repo_name stringlengths 7 71 | file_path stringlengths 5 118 | context list | import_statement stringlengths 45 12.5k | token_num int64 641 99.4k | cropped_code stringlengths 44 17k | all_code stringlengths 43 754k | next_line stringlengths 2 330 | gold_snippet_index int64 0 68 | created_at stringlengths 25 25 | level stringclasses 9
values |
|---|---|---|---|---|---|---|---|---|---|---|
Harvard-Ophthalmology-AI-Lab/FairSeg | SAMed/segment_anything/modeling/image_encoder.py | [
{
"identifier": "LayerNorm2d",
"path": "SAMed/segment_anything/modeling/common.py",
"snippet": "class LayerNorm2d(nn.Module):\n def __init__(self, num_channels: int, eps: float = 1e-6) -> None:\n super().__init__()\n self.weight = nn.Parameter(torch.ones(num_channels))\n self.bia... | import torch
import torch.nn as nn
import torch.nn.functional as F
from icecream import ic
from typing import Optional, Tuple, Type
from .common import LayerNorm2d, MLPBlock | 1,147 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.... | LayerNorm2d(out_chans), | 0 | 2023-11-03 17:05:40+00:00 | 2k |
anand2312/quill-server | quill_server/realtime/events.py | [
{
"identifier": "User",
"path": "quill_server/db/models.py",
"snippet": "class User(Base):\n __tablename__ = \"user\"\n\n id: Mapped[UUID] = mapped_column(pg_UUID(as_uuid=True), primary_key=True, default=uuid4) # noqa: A003\n username: Mapped[str] = mapped_column(unique=True)\n password: Ma... | from enum import StrEnum, auto
from functools import partial
from typing import Any, Generic, TypeVar
from collections.abc import Awaitable
from loguru import logger
from pydantic import BaseModel
from redis.asyncio import Redis
from quill_server.db.models import User
from quill_server.realtime.room import GameMember, ... | 1,548 |
DataT = TypeVar("DataT", bound=BaseModel)
# the excalidraw element event contains many fields
# https://github.com/excalidraw/excalidraw/blob/master/src/element/types.ts#L27-L141
ExcalidrawElement = dict[str, Any]
class Drawing(BaseModel):
|
DataT = TypeVar("DataT", bound=BaseModel)
# the excalidraw element event contains many fields
# https://github.com/excalidraw/excalidraw/blob/master/src/element/types.ts#L27-L141
ExcalidrawElement = dict[str, Any]
class Drawing(BaseModel): | user: GameMember | 1 | 2023-11-03 12:43:18+00:00 | 2k |
OPTML-Group/DeepZero | algorithm/prune/main.py | [
{
"identifier": "zoo_grasp_importance_score",
"path": "algorithm/prune/importance_scores.py",
"snippet": "def zoo_grasp_importance_score(\n model,\n dataloader,\n samples_per_class,\n class_num,\n zoo_rs_size,\n zoo_step_size,\n loss_func = torch.nn.CrossEntropyLoss()\n ):\n\n ... | import torch
from torch.nn.utils import prune
from copy import deepcopy
from .importance_scores import zoo_grasp_importance_score, grasp_importance_score, random_importance_score | 963 |
__all__ = ['global_prune', 'check_sparsity', 'check_grad_sparsity', 'custom_prune', 'extract_mask', 'remove_prune', 'layer_sparsity']
def global_prune(model, ratio, method, class_num=None, dataloader=None, sample_per_classes=25, zoo_sample_size=None, zoo_step_size=None, layer_wise_sparsity=None):
if method == 'gr... |
__all__ = ['global_prune', 'check_sparsity', 'check_grad_sparsity', 'custom_prune', 'extract_mask', 'remove_prune', 'layer_sparsity']
def global_prune(model, ratio, method, class_num=None, dataloader=None, sample_per_classes=25, zoo_sample_size=None, zoo_step_size=None, layer_wise_sparsity=None):
if method == 'gr... | score_dict = zoo_grasp_importance_score(model, dataloader, sample_per_classes, class_num, zoo_sample_size, zoo_step_size) | 0 | 2023-11-01 14:47:38+00:00 | 2k |
S3raphimCS/Hackathon_telehack | backend/SPO_KROT/metrics/admin.py | [
{
"identifier": "ExcelFile",
"path": "backend/SPO_KROT/metrics/models.py",
"snippet": "class ExcelFile(models.Model):\n file = models.FileField(\n upload_to='metrics',\n unique=True,\n blank=True, null=True,\n validators=[FileExtensionValidator(['xlsx', 'xls', 'xlsm'])],\n... | from django.contrib import admin
from .models import ExcelFile, Measurements, Operator, Report | 1,372 |
@admin.register(Operator)
class OperatorAdmin(admin.ModelAdmin):
list_display = ('name',)
list_per_page = 15
search_fields = ("name",)
readonly_fields = ('id',)
|
@admin.register(Operator)
class OperatorAdmin(admin.ModelAdmin):
list_display = ('name',)
list_per_page = 15
search_fields = ("name",)
readonly_fields = ('id',)
| @admin.register(Report) | 3 | 2023-11-09 12:55:04+00:00 | 2k |
lz1oceani/LLM-As-Hierarchical-Policy | hlm/utils/metric_utils.py | [
{
"identifier": "normalize_answer",
"path": "hlm/utils/math_answer_utils.py",
"snippet": "def normalize_answer(text, answer_type=\"text\"):\n ret = normalize_answer_core(text, answer_type)\n try:\n str(ret)\n except:\n ret = None\n return \"No answer!\" if ret is None else ret"... | import os, warnings
import numpy as np, re, time, signal, sympy, scipy
from sympy.utilities.exceptions import SymPyDeprecationWarning
from collections import defaultdict
from numbers import Number
from IPython import embed
from copy import deepcopy
from itertools import chain
from sympy.parsing.latex import parse_latex... | 1,417 |
os.environ["USE_SYMENGINE"] = "1"
warnings.simplefilter("ignore", SyntaxWarning)
warnings.simplefilter("ignore", RuntimeWarning)
warnings.filterwarnings("ignore", category=SymPyDeprecationWarning)
# from sympy import Symbol, Eq, simplify, solve
NO_ANSWER = "No answer!"
SKIP_ANSWER_TEMPLATE = [
"Code cannot b... |
os.environ["USE_SYMENGINE"] = "1"
warnings.simplefilter("ignore", SyntaxWarning)
warnings.simplefilter("ignore", RuntimeWarning)
warnings.filterwarnings("ignore", category=SymPyDeprecationWarning)
# from sympy import Symbol, Eq, simplify, solve
NO_ANSWER = "No answer!"
SKIP_ANSWER_TEMPLATE = [
"Code cannot b... | source = normalize_answer(source, answer_type) | 0 | 2023-11-01 17:15:42+00:00 | 2k |
mitre/arlin | tests/test_dataset/test_collectors/test_sb3_collectors.py | [
{
"identifier": "SB3DQNDataCollector",
"path": "arlin/dataset/collectors/sb3_collectors.py",
"snippet": "class SB3DQNDataCollector(BaseDataCollector):\n \"\"\"Data collector for a model trained with DQN in stable-baselines3.\"\"\"\n\n def __init__(self, datapoint_cls: Type[BaseDatapoint], policy: ... | import pytest
from stable_baselines3 import DQN
from arlin.dataset.collectors import SB3DQNDataCollector, SB3PPODataCollector
from arlin.dataset.collectors.datapoints import SB3DQNDatapoint, SB3PPODatapoint | 1,031 |
@pytest.fixture
def dqn_model(env):
model = DQN("MlpPolicy", env, verbose=1)
model.learn(total_timesteps=int(100))
return model
class TestSB3Collectors:
def test_sb3_ppo_collector(self, ppo_model, env):
|
@pytest.fixture
def dqn_model(env):
model = DQN("MlpPolicy", env, verbose=1)
model.learn(total_timesteps=int(100))
return model
class TestSB3Collectors:
def test_sb3_ppo_collector(self, ppo_model, env): | collector = SB3PPODataCollector(SB3PPODatapoint, ppo_model.policy) | 1 | 2023-11-08 13:57:45+00:00 | 2k |
Giftify-Bot/Giftify-Bot | utils/paginator.py | [
{
"identifier": "ARROW_BACK_EMOJI",
"path": "utils/constants.py",
"snippet": "ARROW_BACK_EMOJI = \"<:GiftifyBack:1120372002939744308>\""
},
{
"identifier": "ARROW_EMOJI",
"path": "utils/constants.py",
"snippet": "ARROW_EMOJI = \"<:GiftifyArrow:1117849870678638653>\""
},
{
"identi... | import abc
import discord
from typing import TYPE_CHECKING, Any, Dict, Generic, List, Optional, TypeVar, Union
from discord.ext import commands
from typing import TypeAlias
from typing_extensions import TypeAlias
from utils.constants import ARROW_BACK_EMOJI, ARROW_EMOJI, STOP_EMOJI
from utils.tree import Intera... | 1,496 | @property
def max_page(self) -> int:
"""The max page count for this paginator."""
return len(self.pages)
@property
def min_page(self) -> int:
"""The min page count for this paginator."""
return 1
@property
def current_page(self) -> int:
"""The current pa... | from __future__ import annotations
try:
except ImportError:
if TYPE_CHECKING:
T = TypeVar("T")
TargetType: TypeAlias = Union[Interaction, commands.Context["Giftify"]]
class BaseButtonPaginator(Generic[T], discord.ui.View, abc.ABC):
"""The base implementation of a button paginator. This class should be inher... | @discord.ui.button(emoji=STOP_EMOJI) | 2 | 2023-11-09 15:00:15+00:00 | 2k |
Zjy0401/CoCoFormer | model/rpr.py | [
{
"identifier": "get_device",
"path": "utilities/device.py",
"snippet": "def get_device():\n\n if((not USE_CUDA) or (TORCH_CUDA_DEVICE is None)):\n return TORCH_CPU_DEVICE\n else:\n return TORCH_CUDA_DEVICE"
},
{
"identifier": "parse_train_args",
"path": "utilities/argume... | import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn.parameter import Parameter
from torch.nn import Module
from torch.nn.modules.transformer import _get_clones
from torch.nn.modules.linear import Linear
from torch.nn.modules.dropout import Dropout
from torch.nn.modules.normalization im... | 1,158 |
# TransformerEncoderRPR
class TransformerEncoderRPR(Module):
def __init__(self, encoder_layer, num_layers, encoder_past, max_seq, c_max_seq, b_max_seq, norm=None):
super(TransformerEncoderRPR, self).__init__()
self.past_layers = _get_clones(encoder_past, 1)
self.layers = _get_clones(enco... |
# TransformerEncoderRPR
class TransformerEncoderRPR(Module):
def __init__(self, encoder_layer, num_layers, encoder_past, max_seq, c_max_seq, b_max_seq, norm=None):
super(TransformerEncoderRPR, self).__init__()
self.past_layers = _get_clones(encoder_past, 1)
self.layers = _get_clones(enco... | args = parse_train_args() | 1 | 2023-11-01 08:33:08+00:00 | 2k |
a16z-infra/sunlight | model/agent.py | [
{
"identifier": "DiffbotClient",
"path": "model/diffbot.py",
"snippet": "class DiffbotClient(object):\n\n BASE_API_URL = 'http://api.diffbot.com'\n TIMEOUT_MS = 15000\n\n def request(self, url, token, api, version=3):\n ''' Issue a request to the Diffbot API and return the response if va... | from datetime import datetime
from threading import Thread
from langchain.callbacks.base import BaseCallbackHandler
from langchain.chains import LLMChain
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from .diffbot import DiffbotClient
from .prompts import BIAS_REPORT, FACTUAL... | 1,398 |
DIFFBOT_API_KEY = os.environ['DIFFBOT_API_KEY']
REQUEST_LOG_FILE = os.environ['REQUEST_LOG_FILE']
MAX_MODEL_CONTEXT = {
'gpt-3.5-turbo': 4096,
'text-davinci-003': 4096,
'gpt-4': 8192,
}
class OpenAIStreamHandler(BaseCallbackHandler):
def __init__(self, stream_queue, *args, **kwargs):
supe... |
DIFFBOT_API_KEY = os.environ['DIFFBOT_API_KEY']
REQUEST_LOG_FILE = os.environ['REQUEST_LOG_FILE']
MAX_MODEL_CONTEXT = {
'gpt-3.5-turbo': 4096,
'text-davinci-003': 4096,
'gpt-4': 8192,
}
class OpenAIStreamHandler(BaseCallbackHandler):
def __init__(self, stream_queue, *args, **kwargs):
supe... | diffbot = DiffbotClient() | 0 | 2023-11-01 17:19:54+00:00 | 2k |
elenacliu/GraspStudio | cameras/realsense.py | [
{
"identifier": "CameraConfig",
"path": "cameras/camera.py",
"snippet": "class CameraConfig(InstantiateConfig):\n \"\"\"Camera Config\"\"\"\n _target: Type = field(default_factory=lambda : Camera)\n # focal length of x axis\n fx: float = 0.0\n # focal length of y axis\n fy: float = 0.0... | from dataclasses import dataclass, field
from typing import Type
from .camera import CameraConfig, Camera
import pyrealsense2 as rs
import numpy as np
import cv2 | 1,298 | # Copyright 2023 Chang Liu. 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 applicable law or a... | # Copyright 2023 Chang Liu. 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 applicable law or a... | class RealSenseCamera(Camera): | 1 | 2023-11-08 09:44:22+00:00 | 2k |
serl-robot/serl | serl/wrappers/pixels.py | [
{
"identifier": "FrameStack",
"path": "serl/wrappers/frame_stack.py",
"snippet": "class FrameStack(gym.Wrapper):\n def __init__(self, env, num_stack: int, stacking_key: str = \"pixels\"):\n super().__init__(env)\n self._num_stack = num_stack\n self._stacking_key = stacking_key\n\... | from typing import Optional, Tuple
from gym.wrappers.pixel_observation import PixelObservationWrapper
from serl.wrappers.frame_stack import FrameStack
from serl.wrappers.repeat_action import RepeatAction
from serl.wrappers.universal_seed import UniversalSeed
import gym | 809 |
def wrap_pixels(
env: gym.Env,
action_repeat: int,
image_size: int = 84,
num_stack: Optional[int] = 3,
camera_id: int = 0,
pixel_keys: Tuple[str, ...] = ("pixels",),
) -> gym.Env:
if action_repeat > 1:
env = RepeatAction(env, action_repeat)
|
def wrap_pixels(
env: gym.Env,
action_repeat: int,
image_size: int = 84,
num_stack: Optional[int] = 3,
camera_id: int = 0,
pixel_keys: Tuple[str, ...] = ("pixels",),
) -> gym.Env:
if action_repeat > 1:
env = RepeatAction(env, action_repeat)
| env = UniversalSeed(env) | 2 | 2023-11-02 23:32:24+00:00 | 2k |
daily-demos/ai-meeting-assistant | server/llm/openai_assistant.py | [
{
"identifier": "Assistant",
"path": "server/llm/assistant.py",
"snippet": "class Assistant(ABC):\n \"\"\"Abstract class defining methods that should be implemented by any assistant\"\"\"\n\n @abstractmethod\n def register_new_context(self, new_text: str,\n name: lis... | import asyncio
import logging
import threading
from collections import deque
from openai import OpenAI
from openai.types.beta import Assistant
from openai.types.chat import ChatCompletionMessageParam, ChatCompletionSystemMessageParam, \
ChatCompletionUserMessageParam
from server.llm.assistant import Assistant, NoCo... | 1,479 | def probe_api_key(api_key: str) -> bool:
"""Probes the OpenAI API with the provided key to ensure it is valid."""
try:
client = OpenAI(api_key=api_key)
client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
ChatCompletionUserMessageParam(
... | """Module that defines an OpenAI assistant."""
_assistant_name = "daily-ai-assistant"
def probe_api_key(api_key: str) -> bool:
"""Probes the OpenAI API with the provided key to ensure it is valid."""
try:
client = OpenAI(api_key=api_key)
client.chat.completions.create(
model="gp... | raise NoContextError() | 1 | 2023-11-02 11:17:16+00:00 | 2k |
Kushalhk/AutoFilter | plugins/inline.py | [
{
"identifier": "get_search_results",
"path": "database/ia_filterdb.py",
"snippet": "async def get_search_results(chat_id, query, file_type=None, max_results=10, offset=0, filter=False):\n \"\"\"For given query return (results, next_offset)\"\"\"\n if chat_id is not None:\n settings = await... | import logging
from pyrogram import Client, emoji, filters
from pyrogram.errors.exceptions.bad_request_400 import QueryIdInvalid
from pyrogram.types import InlineKeyboardButton, InlineKeyboardMarkup, InlineQueryResultCachedDocument, InlineQuery
from database.ia_filterdb import get_search_results
from utils import is_su... | 1,485 |
logger = logging.getLogger(__name__)
cache_time = 0 if AUTH_USERS or AUTH_CHANNEL else CACHE_TIME
async def inline_users(query: InlineQuery):
if AUTH_USERS:
if query.from_user and query.from_user.id in AUTH_USERS:
return True
else:
return False
if query.from_user and qu... |
logger = logging.getLogger(__name__)
cache_time = 0 if AUTH_USERS or AUTH_CHANNEL else CACHE_TIME
async def inline_users(query: InlineQuery):
if AUTH_USERS:
if query.from_user and query.from_user.id in AUTH_USERS:
return True
else:
return False
if query.from_user and qu... | if CUSTOM_FILE_CAPTION: | 7 | 2023-11-03 12:21:26+00:00 | 2k |
tiendatnguyen-vision/Orbit-symmetrize | RotatedMNIST/LPS/emlp-pytorch/tests/model_tests.py | [
{
"identifier": "rel_error",
"path": "RotatedMNIST/LPS/emlp-pytorch/tests/equivariance_tests.py",
"snippet": "def rel_error(t1, t2):\r\n \"\"\" Computes the relative error of two tensors. \"\"\"\r\n error = torch.sqrt(torch.mean(torch.abs(t1-t2)**2))\r\n scale = torch.sqrt(torch.mean(torch.abs(... | import torch
from torch.utils.data import DataLoader
from oil.utils.utils import FixedNumpySeed, FixedPytorchSeed
from emlp_pytorch.nn import EMLP
from emlp_pytorch.groups import S, SO, DirectProduct
from emlp_pytorch.reps import vis, sparsify_basis, V, Rep, LazyKron, T
from .equivariance_tests import rel_error, scale_... | 1,384 | """ Tests for the EMLP model."""
def equivariance_err(model, mb, repin, repout, group):
""" Computes the equivariance error of a model on a minibatch mb. """
x, y = mb
gs = group.samples(x.size(0))
rho_gin = torch.vmap(repin(group).rho_dense)(gs)
rho_gout = torch.vmap(repout(group).rho_dense)(gs)
... | """ Tests for the EMLP model."""
def equivariance_err(model, mb, repin, repout, group):
""" Computes the equivariance error of a model on a minibatch mb. """
x, y = mb
gs = group.samples(x.size(0))
rho_gin = torch.vmap(repin(group).rho_dense)(gs)
rho_gout = torch.vmap(repout(group).rho_dense)(gs)
... | assert rel_error(out1, out2) < 1e-4, "EMLP equivariance fails on bespoke productsubrep" | 0 | 2023-11-01 07:19:02+00:00 | 2k |
crizbae/PictoPlan | backend/mongo_api/app/server/routes/item_routes.py | [
{
"identifier": "collection",
"path": "backend/mongo_api/app/server/database.py",
"snippet": "MONGO_URI = config(\"MONGO_URI\")\ndef item_helper(item) -> dict:\ndef ret_link(item) -> dict:\nasync def retrieve_all_items():\nasync def retrieve_item(item_id: str):\nasync def retrieve_links(session_id: str)... | from fastapi import APIRouter, Depends, HTTPException
from ..database import collection
from ..models.item import Item
from ..database import retrieve_all_items, retrieve_item, update_item_in_db, delete_item_from_db, retrieve_links | 815 |
router = APIRouter()
@router.post("/items/")
def create_item(item: Item):
item_dict = item.dict()
inserted_item = collection.insert_one(item_dict)
item_id = str(inserted_item.inserted_id)
del item_dict["_id"]
item_dict["id"] = item_id
return item_dict
@router.get("/items/")
async def get_all_... |
router = APIRouter()
@router.post("/items/")
def create_item(item: Item):
item_dict = item.dict()
inserted_item = collection.insert_one(item_dict)
item_id = str(inserted_item.inserted_id)
del item_dict["_id"]
item_dict["id"] = item_id
return item_dict
@router.get("/items/")
async def get_all_... | success = await delete_item_from_db(item_id) | 5 | 2023-11-04 16:48:55+00:00 | 2k |
xenxxxx/BitPay-Crypto-Signal-Trading-Bot | tests/data/test_btanalysis.py | [
{
"identifier": "CURRENT_TEST_STRATEGY",
"path": "tests/conftest.py",
"snippet": "CURRENT_TEST_STRATEGY = 'StrategyTestV3'"
},
{
"identifier": "create_mock_trades",
"path": "tests/conftest.py",
"snippet": "def create_mock_trades(fee, is_short: Optional[bool] = False, use_db: bool = True)... | from datetime import datetime, timedelta, timezone
from pathlib import Path
from unittest.mock import MagicMock
from pandas import DataFrame, DateOffset, Timestamp, to_datetime
from freqtrade.configuration import TimeRange
from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.data.btanalysis import (BT_DATA_... | 1,532 |
def test_get_latest_backtest_filename(testdatadir, mocker):
with pytest.raises(ValueError, match=r"Directory .* does not exist\."):
get_latest_backtest_filename(testdatadir / 'does_not_exist')
with pytest.raises(ValueError,
match=r"Directory .* does not seem to contain .*"):
... |
def test_get_latest_backtest_filename(testdatadir, mocker):
with pytest.raises(ValueError, match=r"Directory .* does not exist\."):
get_latest_backtest_filename(testdatadir / 'does_not_exist')
with pytest.raises(ValueError,
match=r"Directory .* does not seem to contain .*"):
... | create_mock_trades(fee, is_short) | 1 | 2023-11-07 18:46:03+00:00 | 2k |
ssajedi/SAiF-GPT | bin/main.py | [
{
"identifier": "anonymize_text",
"path": "utils.py",
"snippet": "def augment_prompt(prompt,ref_doc):\ndef extract_pdf_text(file):"
},
{
"identifier": "extract_pdf_text",
"path": "utils.py",
"snippet": "def extract_pdf_text(file):\n \"\"\"\n Extracts text paragraphs from a PDF file... | import streamlit as st
import random
import time
import openai
import openai
import streamlit as st
from utils import anonymize_text, deanonymize_text, chatbot_response
from utils import extract_pdf_text
from text_effects import highlight_phrases_in_paragraph
from DetectEntity import DetectEntity | 815 |
st.title("AInonymous")
system_prompt="""You are a helpful assistant, your task is to review an uploaded document\
uploaded by a user.\
The user query is delimited by triple asterisks.\
The reference documents in that message are delimited with triple backticks.\
A user might ask follow up questions.
"""
# add a se... |
st.title("AInonymous")
system_prompt="""You are a helpful assistant, your task is to review an uploaded document\
uploaded by a user.\
The user query is delimited by triple asterisks.\
The reference documents in that message are delimited with triple backticks.\
A user might ask follow up questions.
"""
# add a se... | _,chunks = extract_pdf_text(uploaded_file) | 1 | 2023-11-04 18:14:49+00:00 | 2k |
awslabs/optimizing-multitask-training-through-dynamic-pipelines | tests/test_kv_store.py | [
{
"identifier": "_get_from_shared_kv_store",
"path": "dynapipe/pipe/data_loader.py",
"snippet": "def _get_from_shared_kv_store(\n kv_store: RedisKVStore,\n key: str,\n reader_idx: int,\n n_total_readers: int,\n decode: bool = True,\n logger=None,\n):\n reader_count_key = key + \"_rc... | import multiprocessing as mp
import time
import traceback
import traceback
from dynapipe.pipe.data_loader import (
_get_from_shared_kv_store,
_init_kv_store,
_put_to_shared_kv_store,
) | 1,336 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
# Note: this test requires torch
# to run this test, exec:
# DYNAPIPE_DEBUG=DEBUG DYNAPIPE_LOGGING_DEBUG_DIR=./test_debug \
# torchrun --standalone --nnodes=1 --nproc_per_node=1 test_kv_store.py
def _producer... | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
# Note: this test requires torch
# to run this test, exec:
# DYNAPIPE_DEBUG=DEBUG DYNAPIPE_LOGGING_DEBUG_DIR=./test_debug \
# torchrun --standalone --nnodes=1 --nproc_per_node=1 test_kv_store.py
def _producer... | payload = _get_from_shared_kv_store( | 0 | 2023-11-08 07:58:20+00:00 | 2k |
dask-contrib/dask-databricks | dask_databricks/tests/test_databricks.py | [
{
"identifier": "DatabricksCluster",
"path": "dask_databricks/databrickscluster.py",
"snippet": "class DatabricksCluster(Cluster):\n \"\"\"Connect to a Dask cluster deployed via databricks.\"\"\"\n\n def __init__(\n self,\n loop: Optional[IOLoop] = None,\n asynchronous: bool =... | import os
import pytest
from dask.distributed import Client
from distributed.deploy import Cluster, LocalCluster
from dask_databricks import DatabricksCluster, get_client | 669 |
@pytest.fixture(scope="session")
def dask_cluster():
"""Start a LocalCluster to simulate the cluster that would be started on Databricks."""
return LocalCluster(scheduler_port=8786)
@pytest.fixture
def remove_spark_local_ip():
original_spark_local_ip = os.getenv("SPARK_LOCAL_IP")
if original_spark... |
@pytest.fixture(scope="session")
def dask_cluster():
"""Start a LocalCluster to simulate the cluster that would be started on Databricks."""
return LocalCluster(scheduler_port=8786)
@pytest.fixture
def remove_spark_local_ip():
original_spark_local_ip = os.getenv("SPARK_LOCAL_IP")
if original_spark... | DatabricksCluster() | 0 | 2023-11-02 13:49:27+00:00 | 2k |
indiefan/king_smith | custom_components/king_smith/coordinator.py | [
{
"identifier": "DOMAIN",
"path": "custom_components/king_smith/const.py",
"snippet": "DOMAIN = \"king_smith\""
},
{
"identifier": "WalkingPadApi",
"path": "custom_components/king_smith/walking_pad.py",
"snippet": "class WalkingPadApi:\n \"\"\"Walkingpad device.\"\"\"\n\n def __ini... | from datetime import datetime
from homeassistant.core import CALLBACK_TYPE, HassJob, HomeAssistant, callback
from homeassistant.helpers.event import async_call_later
from homeassistant.helpers.update_coordinator import DataUpdateCoordinator
from ph4_walkingpad.pad import WalkingPadCurStatus
from .const import DOMAIN
fr... | 1,339 | """The Walking Pad Coordinator."""
_LOGGER = logging.getLogger(__name__)
NEVER_TIME = -86400.0
DEBOUNCE_SECONDS = 1.0
class WalkingPadCoordinator(DataUpdateCoordinator[None]):
"""Data coordinator for receiving Walking Pad updates."""
def __init__(self, hass: HomeAssistant, walking_pad_api: WalkingPadApi... | """The Walking Pad Coordinator."""
_LOGGER = logging.getLogger(__name__)
NEVER_TIME = -86400.0
DEBOUNCE_SECONDS = 1.0
class WalkingPadCoordinator(DataUpdateCoordinator[None]):
"""Data coordinator for receiving Walking Pad updates."""
def __init__(self, hass: HomeAssistant, walking_pad_api: WalkingPadApi... | name=DOMAIN, | 0 | 2023-11-03 20:45:03+00:00 | 2k |
ndiamant/spice | spice/conditional_histogram.py | [
{
"identifier": "BaseLightning",
"path": "spice/utils.py",
"snippet": "class BaseLightning(LightningModule):\n def _configure_optimizers(self, parameters: Iterator[torch.nn.Parameter]):\n opt = optim.AdamW(\n parameters, lr=self.hparams.lr, weight_decay=self.hparams.wd,\n )\n... | import copy
import math
import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from tqdm import tqdm
from torch import nn
from spice.utils import (
BaseLightning, MLP, unique_quantile,
score_to_q_hat, compute_conformal_metrics,
) | 1,548 |
def select_bins(y: torch.Tensor, n_bins: int) -> torch.Tensor:
return unique_quantile(y, n_bins, first_bin_zero=False)
def discretize(y: torch.Tensor, bins: torch.Tensor) -> torch.Tensor:
return torch.bucketize(y.clip(max=bins[-1] - 1e-5), boundaries=bins)
|
def select_bins(y: torch.Tensor, n_bins: int) -> torch.Tensor:
return unique_quantile(y, n_bins, first_bin_zero=False)
def discretize(y: torch.Tensor, bins: torch.Tensor) -> torch.Tensor:
return torch.bucketize(y.clip(max=bins[-1] - 1e-5), boundaries=bins)
| class ConditionalHist(BaseLightning): | 0 | 2023-11-01 18:04:29+00:00 | 2k |
nik-sm/com-hom-emg | tests/test_data.py | [
{
"identifier": "get_datasets",
"path": "com_hom_emg/data.py",
"snippet": "def get_datasets(\n per_subj_data: dict,\n fold: int,\n n_train_subj: int,\n n_val_subj: int,\n n_test_subj: int,\n use_preprocessed_data: bool,\n return_subj_names: bool = False, # For testing\n) -> Tuple[T... | import torch
from com_hom_emg.data import get_datasets, get_per_subj_data | 1,309 |
def test_get_datasets_disjoint_val_test():
# The subject used for val should be different each time
# Likewise for test
per_subj_data = get_per_subj_data()
all_val_subj = []
all_test_subj = []
n_train = 8
n_val = 1
n_test = 1
expected_train_size = 8 * 1224 # 1224 gestures per s... |
def test_get_datasets_disjoint_val_test():
# The subject used for val should be different each time
# Likewise for test
per_subj_data = get_per_subj_data()
all_val_subj = []
all_test_subj = []
n_train = 8
n_val = 1
n_test = 1
expected_train_size = 8 * 1224 # 1224 gestures per s... | train_set, val_set, test_set, train_subj, val_subj, test_subj = get_datasets( | 0 | 2023-11-01 21:12:05+00:00 | 2k |
alengwenus/ha-sma-ev-charger | custom_components/smaev/select.py | [
{
"identifier": "DOMAIN",
"path": "custom_components/smaev/const.py",
"snippet": "DOMAIN = \"smaev\""
},
{
"identifier": "SMAEV_COORDINATOR",
"path": "custom_components/smaev/const.py",
"snippet": "SMAEV_COORDINATOR = \"coordinator\""
},
{
"identifier": "SMAEV_DEVICE_INFO",
"... | from dataclasses import dataclass, field
from datetime import datetime
from typing import TYPE_CHECKING
from pysmaev.const import SmaEvChargerParameters
from pysmaev.helpers import get_parameters_channel
from homeassistant.components.select import SelectEntity, SelectEntityDescription
from homeassistant.config_entries ... | 1,047 | """Select platform for SMA EV Charger integration."""
from __future__ import annotations
_LOGGER = logging.getLogger(__name__)
@dataclass
class SmaEvChargerSelectEntityDescription(SelectEntityDescription):
"""Describes SMA EV Charger select entities."""
type: str = ""
channel: str = ""
value_map... | """Select platform for SMA EV Charger integration."""
from __future__ import annotations
_LOGGER = logging.getLogger(__name__)
@dataclass
class SmaEvChargerSelectEntityDescription(SelectEntityDescription):
"""Describes SMA EV Charger select entities."""
type: str = ""
channel: str = ""
value_map... | value = channel[SMAEV_VALUE] | 5 | 2023-11-04 07:08:41+00:00 | 2k |
microsoft/promptbase | azureml/components/src/shared/jsonl_utils.py | [
{
"identifier": "JSONLReader",
"path": "azureml/components/src/shared/jsonl_file_utils.py",
"snippet": "class JSONLReader:\n \"\"\"Line-by-line iteration over a JSONL file\n\n Can be used in a 'with' statement, and then iterated over.\n The returned value is a decoded JSON object, rather than\n... | import json
import pathlib
import tempfile
import traceback
from typing import Any, Callable, Tuple
from .jsonl_file_utils import JSONLReader, JSONLWriter
from .logging_utils import get_standard_logger_for_file | 808 | # Copied from Medprompt.... perhaps those utils should go to PyPi?
_logger = get_standard_logger_for_file(__file__)
def line_map(
*,
map_func: Callable[[dict[str, Any]], dict[str, Any] | None],
source_file: pathlib.Path,
dest_file: pathlib.Path,
source_encoding: str,
dest_encoding: str,
... | # Copied from Medprompt.... perhaps those utils should go to PyPi?
_logger = get_standard_logger_for_file(__file__)
def line_map(
*,
map_func: Callable[[dict[str, Any]], dict[str, Any] | None],
source_file: pathlib.Path,
dest_file: pathlib.Path,
source_encoding: str,
dest_encoding: str,
... | with JSONLReader(source_file, source_encoding) as in_file: | 0 | 2023-12-12 08:00:11+00:00 | 2k |
openai/weak-to-strong | weak_to_strong/train.py | [
{
"identifier": "clear_mem",
"path": "weak_to_strong/common.py",
"snippet": "def clear_mem(verbose: bool = False):\n \"\"\"\n This function is used to clear the memory allocated by PyTorch.\n It does so by calling the garbage collector to release unused GPU memory.\n After clearing the memor... | import itertools
import os
import pickle
import time
import datasets
import numpy as np
import torch
import torch_optimizer as toptim
import weak_to_strong.logger as logger
from dataclasses import dataclass
from typing import Callable, Optional
from transformers.modeling_utils import load_sharded_checkpoint
from weak_t... | 1,558 |
@dataclass
class ModelConfig:
name: str
default_lr: float
eval_batch_size: int
custom_kwargs: Optional[dict] = None
gradient_checkpointing: bool = False
model_parallel: bool = False
default_optimizer: str = "adam"
def train_model(
model: torch.nn.Module,
ds: datasets.Dataset,
... |
@dataclass
class ModelConfig:
name: str
default_lr: float
eval_batch_size: int
custom_kwargs: Optional[dict] = None
gradient_checkpointing: bool = False
model_parallel: bool = False
default_optimizer: str = "adam"
def train_model(
model: torch.nn.Module,
ds: datasets.Dataset,
... | loss_fn: Callable = xent_loss, | 2 | 2023-12-13 23:53:13+00:00 | 2k |
SqueezeAILab/LLMCompiler | configs/hotpotqa/configs.py | [
{
"identifier": "OUTPUT_PROMPT",
"path": "configs/hotpotqa/gpt_prompts.py",
"snippet": "OUTPUT_PROMPT = (\n \"Solve a question answering task with interleaving Observation, Thought, and Action steps. Here are some guidelines:\\n\"\n \" - You will be given a Question and some Wikipedia passages, w... | from configs.hotpotqa.gpt_prompts import OUTPUT_PROMPT, PLANNER_PROMPT | 945 |
CONFIGS = {
"default_model": "gpt-3.5-turbo-1106",
"planner_prompt": PLANNER_PROMPT,
|
CONFIGS = {
"default_model": "gpt-3.5-turbo-1106",
"planner_prompt": PLANNER_PROMPT, | "output_prompt": OUTPUT_PROMPT, | 0 | 2023-12-06 21:12:54+00:00 | 2k |
open-compass/MixtralKit | mixtralkit/layers/attention.py | [
{
"identifier": "ModelArgs",
"path": "mixtralkit/layers/utils.py",
"snippet": "class ModelArgs:\n dim: int = 4096\n n_layers: int = 32\n n_heads: int = 32\n n_kv_heads: Optional[int] = None\n vocab_size: int = -1 # defined later by tokenizer\n multiple_of: int = 256 # make SwiGLU hid... | import math
import torch
import torch.nn.functional as F
import fairscale.nn.model_parallel.initialize as fs_init
from typing import Optional, Tuple
from torch import nn
from .utils import ModelArgs, repeat_kv
from .position_embeding import apply_rotary_emb
from fairscale.nn.model_parallel.layers import... | 1,488 | # Copyright (c) OpenMMLab. and affiliates.
# Copyright (c) Meta Platforms, Inc. and affiliates.
class TorchAttention(nn.Module):
"""Multi-head attention module."""
def __init__(self, args: ModelArgs):
"""
Initialize the Attention module.
Args:
args (ModelArgs): Mo... | # Copyright (c) OpenMMLab. and affiliates.
# Copyright (c) Meta Platforms, Inc. and affiliates.
class TorchAttention(nn.Module):
"""Multi-head attention module."""
def __init__(self, args: ModelArgs):
"""
Initialize the Attention module.
Args:
args (ModelArgs): Mo... | xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis) | 2 | 2023-12-09 15:05:26+00:00 | 2k |
aymenfurter/microagents | gradio_ui/agent_manager.py | [
{
"identifier": "MicroAgentManager",
"path": "agents/microagent_manager.py",
"snippet": "class MicroAgentManager:\n \"\"\"\n Manages the creation and retrieval of micro agents.\n \"\"\"\n\n def __init__(self, api_key: str, max_agents: int = 20, db_filename=\"agents.db\"):\n self.api_k... | import logging
from typing import Any, List
from agents.microagent_manager import MicroAgentManager
from agents.microagent import MicroAgent | 1,527 |
logger = logging.getLogger(__name__)
class GradioAgentManager:
"""
A wrapper class for interacting with MicroAgentManager in a Gradio interface.
"""
def __init__(self, api_key: str):
self.manager = MicroAgentManager(api_key)
self.manager.create_agents()
def get_agents_info(self)... |
logger = logging.getLogger(__name__)
class GradioAgentManager:
"""
A wrapper class for interacting with MicroAgentManager in a Gradio interface.
"""
def __init__(self, api_key: str):
self.manager = MicroAgentManager(api_key)
self.manager.create_agents()
def get_agents_info(self)... | def format_agent_info(self, agent: MicroAgent) -> dict: | 1 | 2023-12-11 08:17:09+00:00 | 2k |
bytedance/ImageDream | extern/ldm_zero123/thirdp/psp/model_irse.py | [
{
"identifier": "Flatten",
"path": "extern/ldm_zero123/thirdp/psp/helpers.py",
"snippet": "class Flatten(Module):\n def forward(self, input):\n return input.view(input.size(0), -1)"
},
{
"identifier": "bottleneck_IR",
"path": "extern/ldm_zero123/thirdp/psp/helpers.py",
"snippet... | from torch.nn import (
BatchNorm1d,
BatchNorm2d,
Conv2d,
Dropout,
Linear,
Module,
PReLU,
Sequential,
)
from extern.ldm_zero123.thirdp.psp.helpers import (
Flatten,
bottleneck_IR,
bottleneck_IR_SE,
get_blocks,
l2_norm,
) | 1,205 | # https://github.com/eladrich/pixel2style2pixel
"""
Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
"""
class Backbone(Module):
def __init__(self, input_size, num_layers, mode="ir", drop_ratio=0.4, affine=True):
super(Backbone, self).__init__()
as... | # https://github.com/eladrich/pixel2style2pixel
"""
Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
"""
class Backbone(Module):
def __init__(self, input_size, num_layers, mode="ir", drop_ratio=0.4, affine=True):
super(Backbone, self).__init__()
as... | unit_module = bottleneck_IR | 1 | 2023-12-13 21:09:37+00:00 | 2k |
TencentARC/MotionCtrl | lvdm/modules/attention_temporal.py | [
{
"identifier": "checkpoint",
"path": "lvdm/common.py",
"snippet": "def checkpoint(func, inputs, params, flag):\n \"\"\"\n Evaluate a function without caching intermediate activations, allowing for\n reduced memory at the expense of extra compute in the backward pass.\n :param func: the func... | import math
import torch
import torch as th
import torch.nn.functional as F
import xformers
import xformers.ops
from inspect import isfunction
from torch import nn, einsum
from einops import rearrange, repeat
from lvdm.common import (
checkpoint,
exists,
uniq,
default,
max_neg_value,
ini... | 842 |
try:
XFORMERS_IS_AVAILBLE = True
except:
XFORMERS_IS_AVAILBLE = False
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x... |
try:
XFORMERS_IS_AVAILBLE = True
except:
XFORMERS_IS_AVAILBLE = False
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x... | dim_out = default(dim_out, dim) | 3 | 2023-12-06 07:27:45+00:00 | 2k |
s-casci/tinyzero | tictactoe/one_dim/eval.py | [
{
"identifier": "LinearNetwork",
"path": "models.py",
"snippet": "class LinearNetwork(nn.Module):\n def __init__(self, input_shape, action_space, first_layer_size=512, second_layer_size=256):\n super().__init__()\n self.first_layer = nn.Linear(input_shape[0], first_layer_size)\n self.second_la... | from game import TicTacToe
from train import OUT_DIR, SEARCH_ITERATIONS
from tqdm import tqdm
from models import LinearNetwork # noqa: E402
from agents import AlphaZeroAgent, ClassicMCTSAgent # noqa: E402
from mcts import pit # noqa: E402
import torch
import os
import sys | 948 |
sys.path.append(os.getcwd())
EVAL_GAMES = 100
if __name__ == "__main__":
game = TicTacToe()
model = LinearNetwork(game.observation_shape, game.action_space)
model.load_state_dict(torch.load(f"{OUT_DIR}/model.pth"))
agent = AlphaZeroAgent(model)
agent_play_kwargs = {"search_iterations": SEARCH_ITERATIONS... |
sys.path.append(os.getcwd())
EVAL_GAMES = 100
if __name__ == "__main__":
game = TicTacToe()
model = LinearNetwork(game.observation_shape, game.action_space)
model.load_state_dict(torch.load(f"{OUT_DIR}/model.pth"))
agent = AlphaZeroAgent(model)
agent_play_kwargs = {"search_iterations": SEARCH_ITERATIONS... | result = pit( | 3 | 2023-12-14 11:36:50+00:00 | 2k |
facebookresearch/PurpleLlama | CybersecurityBenchmarks/insecure_code_detector/tests/test_python_insecure_code_detector.py | [
{
"identifier": "Language",
"path": "CybersecurityBenchmarks/insecure_code_detector/languages.py",
"snippet": "class Language(str, enum.Enum):\n C = \"c\"\n CPP = \"cpp\"\n CSHARP = \"csharp\"\n HACK = \"hack\"\n JAVA = \"java\"\n JAVASCRIPT = \"javascript\"\n KOTLIN = \"kotlin\"\n ... | from ..languages import Language
from .insecure_code_detector_test import InsecureCodeDetectorTest | 716 | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# the following test cases contain an input string, and the corresponding number of expected insecure pattern matches
PYTHON_TEST_CASES = ... | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# the following test cases contain an input string, and the corresponding number of expected insecure pattern matches
PYTHON_TEST_CASES = ... | class TestPythonInsecureCodeDetector(InsecureCodeDetectorTest): | 1 | 2023-12-06 21:29:41+00:00 | 2k |
allenai/unified-io-2 | t5x/examples/unified_io/modality_processing.py | [
{
"identifier": "AudioEncoder",
"path": "t5x/examples/unified_io/audio_encoder.py",
"snippet": "class AudioEncoder(nn.Module):\n \"\"\"Encodes raw audio spectrograms as features\"\"\"\n config: Union[ImageVitFeatureConfig, AudioVitFeatureConfig]\n\n def setup(self):\n cfg = self.config\n # `vis... | from collections import OrderedDict
from typing import Mapping
from flax import traverse_util
from seqio import TaskRegistry, FeatureConverter
from t5x.examples.unified_io.audio_encoder import AudioEncoder
from t5x.examples.unified_io.image_encoder import ImageEncoder
from t5x.examples.unified_io.input_modalities impor... | 890 | """Code for handling modalities"""
@gin.configurable
def get_target_modalities(
target_modality=['text', 'image', 'audio'],
image_vae_config: ImageViTVQGANConfig=VAEConfig(),
audio_vae_config: AudioViTVQGANConfig=AudioViTVQGANConfig(),
) -> Dict[str, ModalityEncoder]:
"""Return the encoders to use ... | """Code for handling modalities"""
@gin.configurable
def get_target_modalities(
target_modality=['text', 'image', 'audio'],
image_vae_config: ImageViTVQGANConfig=VAEConfig(),
audio_vae_config: AudioViTVQGANConfig=AudioViTVQGANConfig(),
) -> Dict[str, ModalityEncoder]:
"""Return the encoders to use ... | audio_encoder = AudioEncoder(audio_vit_cfg) | 0 | 2023-12-12 20:23:33+00:00 | 2k |
zju3dv/EasyVolcap | scripts/gaussian/merge_pcd.py | [
{
"identifier": "load_pts",
"path": "easyvolcap/utils/data_utils.py",
"snippet": "def load_pts(filename: str):\n from pyntcloud import PyntCloud\n cloud = PyntCloud.from_file(filename)\n verts = cloud.xyz\n if 'red' in cloud.points and 'green' in cloud.points and 'blue' in cloud.points:\n ... | from easyvolcap.utils.console_utils import *
from easyvolcap.utils.data_utils import load_pts, export_pts
from os.path import join
import argparse
import numpy as np | 1,196 | """
This script will load and convert a .ply visual hull to a points3D file
"""
@catch_throw
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('--data_root', default='data/enerf_outdoor/actor2_3')
parser.add_argument('--vhulls_dir', default='merged')
parser.add_ar... | """
This script will load and convert a .ply visual hull to a points3D file
"""
@catch_throw
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('--data_root', default='data/enerf_outdoor/actor2_3')
parser.add_argument('--vhulls_dir', default='merged')
parser.add_ar... | v, c, n, s = load_pts(vhull) | 0 | 2023-12-07 08:53:42+00:00 | 2k |
minghanqin/LangSplat | scene/cameras.py | [
{
"identifier": "getWorld2View2",
"path": "utils/graphics_utils.py",
"snippet": "def getWorld2View2(R, t, translate=np.array([.0, .0, .0]), scale=1.0):\n Rt = np.zeros((4, 4))\n Rt[:3, :3] = R.transpose()\n Rt[:3, 3] = t\n Rt[3, 3] = 1.0\n\n C2W = np.linalg.inv(Rt)\n cam_center = C2W[:... | import os
import pickle
import torch
import numpy as np
from torch import nn
from utils.graphics_utils import getWorld2View2, getProjectionMatrix | 922 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
class Camera(nn.Module):
def _... | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
class Camera(nn.Module):
def _... | self.projection_matrix = getProjectionMatrix(znear=self.znear, zfar=self.zfar, fovX=self.FoVx, fovY=self.FoVy).transpose(0,1).cuda() | 1 | 2023-12-11 06:33:35+00:00 | 2k |
SciPhi-AI/agent-search | agent_search/search/base.py | [
{
"identifier": "AgentSearchResult",
"path": "agent_search/core/search_types.py",
"snippet": "class AgentSearchResult(BaseModel):\n \"\"\"A dataclass to store the search result\"\"\"\n\n score: float\n url: str\n title: Optional[str]\n dataset: Optional[str]\n # TODO - Add dict(str, [s... | import csv
import json
import logging
import os
import numpy as np
import psycopg2
import psycopg2
from typing import List
from qdrant_client import QdrantClient
from transformers import AutoModel
from agent_search.core import AgentSearchResult
from agent_search.core.utils import (
cosine_similarity,
... | 650 |
logger = logging.getLogger(__name__)
class WebSearchEngine:
"""A simple search client for the OpenSearch collection"""
def __init__(
self,
):
try:
except ImportError as e:
raise ImportError(
f"Error {e} while imoprting psycopg2. Please install it wit... |
logger = logging.getLogger(__name__)
class WebSearchEngine:
"""A simple search client for the OpenSearch collection"""
def __init__(
self,
):
try:
except ImportError as e:
raise ImportError(
f"Error {e} while imoprting psycopg2. Please install it wit... | self.config = load_config()["agent_search"] | 3 | 2023-12-11 17:41:03+00:00 | 2k |
yohanshin/WHAM | lib/data/_dataset.py | [
{
"identifier": "constants",
"path": "configs/constants.py",
"snippet": "IMG_FEAT_DIM = {\n 'resnet': 2048,\n 'vit': 1024\n}\nN_JOINTS = 17\n PARSED_DATA = f'{root}/parsed_data'\n THREEDPW_PTH = f'{root}/3DPW'\n RICH_PTH = f'{root}/RICH'\n EMDB_PTH = f'{root}/EMDB'\n NUM_JOINTS = N_... | import torch
import numpy as np
from skimage.util.shape import view_as_windows
from configs import constants as _C
from .normalizer import Normalizer
from lib.utils.imutils import transform | 1,499 | from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
class BaseDataset(torch.utils.data.Dataset):
def __init__(self, cfg, training=True):
super(BaseDataset, self).__init__()
self.n_joints = _C.KEYPOINTS.NUM_JOINTS
self.epoch = 0
... | from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
class BaseDataset(torch.utils.data.Dataset):
def __init__(self, cfg, training=True):
super(BaseDataset, self).__init__()
self.n_joints = _C.KEYPOINTS.NUM_JOINTS
self.epoch = 0
... | self.keypoints_normalizer = Normalizer(cfg) | 1 | 2023-12-08 09:17:54+00:00 | 2k |
octo-models/octo | octo/data/oxe/oxe_standardization_transforms.py | [
{
"identifier": "binarize_gripper_actions",
"path": "octo/data/utils/data_utils.py",
"snippet": "def binarize_gripper_actions(actions: tf.Tensor) -> tf.Tensor:\n \"\"\"Converts gripper actions from continous to binary values (0 and 1).\n\n We exploit that fact that most of the time, the gripper is... | from typing import Any, Dict
from octo.data.utils.data_utils import (
binarize_gripper_actions,
invert_gripper_actions,
rel2abs_gripper_actions,
relabel_actions,
)
import tensorflow as tf
import tensorflow_graphics.geometry.transformation as tft
import tensorflow_graphics.geometry.transformation... | 1,251 | """Open X-Embodiment Dataset Transforms
input: dict of features, each is batched, i.e. has leading time dimension
expected output:
step = {
'observation': {
<image_keys, depth_image_keys>
state in chosen state representation
},
'action': action in chosen action representation,
'language... | """Open X-Embodiment Dataset Transforms
input: dict of features, each is batched, i.e. has leading time dimension
expected output:
step = {
'observation': {
<image_keys, depth_image_keys>
state in chosen state representation
},
'action': action in chosen action representation,
'language... | binarize_gripper_actions(trajectory["action"][:, -1])[:, None], | 0 | 2023-12-13 09:58:56+00:00 | 2k |
mistralai/client-python | tests/test_chat.py | [
{
"identifier": "mock_chat_response_payload",
"path": "tests/utils.py",
"snippet": "def mock_chat_response_payload():\n return orjson.dumps(\n {\n \"id\": \"chat-98c8c60e3fbf4fc49658eddaf447357c\",\n \"object\": \"chat.completion\",\n \"created\": 1703165682,\n... | import unittest.mock as mock
import pytest
from mistralai.client import MistralClient
from mistralai.models.chat_completion import (
ChatCompletionResponse,
ChatCompletionStreamResponse,
ChatMessage,
)
from .utils import (
mock_chat_response_payload,
mock_chat_response_streaming_payload,
mock_re... | 1,007 |
@pytest.fixture()
def client():
client = MistralClient()
client._client = mock.MagicMock()
return client
class TestChat:
def test_chat(self, client):
client._client.request.return_value = mock_response(
200,
mock_chat_response_payload(),
)
result = ... |
@pytest.fixture()
def client():
client = MistralClient()
client._client = mock.MagicMock()
return client
class TestChat:
def test_chat(self, client):
client._client.request.return_value = mock_response(
200,
mock_chat_response_payload(),
)
result = ... | client._client.stream.return_value = mock_stream_response( | 3 | 2023-12-07 10:09:51+00:00 | 2k |
kijai/ComfyUI-Marigold | marigold/model/marigold_pipeline.py | [
{
"identifier": "RGBEncoder",
"path": "marigold/model/rgb_encoder.py",
"snippet": "class RGBEncoder(nn.Module):\n \"\"\"\n The encoder of pretrained Stable Diffusion VAE\n \"\"\"\n \n def __init__(self, pretrained_path, subfolder=None) -> None:\n super().__init__()\n \n ... | import logging
import numpy as np
import torch
from typing import Dict
from diffusers import (
DDIMScheduler,
DDPMScheduler,
PNDMScheduler,
DEISMultistepScheduler,
SchedulerMixin,
UNet2DConditionModel,
)
from torch import nn
from torch.nn import Conv2d
from torch.nn.parameter import Parameter
fr... | 1,225 | # Author: Bingxin Ke
# Last modified: 2023-12-11
class MarigoldPipeline(nn.Module):
"""
Marigold monocular depth estimator.
"""
def __init__(
self,
unet_pretrained_path: Dict, # {path: xxx, subfolder: xxx}
rgb_encoder_pretrained_path: Dict,
depht_ae_pretrained_path... | # Author: Bingxin Ke
# Last modified: 2023-12-11
class MarigoldPipeline(nn.Module):
"""
Marigold monocular depth estimator.
"""
def __init__(
self,
unet_pretrained_path: Dict, # {path: xxx, subfolder: xxx}
rgb_encoder_pretrained_path: Dict,
depht_ae_pretrained_path... | self.rgb_encoder = RGBEncoder( | 0 | 2023-12-12 12:25:52+00:00 | 2k |
modelscope/richdreamer | extern/ldm_zero123/thirdp/psp/model_irse.py | [
{
"identifier": "Flatten",
"path": "extern/ldm_zero123/thirdp/psp/helpers.py",
"snippet": "class Flatten(Module):\n def forward(self, input):\n return input.view(input.size(0), -1)"
},
{
"identifier": "bottleneck_IR",
"path": "extern/ldm_zero123/thirdp/psp/helpers.py",
"snippet... | from torch.nn import (BatchNorm1d, BatchNorm2d, Conv2d, Dropout, Linear,
Module, PReLU, Sequential,)
from extern.ldm_zero123.thirdp.psp.helpers import (Flatten, bottleneck_IR,
bottleneck_IR_SE,
ge... | 1,210 | # https://github.com/eladrich/pixel2style2pixel
"""
Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
"""
class Backbone(Module):
def __init__(self, input_size, num_layers, mode="ir", drop_ratio=0.4, affine=True):
super(Backbone, self).__init__()
as... | # https://github.com/eladrich/pixel2style2pixel
"""
Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
"""
class Backbone(Module):
def __init__(self, input_size, num_layers, mode="ir", drop_ratio=0.4, affine=True):
super(Backbone, self).__init__()
as... | unit_module = bottleneck_IR_SE | 2 | 2023-12-06 07:53:11+00:00 | 2k |
rehg-lab/RAVE | annotator/mmpkg/mmcv/runner/base_module.py | [
{
"identifier": "master_only",
"path": "annotator/mmpkg/mmcv/runner/dist_utils.py",
"snippet": "def master_only(func):\n\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n rank, _ = get_dist_info()\n if rank == 0:\n return func(*args, **kwargs)\n\n return wrappe... | import copy
import warnings
import torch.nn as nn
from abc import ABCMeta
from collections import defaultdict
from logging import FileHandler
from annotator.mmpkg.mmcv.runner.dist_utils import master_only
from annotator.mmpkg.mmcv.utils.logging import get_logger, logger_initialized, print_log
from ..cnn import ... | 999 | # Copyright (c) OpenMMLab. All rights reserved.
class BaseModule(nn.Module, metaclass=ABCMeta):
"""Base module for all modules in openmmlab.
``BaseModule`` is a wrapper of ``torch.nn.Module`` with additional
functionality of parameter initialization. Compared with
``torch.nn.Module``, ``BaseModule`... | # Copyright (c) OpenMMLab. All rights reserved.
class BaseModule(nn.Module, metaclass=ABCMeta):
"""Base module for all modules in openmmlab.
``BaseModule`` is a wrapper of ``torch.nn.Module`` with additional
functionality of parameter initialization. Compared with
``torch.nn.Module``, ``BaseModule`... | print_log( | 1 | 2023-12-05 02:51:53+00:00 | 2k |
worldcoin/open-iris | tests/e2e_tests/pipelines/test_e2e_iris_pipeline.py | [
{
"identifier": "compare_debug_pipeline_outputs",
"path": "tests/e2e_tests/utils.py",
"snippet": "def compare_debug_pipeline_outputs(pipeline_output_1: Dict[str, Any], pipeline_output_2: Dict[str, Any]):\n \"\"\"Compare two IRISPipeline outputs for debugging.\n\n Args:\n pipeline_output_1 (... | import os
import pickle
import cv2
import numpy as np
import pytest
from typing import Any, Dict
from iris.pipelines.iris_pipeline import IRISPipeline
from tests.e2e_tests.utils import compare_debug_pipeline_outputs, compare_iris_pipeline_outputs | 906 |
@pytest.fixture
def ir_image() -> np.ndarray:
ir_image_path = os.path.join(os.path.dirname(__file__), "mocks", "inputs", "anonymized.png")
img_data = cv2.imread(ir_image_path, cv2.IMREAD_GRAYSCALE)
return img_data
@pytest.fixture
def expected_iris_pipeline_output() -> Dict[str, Any]:
expected_iris... |
@pytest.fixture
def ir_image() -> np.ndarray:
ir_image_path = os.path.join(os.path.dirname(__file__), "mocks", "inputs", "anonymized.png")
img_data = cv2.imread(ir_image_path, cv2.IMREAD_GRAYSCALE)
return img_data
@pytest.fixture
def expected_iris_pipeline_output() -> Dict[str, Any]:
expected_iris... | compare_debug_pipeline_outputs(computed_pipeline_output, expected_debug_pipeline_output) | 0 | 2023-12-09 22:43:09+00:00 | 2k |
laixintao/mactop | mactop/panels/cpu_percpu_usage.py | [
{
"identifier": "LabeledColorBar",
"path": "mactop/widgets/labeled_colorbar.py",
"snippet": "class LabeledColorBar(Static):\n percentages = reactive(None)\n\n DEFAULT_CSS = \"\"\"\n LabeledColorBar {\n layout: horizontal;\n }\n LabeledColorBar > ColorBar {\n width: 1fr;\n ... | import logging
from functools import partial
from textual.app import ComposeResult
from mactop.widgets import LabeledColorBar
from mactop.metrics_store import metrics
from mactop.utils.formatting import render_cpu_percentage_100
from ._base import BaseStatic
from mactop import const | 1,272 |
logger = logging.getLogger(__name__)
def get_percpu_percent(index):
cpus = metrics.psutilmetrics.cpu_percent_percpu
if not cpus:
return [0, 0, 0, 0]
cpu_percent = cpus[index]
return [
cpu_percent.user,
cpu_percent.nice,
cpu_percent.system,
cpu_percent.idle,... |
logger = logging.getLogger(__name__)
def get_percpu_percent(index):
cpus = metrics.psutilmetrics.cpu_percent_percpu
if not cpus:
return [0, 0, 0, 0]
cpu_percent = cpus[index]
return [
cpu_percent.user,
cpu_percent.nice,
cpu_percent.system,
cpu_percent.idle,... | value_render_fn=render_cpu_percentage_100, | 2 | 2023-12-05 09:12:42+00:00 | 2k |
geopavlakos/hamer | hamer/datasets/vitdet_dataset.py | [
{
"identifier": "convert_cvimg_to_tensor",
"path": "hamer/datasets/utils.py",
"snippet": "def convert_cvimg_to_tensor(cvimg: np.array):\n \"\"\"\n Convert image from HWC to CHW format.\n Args:\n cvimg (np.array): Image of shape (H, W, 3) as loaded by OpenCV.\n Returns:\n np.arr... | from typing import Dict
from skimage.filters import gaussian
from yacs.config import CfgNode
from .utils import (convert_cvimg_to_tensor,
expand_to_aspect_ratio,
generate_image_patch_cv2)
import cv2
import numpy as np
import torch | 1,342 |
DEFAULT_MEAN = 255. * np.array([0.485, 0.456, 0.406])
DEFAULT_STD = 255. * np.array([0.229, 0.224, 0.225])
class ViTDetDataset(torch.utils.data.Dataset):
def __init__(self,
cfg: CfgNode,
img_cv2: np.array,
boxes: np.array,
right: np.array,
... |
DEFAULT_MEAN = 255. * np.array([0.485, 0.456, 0.406])
DEFAULT_STD = 255. * np.array([0.229, 0.224, 0.225])
class ViTDetDataset(torch.utils.data.Dataset):
def __init__(self,
cfg: CfgNode,
img_cv2: np.array,
boxes: np.array,
right: np.array,
... | bbox_size = expand_to_aspect_ratio(scale*200, target_aspect_ratio=BBOX_SHAPE).max() | 1 | 2023-12-08 09:07:07+00:00 | 2k |
rogeriochaves/driver | driver/annotator.py | [
{
"identifier": "detect_components",
"path": "driver/UIED/run_single.py",
"snippet": "def detect_components(\n input_path_img, ocr_result: AnnotatedImage, showOCR=False, showUIED=False\n) -> DetectElementsResponse:\n output_root = \"output\"\n\n # Resizes the image to be smaller because this pr... | import math
import os
import cv2
from PIL import Image, ImageDraw, ImageFont
from driver.UIED.run_single import detect_components
from driver.UIED.utils import show_image
from driver.ocr_call import ocr_text_detection
from driver.types import DebugConfig, ImgMultiplierFactor, LabelMap
from driver.utils import is_retina... | 1,310 |
def annotate_image(input_image_path, debug: DebugConfig):
ocr_result = ocr_text_detection(input_image_path, debug)
components = detect_components(
input_image_path,
ocr_result,
showOCR=debug["ocr"],
showUIED=debug["uied"],
)
original_image = Image.open(input_image_p... |
def annotate_image(input_image_path, debug: DebugConfig):
ocr_result = ocr_text_detection(input_image_path, debug)
components = detect_components(
input_image_path,
ocr_result,
showOCR=debug["ocr"],
showUIED=debug["uied"],
)
original_image = Image.open(input_image_p... | label_map: LabelMap = {} | 3 | 2023-12-10 17:18:28+00:00 | 2k |
baidubce/app-builder | appbuilder/core/components/embeddings/base.py | [
{
"identifier": "Component",
"path": "appbuilder/core/component.py",
"snippet": "class Component:\n r\"\"\"Component基类, 其它实现的Component子类需要继承该基类，并至少实现run方法.\"\"\"\n\n def __init__(self,\n meta: Optional[ComponentArguments] = ComponentArguments(),\n secret_key: Option... | from abc import abstractmethod
from typing import List, Union
from appbuilder.core.component import Component
from appbuilder.core.message import Message
from appbuilder.core.component import ComponentArguments | 1,120 | """
base
"""
# Copyright (c) 2023 Baidu, Inc. 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 a... | """
base
"""
# Copyright (c) 2023 Baidu, Inc. 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 a... | meta: ComponentArguments | 2 | 2023-12-05 01:48:12+00:00 | 2k |
corfyi/UCMCTrack | run_mot20_test.py | [
{
"identifier": "run_ucmc",
"path": "util/run_ucmc.py",
"snippet": "def run_ucmc(args, det_path = \"det_results/mot17/yolox_x_ablation\",\n cam_path = \"cam_para/mot17\",\n gmc_path = \"gmc/mot17\",\n out_path = \"output/mot17\",\n ... | from util.run_ucmc import run_ucmc, make_args | 1,416 |
if __name__ == '__main__':
det_path = "det_results/mot20"
cam_path = "cam_para/mot20"
gmc_path = "gmc/mot20"
out_path = "output/mot20"
exp_name = "test"
dataset = "MOT20"
|
if __name__ == '__main__':
det_path = "det_results/mot20"
cam_path = "cam_para/mot20"
gmc_path = "gmc/mot20"
out_path = "output/mot20"
exp_name = "test"
dataset = "MOT20" | args = make_args() | 1 | 2023-12-12 07:29:20+00:00 | 2k |
ingra14m/Specular-Gaussians | metrics.py | [
{
"identifier": "ssim",
"path": "utils/loss_utils.py",
"snippet": "def ssim(img1, img2, window_size=11, size_average=True):\n channel = img1.size(-3)\n window = create_window(window_size, channel)\n\n if img1.is_cuda:\n window = window.cuda(img1.get_device())\n window = window.type_as... | from pathlib import Path
from PIL import Image
from utils.loss_utils import ssim
from tqdm import tqdm
from utils.image_utils import psnr
from argparse import ArgumentParser
import os
import torch
import torchvision.transforms.functional as tf
import lpips
import json | 721 | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
# from lpipsPyTorch import lpips
... | #
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
# from lpipsPyTorch import lpips
... | ssims.append(ssim(renders[idx], gts[idx])) | 0 | 2023-12-12 14:59:01+00:00 | 2k |
u2seg/U2Seg | detectron2/evaluation/evaluator.py | [
{
"identifier": "get_world_size",
"path": "detectron2/utils/comm.py",
"snippet": "def get_world_size() -> int:\n if not dist.is_available():\n return 1\n if not dist.is_initialized():\n return 1\n return dist.get_world_size()"
},
{
"identifier": "is_main_process",
"pat... | import datetime
import logging
import time
import torch
from collections import OrderedDict, abc
from contextlib import ExitStack, contextmanager
from typing import List, Union
from torch import nn
from detectron2.utils.comm import get_world_size, is_main_process
from detectron2.utils.logger import log_every_n_seconds | 1,151 | # Copyright (c) Facebook, Inc. and its affiliates.
class DatasetEvaluator:
"""
Base class for a dataset evaluator.
The function :func:`inference_on_dataset` runs the model over
all samples in the dataset, and have a DatasetEvaluator to process the inputs/outputs.
This class will accumulate info... | # Copyright (c) Facebook, Inc. and its affiliates.
class DatasetEvaluator:
"""
Base class for a dataset evaluator.
The function :func:`inference_on_dataset` runs the model over
all samples in the dataset, and have a DatasetEvaluator to process the inputs/outputs.
This class will accumulate info... | num_devices = get_world_size() | 0 | 2023-12-05 01:13:31+00:00 | 2k |
upfusion3d/upfusion | control_net/cldm/ddim_hacked.py | [
{
"identifier": "make_ddim_sampling_parameters",
"path": "control_net/ldm/modules/diffusionmodules/util.py",
"snippet": "def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):\n # select alphas for computing the variance schedule\n alphas = alphacums[ddim_timesteps]\n ... | import torch
import numpy as np
from tqdm import tqdm
from control_net.ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor | 844 | """SAMPLING ONLY."""
class DDIMSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
def register_buffer(self, name, attr):
if type(attr) =... | """SAMPLING ONLY."""
class DDIMSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
def register_buffer(self, name, attr):
if type(attr) =... | self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, | 1 | 2023-12-12 00:49:11+00:00 | 2k |
modelscope/normal-depth-diffusion | libs/ControlNet-v1-1-nightly/annotator/normalbae/models/baseline.py | [
{
"identifier": "UpSampleBN",
"path": "libs/ControlNet-v1-1-nightly/annotator/normalbae/models/submodules/submodules.py",
"snippet": "class UpSampleBN(nn.Module):\n\n def __init__(self, skip_input, output_features):\n super(UpSampleBN, self).__init__()\n\n self._net = nn.Sequential(\n ... | import torch
import torch.nn as nn
import torch.nn.functional as F
from .submodules.submodules import UpSampleBN, norm_normalize | 928 |
# This is the baseline encoder-decoder we used in the ablation study
class NNET(nn.Module):
def __init__(self, args=None):
super(NNET, self).__init__()
self.encoder = Encoder()
self.decoder = Decoder(num_classes=4)
def forward(self, x, **kwargs):
out = self.decoder(self.enco... |
# This is the baseline encoder-decoder we used in the ablation study
class NNET(nn.Module):
def __init__(self, args=None):
super(NNET, self).__init__()
self.encoder = Encoder()
self.decoder = Decoder(num_classes=4)
def forward(self, x, **kwargs):
out = self.decoder(self.enco... | self.up1 = UpSampleBN(skip_input=2048 + 176, output_features=1024) | 0 | 2023-12-06 07:29:34+00:00 | 2k |
daswer123/xtts-webui | scripts/resemble_enhance/denoiser/inference.py | [
{
"identifier": "inference",
"path": "scripts/resemble_enhance/inference.py",
"snippet": "def inference(model, dwav, sr, device, chunk_seconds: float = 30.0, overlap_seconds: float = 1.0):\n remove_weight_norm_recursively(model)\n\n hp: HParams = model.hp\n\n dwav = resample(\n dwav,\n ... | import logging
import torch
from functools import cache
from ..inference import inference
from .train import Denoiser, HParams | 664 |
logger = logging.getLogger(__name__)
@cache
def load_denoiser(run_dir, device):
if run_dir is None:
|
logger = logging.getLogger(__name__)
@cache
def load_denoiser(run_dir, device):
if run_dir is None: | return Denoiser(HParams()) | 1 | 2023-12-14 06:34:12+00:00 | 2k |
FrozenBurning/PrimDiffusion | dva/io.py | [
{
"identifier": "AttrDict",
"path": "dva/attr_dict.py",
"snippet": "class AttrDict:\n def __init__(self, entries):\n self.add_entries_(entries)\n\n def keys(self):\n return self.__dict__.keys()\n\n def values(self):\n return self.__dict__.values()\n\n def __getitem__(sel... | import json
import cv2
import numpy as np
import copy
import importlib
import pickle
import os
from typing import Any, Dict
from dva.attr_dict import AttrDict
from dva.geom import compute_v2uv, compute_neighbours | 1,514 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
def load_module(module_name, class_name=None, silent: bool = False):
module = importlib.import_module(module_name)... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
def load_module(module_name, class_name=None, silent: bool = False):
module = importlib.import_module(module_name)... | topology["v2uv"] = compute_v2uv( | 1 | 2023-12-06 05:12:55+00:00 | 2k |
Nearcyan/papers.day | backend/admin.py | [
{
"identifier": "ArxivPaper",
"path": "backend/models.py",
"snippet": "class ArxivPaper(models.Model):\n created_at = models.DateTimeField(auto_now_add=True)\n modified_at = models.DateTimeField(auto_now=True)\n arxiv_id = models.CharField(max_length=20, unique=True)\n\n # fields scraped fro... | from django.contrib import admin
from .models import ArxivPaper, Author, Subject, PaperImage, PaperSource | 1,096 |
class ArxivPaperAdmin(admin.ModelAdmin):
list_display = ('title', 'citations', 'total_author_citations', 'summary', 'publication_date', 'arxiv_id',
'created_at')
search_fields = ('title', 'abstract', 'arxiv_id')
readonly_fields = ('created_at', 'modified_at')
ordering = ('-publica... |
class ArxivPaperAdmin(admin.ModelAdmin):
list_display = ('title', 'citations', 'total_author_citations', 'summary', 'publication_date', 'arxiv_id',
'created_at')
search_fields = ('title', 'abstract', 'arxiv_id')
readonly_fields = ('created_at', 'modified_at')
ordering = ('-publica... | admin.site.register(ArxivPaper, ArxivPaperAdmin) | 0 | 2023-12-14 08:23:05+00:00 | 2k |
LSimon95/megatts2 | models/trainer.py | [
{
"identifier": "MegaVQ",
"path": "models/megatts2.py",
"snippet": "class MegaVQ(nn.Module):\n def __init__(\n self,\n mrte: MRTE,\n vqpe: VQProsodyEncoder,\n decoder: ConvNet,\n ):\n super(MegaVQ, self).__init__()\n\n self.mrte = mrte\n ... | import lightning.pytorch as pl
import torch
import torchaudio
import torch.nn.functional as F
import transformers
import numpy as np
import math
from .megatts2 import MegaVQ
from modules.dscrm import Discriminator
from utils.utils import plot_spectrogram_to_numpy | 1,002 |
class MegaGANTrainer(pl.LightningModule):
def __init__(
self,
|
class MegaGANTrainer(pl.LightningModule):
def __init__(
self, | G: MegaVQ, | 0 | 2023-12-10 15:02:54+00:00 | 2k |
wanghao-cst/Omni-VideoAssistant | llava/serve/controller.py | [
{
"identifier": "CONTROLLER_HEART_BEAT_EXPIRATION",
"path": "llava/constants.py",
"snippet": "CONTROLLER_HEART_BEAT_EXPIRATION = 30"
},
{
"identifier": "build_logger",
"path": "llava/utils.py",
"snippet": "def build_logger(logger_name, logger_filename):\n def __init__(self, logger, lo... | import argparse
import asyncio
import dataclasses
import json
import logging
import time
import threading
import numpy as np
import requests
import uvicorn
from enum import Enum, auto
from typing import List, Union
from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
from llava.constants... | 1,519 | if not worker_status:
return False
self.worker_info[worker_name] = WorkerInfo(
worker_status["model_names"], worker_status["speed"], worker_status["queue_length"],
check_heart_beat, time.time())
logger.info(f"Register done: {worker_name}, {worker_status}")
... | """
A controller manages distributed workers.
It sends worker addresses to clients.
"""
logger = build_logger("controller", "controller.log")
class DispatchMethod(Enum):
LOTTERY = auto()
SHORTEST_QUEUE = auto()
@classmethod
def from_str(cls, name):
if name == "lottery":
return... | "text": server_error_msg, | 1 | 2023-12-05 08:02:17+00:00 | 2k |
RobertCsordas/moe_attention | layers/transformer/transformer.py | [
{
"identifier": "MultiHeadAttention",
"path": "layers/transformer/multi_head_attention.py",
"snippet": "class MultiHeadAttention(AttentionMergeMixin, AbsPosAttentionBase):\n def __init__(self, state_size: int, n_heads: int, dropout: float = 0.1, input_size: Optional[int] = None,\n out... | import torch
import torch.nn
import torch.nn.functional as F
from .multi_head_attention import MultiHeadAttention, AttentionMask
from typing import Optional, Callable, Dict, Type, Sequence, Union
from dataclasses import dataclass | 686 | # This file is based on PyTorch's internal implementation
ActivationFunction = Callable[[torch.Tensor], torch.Tensor]
class TransformerEncoderLayer(torch.nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation: ActivationFunction = F.relu,
attention_dropout=0... | # This file is based on PyTorch's internal implementation
ActivationFunction = Callable[[torch.Tensor], torch.Tensor]
class TransformerEncoderLayer(torch.nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation: ActivationFunction = F.relu,
attention_dropout=0... | def forward(self, src: torch.Tensor, mask: Optional[AttentionMask] = None) -> torch.Tensor: | 1 | 2023-12-13 08:45:02+00:00 | 2k |
riccardomusmeci/mlx-llm | src/mlx_llm/model/_registry.py | [
{
"identifier": "phi2",
"path": "src/mlx_llm/model/phi2.py",
"snippet": "def phi2() -> Phi2:\n return Phi2(\n dim=2560,\n vocab_size=51200,\n n_heads=32,\n n_layers=32,\n rotary_dim=32\n )"
},
{
"identifier": "llama_2_7B_chat",
"path": "src/mlx_llm/mo... | from .phi2 import phi2
from .transformer import (
llama_2_7B_chat,
tiny_llama_chat_v06,
openhermes_25_mistral_7B,
# mistral_7B_instruct_v01,
mistral_7B_instruct_v02,
e5_mistral_7b_instruct
) | 756 |
MODEL_ENTRYPOINTS = {
"Phi2": phi2,
"LLaMA-2-7B-chat": llama_2_7B_chat,
"TinyLlama-1.1B-Chat-v0.6": tiny_llama_chat_v06,
# "Mistral-7B-Instruct-v0.1": mistral_7B_instruct_v01,
|
MODEL_ENTRYPOINTS = {
"Phi2": phi2,
"LLaMA-2-7B-chat": llama_2_7B_chat,
"TinyLlama-1.1B-Chat-v0.6": tiny_llama_chat_v06,
# "Mistral-7B-Instruct-v0.1": mistral_7B_instruct_v01, | "Mistral-7B-Instruct-v0.2": mistral_7B_instruct_v02, | 4 | 2023-12-07 16:19:47+00:00 | 2k |
xetdata/xetcache | xetcache/xetmemo_kernel_extension.py | [
{
"identifier": "hash_anything",
"path": "xetcache/util.py",
"snippet": "def hash_anything(x):\n return hashlib.sha256(pickle.dumps(x)).hexdigest()"
},
{
"identifier": "probe_memo",
"path": "xetcache/util.py",
"snippet": "def probe_memo(memopath, inputhashstr, key=None):\n \"\"\"\n... | import os
import time
from .util import hash_anything, probe_memo, store_memo
from .config import get_memo_path, get_runtime_threshold
from IPython.core.magic import Magics, magics_class, cell_magic | 1,389 |
@magics_class
class XMemoMagics(Magics):
"""Memoization for data science tasks
%load_ext xetcache
to load the extension
"""
def __init__(self, *args, **kwargs):
print(self.xetmemo.__doc__)
memopath = get_memo_path()
print(f"Memoizing to {memopath}")
super()._... |
@magics_class
class XMemoMagics(Magics):
"""Memoization for data science tasks
%load_ext xetcache
to load the extension
"""
def __init__(self, *args, **kwargs):
print(self.xetmemo.__doc__)
memopath = get_memo_path()
print(f"Memoizing to {memopath}")
super()._... | inputhashes = [hash_anything(line), hash_anything(cell)] | 0 | 2023-12-05 21:59:08+00:00 | 2k |
open-compass/T-Eval | teval/evaluators/planning_evaluator.py | [
{
"identifier": "format_load",
"path": "teval/utils/format_load.py",
"snippet": "def format_load(raw_data: str, start_character: str = '', end_character: str = ''):\n \"\"\"Format the raw data into the format that can be evaluated.\n\n Args:\n raw_data (str): The raw data.\n start_ch... | from collections import defaultdict
from numpy import mean
from mmengine import load
from teval.utils.format_load import format_load
from tqdm import tqdm
from teval.schema import ResponseDataSample
from sentence_transformers import SentenceTransformer, util
import json
import itertools
import networkx as nx
import num... | 1,293 | # import evaluate
class PlanningEvaluator:
"""Planning Evaluation
Args:
dataset_path(str): File path of evaluation dataset
name_weight(float): the weight of action_name in bert_score match, default = 0.9
args_weight(float): the weight of action_args in bert_score match, default = 0.1
... | # import evaluate
class PlanningEvaluator:
"""Planning Evaluation
Args:
dataset_path(str): File path of evaluation dataset
name_weight(float): the weight of action_name in bert_score match, default = 0.9
args_weight(float): the weight of action_args in bert_score match, default = 0.1
... | ) -> ResponseDataSample: | 1 | 2023-12-10 05:18:46+00:00 | 2k |
rabilrbl/gemini-pro-bot | gemini_pro_bot/handlers.py | [
{
"identifier": "model",
"path": "gemini_pro_bot/llm.py",
"snippet": "SAFETY_SETTINGS = {\n HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,\n HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,\n HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBl... | import asyncio
import PIL.Image as load_image
from gemini_pro_bot.llm import model, img_model
from google.generativeai.types.generation_types import (
StopCandidateException,
BlockedPromptException,
)
from telegram import Update
from telegram.ext import (
ContextTypes,
)
from telegram.error import NetworkEr... | 1,017 |
def new_chat(context: ContextTypes.DEFAULT_TYPE) -> None:
context.chat_data["chat"] = model.start_chat()
async def start(update: Update, _: ContextTypes.DEFAULT_TYPE) -> None:
"""Send a message when the command /start is issued."""
user = update.effective_user
await update.message.reply_html(
... |
def new_chat(context: ContextTypes.DEFAULT_TYPE) -> None:
context.chat_data["chat"] = model.start_chat()
async def start(update: Update, _: ContextTypes.DEFAULT_TYPE) -> None:
"""Send a message when the command /start is issued."""
user = update.effective_user
await update.message.reply_html(
... | message = format_message(full_plain_message) | 1 | 2023-12-14 16:57:14+00:00 | 2k |
nox-410/tvm.tl | python/tvm/target/x86.py | [
{
"identifier": "register_func",
"path": "python/tvm/_ffi/registry.py",
"snippet": "def register_func(func_name, f=None, override=False):\n \"\"\"Register global function\n\n Parameters\n ----------\n func_name : str or function\n The function name\n\n f : function, optional\n ... | from .._ffi import register_func
from .codegen import target_has_features | 940 | # Licensed to the Apache Software Foundation (ASF) 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 u... | # Licensed to the Apache Software Foundation (ASF) 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 u... | if target_has_features(["avx512bw", "avx512f"]): | 1 | 2023-12-14 02:37:47+00:00 | 2k |
kakaobrain/honeybee | tasks/mme/mme_dataset.py | [
{
"identifier": "TaskDataset",
"path": "tasks/base_dataset.py",
"snippet": "class TaskDataset(Dataset):\n def build_prompt(self, question, image_prompt=\"Human: <image>\"):\n prompt = f\"\"\"{SYSTEM}\n{image_prompt}\nHuman: {question}\nAI: \"\"\"\n return prompt\n\n def collate_fn(se... | from pathlib import Path
from PIL import Image
from tasks.base_dataset import TaskDataset, Example
import utils | 763 |
EVAL_TYPE_DICT = {
"Perception": ["existence", "count", "position", "color", "posters", "celebrity", "scene", "landmark", "artwork", "OCR"],
"Cognition": ["commonsense_reasoning", "numerical_calculation", "text_translation", "code_reasoning"]
}
def load_subset(dir_path):
root = Path(dir_path)
dset_n... |
EVAL_TYPE_DICT = {
"Perception": ["existence", "count", "position", "color", "posters", "celebrity", "scene", "landmark", "artwork", "OCR"],
"Cognition": ["commonsense_reasoning", "numerical_calculation", "text_translation", "code_reasoning"]
}
def load_subset(dir_path):
root = Path(dir_path)
dset_n... | ex = Example(index, image, prompt, data) | 1 | 2023-12-06 14:48:41+00:00 | 2k |
NVlabs/RADIO | radio/hf_model.py | [
{
"identifier": "eradio",
"path": "radio/eradio_model.py",
"snippet": "@register_model\ndef eradio(pretrained=False, **kwargs):\n return fastervit2_large_fullres_ws16(pretrained=pretrained, **kwargs)"
},
{
"identifier": "create_model_from_args",
"path": "radio/radio_model.py",
"snippe... | from collections import namedtuple
from typing import Optional
from timm.models import VisionTransformer
from transformers import PretrainedConfig, PreTrainedModel
from .eradio_model import eradio
from .radio_model import create_model_from_args
from .radio_model import RADIOModel as RADIOModelBase
from .input_condition... | 1,421 | # Copyright (c) 2023, NVIDIA CORPORATION. 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 appli... | # Copyright (c) 2023, NVIDIA CORPORATION. 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 appli... | model = create_model_from_args(args) | 1 | 2023-12-08 19:53:01+00:00 | 2k |
taikinman/langrila | src/langrila/database/chroma.py | [
{
"identifier": "BaseModule",
"path": "src/langrila/base.py",
"snippet": "class BaseModule(ABC):\n @abstractmethod\n def run(self, *args, **kwargs):\n raise NotImplementedError\n\n async def arun(self, *args, **kwargs):\n raise NotImplementedError\n\n def stream(self, *args, **... | import sys
import chromadb
from pathlib import Path
from typing import Optional
from ..base import BaseModule
from ..result import RetrievalResult
from ..usage import Usage | 1,570 |
python_version = sys.version_info
# NOTE : Python version < 3.10 is bundled by lower version sqlite client, so in that case sqlite modules is override
# https://docs.trychroma.com/troubleshooting#sqlite
__import__("pysqlite3")
sys.modules["sqlite3"] = sys.modules.pop("pysqlite3")
class ChromaCollectionModule(Base... |
python_version = sys.version_info
# NOTE : Python version < 3.10 is bundled by lower version sqlite client, so in that case sqlite modules is override
# https://docs.trychroma.com/troubleshooting#sqlite
__import__("pysqlite3")
sys.modules["sqlite3"] = sys.modules.pop("pysqlite3")
class ChromaCollectionModule(Base... | usage=Usage( | 2 | 2023-12-10 09:42:35+00:00 | 2k |
Open-All-Scale-Causal-Engine/OpenASCE | openasce/inference/learner/dml_test.py | [
{
"identifier": "DML",
"path": "openasce/inference/learner/dml.py",
"snippet": "class DML(_DML, InferenceModel):\n def fit(\n self,\n *,\n X: Iterable[np.ndarray],\n Y: Iterable[np.ndarray],\n T: Iterable[np.ndarray],\n **kwargs\n ):\n \"\"\"Feed th... | from unittest import TestCase
from econml.sklearn_extensions.linear_model import WeightedLassoCVWrapper
from sklearn.linear_model import LassoCV
from openasce.inference.learner.dml import DML
from tests.datasets.ihdp_data import get_ihdp_data
from openasce.utils.logger import logger
import numpy as np | 1,324 | # Copyright 2023 AntGroup CO., Ltd.
#
# 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 ... | # Copyright 2023 AntGroup CO., Ltd.
#
# 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 ... | logger.info(f"dml result: {avg}") | 2 | 2023-12-06 05:54:36+00:00 | 2k |
latorc/Wechat-AI-Assistant | chatbot.py | [
{
"identifier": "WcfWrapper",
"path": "wcf_wrapper.py",
"snippet": "class WcfWrapper:\r\n def __init__(self) -> None:\r\n def __del__(self):\r\n def msg_preview_str(self, msg:WxMsg) -> str:\r\n def wxid_to_nickname(self, wxid) -> str:\r\n def wxid_to_wxcode(self, wxid) -> str:\r\n def ... | import queue
import re
import config
import common
import openai_wrapper
import preset
from typing import Tuple
from wcf_wrapper import WcfWrapper, ContentType
from wcferry import WxMsg
from config import AdminCmd
from common import ContentType, ChatMsg
| 1,525 |
class Chatbot():
""" 管理微信机器人逻辑. 管理与微信客户端 (如Wechat Ferry) 和 AI 客户端 (如 OpenAI )的交互逻辑 """
def __init__(self, config: config.Config, wcfw: WcfWrapper, oaiw: openai_wrapper.OpenAIWrapper) -> None:
""" 初始化
args:
config (Config): Config对象
wcfw (WcfWrapper): Wechat Fer... |
class Chatbot():
""" 管理微信机器人逻辑. 管理与微信客户端 (如Wechat Ferry) 和 AI 客户端 (如 OpenAI )的交互逻辑 """
def __init__(self, config: config.Config, wcfw: WcfWrapper, oaiw: openai_wrapper.OpenAIWrapper) -> None:
""" 初始化
args:
config (Config): Config对象
wcfw (WcfWrapper): Wechat Fer... | def callback_msg(msg:ChatMsg) -> int:
| 3 | 2023-12-07 12:17:15+00:00 | 2k |
tensorsense/faceflow | params/datamodule.py | [
{
"identifier": "LocalNaturalDatasetCfg",
"path": "lib/data/cfg/local.py",
"snippet": "class LocalNaturalDatasetCfg:\n name: str\n root: str\n labels_filename: str = \"au.csv\"\n crops_dir: str = \"crops\"\n aus: List[str] = field(\n default_factory=lambda: [\n \"AU1\",\... | import albumentations as A
import wandb
from albumentations.pytorch import ToTensorV2
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from lib.data.cfg.local import LocalNaturalDatasetCfg
from lib.data.datamodules.vanilla import AUDataModule | 1,019 |
project = "disfa"
aus = [
"AU1",
"AU2",
"AU4",
"AU5",
"AU6",
"AU9",
"AU12",
"AU15",
"AU17",
"AU20",
"AU26",
]
TRAIN_LABELED = [
|
project = "disfa"
aus = [
"AU1",
"AU2",
"AU4",
"AU5",
"AU6",
"AU9",
"AU12",
"AU15",
"AU17",
"AU20",
"AU26",
]
TRAIN_LABELED = [ | LocalNaturalDatasetCfg( | 0 | 2023-12-05 13:15:58+00:00 | 2k |
Psivant/femto | femto/fe/atm/_setup.py | [
{
"identifier": "OpenMMForceGroup",
"path": "femto/md/constants.py",
"snippet": "class OpenMMForceGroup(enum.IntEnum):\n \"\"\"Standard force groups to assign to common OpenMM forces to make them easier to\n identify.\"\"\"\n\n BOND = 0\n ANGLE = 1\n DIHEDRAL = 2\n\n NONBONDED = 3\n\n ... | import logging
import tempfile
import typing
import numpy
import openmm
import openmm.app
import openmm.unit
import parmed
import scipy.spatial.distance
import femto.fe.reference
import femto.md.rest
import femto.md.restraints
import femto.md.solvate
import femto.md.system
import femto.md.utils.openmm
import femto.... | 1,395 |
_LOGGER = logging.getLogger(__name__)
def select_displacement(
receptor: parmed.amber.AmberParm,
ligand_1: parmed.amber.AmberParm,
ligand_2: parmed.amber.AmberParm | None,
distance: openmm.unit.Quantity,
) -> openmm.unit.Quantity:
"""Attempts to automatically select a displacement vector for the ... | """Set up the system for ATM calculations."""
if typing.TYPE_CHECKING:
_LOGGER = logging.getLogger(__name__)
def select_displacement(
receptor: parmed.amber.AmberParm,
ligand_1: parmed.amber.AmberParm,
ligand_2: parmed.amber.AmberParm | None,
distance: openmm.unit.Quantity,
) -> openmm.unit.Quanti... | com_restraint.setName(OpenMMForceName.COM_RESTRAINT) | 1 | 2023-12-07 15:28:18+00:00 | 2k |
AIFSH/NativeDancer | nativedancer/third_part/detectron2/evaluation/cityscapes_evaluation.py | [
{
"identifier": "MetadataCatalog",
"path": "nativedancer/third_part/detectron2/data/catalog.py",
"snippet": "class _DatasetCatalog(UserDict):\nclass Metadata(types.SimpleNamespace):\nclass _MetadataCatalog(UserDict):\n def register(self, name, func):\n def get(self, name):\n def list(self) -> L... | import glob
import logging
import numpy as np
import os
import tempfile
import torch
import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval
import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval
from collections import OrderedDict
from PIL... | 1,295 | # Copyright (c) Facebook, Inc. and its affiliates.
class CityscapesEvaluator(DatasetEvaluator):
"""
Base class for evaluation using cityscapes API.
"""
def __init__(self, dataset_name):
"""
Args:
dataset_name (str): the name of the dataset.
It must have t... | # Copyright (c) Facebook, Inc. and its affiliates.
class CityscapesEvaluator(DatasetEvaluator):
"""
Base class for evaluation using cityscapes API.
"""
def __init__(self, dataset_name):
"""
Args:
dataset_name (str): the name of the dataset.
It must have t... | comm.get_local_size() == comm.get_world_size() | 1 | 2023-12-10 20:14:00+00:00 | 2k |
ethanweber/nerfiller | nerfiller/inpaint/saicinpainting/training/modules/base.py | [
{
"identifier": "DepthWiseSeperableConv",
"path": "nerfiller/inpaint/saicinpainting/training/modules/depthwise_sep_conv.py",
"snippet": "class DepthWiseSeperableConv(nn.Module):\n def __init__(self, in_dim, out_dim, *args, **kwargs):\n super().__init__()\n if \"groups\" in kwargs:\n ... | import abc
import torch
import torch.nn as nn
from typing import Tuple, List
from nerfiller.inpaint.saicinpainting.training.modules.depthwise_sep_conv import (
DepthWiseSeperableConv,
)
from nerfiller.inpaint.saicinpainting.training.modules.multidilated_conv import (
MultidilatedConv,
) | 1,459 |
class BaseDiscriminator(nn.Module):
@abc.abstractmethod
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
"""
Predict scores and get intermediate activations. Useful for feature matching loss
:return tuple (scores, list of intermediate activations)
... |
class BaseDiscriminator(nn.Module):
@abc.abstractmethod
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
"""
Predict scores and get intermediate activations. Useful for feature matching loss
:return tuple (scores, list of intermediate activations)
... | return MultidilatedConv | 1 | 2023-12-07 19:12:08+00:00 | 2k |
nnanhuang/Customize-it-3D | ldm/models/diffusion/plms.py | [
{
"identifier": "make_ddim_sampling_parameters",
"path": "ldm/modules/diffusionmodules/util.py",
"snippet": "def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):\n # select alphas for computing the variance schedule\n alphas = alphacums[ddim_timesteps]\n alphas_prev ... | import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
from ldm.models.diffusion.sampling_util import norm_thresholding | 868 | """SAMPLING ONLY."""
class PLMSSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
def register_buffer(self, name, attr):
if type(attr) =... | """SAMPLING ONLY."""
class PLMSSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
def register_buffer(self, name, attr):
if type(attr) =... | self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, | 1 | 2023-12-14 11:03:35+00:00 | 2k |
TaoHuang13/diffusion_reward | diffusion_reward/models/codec_models/vqgan/vqgan.py | [
{
"identifier": "Codebook",
"path": "diffusion_reward/models/codec_models/vqgan/codebook.py",
"snippet": "class Codebook(nn.Module):\n def __init__(self, args):\n super(Codebook, self).__init__()\n self.num_codebook_vectors = args.num_codebook_vectors\n self.latent_dim = args.lat... | import torch
import torch.nn as nn
from .codebook import Codebook
from .decoder import Decoder
from .encoder import Encoder | 1,162 |
class VQGAN(nn.Module):
def __init__(self, args):
super(VQGAN, self).__init__()
self.encoder = Encoder(args).to(device=args.device)
|
class VQGAN(nn.Module):
def __init__(self, args):
super(VQGAN, self).__init__()
self.encoder = Encoder(args).to(device=args.device) | self.decoder = Decoder(args).to(device=args.device) | 1 | 2023-12-05 02:42:28+00:00 | 2k |
its0x4d/fastapi-jet | fastapi_jet/commands/startproject.py | [
{
"identifier": "app",
"path": "fastapi_jet/cli.py",
"snippet": "def _version_callback(value: bool) -> None:\ndef _register_commands() -> None:\ndef main(\n version: Optional[bool] = typer.Option(\n None,\n \"--version\",\n \"-v\",\n help=\"Show the app... | import os
import typer
from questionary.form import form
from fastapi_jet.cli import app
from fastapi_jet.context import ProjectContext
from fastapi_jet.generator import generate_template
from fastapi_jet.utils import binary_question, name_fixer | 1,211 |
@app.command(name="startproject")
def startproject(
name: str = typer.Argument(
..., help="Name of the project",
callback=lambda name: name_fixer(name),
metavar="PROJECT_NAME"
),
interactive: bool = typer.Option(False, "--interactive", "-i", help="Interact... |
@app.command(name="startproject")
def startproject(
name: str = typer.Argument(
..., help="Name of the project",
callback=lambda name: name_fixer(name),
metavar="PROJECT_NAME"
),
interactive: bool = typer.Option(False, "--interactive", "-i", help="Interact... | use_templates=binary_question("Do you want to use templates?", default=True), | 3 | 2023-12-12 00:15:53+00:00 | 2k |
WithSecureLabs/damn-vulnerable-llm-agent | main.py | [
{
"identifier": "get_current_user_tool",
"path": "tools.py",
"snippet": "def get_current_user(input : str):\ndef get_transactions(userId : str):"
},
{
"identifier": "display_instructions",
"path": "utils.py",
"snippet": "def display_instructions():\n # Markdown with some basic CSS sty... | import langchain
import streamlit as st
from dotenv import load_dotenv
from langchain.agents import ConversationalChatAgent, AgentExecutor
from langchain.callbacks import StreamlitCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.memory.cha... | 1,244 |
load_dotenv()
# Initialise tools
tools = [get_current_user_tool, get_recent_transactions_tool]
system_msg = """Assistant helps the current user retrieve the list of their recent bank transactions ans shows them as a table. Assistant will ONLY operate on the userId returned by the GetCurrentUser() tool, and REFUSE t... |
load_dotenv()
# Initialise tools
tools = [get_current_user_tool, get_recent_transactions_tool]
system_msg = """Assistant helps the current user retrieve the list of their recent bank transactions ans shows them as a table. Assistant will ONLY operate on the userId returned by the GetCurrentUser() tool, and REFUSE t... | display_logo() | 2 | 2023-12-07 09:37:47+00:00 | 2k |
MarcoGorelli/polars-upgrade | polars_upgrade/_plugins/map_dict.py | [
{
"identifier": "ast_to_offset",
"path": "polars_upgrade/_ast_helpers.py",
"snippet": "def ast_to_offset(node: ast.expr | ast.stmt) -> Offset:\n return Offset(node.lineno, node.col_offset)"
},
{
"identifier": "register",
"path": "polars_upgrade/_data.py",
"snippet": "def register(tp: ... | import ast
import functools
from typing import Iterable
from tokenize_rt import NON_CODING_TOKENS
from tokenize_rt import Offset
from tokenize_rt import Token
from polars_upgrade._ast_helpers import ast_to_offset
from polars_upgrade._data import register
from polars_upgrade._data import State
from polars_upgrade._data ... | 1,010 | from __future__ import annotations
def rename(
i: int,
tokens: list[Token],
*,
name: str,
new: str,
) -> None:
while not (tokens[i].name == 'NAME' and tokens[i].src == name):
i += 1
tokens[i] = tokens[i]._replace(src=new)
def rename_and_add_default(
i: int,
tokens: lis... | from __future__ import annotations
def rename(
i: int,
tokens: list[Token],
*,
name: str,
new: str,
) -> None:
while not (tokens[i].name == 'NAME' and tokens[i].src == name):
i += 1
tokens[i] = tokens[i]._replace(src=new)
def rename_and_add_default(
i: int,
tokens: lis... | is_simple_expression(node.func.value, state.aliases) and | 5 | 2023-12-09 19:31:35+00:00 | 2k |
I-am-PUID-0/pd_zurg | main.py | [
{
"identifier": "rclone",
"path": "rclone_rd/rclone.py",
"snippet": "def get_port_from_config(config_file_path, key_type):\ndef setup():\n RCLONEMN_RD = f\"{RCLONEMN}_RD\"\n RCLONEMN_AD = f\"{RCLONEMN}_AD\"\n RCLONEMN_RD = RCLONEMN_AD = RCLONEMN"
},
{
"identifier... | from base import *
from rclone_rd import rclone
from cleanup import duplicate_cleanup
from update import auto_update
import plex_debrid_ as p
import zurg as z | 720 |
def main():
logger = get_logger()
version = '2.0.1'
ascii_art = f'''
_______ ______ _______ _______ _______
( ____ )( __ \ / ___ )|\ /|( ____ )( ____ \\
| ( )|| ( \ ) \/ ) || ) ( ||... |
def main():
logger = get_logger()
version = '2.0.1'
ascii_art = f'''
_______ ______ _______ _______ _______
( ____ )( __ \ / ___ )|\ /|( ____ )( ____ \\
| ( )|| ( \ ) \/ ) || ) ( ||... | z_updater.auto_update('Zurg',True) | 2 | 2023-12-05 14:49:38+00:00 | 2k |
JeffersonQin/DungeonAssistant | registration.py | [
{
"identifier": "o3dobj",
"path": "utils/o3dobj.py",
"snippet": "def get_o3d_unit_block_at_origin():\ndef get_o3d_trajectory_object(points, color=(1, 0, 0)):\n def transform_o3d_format(points):"
},
{
"identifier": "io",
"path": "utils/io.py",
"snippet": "def load_point_clouds(\n po... | import json
import argparse
import os
import os.path as osp
import time
import open3d as o3d
import numpy as np
import copy
import matplotlib.pyplot as plt
from utils import o3dobj
from utils import io
from utils import tfm | 1,505 | default=0.05,
help="voxel size for global fast registration downsampling. default is 0.05",
)
parser.add_argument(
"--voxel_size_icp",
type=float,
default=0.05,
help="voxel size for icp downsampling. default is 0.05",
)
parser.add_argument("--skip_icp", action="store_true", help="skip icp and on... |
parser = argparse.ArgumentParser()
parser.add_argument(
"--pointcloud1",
type=str,
default="pointcloud1.ply",
help="first point cloud file path (1 --[transform]-> 2)",
)
parser.add_argument(
"--pointcloud2",
type=str,
default="pointcloud2.ply",
help="second point cloud file path (1 --... | unit_block = o3dobj.get_o3d_unit_block_at_origin() | 0 | 2023-12-08 19:52:08+00:00 | 2k |
KAIST-VICLab/From_Ground_To_Objects | networks/depth_decoder.py | [
{
"identifier": "ConvBlock",
"path": "networks/layers.py",
"snippet": "class ConvBlock(nn.Module):\r\n \"\"\"Layer to perform a convolution followed by ELU\r\n \"\"\"\r\n\r\n def __init__(self, in_channels, out_channels):\r\n super(ConvBlock, self).__init__()\r\n\r\n self.conv = C... | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from .layers import ConvBlock, Conv3x3, upsample, disp_to_depth, coords_to_normals
from timm.models.layers import trunc_normal_
from .cadepth import SPM, DEM | 1,539 | # Copyright Niantic 2021. Patent Pending. All rights reserved.
#
# This software is licensed under the terms of the ManyDepth licence
# which allows for non-commercial use only, the full terms of which are made
# available in the LICENSE file.
class DepthDecoder(nn.Module):
def __init__(self, num_ch_enc, scales... | # Copyright Niantic 2021. Patent Pending. All rights reserved.
#
# This software is licensed under the terms of the ManyDepth licence
# which allows for non-commercial use only, the full terms of which are made
# available in the LICENSE file.
class DepthDecoder(nn.Module):
def __init__(self, num_ch_enc, scales... | self.convs[("dispconv", s)] = Conv3x3(self.num_ch_dec[s], self.num_output_channels) | 1 | 2023-12-12 08:29:30+00:00 | 2k |
marc-rigter/polygrad-world-models | polygrad/agent/a2c.py | [
{
"identifier": "EMA",
"path": "polygrad/utils/training.py",
"snippet": "class EMA():\n '''\n empirical moving average\n '''\n def __init__(self, beta):\n super().__init__()\n self.beta = beta\n\n def update_model_average(self, ma_model, current_model):\n for curr... | import torch
import copy
import torch.nn as nn
import copy
import torch.nn.functional as F
import torch.distributions as D
import importlib
import wandb
from torch import Tensor
from polygrad.utils.training import EMA
from .functions import *
from .common import *
from polygrad.utils.evaluation import get_standardized_... | 1,009 |
class ActorCritic(nn.Module):
def __init__(self,
in_dim,
out_actions,
normalizer,
device="cuda:0",
hidden_dim=256,
min_std=0.01,
fixed_std=False,
decay_std_steps=500000,
... |
class ActorCritic(nn.Module):
def __init__(self,
in_dim,
out_actions,
normalizer,
device="cuda:0",
hidden_dim=256,
min_std=0.01,
fixed_std=False,
decay_std_steps=500000,
... | self.ema = EMA(ema) | 0 | 2023-12-12 21:05:26+00:00 | 2k |
Chat-3D/Chat-3D-v2 | utils/logger.py | [
{
"identifier": "get_rank",
"path": "utils/distributed.py",
"snippet": "def get_rank():\n if not is_dist_avail_and_initialized():\n return 0\n return dist.get_rank()"
},
{
"identifier": "is_main_process",
"path": "utils/distributed.py",
"snippet": "def is_main_process():\n ... | import functools
import logging
import os
import sys
import time
import wandb
import torch
from typing import Any, Dict, Union
from .distributed import get_rank, is_main_process
from termcolor import colored
from torch.utils.tensorboard import SummaryWriter | 831 | # from MMF: https://github.com/facebookresearch/mmf/blob/master/mmf/utils/logger.py
# Copyright (c) Facebook, Inc. and its affiliates.
def log_dict_to_wandb(log_dict, step, prefix=""):
"""include a separator `/` at the end of `prefix`"""
if not is_main_process():
return
log_dict = {f"{prefix}{k... | # from MMF: https://github.com/facebookresearch/mmf/blob/master/mmf/utils/logger.py
# Copyright (c) Facebook, Inc. and its affiliates.
def log_dict_to_wandb(log_dict, step, prefix=""):
"""include a separator `/` at the end of `prefix`"""
if not is_main_process():
return
log_dict = {f"{prefix}{k... | distributed_rank = get_rank() | 0 | 2023-12-11 14:39:58+00:00 | 2k |
SqueezeBits/owlite | owlite/calib/mse_calibrator.py | [
{
"identifier": "log",
"path": "owlite/logger.py",
"snippet": "class Logger(logging.Logger):\n class _WarningFilterContext:\n class WarningFilter(logging.Filter):\n ENV_VAR = \"OWLITE_LOG_LEVEL\"\n DEBUG_WARNING = 15\n ULTRA_VERBOSE = -10\n def ignore_warnings(self):\n ... | import torch
from ..logger import log
from ._histogram_calibrator import _HistogramCalibrator | 1,461 | """MSE(Mean Squared Error) calibrator"""
class MSECalibrator(_HistogramCalibrator):
"""MSE Calibrator Class"""
def update(self):
# update step_size using "mse"
if self.quantizer.histogram is None or self.quantizer.bin_edges is None:
| """MSE(Mean Squared Error) calibrator"""
class MSECalibrator(_HistogramCalibrator):
"""MSE Calibrator Class"""
def update(self):
# update step_size using "mse"
if self.quantizer.histogram is None or self.quantizer.bin_edges is None: | log.error(f"quantizer.histogram : {self.quantizer.histogram}") | 0 | 2023-12-08 06:41:50+00:00 | 2k |
ximinng/PyTorch-SVGRender | pytorch_svgrender/svgtools/process.py | [
{
"identifier": "circle_tag",
"path": "pytorch_svgrender/svgtools/shape.py",
"snippet": "def circle_tag(cx: float, cy: float, r: float, transform: str = None):\n attrib = {\n 'cx': f'{cx}', 'cy': f'{cy}', 'r': f'{r}'\n }\n if transform is not None:\n attrib['transform'] = transfor... | import xml.etree.ElementTree as ET
import omegaconf
from typing import Tuple
from .shape import circle_tag, rect_tag
from .type import is_valid_svg | 768 | # -*- coding: utf-8 -*-
# Author: ximing
# Description: process
# Copyright (c) 2023, XiMing Xing.
# License: MIT License
def delete_empty_path(input_svg: str, output_svg: str):
is_valid_svg(input_svg)
# read svg
tree = ET.parse(input_svg)
root = tree.getroot()
group = ET.Element('g')
for ... | # -*- coding: utf-8 -*-
# Author: ximing
# Description: process
# Copyright (c) 2023, XiMing Xing.
# License: MIT License
def delete_empty_path(input_svg: str, output_svg: str):
is_valid_svg(input_svg)
# read svg
tree = ET.parse(input_svg)
root = tree.getroot()
group = ET.Element('g')
for ... | circle_tag(cx=attrs.cx, cy=attrs.cy, r=attrs.r) | 0 | 2023-12-13 08:18:01+00:00 | 2k |
lyhisme/DeST | libs/models/SP.py | [
{
"identifier": "Graph",
"path": "libs/models/graph/graph.py",
"snippet": "class Graph:\n def __init__(self, labeling_mode='spatial', layout='MCFS-22'):\n\n self.get_edge(layout)\n self.A = self.get_adjacency_matrix(labeling_mode)\n\n def get_edge(self, layout):\n if layout ==... | import torch
import torch.nn as nn
import numpy as np
from .graph.graph import Graph
from .graph.tools import k_adjacency, normalize_adjacency_matrix, get_adjacency_matrix | 1,214 |
class MultiScale_GraphConv(nn.Module):
def __init__(self,
num_scales, # 13
in_channels,
out_channels,
dataset,
disentangled_agg=True,
use_mask=True,
dropout=0,
activation='relu... |
class MultiScale_GraphConv(nn.Module):
def __init__(self,
num_scales, # 13
in_channels,
out_channels,
dataset,
disentangled_agg=True,
use_mask=True,
dropout=0,
activation='relu... | self.graph = Graph(labeling_mode='spatial', layout=dataset) | 0 | 2023-12-12 02:27:15+00:00 | 2k |
soCzech/GenHowTo | genhowto.py | [
{
"identifier": "load_genhowto_model",
"path": "genhowto_utils.py",
"snippet": "def load_genhowto_model(weights_path, device=\"cpu\"):\n with open(os.path.join(weights_path, \"GenHowTo_controlnet_config.json\")) as file:\n gef_controlnet_config = json.load(file)\n\n controlnet = ControlNetM... | import os
import math
import torch
import argparse
import numpy as np
from PIL import Image
from genhowto_utils import load_genhowto_model, DDIMSkipScheduler | 1,103 |
def main(args):
if os.path.exists(args.output_path):
print(f"{args.output_path} already exists.")
return
pipe = load_genhowto_model(args.weights_path, device=args.device)
pipe.scheduler.set_timesteps(args.num_inference_steps)
if args.num_steps_to_skip is not None: # possibly do not... |
def main(args):
if os.path.exists(args.output_path):
print(f"{args.output_path} already exists.")
return
pipe = load_genhowto_model(args.weights_path, device=args.device)
pipe.scheduler.set_timesteps(args.num_inference_steps)
if args.num_steps_to_skip is not None: # possibly do not... | pipe.scheduler = DDIMSkipScheduler.from_config(pipe.scheduler.config) | 1 | 2023-12-11 08:47:51+00:00 | 2k |
bolna-ai/bolna | bolna/helpers/utils.py | [
{
"identifier": "configure_logger",
"path": "bolna/helpers/logger_config.py",
"snippet": "def configure_logger(file_name, enabled=True, logging_level='INFO'):\n if logging_level not in VALID_LOGGING_LEVELS:\n logging_level = \"INFO\"\n\n logging.basicConfig(\n level=logging_level,\n ... | import json
import asyncio
import re
import numpy as np
import copy
import hashlib
import os
import traceback
import ast
from botocore.exceptions import BotoCoreError, ClientError
from aiobotocore.session import AioSession
from contextlib import AsyncExitStack
from dotenv import load_dotenv
from pydantic import BaseMod... | 1,049 |
logger = configure_logger(__name__)
load_dotenv()
BUCKET_NAME = os.getenv('BUCKET_NAME')
def load_file(file_path, is_json=False):
data = None
with open(file_path, "r") as f:
if is_json:
data = json.load(f)
else:
data = f.read()
return data
def write_json_file(f... |
logger = configure_logger(__name__)
load_dotenv()
BUCKET_NAME = os.getenv('BUCKET_NAME')
def load_file(file_path, is_json=False):
data = None
with open(file_path, "r") as f:
if is_json:
data = json.load(f)
else:
data = f.read()
return data
def write_json_file(f... | file_name = f"{PREPROCESS_DIR}/{agent_name}/{audio_format}/{b64_string}.{audio_format}" | 1 | 2023-12-13 09:07:35+00:00 | 2k |
relari-ai/continuous-eval | continuous_eval/metrics/generation_LLM_based_metrics.py | [
{
"identifier": "DefaultLLM",
"path": "continuous_eval/llm_factory.py",
"snippet": " GOOGLE_GENAI_AVAILABLE = True\n GOOGLE_GENAI_AVAILABLE = False\n ANTHROPIC_AVAILABLE = True\n ANTHROPIC_AVAILABLE = False\nclass LLMInterface(ABC):\nclass LLMFactory(LLMInterface):\n def run(self, prompt,... | from continuous_eval.llm_factory import DefaultLLM, LLMInterface
from continuous_eval.metrics.base import LLMBasedMetric
from continuous_eval.metrics.retrieval_LLM_based_metrics import LLMBasedContextCoverage | 1,293 |
class LLMBasedFaithfulness(LLMBasedMetric):
"""
The LLM based faithfulness metric.
Measures whether the generated answer is faithful to the retrieved context.
"""
def __init__(
self,
model: LLMInterface = DefaultLLM,
use_few_shot: bool = True,
classify_by_statement... |
class LLMBasedFaithfulness(LLMBasedMetric):
"""
The LLM based faithfulness metric.
Measures whether the generated answer is faithful to the retrieved context.
"""
def __init__(
self,
model: LLMInterface = DefaultLLM,
use_few_shot: bool = True,
classify_by_statement... | context_coverage = LLMBasedContextCoverage(use_few_shot=self.use_few_shot) | 2 | 2023-12-08 21:30:39+00:00 | 2k |
ryanhe312/STSSNet-AAAI2024 | eval.py | [
{
"identifier": "matlab_metric",
"path": "utils/matlab_metric.py",
"snippet": "def rgb2ycbcr(img, only_y=True):\ndef calc_metrics(img1, img2, crop_border, test_Y=True, norm=False, mask=None):\ndef calc_metrics_y(img1, img2, crop_border, test_Y=True):\ndef calc_psnr(img1, img2, mask=None):\ndef ssim(img1... | import os
import cv2
import lpips
import torch
import numpy as np
import torch.nn.functional as F
import torch.utils.data as data
import matplotlib.pyplot as plt
from tqdm import tqdm
from utils import matlab_metric, metrics
from dataloaders import *
from model import STSSNet | 1,184 |
def ImgWrite(mPath,prefix,idx,img):
cv2.imwrite(os.path.join(mPath,prefix+"."+str(idx).zfill(4)+".png"),img)
@torch.no_grad()
def save_res(dataLoaderIns, model, modelPath, save_dir, save_img=True, mode='all'):
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if modelPath.endswith(".tar"):... |
def ImgWrite(mPath,prefix,idx,img):
cv2.imwrite(os.path.join(mPath,prefix+"."+str(idx).zfill(4)+".png"),img)
@torch.no_grad()
def save_res(dataLoaderIns, model, modelPath, save_dir, save_img=True, mode='all'):
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if modelPath.endswith(".tar"):... | psnr, ssim = matlab_metric.calc_metrics(res[0].permute(1,2,0).detach().cpu().numpy(), label[0].permute(1,2,0).detach().cpu().numpy(), 0, norm=True, mask=mask) | 0 | 2023-12-10 02:02:37+00:00 | 2k |
Seunggu0305/VLCounter | tools/models/ViT_Encoder_add.py | [
{
"identifier": "LayerNorm",
"path": "tools/models/Encoder_utils.py",
"snippet": "class LayerNorm(nn.LayerNorm):\n \"\"\"Subclass torch's LayerNorm to handle fp16.\"\"\"\n\n def forward(self, x: torch.Tensor):\n orig_type = x.dtype\n ret = super().forward(x.type(torch.float32))\n ... | import torch
import torch.nn.functional as F
import math
from torch.nn import Dropout
from torch import nn
from functools import reduce
from operator import mul
from .Encoder_utils import LayerNorm, Transformer, Attention | 1,124 |
class SPTCLIPVisionTransformer(nn.Module):
def __init__(self, input_resolution=384, patch_size=16, width=768, layers=12, heads=12, output_dim=512, drop_path_rate=0.1, out_indices=[5,6,7,8,11], pretrained=None, get_embeddings=True,
num_tokens=10, prompt_dim=768, total_d_layer=11, **kwargs):
... |
class SPTCLIPVisionTransformer(nn.Module):
def __init__(self, input_resolution=384, patch_size=16, width=768, layers=12, heads=12, output_dim=512, drop_path_rate=0.1, out_indices=[5,6,7,8,11], pretrained=None, get_embeddings=True,
num_tokens=10, prompt_dim=768, total_d_layer=11, **kwargs):
... | self.ln_pre = LayerNorm(width) | 0 | 2023-12-13 08:00:28+00:00 | 2k |
qitan/devops-backend-lite | apps/workflow/serializers.py | [
{
"identifier": "Product",
"path": "dbapp/models.py",
"snippet": ""
},
{
"identifier": "RecursiveField",
"path": "common/recursive.py",
"snippet": "class RecursiveField(Field):\n \"\"\"\n A field that gets its representation from its parent.\n\n This method could be used to seri... | from rest_framework import serializers
from dbapp.models import Product, Project
from common.recursive import RecursiveField
from dbapp.models import UserProfile
from dbapp.models import WorkflowCategory, Workflow, WorkflowNodeHistory, WorkflowTemplate, \
WorkflowTemplateRevisionHistory, WorkflowNodeHistoryCallback... | 1,600 | """
@Author : Ken Chen
@Contact : 316084217@qq.com
@Time : 2021/11/2 上午9:50
"""
logger = logging.getLogger(__name__)
class WorkflowTemplateSerializer(ModelSerializer):
projects_info = serializers.SerializerMethodField()
env_info = serializers.SerializerMethodField()
def get_env_info(self, instance):
... | """
@Author : Ken Chen
@Contact : 316084217@qq.com
@Time : 2021/11/2 上午9:50
"""
logger = logging.getLogger(__name__)
class WorkflowTemplateSerializer(ModelSerializer):
projects_info = serializers.SerializerMethodField()
env_info = serializers.SerializerMethodField()
def get_env_info(self, instance):
... | model = WorkflowTemplateRevisionHistory | 3 | 2023-12-13 03:09:32+00:00 | 2k |
timo-reymann/python-oauth2-cli-auth | oauth2_cli_auth/simplified_flow.py | [
{
"identifier": "OAuthCallbackHttpServer",
"path": "oauth2_cli_auth/http_server.py",
"snippet": "class OAuthCallbackHttpServer(HTTPServer):\n \"\"\"\n Simplistic HTTP Server to provide local callback URL for oauth2 provider\n \"\"\"\n\n def __init__(self, port):\n super().__init__((\"... | from oauth2_cli_auth import OAuthCallbackHttpServer, get_auth_url, exchange_code_for_access_token, OAuth2ClientInfo, \
open_browser | 1,227 |
def get_access_token_with_browser_open(client_info: OAuth2ClientInfo, server_port: int = 8080) -> str:
"""
Provides a simplified API to:
- Spin up the callback server
- Open the browser with the authorization URL
- Wait for the code to arrive
- Get access token from code
:param client_inf... |
def get_access_token_with_browser_open(client_info: OAuth2ClientInfo, server_port: int = 8080) -> str:
"""
Provides a simplified API to:
- Spin up the callback server
- Open the browser with the authorization URL
- Wait for the code to arrive
- Get access token from code
:param client_inf... | open_browser(auth_url) | 4 | 2023-12-09 12:14:33+00:00 | 2k |
solanav/phishflood | phishflood/__main__.py | [
{
"identifier": "extract_inputs",
"path": "credfind/utils.py",
"snippet": "def extract_inputs(html: str) -> InputList:\n \"\"\"Given an HTML page, returns a list of inputs or None if nothing was found\"\"\"\n soup = BeautifulSoup(html, \"html.parser\")\n\n print(\"Finding all forms in the page\... | import json
import os
import sys
import time
import requests
from hashlib import sha256
from typing import Any, Dict, List, Optional, Tuple
from credfind.utils import extract_inputs
from credfind.objects import Input, InputList, InputType
from playwright.sync_api import sync_playwright, TimeoutError, Page
from credgen.... | 1,591 |
SCREENSHOT_I = 0
Actions = List[Dict[str, Any]]
def screenshot(page: Page):
global SCREENSHOT_I
SCREENSHOT_I += 1
page.screenshot(path=f"samples/{SCREENSHOT_I}.png")
def hash_inputs(inputs: List[Input]) -> str:
"""Returns a unique string identifying the inputs in the website"""
return sha256("... |
SCREENSHOT_I = 0
Actions = List[Dict[str, Any]]
def screenshot(page: Page):
global SCREENSHOT_I
SCREENSHOT_I += 1
page.screenshot(path=f"samples/{SCREENSHOT_I}.png")
def hash_inputs(inputs: List[Input]) -> str:
"""Returns a unique string identifying the inputs in the website"""
return sha256("... | text = creds_from_input(inp) | 2 | 2023-12-11 16:38:36+00:00 | 2k |
abing7k/redroid-script | stuffs/ndk.py | [
{
"identifier": "General",
"path": "stuffs/general.py",
"snippet": "class General:\n def download(self):\n loc_md5 = \"\"\n if os.path.isfile(self.dl_file_name):\n with open(self.dl_file_name,\"rb\") as f:\n bytes = f.read()\n loc_md5 = hashlib.m... | import os
import shutil
from stuffs.general import General
from tools.helper import bcolors, get_download_dir, print_color, run | 845 |
class Ndk(General):
download_loc = get_download_dir()
copy_dir = "./ndk"
dl_link = "https://github.com/supremegamers/vendor_google_proprietary_ndk_translation-prebuilt/archive/181d9290a69309511185c4417ba3d890b3caaaa8.zip"
dl_file_name = os.path.join(download_loc, "libndktranslation.zip")
extract_to... |
class Ndk(General):
download_loc = get_download_dir()
copy_dir = "./ndk"
dl_link = "https://github.com/supremegamers/vendor_google_proprietary_ndk_translation-prebuilt/archive/181d9290a69309511185c4417ba3d890b3caaaa8.zip"
dl_file_name = os.path.join(download_loc, "libndktranslation.zip")
extract_to... | print_color("Downloading libndk now .....", bcolors.GREEN) | 3 | 2023-12-06 09:03:05+00:00 | 2k |
zvict/papr | dataset/dataset.py | [
{
"identifier": "load_meta_data",
"path": "dataset/utils.py",
"snippet": "def load_meta_data(args, mode=\"train\"):\n \"\"\"\n 0 -----------> W\n |\n |\n |\n ⬇\n H\n [H, W, 4]\n \"\"\"\n image_paths = None\n\n if args.type == \"synthetic\":\n images, poses, hwf, i... | import torch
import numpy as np
import imageio
from torch.utils.data import Dataset
from PIL import Image
from .utils import load_meta_data, get_rays, extract_patches | 1,572 |
class RINDataset(Dataset):
""" Ray Image Normal Dataset """
def __init__(self, args, mode='train'):
self.args = args
|
class RINDataset(Dataset):
""" Ray Image Normal Dataset """
def __init__(self, args, mode='train'):
self.args = args | images, c2w, H, W, focal_x, focal_y, image_paths = load_meta_data( | 0 | 2023-12-08 19:51:42+00:00 | 2k |
rinnakk/nue-asr | nue_asr/cli.py | [
{
"identifier": "transcribe",
"path": "nue_asr/transcribe.py",
"snippet": "@torch.inference_mode()\ndef transcribe(\n model: NueASRModel,\n tokenizer: PreTrainedTokenizer,\n audio: Union[str, np.ndarray, torch.Tensor],\n **decode_options,\n) -> ASRResult:\n device = model.device\n sr =... | import argparse
import os
import torch
from .transcribe import transcribe
from .utils import load_model, load_tokenizer, set_seed, str2bool | 1,542 | #!/usr/bin/env python3
# Copyright 2023 rinna Co., Ltd.
#
# 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 la... | #!/usr/bin/env python3
# Copyright 2023 rinna Co., Ltd.
#
# 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 la... | "--fp16", type=str2bool, default=True, help="Whether to fp16 inference." | 4 | 2023-12-07 01:37:23+00:00 | 2k |
AdaCheng/EgoThink | models/instruct_blip/processors/blip_processors.py | [
{
"identifier": "registry",
"path": "models/instruct_blip/common/registry.py",
"snippet": "class Registry:\n def register_model(cls, name):\n def wrap(model_cls):\n def register_processor(cls, name):\n def wrap(processor_cls):\n def register_lr_scheduler(cls, name):\n def w... | import re
from ..common.registry import registry
from .base_processor import BaseProcessor
from .randaugment import RandomAugment
from omegaconf import OmegaConf
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode | 805 | """
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
class BlipImageBaseProcessor(BaseProcessor):
def __init__(self, mean=None, std=None):
... | """
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
class BlipImageBaseProcessor(BaseProcessor):
def __init__(self, mean=None, std=None):
... | @registry.register_processor("blip_caption") | 0 | 2023-12-05 14:17:17+00:00 | 2k |
TristanBilot/mlx-GCN | main_torch.py | [
{
"identifier": "download_cora",
"path": "datasets.py",
"snippet": "def download_cora():\n \"\"\"Downloads the cora dataset into a local cora folder.\"\"\"\n\n url = \"https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz\"\n extract_to = \".\"\n\n if os.path.exists(os.path.join(extract_to, \"... | from argparse import ArgumentParser
from time import time
from datasets import download_cora, load_data, train_val_test_mask
import torch
import torch.nn as nn | 1,238 |
class GCNLayer(nn.Module):
def __init__(self, x_dim, h_dim, bias=True):
super(GCNLayer, self).__init__()
self.weight = nn.Parameter(torch.FloatTensor(torch.zeros(size=(x_dim, h_dim))))
if bias:
self.bias = nn.Parameter(torch.FloatTensor(torch.zeros(size=(h_dim,))))
el... |
class GCNLayer(nn.Module):
def __init__(self, x_dim, h_dim, bias=True):
super(GCNLayer, self).__init__()
self.weight = nn.Parameter(torch.FloatTensor(torch.zeros(size=(x_dim, h_dim))))
if bias:
self.bias = nn.Parameter(torch.FloatTensor(torch.zeros(size=(h_dim,))))
el... | train_mask, val_mask, test_mask = train_val_test_mask(y, args.nb_classes) | 2 | 2023-12-11 09:40:09+00:00 | 2k |
3dlg-hcvc/cage | models/denoiser.py | [
{
"identifier": "FinalLayer",
"path": "models/utils.py",
"snippet": "class FinalLayer(nn.Module):\n def __init__(self, in_ch, out_ch=None, dropout=0.):\n super().__init__()\n out_ch = in_ch if out_ch is None else out_ch\n self.linear = nn.Linear(in_ch, out_ch)\n self.norm ... | import torch
import models
from torch import nn
from models.utils import FinalLayer, PEmbeder, AAB | 1,352 |
@models.register('denoiser')
class AABModel(nn.Module):
'''
Denoiser based on Attribute Attention Block (AAB)
3 sequential attentions: local -> global -> graph
'''
def __init__(self, hparams):
super(AABModel, self).__init__()
self.hparams = hparams
in_ch = hparams.in_ch
... |
@models.register('denoiser')
class AABModel(nn.Module):
'''
Denoiser based on Attribute Attention Block (AAB)
3 sequential attentions: local -> global -> graph
'''
def __init__(self, hparams):
super(AABModel, self).__init__()
self.hparams = hparams
in_ch = hparams.in_ch
... | self.final_layer = FinalLayer(attn_dim, in_ch, dropout=dropout) | 0 | 2023-12-06 23:08:41+00:00 | 2k |
modelscope/llmuses | llmuses/benchmarks/data_adapter.py | [
{
"identifier": "Benchmark",
"path": "llmuses/benchmarks/benchmark.py",
"snippet": "class Benchmark(object):\n \"\"\"\n Wrapper for loading datasets from ModelScope or HuggingFace.\n \"\"\"\n\n def __init__(self):\n ...\n\n @staticmethod\n def load(dataset_name: str,\n ... | from abc import ABC, abstractmethod
from typing import Any, Optional
from llmuses.benchmarks import Benchmark
from llmuses.constants import DEFAULT_ROOT_CACHE_DIR, AnswerKeys
from llmuses.utils.logger import get_logger
import random | 1,377 | # Copyright (c) Alibaba, Inc. and its affiliates.
logger = get_logger()
class DataAdapter(ABC):
def __init__(self,
subset_list: list,
metric_list: list,
few_shot_num: Optional[int] = 0,
train_split: Optional[str] = None,
eval... | # Copyright (c) Alibaba, Inc. and its affiliates.
logger = get_logger()
class DataAdapter(ABC):
def __init__(self,
subset_list: list,
metric_list: list,
few_shot_num: Optional[int] = 0,
train_split: Optional[str] = None,
eval... | dataset = Benchmark.load(dataset_name=dataset_name_or_path, | 0 | 2023-12-07 06:10:49+00:00 | 2k |
Subsets and Splits
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that have consistent code formatting levels across multiple scales (2k, 4k, 8k, 12k) and reveals the structured formatting patterns within these repositories.
SQL Console for tianyang/repobench_python_v1.1
Compares cross-file and in-file code structure patterns across different complexity levels, revealing how file organization strategies vary with code size and potentially informing better code architecture decisions.
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that have complete performance data across all seven code complexity levels, revealing consistent benchmarking patterns across different code sizes.
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that contain all 7 distinct quality levels (2k through 32k), revealing complete datasets that might be useful for comprehensive analysis.