OAuth2Credentials
from backend.integrations.providers import ProviderName
logger = logging.getLogger(__name__)
class BaseOAuthHandler(ABC):
# --8<-- [start:BaseOAuthHandler1]
P... | import logging
import time
from abc import ABC, abstractmethod
from typing import ClassVar, Optional
from backend.data.model import OAuth2Credentials
from backend.integrations.providers import ProviderName
logger = logging.getLogger(__name__)
class BaseOAuthHandler(ABC):
# --8<-- [start:BaseOAuthHandler1]
P... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from mmcv.runner import BaseModule
class BaseMaskHead(BaseModule, metaclass=ABCMeta):
"""Base class for mask heads used in One-Stage Instance Segmentation."""
def __init__(self, init_cfg):
super(BaseMaskHead, sel... | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from mmcv.runner import BaseModule
class BaseMaskHead(BaseModule, metaclass=ABCMeta):
"""Base class for mask heads used in One-Stage Instance Segmentation."""
def __init__(self, init_cfg):
super(BaseMaskHead, sel... |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | # Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
# model settings
preprocess_cfg = dict(
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True,
pad_size_divisor=32)
model = dict(
type='MaskRCNN',
preprocess_cfg=preprocess_cfg,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indi... | # model settings
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
model = dict(
type='MaskRCNN',
img_norm_cfg=img_norm_cfg,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages... |
from pathlib import Path
from typing import Any, Callable, Optional, Tuple
import PIL.Image
from .utils import download_and_extract_archive
from .vision import VisionDataset
class SUN397(VisionDataset):
"""`The SUN397 Data Set <https://vision.princeton.edu/projects/2010/SUN/>`_.
The SUN397 or Scene UNderst... | from pathlib import Path
from typing import Any, Callable, Optional, Tuple
import PIL.Image
from .utils import download_and_extract_archive
from .vision import VisionDataset
class SUN397(VisionDataset):
"""`The SUN397 Data Set <https://vision.princeton.edu/projects/2010/SUN/>`_.
The SUN397 or Scene UNderst... |
# Copyright (c) OpenMMLab. All rights reserved.
import time
import pytest
import mmengine
def test_timer_init():
timer = mmengine.Timer(start=False)
assert not timer.is_running
timer.start()
assert timer.is_running
timer = mmengine.Timer()
assert timer.is_running
def test_timer_run():
... | # Copyright (c) OpenMMLab. All rights reserved.
import time
import mmcv
import pytest
def test_timer_init():
timer = mmcv.Timer(start=False)
assert not timer.is_running
timer.start()
assert timer.is_running
timer = mmcv.Timer()
assert timer.is_running
def test_timer_run():
timer = mmcv.... |
_base_ = '../rpn/rpn_r50_caffe_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='GARPNHead',
in_channels=256,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=8,
scales_per_octave=3,... | _base_ = '../rpn/rpn_r50_caffe_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='GARPNHead',
in_channels=256,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=8,
scales_per_octave=3,... |
import multiprocessing
import os
import signal
import time
import pytest
from jina import Document, DocumentArray, Executor, requests
from jina.clients.request import request_generator
from jina.parsers import set_gateway_parser
from jina.serve.networking.utils import send_request_sync
from jina_cli.api import execut... | import multiprocessing
import os
import signal
import time
import pytest
from jina import Document, DocumentArray, Executor, requests
from jina.clients.request import request_generator
from jina.parsers import set_gateway_parser
from jina.serve.networking.utils import send_request_sync
from jina_cli.api import execut... |
from typing import Union
from fastapi import FastAPI
from pydantic import BaseModel
class Item(BaseModel):
name: str
description: Union[str, None] = None
price: float
tax: Union[float, None] = None
app = FastAPI()
@app.post("/items/")
async def create_item(item: Item):
item_dict = item.dict()... | from typing import Union
from fastapi import FastAPI
from pydantic import BaseModel
class Item(BaseModel):
name: str
description: Union[str, None] = None
price: float
tax: Union[float, None] = None
app = FastAPI()
@app.post("/items/")
async def create_item(item: Item):
item_dict = item.dict()... |
"""Chat Message."""
from typing import Any, Literal
from typing_extensions import override
from langchain_core.messages.base import (
BaseMessage,
BaseMessageChunk,
merge_content,
)
from langchain_core.utils._merge import merge_dicts
class ChatMessage(BaseMessage):
"""Message that can be assigned a... | """Chat Message."""
from typing import Any, Literal
from typing_extensions import override
from langchain_core.messages.base import (
BaseMessage,
BaseMessageChunk,
merge_content,
)
from langchain_core.utils._merge import merge_dicts
class ChatMessage(BaseMessage):
"""Message that can be assigned a... |
"""Test HyDE."""
from typing import Any, Optional
import numpy as np
from langchain_core.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.embeddings import Embeddings
from langchain_core.language_models.llms import BaseLLM
from langchain_core.outputs im... | """Test HyDE."""
from typing import Any, Optional
import numpy as np
from langchain_core.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.embeddings import Embeddings
from langchain_core.language_models.llms import BaseLLM
from langchain_core.outputs im... |
_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32)
model = dict(
# use caffe img_norm
preprocess_cfg=preprocess_cfg,
backbone=dict(
norm_cfg=dict(requires_grad=False),
styl... | _base_ = './mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.5... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
from typing import Optional
import fire
from llama import Llama
def main(
ckpt_dir: str,
tokenizer_path: str,
temperature: float = 0.6,
... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
from typing import Optional
import fire
from llama import Llama
def main(
ckpt_dir: str,
tokenizer_path: str,
temperature: float = 0.6,
... |
import os
import sys
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
sys.path.append("tests/python")
import test_basic_models as test_bm
# Don't import the test class, otherwise they will run twice.
import test_callback as test_cb # noqa
rng = np.random.RandomState(1994)
... | import os
import sys
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
sys.path.append("tests/python")
import test_basic_models as test_bm
# Don't import the test class, otherwise they will run twice.
import test_callback as test_cb # noqa
rng = np.random.RandomState(1994)
... |
from typing_extensions import TYPE_CHECKING
if TYPE_CHECKING:
from rich.console import Console, ConsoleOptions, RenderResult
from rich.measure import Measurement
from docarray.typing.tensor.abstract_tensor import AbstractTensor
class TensorDisplay:
"""
Rich representation of a tensor.
"""
... | from typing_extensions import TYPE_CHECKING
if TYPE_CHECKING:
from rich.console import Console, ConsoleOptions, RenderResult
from rich.measure import Measurement
from docarray.typing.tensor.abstract_tensor import AbstractTensor
class TensorDisplay:
"""
Rich representation of a tensor.
"""
... |
_base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=0.33, widen_factor=0.375),
neck=dict(in_channels=[96, 192, 384], out_channels=96),
bbox_head=dict(in_channels=96, feat_channels=96))
# dataset settings
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], ... | _base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=0.33, widen_factor=0.375),
neck=dict(in_channels=[96, 192, 384], out_channels=96),
bbox_head=dict(in_channels=96, feat_channels=96))
# dataset settings
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], ... |
from .dpr_reader import DPRReaderRanker
| from .dpr_reader import DPRReaderRanker |
import logging
import os
from typing import Optional
from jina import __default_host__
from jina.importer import ImportExtensions
from jina.serve.gateway import BaseGateway
from jina.serve.runtimes.gateway.websocket.app import get_fastapi_app
class WebSocketGateway(BaseGateway):
"""WebSocket Gateway implementati... | import logging
import os
from typing import Optional
from jina import __default_host__
from jina.importer import ImportExtensions
from jina.serve.gateway import BaseGateway
from jina.serve.runtimes.gateway.websocket.app import get_fastapi_app
class WebSocketGateway(BaseGateway):
"""WebSocket Gateway implementati... |
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from typing import Literal
from pydantic import BaseModel, ConfigDict, SecretStr
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import (
CredentialsField,
C... | import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from pydantic import BaseModel, ConfigDict
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import BlockSecret, SchemaField, SecretField
class EmailCredentials(Base... |
"""
Demo for prediction using individual trees and model slices
===========================================================
"""
import os
import numpy as np
from scipy.special import logit
from sklearn.datasets import load_svmlight_file
import xgboost as xgb
CURRENT_DIR = os.path.dirname(__file__)
train = os.path.jo... | """
Demo for prediction using individual trees and model slices
===========================================================
"""
import os
import numpy as np
from scipy.special import logit
from sklearn.datasets import load_svmlight_file
import xgboost as xgb
CURRENT_DIR = os.path.dirname(__file__)
train = os.path.jo... |
from llama_index.core.storage.kvstore.types import BaseKVStore
from llama_index.storage.docstore.azurecosmosnosql import AzureCosmosNoSqlDocumentStore
def test_class():
names_of_base_classes = [b.__name__ for b in AzureCosmosNoSqlDocumentStore.__mro__]
assert BaseKVStore.__name__ in names_of_base_classes
| from llama_index.core.storage.docstore.keyval_docstore import KVDocumentStore
from llama_index.storage.docstore.azurecosmosnosql import AzureCosmosNoSqlDocumentStore
def test_class():
names_of_base_classes = [b.__name__ for b in AzureCosmosNoSqlDocumentStore.__mro__]
assert KVDocumentStore.__name__ in names_o... |
"""Argparser module for WorkerRuntime"""
from jina import __default_host__, helper
from jina.parsers.helper import KVAppendAction
def mixin_base_runtime_parser(arg_group):
"""Mixing in arguments required by any class that extends :class:`AsynNewLoopRuntime` into the given parser.
:param arg_group: the parser... | """Argparser module for WorkerRuntime"""
from jina import __default_host__, helper
from jina.parsers.helper import KVAppendAction, add_arg_group
def mixin_base_runtime_parser(arg_group):
"""Mixing in arguments required by any class that extends :class:`AsynNewLoopRuntime` into the given parser.
:param arg_gro... |
_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, ... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.runner import BaseModule
from mmdet.registry import MODELS
from ...core import bbox_cxcywh_to_xyxy
@MODELS.register_module()
class EmbeddingRPNHead(BaseModule):
"""RPNHead in the `Sparse R-CNN <https://arxiv.org/abs/2011... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.runner import BaseModule
from mmdet.models.builder import HEADS
from ...core import bbox_cxcywh_to_xyxy
@HEADS.register_module()
class EmbeddingRPNHead(BaseModule):
"""RPNHead in the `Sparse R-CNN <https://arxiv.org/abs/... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.