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 |
|---|---|---|---|---|---|---|---|---|---|---|
YaoFANGUK/video-subtitle-remover | backend/inpaint/video/raft/corr.py | [
{
"identifier": "bilinear_sampler",
"path": "backend/inpaint/video/raft/utils/utils.py",
"snippet": "def bilinear_sampler(img, coords, mode='bilinear', mask=False):\n \"\"\" Wrapper for grid_sample, uses pixel coordinates \"\"\"\n H, W = img.shape[-2:]\n xgrid, ygrid = coords.split([1,1], dim=-... | import torch
import torch.nn.functional as F
import alt_cuda_corr
from .utils.utils import bilinear_sampler, coords_grid | 673 |
try:
except:
# alt_cuda_corr is not compiled
pass
class CorrBlock:
def __init__(self, fmap1, fmap2, num_levels=4, radius=4):
self.num_levels = num_levels
self.radius = radius
self.corr_pyramid = []
# all pairs correlation
corr = CorrBlock.corr(fmap1, fmap2)
... |
try:
except:
# alt_cuda_corr is not compiled
pass
class CorrBlock:
def __init__(self, fmap1, fmap2, num_levels=4, radius=4):
self.num_levels = num_levels
self.radius = radius
self.corr_pyramid = []
# all pairs correlation
corr = CorrBlock.corr(fmap1, fmap2)
... | corr = bilinear_sampler(corr, coords_lvl) | 0 | 2023-10-25 02:50:01+00:00 | 2k |
Genesis-Embodied-AI/RoboGen | objaverse_utils/find_uid_utils.py | [
{
"identifier": "text_to_uid_dict",
"path": "objaverse_utils/utils.py",
"snippet": ""
},
{
"identifier": "check_text_similarity",
"path": "gpt_4/verification.py",
"snippet": "def check_text_similarity(text, check_list=None, check_embeddings=None):\n global sentence_bert_model\n if ... | import pandas as pd
import torch
import numpy as np
import json
from objaverse_utils.utils import text_to_uid_dict
from gpt_4.verification import check_text_similarity
from gpt_4.bard_verify import verify_objaverse_object | 1,182 |
objaverse_csv = pd.read_csv('objaverse_utils/Cap3D_automated_Objaverse.csv')
objaverse_csv = objaverse_csv.dropna()
objaverse_csv_uids = list(objaverse_csv.iloc[:, 0].values)
objaverse_csv_annotations = list(objaverse_csv.iloc[:, 1].values)
objaverse_csv_annotations_embeddings = torch.load("objaverse_utils/data/cap3d_... |
objaverse_csv = pd.read_csv('objaverse_utils/Cap3D_automated_Objaverse.csv')
objaverse_csv = objaverse_csv.dropna()
objaverse_csv_uids = list(objaverse_csv.iloc[:, 0].values)
objaverse_csv_annotations = list(objaverse_csv.iloc[:, 1].values)
objaverse_csv_annotations_embeddings = torch.load("objaverse_utils/data/cap3d_... | uids = text_to_uid_dict.get(obj_descrption, None) | 0 | 2023-10-31 19:44:09+00:00 | 2k |
junhoyeo/BetterOCR | betterocr/detect.py | [
{
"identifier": "extract_json",
"path": "betterocr/parsers.py",
"snippet": "def extract_json(input_string):\n # Find the JSON in the string\n matches = re.findall(r'{\\s*\"data\"\\s*:\\s*\"(.*?)\"\\s*}', input_string, re.DOTALL)\n if matches:\n # Correctly escape special characters\n ... | from threading import Thread
from queue import Queue
from openai import OpenAI
from .parsers import extract_json, extract_list, rectangle_corners
from .wrappers import (
job_easy_ocr,
job_easy_ocr_boxes,
job_tesseract,
job_tesseract_boxes,
)
from .wrappers.easy_pororo_ocr import job_easy... | 1,298 |
def wrapper(func, args, queue):
queue.put(func(args))
# custom error
class NoTextDetectedError(Exception):
pass
def detect():
"""Unimplemented"""
raise NotImplementedError
def detect_async():
"""Unimplemented"""
raise NotImplementedError
def get_jobs(languages: list[str], boxes=False... |
def wrapper(func, args, queue):
queue.put(func(args))
# custom error
class NoTextDetectedError(Exception):
pass
def detect():
"""Unimplemented"""
raise NotImplementedError
def detect_async():
"""Unimplemented"""
raise NotImplementedError
def get_jobs(languages: list[str], boxes=False... | job_easy_ocr if not boxes else job_easy_ocr_boxes, | 3 | 2023-10-26 11:26:25+00:00 | 2k |
KoeAI/LLVC | infer.py | [
{
"identifier": "Net",
"path": "model.py",
"snippet": "class Net(nn.Module):\n def __init__(self, label_len, L=8,\n enc_dim=512, num_enc_layers=10,\n dec_dim=256, dec_buf_len=100, num_dec_layers=2,\n dec_chunk_size=72, out_buf_len=2,\n u... | from model import Net
from utils import glob_audio_files
from tqdm import tqdm
import torch
import torchaudio
import time
import numpy as np
import argparse
import json
import os | 1,507 |
def load_model(checkpoint_path, config_path):
with open(config_path) as f:
config = json.load(f)
|
def load_model(checkpoint_path, config_path):
with open(config_path) as f:
config = json.load(f) | model = Net(**config['model_params']) | 0 | 2023-10-28 01:58:49+00:00 | 2k |
aurelio-labs/semantic-router | semantic_router/llms/llamacpp.py | [
{
"identifier": "BaseLLM",
"path": "semantic_router/llms/base.py",
"snippet": "class BaseLLM(BaseModel):\n name: str\n\n class Config:\n arbitrary_types_allowed = True\n\n def __init__(self, name: str, **kwargs):\n super().__init__(name=name, **kwargs)\n\n def __call__(self, me... | from contextlib import contextmanager
from pathlib import Path
from typing import Any, Optional
from llama_cpp import Llama, LlamaGrammar
from semantic_router.llms.base import BaseLLM
from semantic_router.schema import Message
from semantic_router.utils.logger import logger | 1,212 |
class LlamaCppLLM(BaseLLM):
llm: Llama
temperature: float
max_tokens: Optional[int] = 200
grammar: Optional[LlamaGrammar] = None
def __init__(
self,
llm: Llama,
name: str = "llama.cpp",
temperature: float = 0.2,
max_tokens: Optional[int] = 200,
gr... |
class LlamaCppLLM(BaseLLM):
llm: Llama
temperature: float
max_tokens: Optional[int] = 200
grammar: Optional[LlamaGrammar] = None
def __init__(
self,
llm: Llama,
name: str = "llama.cpp",
temperature: float = 0.2,
max_tokens: Optional[int] = 200,
gr... | logger.error(f"LLM error: {e}") | 2 | 2023-10-30 12:12:45+00:00 | 2k |
baaivision/JudgeLM | judgelm/llm_judge/gen_model_judgement_mmvet.py | [
{
"identifier": "load_questions",
"path": "judgelm/llm_judge/common.py",
"snippet": "def parse_score(review):\ndef translate_score_to_win_list(score_list, T=0.0):\ndef generate_question_template(domain, question1, question2):\ndef reorg_answer_file(answer_file):\n def __init__(self, keywords, tokeniz... | import argparse
import json
import os
import time
import shortuuid
import torch
import sys
import random
import ray
from tqdm import tqdm
from pathlib import Path # if you haven't already done so
from judgelm.llm_judge.common import load_questions, reorg_answer_file, conv_judge_vqa_single_answer, Ke... | 937 | """Generate answers with local models.
"""
file = Path(__file__).resolve()
root = file.parents[2]
sys.path.append(str(root))
print(sys.path)
def run_eval(
model_path,
model_id,
question_file,
question_begin,
question_end,
answer_file,
max_new_token,
num_gpus_per_model,
num_gpus... | """Generate answers with local models.
"""
file = Path(__file__).resolve()
root = file.parents[2]
sys.path.append(str(root))
print(sys.path)
def run_eval(
model_path,
model_id,
question_file,
question_begin,
question_end,
answer_file,
max_new_token,
num_gpus_per_model,
num_gpus... | model, tokenizer = load_model( | 1 | 2023-10-26 19:41:07+00:00 | 2k |
EulerSearch/embedding_studio | embedding_studio/api/api_v1/endpoints/fine_tuning.py | [
{
"identifier": "FineTuningTaskCreate",
"path": "embedding_studio/api/api_v1/schemas/fine_tuning.py",
"snippet": "class FineTuningTaskCreate(BaseModel):\n fine_tuning_method: str\n batch_id: Optional[str] = None\n metadata: Optional[Dict] = None\n idempotency_key: Optional[uuid.UUID] = None"... | import logging
from typing import Any, List
from dramatiq_abort import abort as dramatiq_abort
from fastapi import APIRouter, HTTPException, status
from embedding_studio.api.api_v1.schemas.fine_tuning import (
FineTuningTaskCreate,
FineTuningTaskResponse,
)
from embedding_studio.context.app_context import conte... | 1,238 |
logger = logging.getLogger(__name__)
router = APIRouter()
@router.post(
"/task",
response_model=FineTuningTaskResponse,
response_model_by_alias=False,
response_model_exclude_none=True,
)
def create_fine_tuning_task(
|
logger = logging.getLogger(__name__)
router = APIRouter()
@router.post(
"/task",
response_model=FineTuningTaskResponse,
response_model_by_alias=False,
response_model_exclude_none=True,
)
def create_fine_tuning_task( | body: FineTuningTaskCreate, | 0 | 2023-10-31 00:33:13+00:00 | 2k |
reworkd/bananalyzer | tests/test_examples.py | [
{
"identifier": "download_examples",
"path": "bananalyzer/data/examples.py",
"snippet": "def are_examples_available(path: Path) -> bool:\ndef get_examples_path() -> Path:\ndef convert_to_crlf(file_path: Path) -> None:\ndef download_examples() -> None:\ndef load_examples_at_path(path: Path, examples_json... | import json
import os
import shutil
import pytest
from pathlib import Path
from typing import List
from unittest.mock import mock_open
from pytest_mock import MockFixture
from bananalyzer.data.examples import (
download_examples,
downloaded_examples_path,
get_all_examples,
get_example_by_url,
get_ex... | 932 |
def test_load_examples_at_path_success(mocker: MockFixture) -> None:
data: List[Example] = []
mocker.patch("builtins.open", mock_open(read_data=json.dumps(data)))
loaded_examples = load_examples_at_path(Path("/fake/path"), "fake.json")
assert len(loaded_examples) == len(data)
assert all(isinstan... |
def test_load_examples_at_path_success(mocker: MockFixture) -> None:
data: List[Example] = []
mocker.patch("builtins.open", mock_open(read_data=json.dumps(data)))
loaded_examples = load_examples_at_path(Path("/fake/path"), "fake.json")
assert len(loaded_examples) == len(data)
assert all(isinstan... | assert get_examples_path() == local_examples_path | 0 | 2023-10-30 16:40:57+00:00 | 2k |
OpenMask3D/openmask3d | openmask3d/mask_features_computation/features_extractor.py | [
{
"identifier": "Camera",
"path": "openmask3d/data/load.py",
"snippet": "class Camera:\n def __init__(self, \n intrinsic_path, \n intrinsic_resolution, \n poses_path, \n depths_path, \n extension_depth, \n ... | import clip
import numpy as np
import imageio
import torch
import os
from tqdm import tqdm
from openmask3d.data.load import Camera, InstanceMasks3D, Images, PointCloud, get_number_of_images
from openmask3d.mask_features_computation.utils import initialize_sam_model, mask2box_multi_level, run_sam | 1,519 |
class PointProjector:
def __init__(self, camera: Camera,
point_cloud: PointCloud,
|
class PointProjector:
def __init__(self, camera: Camera,
point_cloud: PointCloud, | masks: InstanceMasks3D, | 1 | 2023-10-31 14:58:50+00:00 | 2k |
nv-tlabs/vid2player3d | embodied_pose/models/im_network_builder.py | [
{
"identifier": "RunningNorm",
"path": "embodied_pose/models/running_norm.py",
"snippet": "class RunningNorm(nn.Module):\n \"\"\"\n y = (x-mean)/std\n using running estimates of mean,std\n \"\"\"\n\n def __init__(self, dim, demean=True, destd=True, clip=5.0):\n super().__init__()\n... | from rl_games.algos_torch import network_builder
from rl_games.algos_torch.running_mean_std import RunningMeanStd
from isaacgym.torch_utils import *
from .running_norm import RunningNorm
from utils import torch_utils
from utils.torch_transform import heading_to_vec, rotation_matrix_to_angle_axis, rotation_matrix_to_qua... | 1,231 |
DISC_LOGIT_INIT_SCALE = 1.0
mujoco_joint_names = [
'Pelvis', 'L_Hip', 'L_Knee', 'L_Ankle', 'L_Toe', 'R_Hip', 'R_Knee',
'R_Ankle', 'R_Toe', 'Torso', 'Spine', 'Chest', 'Neck', 'Head', 'L_Thorax',
'L_Shoulder', 'L_Elbow', 'L_Wrist', 'L_Hand', 'R_Thorax', 'R_Shoulder',
'R_Elbow', 'R_Wrist', 'R_Hand'
]
s... |
DISC_LOGIT_INIT_SCALE = 1.0
mujoco_joint_names = [
'Pelvis', 'L_Hip', 'L_Knee', 'L_Ankle', 'L_Toe', 'R_Hip', 'R_Knee',
'R_Ankle', 'R_Toe', 'Torso', 'Spine', 'Chest', 'Neck', 'Head', 'L_Thorax',
'L_Shoulder', 'L_Elbow', 'L_Wrist', 'L_Hand', 'R_Thorax', 'R_Shoulder',
'R_Elbow', 'R_Wrist', 'R_Hand'
]
s... | self.running_obs = RunningNorm(self.humanoid_obs_dim) | 0 | 2023-10-30 20:43:43+00:00 | 2k |
vLAR-group/RayDF | net_multiview/network.py | [
{
"identifier": "DualVisClassifier",
"path": "net_classifier/network.py",
"snippet": "class DualVisClassifier(nn.Module):\n def __init__(self, D=8, W=512, ext_layer=1, input_ch=11, w0_init=30.):\n super(DualVisClassifier, self).__init__()\n\n self.layer_ray = nn.ModuleList(\n ... | import os
import torch
import torch.nn as nn
import sys
from net_classifier.network import DualVisClassifier
from utils.layer import Siren
from utils.ray import get_rayparam_func | 1,173 | sys.path.append('../')
EPS = 1e-8
class RaySurfDNet(nn.Module):
def __init__(self, D=8, W=256, input_ch=4, rgb_layer=0, w0_init=30.):
super(RaySurfDNet, self).__init__()
self.predict_rgb = True if rgb_layer > 0 else False
n_ext = max(rgb_layer, 1)
self.lf_encoder = nn.ModuleList... | sys.path.append('../')
EPS = 1e-8
class RaySurfDNet(nn.Module):
def __init__(self, D=8, W=256, input_ch=4, rgb_layer=0, w0_init=30.):
super(RaySurfDNet, self).__init__()
self.predict_rgb = True if rgb_layer > 0 else False
n_ext = max(rgb_layer, 1)
self.lf_encoder = nn.ModuleList... | ray_fn, input_ch = get_rayparam_func(scene_info) | 2 | 2023-10-30 14:05:51+00:00 | 2k |
francescofugazzi/3dgsconverter | gsconverter/utils/utility.py | [
{
"identifier": "debug_print",
"path": "gsconverter/utils/utility_functions.py",
"snippet": "def debug_print(message):\n if config.DEBUG:\n print(message)"
},
{
"identifier": "init_worker",
"path": "gsconverter/utils/utility_functions.py",
"snippet": "def init_worker():\n si... | import numpy as np
import multiprocessing
from multiprocessing import Pool, cpu_count
from .utility_functions import debug_print, init_worker | 913 | """
3D Gaussian Splatting Converter
Copyright (c) 2023 Francesco Fugazzi
This software is released under the MIT License.
For more information about the license, please see the LICENSE file.
"""
class Utility:
@staticmethod
def text_based_detect_format(file_path):
debug_print("[DEBUG] Executing 'text... | """
3D Gaussian Splatting Converter
Copyright (c) 2023 Francesco Fugazzi
This software is released under the MIT License.
For more information about the license, please see the LICENSE file.
"""
class Utility:
@staticmethod
def text_based_detect_format(file_path):
debug_print("[DEBUG] Executing 'text... | with Pool(processes=num_cores, initializer=init_worker) as pool: | 1 | 2023-10-28 15:09:50+00:00 | 2k |
solangii/MICS | models/network/resnet20.py | [
{
"identifier": "to_one_hot",
"path": "utils/mixup_utils.py",
"snippet": "def to_one_hot(inp, num_classes):\n y_onehot = torch.FloatTensor(inp.size(0), num_classes)\n y_onehot.zero_()\n\n y_onehot.scatter_(1, inp.unsqueeze(1).data.cpu(), 1)\n\n return Variable(y_onehot.cuda(), requires_grad=... | import torch
import torch.nn as nn
import numpy as np
import random
from utils.mixup_utils import to_one_hot, middle_mixup_process, get_lambda
from torch.autograd import Variable | 1,436 |
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=Non... |
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=Non... | out, target_reweighted, mix_label_mask = middle_mixup_process(out, target_reweighted, num_base_classes, | 1 | 2023-10-25 16:50:51+00:00 | 2k |
megvii-research/WACV2024-SAFA | model/flownet.py | [
{
"identifier": "warp",
"path": "model/warplayer.py",
"snippet": "def warp(tenInput, tenFlow, mode='bilinear'):\n k = (str(tenFlow.device), str(tenFlow.size()))\n if k not in backwarp_tenGrid:\n tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3]).view(1, 1, 1, tenFlow.shape[3]).expa... | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
from model.warplayer import warp
from model.head import Head | 1,255 |
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1, groups=1):
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilati... |
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1, groups=1):
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilati... | i0 = warp(i0, flow_down[:, :2] * 0.5) | 0 | 2023-10-26 09:24:29+00:00 | 2k |
Z4kSec/IoctlHunter | ioctl_hunter/ui/keys_reader.py | [
{
"identifier": "State",
"path": "ioctl_hunter/lib/state.py",
"snippet": "class State:\n results = Results()\n\n script = None\n cur_proc = None\n\n quiet = False\n running = True\n hook_enabled = False\n debug_enabled = False\n hex_out_enabled = False\n\n included_drivers = [... | import sys
import threading
import time
import logging
import msvcrt
from colorama import init, Fore, Style
from ..lib.state import State
from ..ui.display import (
print_enable_debugger,
print_disable_debugger,
print_dynamic_helper,
) | 1,022 |
logger = logging.getLogger("ioctl-hunter")
class KeysListenner(threading.Thread):
is_debugger_enabled = False
def __init__(self):
super(KeysListenner, self).__init__(daemon=True)
init(convert=True)
self.start()
def run(self):
while not msvcrt.kbhit():
time.sl... |
logger = logging.getLogger("ioctl-hunter")
class KeysListenner(threading.Thread):
is_debugger_enabled = False
def __init__(self):
super(KeysListenner, self).__init__(daemon=True)
init(convert=True)
self.start()
def run(self):
while not msvcrt.kbhit():
time.sl... | print_enable_debugger() | 1 | 2023-10-31 22:38:36+00:00 | 2k |
masked-spacetime-hashing/msth | nerfstudio/process_data/process_data_utils.py | [
{
"identifier": "status",
"path": "nerfstudio/utils/rich_utils.py",
"snippet": "def status(msg: str, spinner: str = \"bouncingBall\", verbose: bool = False):\n \"\"\"A context manager that does nothing is verbose is True. Otherwise it hides logs under a message.\n\n Args:\n msg: The message... | import os
import shutil
import sys
import cv2
import numpy as np
from enum import Enum
from pathlib import Path
from typing import List, Optional, Tuple
from rich.console import Console
from typing_extensions import Literal, OrderedDict
from nerfstudio.utils.rich_utils import status
from nerfstudio.utils.scripts import... | 1,336 | # Copyright 2022 The Nerfstudio Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | # Copyright 2022 The Nerfstudio Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | with status(msg="Converting video to images...", spinner="bouncingBall", verbose=verbose): | 0 | 2023-10-26 04:39:15+00:00 | 2k |
sehyunkwon/ICTC | step2b.py | [
{
"identifier": "args",
"path": "utils/argument.py",
"snippet": "def str2bool(v):"
},
{
"identifier": "get_gpt_response",
"path": "utils/llm_utils.py",
"snippet": "def get_gpt_response(system_prompt, user_prompt, api_key, user, model):\n\n headers = {\n \"Content-Type\"... | import os
from dotenv import load_dotenv
from utils.argument import args
from utils.llm_utils import get_gpt_response, get_llama_response | 1,139 |
### Requires the file to be in the following format: "Image-file ... ; Answer: {label}"
def post_process():
answer_list = {}
# read line by line
with open(args.step2a_result_path, 'r') as answers:
answers = answers.readlines()
for answer in answers:
if "Image file-" in answ... |
### Requires the file to be in the following format: "Image-file ... ; Answer: {label}"
def post_process():
answer_list = {}
# read line by line
with open(args.step2a_result_path, 'r') as answers:
answers = answers.readlines()
for answer in answers:
if "Image file-" in answ... | response = get_gpt_response(system_prompt, user_prompt, api_key, user, model) | 1 | 2023-10-27 05:00:14+00:00 | 2k |
phineas-pta/comfy-trt-test | comfy_trt/model_manager.py | [
{
"identifier": "ModelConfig",
"path": "comfy_trt/datastructures.py",
"snippet": "class ModelConfig:\n\tprofile: dict\n\tstatic_shapes: bool = False\n\tfp32: bool = False\n\tbaseline_model: str = \"SD15\" # save model info, for values see `comfy/supported_models.py`, breaking change incompatible A1111\... | import hashlib
import json
import os
import logging
import copy
import torch
from .datastructures import ModelConfig, ModelConfigEncoder | 1,538 | # -*- coding: utf-8 -*-
# modified from https://github.com/NVIDIA/Stable-Diffusion-WebUI-TensorRT/blob/main/model_manager.py
# CHANGE: retrieve checkpoint info from comfy
# STATUS: ok i guess
BASE_PATH = os.path.dirname(os.path.realpath(__file__))
ONNX_MODEL_DIR = os.path.join(BASE_PATH, "Unet-onnx")
if not os.pat... | # -*- coding: utf-8 -*-
# modified from https://github.com/NVIDIA/Stable-Diffusion-WebUI-TensorRT/blob/main/model_manager.py
# CHANGE: retrieve checkpoint info from comfy
# STATUS: ok i guess
BASE_PATH = os.path.dirname(os.path.realpath(__file__))
ONNX_MODEL_DIR = os.path.join(BASE_PATH, "Unet-onnx")
if not os.pat... | config = ModelConfig(profile, static_shapes, fp32, baseline_model, prediction_type, inpaint, refit, lora, unet_hidden_dim) | 0 | 2023-10-25 23:58:12+00:00 | 2k |
hydrogram/hydrogram | hydrogram/raw/core/gzip_packed.py | [
{
"identifier": "Bytes",
"path": "hydrogram/raw/core/primitives/bytes.py",
"snippet": "class Bytes(bytes, TLObject):\n @classmethod\n def read(cls, data: BytesIO, *args: Any) -> bytes:\n length = int.from_bytes(data.read(1), \"little\")\n\n if length <= 253:\n x = data.rea... | from gzip import compress, decompress
from io import BytesIO
from typing import Any, cast
from .primitives.bytes import Bytes
from .primitives.int import Int
from .tl_object import TLObject | 1,159 | # Hydrogram - Telegram MTProto API Client Library for Python
# Copyright (C) 2017-2023 Dan <https://github.com/delivrance>
# Copyright (C) 2023-present Hydrogram <https://hydrogram.org>
#
# This file is part of Hydrogram.
#
# Hydrogram is free software: you can redistribute it and/or modify
# it under the terms o... | # Hydrogram - Telegram MTProto API Client Library for Python
# Copyright (C) 2017-2023 Dan <https://github.com/delivrance>
# Copyright (C) 2023-present Hydrogram <https://hydrogram.org>
#
# This file is part of Hydrogram.
#
# Hydrogram is free software: you can redistribute it and/or modify
# it under the terms o... | return cast(GzipPacked, TLObject.read(BytesIO(decompress(Bytes.read(data))))) | 0 | 2023-10-29 16:16:37+00:00 | 2k |
chenruduan/OAReactDiff | oa_reactdiff/tests/utils/test_graph_tools.py | [
{
"identifier": "get_edges_index",
"path": "oa_reactdiff/utils/_graph_tools.py",
"snippet": "def get_edges_index(\n combined_mask: Tensor,\n pos: Optional[Tensor] = None,\n edge_cutoff: Optional[float] = None,\n remove_self_edge: bool = False,\n) -> Tensor:\n r\"\"\"\n\n Args:\n ... | import unittest
import torch
from torch import Tensor, tensor
from oa_reactdiff.utils import (
get_edges_index,
get_subgraph_mask,
get_n_frag_switch,
get_mask_for_frag,
) | 1,138 |
class TestBasics(unittest.TestCase):
def test_get_mask_for_frag(self):
natms = Tensor([2, 0, 3]).long()
res = get_mask_for_frag(natms)
self.assertTrue(torch.allclose(res, Tensor([0, 0, 2, 2, 2]).long()))
def test_get_n_frag_switch(self):
natm_list = [tensor([2, 0]), tensor([... |
class TestBasics(unittest.TestCase):
def test_get_mask_for_frag(self):
natms = Tensor([2, 0, 3]).long()
res = get_mask_for_frag(natms)
self.assertTrue(torch.allclose(res, Tensor([0, 0, 2, 2, 2]).long()))
def test_get_n_frag_switch(self):
natm_list = [tensor([2, 0]), tensor([... | res = get_n_frag_switch(natm_list) | 2 | 2023-10-30 02:53:38+00:00 | 2k |
lewandofskee/DiAD | ldm/models/diffusion/ddim.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 ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor | 1,171 | """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) =... | ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), | 0 | 2023-10-30 14:21:09+00:00 | 2k |
nv-tlabs/trace | tbsim/models/temporal.py | [
{
"identifier": "SinusoidalPosEmb",
"path": "tbsim/models/trace_helpers.py",
"snippet": "class SinusoidalPosEmb(nn.Module):\n def __init__(self, dim):\n super().__init__()\n self.dim = dim\n\n def forward(self, x):\n device = x.device\n half_dim = self.dim // 2\n ... | import torch
import torch.nn as nn
import einops
from einops.layers.torch import Rearrange
from .trace_helpers import (
SinusoidalPosEmb,
Downsample1d,
Upsample1d,
Conv1dBlock,
) | 935 | #
# Based on Diffuser: https://github.com/jannerm/diffuser/blob/main/diffuser/models/temporal.py
#
class ResidualTemporalMapBlockConcat(nn.Module):
def __init__(self, inp_channels, out_channels, time_embed_dim, horizon, kernel_size=5):
super().__init__()
self.time_mlp = nn.Sequential(
... | #
# Based on Diffuser: https://github.com/jannerm/diffuser/blob/main/diffuser/models/temporal.py
#
class ResidualTemporalMapBlockConcat(nn.Module):
def __init__(self, inp_channels, out_channels, time_embed_dim, horizon, kernel_size=5):
super().__init__()
self.time_mlp = nn.Sequential(
... | SinusoidalPosEmb(time_dim), | 0 | 2023-10-31 18:43:07+00:00 | 2k |
gydpku/PPTC | src/evaluate.py | [
{
"identifier": "api_doc",
"path": "src/api_doc.py",
"snippet": "class API(object):\n def __init__(self, name, parameters, description,\n parameter_description=\"\", composition_instruction=\"\", example=\"\", api_desc=\"\",\n type=\"\",\n implementatio... | from src import api_doc
from src import prompt_factor
from src import ppt_reader, utils
from pptx import Presentation
from src import pptx_check
from sacremoses import MosesTokenizer
from tqdm import tqdm
import mosestokenizer
import os | 1,181 |
def calc_token_cost(path):
text = open(path,'r').read()
tokenizer = MosesTokenizer()
tokens = tokenizer.tokenize(text)
return len(tokens)
def calc_acc(label_path, pred_path, instruction, additional_restrictions=[]):
pos_total, pos_correct, str_correct = 0,0,0
# position
splitted = inst... |
def calc_token_cost(path):
text = open(path,'r').read()
tokenizer = MosesTokenizer()
tokens = tokenizer.tokenize(text)
return len(tokens)
def calc_acc(label_path, pred_path, instruction, additional_restrictions=[]):
pos_total, pos_correct, str_correct = 0,0,0
# position
splitted = inst... | label_string = ppt_reader.eval_get_contents(need_text=True, need_style=True, need_position=False,need_shape_list=None,ppt=Presentation(label_path)) | 2 | 2023-10-25 13:14:46+00:00 | 2k |
secarri/MipFlooding | mipflooding/image_processing.py | [
{
"identifier": "setup_logger",
"path": "mipflooding/logger.py",
"snippet": "def setup_logger(logger_name: str, abs_log_path: str) -> logging.Logger:\n \"\"\"Set up a logger with the specified name and log to the given absolute path, returning the logger instance.\"\"\"\n logger = logging.getLogge... | import logging
import math
import os
import time
from pathlib import Path
from typing import List, Optional
from PIL import Image
from .logger import setup_logger, terminate_loggers
from .file_utils import clear_log_file, get_output_directory, get_output_filename | 1,599 | # Default packages
# Third party packages
# From self package
def _open_image_inputs(color: str, alpha: str, logger: logging.Logger) -> List:
"""Open and return the color and alpha images as a list of Image objects."""
logger.info("--- Opening images in memory...")
if not color:
color = str(None... | # Default packages
# Third party packages
# From self package
def _open_image_inputs(color: str, alpha: str, logger: logging.Logger) -> List:
"""Open and return the color and alpha images as a list of Image objects."""
logger.info("--- Opening images in memory...")
if not color:
color = str(None... | return setup_logger("mipmap_flooding", out_log_file.__str__()) | 0 | 2023-10-25 11:05:59+00:00 | 2k |
Lin-jun-xiang/chatgpt-line-bot | chatgpt_linebot/modules/horoscope.py | [
{
"identifier": "chat_completion",
"path": "chatgpt_linebot/modules/gpt.py",
"snippet": "def chat_completion(message: List[Dict]) -> str:\n \"\"\"Use OpenAI API via gpt4free providers\"\"\"\n try:\n response = g4f.ChatCompletion.create(\n model=g4f.models.default,\n me... | import json
import re
import requests
from bs4 import BeautifulSoup
from chatgpt_linebot.modules.gpt import chat_completion
from chatgpt_linebot.prompts import horoscope_template | 668 |
class Horoscope:
HOST = "https://www.cosmopolitan.com/tw/horoscopes/"
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
}
error_msg = (
"Cannot get the horoscope, please try again.🥶\n"
... |
class Horoscope:
HOST = "https://www.cosmopolitan.com/tw/horoscopes/"
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
}
error_msg = (
"Cannot get the horoscope, please try again.🥶\n"
... | response = chat_completion( | 0 | 2023-10-24 09:01:13+00:00 | 2k |
nv-tlabs/pacer | pacer/env/tasks/vec_task_wrappers.py | [
{
"identifier": "VecTaskCPU",
"path": "pacer/env/tasks/vec_task.py",
"snippet": "class VecTaskCPU(VecTask):\n\n def __init__(self, task, rl_device, sync_frame_time=False, clip_observations=5.0):\n super().__init__(task, rl_device, clip_observations=clip_observations)\n self.sync_frame_t... | from gym import spaces
from pacer.env.tasks.vec_task import VecTaskCPU, VecTaskGPU, VecTaskPython
import numpy as np
import torch | 983 | # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and relat... | # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and relat... | class VecTaskGPUWrapper(VecTaskGPU): | 1 | 2023-10-31 20:47:12+00:00 | 2k |
Improbable-AI/dexenv | dexenv/models/state_model.py | [
{
"identifier": "DiagGaussianPolicy",
"path": "dexenv/models/diag_gaussian_pol/diag_gaussian_policy.py",
"snippet": "class DiagGaussianPolicy(nn.Module):\n def __init__(self,\n body_net,\n action_dim,\n init_log_std=-0.2,\n std_cond_in=F... | import gym
import torch.nn as nn
from collections.abc import Sequence
from loguru import logger
from dexenv.models.diag_gaussian_pol.diag_gaussian_policy import \
DiagGaussianPolicy
from dexenv.models.utils import get_activation
from dexenv.models.value_nets.value_net import ValueNet | 1,138 |
class SimpleMLP(nn.Module):
def __init__(self, in_dim, out_dim, act):
super().__init__()
act = get_activation(act)
self.body = nn.Sequential(
nn.Linear(in_dim, 512),
act(),
nn.Linear(512, 256),
act(),
nn.Linear(256, out_dim),
... |
class SimpleMLP(nn.Module):
def __init__(self, in_dim, out_dim, act):
super().__init__()
act = get_activation(act)
self.body = nn.Sequential(
nn.Linear(in_dim, 512),
act(),
nn.Linear(512, 256),
act(),
nn.Linear(256, out_dim),
... | critic = ValueNet(critic_body, | 2 | 2023-10-25 17:22:41+00:00 | 2k |
ai-safety-foundation/sparse_autoencoder | sparse_autoencoder/autoencoder/components/tests/test_tied_bias.py | [
{
"identifier": "TiedBias",
"path": "sparse_autoencoder/autoencoder/components/tied_bias.py",
"snippet": "class TiedBias(Module):\n \"\"\"Tied Bias Layer.\n\n The tied pre-encoder bias is a learned bias term that is subtracted from the input before\n encoding, and added back after decoding.\n\n... | from jaxtyping import Float
from torch import Tensor
from torch.nn import Parameter
from sparse_autoencoder.autoencoder.components.tied_bias import TiedBias, TiedBiasPosition
from sparse_autoencoder.tensor_types import Axis
import torch | 1,467 | """Tied Bias Tests."""
def test_pre_encoder_subtracts_bias() -> None:
"""Check that the pre-encoder bias subtracts the bias."""
encoder_input: Float[Tensor, Axis.names(Axis.BATCH, Axis.INPUT_OUTPUT_FEATURE)] = torch.tensor(
[[5.0, 3.0, 1.0]]
)
bias = Parameter(torch.tensor([2.0, 4.0, 6.0]))
... | """Tied Bias Tests."""
def test_pre_encoder_subtracts_bias() -> None:
"""Check that the pre-encoder bias subtracts the bias."""
encoder_input: Float[Tensor, Axis.names(Axis.BATCH, Axis.INPUT_OUTPUT_FEATURE)] = torch.tensor(
[[5.0, 3.0, 1.0]]
)
bias = Parameter(torch.tensor([2.0, 4.0, 6.0]))
... | pre_encoder = TiedBias(bias, TiedBiasPosition.PRE_ENCODER) | 0 | 2023-10-27 07:37:15+00:00 | 2k |
vb000/SemanticHearing | src/training/train.py | [
{
"identifier": "utils",
"path": "src/helpers/utils.py",
"snippet": "class Params():\n def __init__(self, json_path):\n def save(self, json_path):\n def update(self, json_path):\n def dict(self):\ndef save_graph(train_metrics, test_metrics, save_dir):\ndef import_attr(import_path):\ndef set_... | import argparse
import multiprocessing
import os
import logging
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import traceback # pylint: disable=import-outside-toplevel
import wandb
from pathlib import Path
from torch.uti... | 1,165 | """
The main training script for training on synthetic data
"""
def train_epoch(model: nn.Module, device: torch.device,
optimizer: optim.Optimizer,
| """
The main training script for training on synthetic data
"""
def train_epoch(model: nn.Module, device: torch.device,
optimizer: optim.Optimizer, | train_loader: torch.utils.data.dataloader.DataLoader, | 0 | 2023-10-30 05:36:07+00:00 | 2k |
openai/bugbounty-gpt | tests/test_openai_classification.py | [
{
"identifier": "OpenAIHandler",
"path": "bugbounty_gpt/handlers/openai_handler.py",
"snippet": "class OpenAIHandler:\n @staticmethod\n def _classifications_sanitization(input_string):\n \"\"\"\n Sanitizes the input string by removing spaces, converting to upper case, and replacing s... | from bugbounty_gpt.handlers.openai_handler import OpenAIHandler
from unittest.mock import patch, AsyncMock
from bugbounty_gpt.env import OPENAI_PROMPT, OPENAI_MODEL, DEFAULT_CATEGORY
import pytest, asyncio | 915 |
def test_classifications_sanitization():
assert OpenAIHandler._classifications_sanitization(" Test Category ") == "TEST_CATEGORY"
def test_build_request_data():
submission_content = "Sample content"
expected_data = {
"model": OPENAI_MODEL,
"temperature": 0,
"max_tokens": 512,
... |
def test_classifications_sanitization():
assert OpenAIHandler._classifications_sanitization(" Test Category ") == "TEST_CATEGORY"
def test_build_request_data():
submission_content = "Sample content"
expected_data = {
"model": OPENAI_MODEL,
"temperature": 0,
"max_tokens": 512,
... | {"role": "system", "content": OPENAI_PROMPT}, | 1 | 2023-10-27 22:41:24+00:00 | 2k |
LeapLabTHU/FamO2O | jax_cql/JaxCQL/sac.py | [
{
"identifier": "next_rng",
"path": "jax_cql/JaxCQL/jax_utils.py",
"snippet": "def next_rng(*args, **kwargs):\n global jax_utils_rng\n return jax_utils_rng(*args, **kwargs)"
},
{
"identifier": "value_and_multi_grad",
"path": "jax_cql/JaxCQL/jax_utils.py",
"snippet": "def value_and_... | from collections import OrderedDict
from copy import deepcopy
from functools import partial
from ml_collections import ConfigDict
from flax.training.train_state import TrainState
from .jax_utils import (
next_rng, value_and_multi_grad, mse_loss, JaxRNG, wrap_function_with_rng,
collect_jax_metrics
)
from .model ... | 1,576 |
class SAC(object):
@staticmethod
def get_default_config(updates=None):
config = ConfigDict()
config.discount = 0.99
config.alpha_multiplier = 1.0
config.use_automatic_entropy_tuning = True
config.backup_entropy = False
config.target_entropy = 0.0
con... |
class SAC(object):
@staticmethod
def get_default_config(updates=None):
config = ConfigDict()
config.discount = 0.99
config.alpha_multiplier = 1.0
config.use_automatic_entropy_tuning = True
config.backup_entropy = False
config.target_entropy = 0.0
con... | self.log_alpha = Scalar(0.0) | 6 | 2023-10-25 11:53:25+00:00 | 2k |
RenShuhuai-Andy/TESTA | data/pretrain_dataset.py | [
{
"identifier": "pre_caption",
"path": "data/utils.py",
"snippet": "def pre_caption(caption, max_words=50):\n caption = re.sub(\n r\"([!\\\"()*#~])\", #r\"([!\\\"()*#:;~])\" #r\"([.!\\\"()*#:;~])\",\n ' ',\n caption.lower(),\n )\n caption = re.sub(\n r\"\\s{2,}\",\n... | import json
import os
import random
import torch
import torch
import numpy as np
import decord
import os,glob
from pandas import Categorical
from torch.utils.data import Dataset
from PIL import Image
from PIL import ImageFile
from decord import VideoReader
from data.utils import pre_caption
from .randaugment import Tem... | 1,012 |
ImageFile.LOAD_TRUNCATED_IMAGES = True
Image.MAX_IMAGE_PIXELS = None
decord.bridge.set_bridge('torch')
class pretrain_dataset(Dataset):
def __init__(self, ann_file, laion_path, transform):
self.ann_pretrain = []
for f in ann_file:
print('loading '+f)
ann = json.load(op... |
ImageFile.LOAD_TRUNCATED_IMAGES = True
Image.MAX_IMAGE_PIXELS = None
decord.bridge.set_bridge('torch')
class pretrain_dataset(Dataset):
def __init__(self, ann_file, laion_path, transform):
self.ann_pretrain = []
for f in ann_file:
print('loading '+f)
ann = json.load(op... | caption = pre_caption(ann['caption'],30) | 0 | 2023-10-29 12:09:38+00:00 | 2k |
flbraun/poe-palette | data/beasts.py | [
{
"identifier": "League",
"path": "data/leagues.py",
"snippet": "class League:\n type_: LeagueType\n title: str # e.g. \"Ancestor\"\n slug: str # e.g. \"ancestor\"\n is_hardcore: bool"
},
{
"identifier": "get_ninja_index",
"path": "data/ninja.py",
"snippet": "@functools.cac... | from collections.abc import Generator
from .leagues import League
from .ninja import get_ninja_index, make_ninja_url
from .trade import make_trade_url
from .types import NinjaCategory
from .utils import Entry, make_wiki_url | 1,262 |
def get_beasts(league: League) -> Generator[Entry, None, None]:
index = get_ninja_index(league)
for beast in index.raw[NinjaCategory.BEASTS]:
yield Entry(
display_text=beast,
wiki_url=make_wiki_url(beast),
ninja_url=make_ninja_url(league, beast, None, NinjaCategor... |
def get_beasts(league: League) -> Generator[Entry, None, None]:
index = get_ninja_index(league)
for beast in index.raw[NinjaCategory.BEASTS]:
yield Entry(
display_text=beast,
wiki_url=make_wiki_url(beast),
ninja_url=make_ninja_url(league, beast, None, NinjaCategor... | trade_url=make_trade_url(league, beast), | 3 | 2023-10-27 11:33:43+00:00 | 2k |
ATR-DBI/CityRefer | models/cityrefer.py | [
{
"identifier": "SparseConvEncoder",
"path": "models/basic_blocks.py",
"snippet": "class SparseConvEncoder(nn.Module):\n def __init__(self, input_dim):\n super().__init__()\n\n self.stem = nn.Sequential(\n BasicConvolutionBlock(input_dim, 32, 3)\n )\n\n self.sta... | import sys
import os
import importlib
import models
import torch
import torch.nn as nn
import torchsparse.nn as spnn
from torch.nn.utils.rnn import pad_sequence
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from torchsparse.utils.collate import sparse_collate
from transformers import BertConf... | 1,357 |
importlib.reload(models)
sys.path.append(os.path.join(os.getcwd(), "lib")) # HACK add the lib folder
sys.path.append(os.path.join(os.getcwd(), "models")) # HACK add the lib folder
class CityRefer(nn.Module):
def __init__(self, args, input_feature_dim=0, num_object_class=None, vocab_size=None, pad_token_id=... |
importlib.reload(models)
sys.path.append(os.path.join(os.getcwd(), "lib")) # HACK add the lib folder
sys.path.append(os.path.join(os.getcwd(), "models")) # HACK add the lib folder
class CityRefer(nn.Module):
def __init__(self, args, input_feature_dim=0, num_object_class=None, vocab_size=None, pad_token_id=... | self.sparse_conv = SparseConvEncoder(self.input_feature_dim) # self.input_feature_dim = 3 -> 128 | 0 | 2023-10-25 10:02:28+00:00 | 2k |
OATML-Markslab/ProteinNPT | baselines/data_processing.py | [
{
"identifier": "slice_sequences",
"path": "utils/data_utils.py",
"snippet": "def slice_sequences(list_mutant_mutated_seq_pairs, max_positions=1024, method=\"rolling\", rolling_overlap=100, eval_mode=True, batch_target_labels=None, batch_masked_targets=None, target_names=None, start_idx=1, num_extra_tok... | import sys
import numpy as np
import h5py
import torch
from collections import defaultdict
from utils.data_utils import slice_sequences, get_indices_retrieved_embeddings
from utils.msa_utils import weighted_sample_MSA | 1,219 |
def process_batch(batch, model, alphabet, args, device, MSA_sequences=None, MSA_weights=None, MSA_start_position=None, MSA_end_position=None, eval_mode = True, indel_mode=False, start_idx=1):
"""
start_idx is the one-indexed postion of the first residue in the sequence. If full sequence is passed (as always as... |
def process_batch(batch, model, alphabet, args, device, MSA_sequences=None, MSA_weights=None, MSA_start_position=None, MSA_end_position=None, eval_mode = True, indel_mode=False, start_idx=1):
"""
start_idx is the one-indexed postion of the first residue in the sequence. If full sequence is passed (as always as... | indices_retrieved_embeddings = get_indices_retrieved_embeddings(batch,args.sequence_embeddings_location) | 1 | 2023-10-28 11:41:05+00:00 | 2k |
dyhBUPT/iKUN | test.py | [
{
"identifier": "opt",
"path": "opts.py",
"snippet": "class opts:\n def __init__(self):\n def parse(self, args=''):"
},
{
"identifier": "get_model",
"path": "model.py",
"snippet": "def get_model(opt, name='Model'):\n model = eval(name)(opt)\n model.cuda()\n model = nn.Data... | import os
import json
import shutil
import numpy as np
import torch
import torch.nn.functional as F
import warnings
from tqdm import tqdm
from os.path import join, exists
from collections import defaultdict
from torch import nn
from torchvision.utils import save_image
from opts import opt
from utils import *
from model... | 916 |
warnings.filterwarnings('ignore')
# import `opts` first to set gpus
def test_accuracy_v1(model, dataloader, save_img=False):
model.eval()
TP, FP, FN = 0, 0, 0
assert dataloader.batch_size == 1
if save_img:
save_dir = join(opt.save_dir, 'images')
os.makedirs(save_dir, exist_ok=True)... |
warnings.filterwarnings('ignore')
# import `opts` first to set gpus
def test_accuracy_v1(model, dataloader, save_img=False):
model.eval()
TP, FP, FN = 0, 0, 0
assert dataloader.batch_size == 1
if save_img:
save_dir = join(opt.save_dir, 'images')
os.makedirs(save_dir, exist_ok=True)... | un_norm = get_transform('unnorm', opt, -1) | 3 | 2023-10-31 07:08:37+00:00 | 2k |
CVHub520/yolov5_obb | utils/augmentations.py | [
{
"identifier": "LOGGER",
"path": "utils/general.py",
"snippet": "LOGGER = set_logging(__name__) # define globally (used in train.py, val.py, detect.py, etc.)"
},
{
"identifier": "check_version",
"path": "utils/general.py",
"snippet": "def check_version(current='0.0.0', minimum='0.0.0',... | import math
import random
import cv2
import numpy as np
import albumentations as A
from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box
from utils.metrics import bbox_ioa
from utils.rboxs_utils import poly_filter | 1,519 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Image augmentation functions
"""
class Albumentations:
# YOLOv5 Albumentations class (optional, only used if package is installed)
def __init__(self):
self.transform = None
try:
| # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Image augmentation functions
"""
class Albumentations:
# YOLOv5 Albumentations class (optional, only used if package is installed)
def __init__(self):
self.transform = None
try: | check_version(A.__version__, '1.0.3', hard=True) # version requirement | 1 | 2023-10-31 06:06:41+00:00 | 2k |
hyw-dev/AFI-ForwardDeduplicate | models/gmflow/matching.py | [
{
"identifier": "coords_grid",
"path": "models/gmflow/geometry.py",
"snippet": "def coords_grid(b, h, w, homogeneous=False, device=None, dtype: torch.dtype=torch.float32):\r\n k = (str(device), str((b, h, w)))\r\n if k in coords_grid_cache:\r\n return coords_grid_cache[k]\r\n y, x = torc... | import torch
import torch.nn.functional as F
from models.gmflow.geometry import coords_grid, generate_window_grid, normalize_coords
| 767 |
def global_correlation_softmax(feature0, feature1,
pred_bidir_flow=False,
):
# global correlation
b, c, h, w = feature0.shape
feature0 = feature0.view(b, c, -1).permute(0, 2, 1) # [B, H*W, C]
feature1 = feature1.view(b, c, -1) #... |
def global_correlation_softmax(feature0, feature1,
pred_bidir_flow=False,
):
# global correlation
b, c, h, w = feature0.shape
feature0 = feature0.view(b, c, -1).permute(0, 2, 1) # [B, H*W, C]
feature1 = feature1.view(b, c, -1) #... | init_grid = coords_grid(b, h, w, device=correlation.device, dtype=feature0.dtype) # [B, 2, H, W]
| 0 | 2023-10-29 18:25:36+00:00 | 2k |
bmrussell/LGBattery | device.py | [
{
"identifier": "Shared",
"path": "globals.py",
"snippet": "class Shared:\n \"\"\"_Configuration_\n\n Args:\n Singleton class for application configuration\n \"\"\"\n\n appname = 'lgbattery'\n quit_selected = False\n datadir = f'{os.getenv(\"APPDATA\")}\\\\{app... | import asyncio
import json
import logging
import websockets
from globals import Shared
from icons import get_icon | 1,575 |
def get_device_by_id(id):
for dev in Shared.devices:
if dev.id == id:
return dev
return None
class Device:
def __init__(self, id, unitId, name, batteryLevel, charging):
self.id = id
self.unitId = unitId
self.name = name
self.batteryLevel = batteryLeve... |
def get_device_by_id(id):
for dev in Shared.devices:
if dev.id == id:
return dev
return None
class Device:
def __init__(self, id, unitId, name, batteryLevel, charging):
self.id = id
self.unitId = unitId
self.name = name
self.batteryLevel = batteryLeve... | icon = get_icon(self.batteryLevel) | 1 | 2023-10-25 20:37:43+00:00 | 2k |
Kiteretsu77/VCISR-official | degradation/ESR/usm_sharp.py | [
{
"identifier": "filter2D",
"path": "degradation/ESR/utils.py",
"snippet": "def filter2D(img, kernel):\n \"\"\"PyTorch version of cv2.filter2D\n\n Args:\n img (Tensor): (b, c, h, w)\n kernel (Tensor): (b, k, k)\n \"\"\"\n k = kernel.size(-1)\n b, c, h, w = img.size()\n if... | import cv2
import numpy as np
import torch
import os, sys
from torch.nn import functional as F
from degradation.ESR.utils import filter2D, np2tensor, tensor2np | 948 | # -*- coding: utf-8 -*-
root_path = os.path.abspath('.')
sys.path.append(root_path)
def usm_sharp_func(img, weight=0.5, radius=50, threshold=10):
"""USM sharpening.
Input image: I; Blurry image: B.
1. sharp = I + weight * (I - B)
2. Mask = 1 if abs(I - B) > threshold, else: 0
3. Blur mask:
... | # -*- coding: utf-8 -*-
root_path = os.path.abspath('.')
sys.path.append(root_path)
def usm_sharp_func(img, weight=0.5, radius=50, threshold=10):
"""USM sharpening.
Input image: I; Blurry image: B.
1. sharp = I + weight * (I - B)
2. Mask = 1 if abs(I - B) > threshold, else: 0
3. Blur mask:
... | img = np2tensor(img) | 1 | 2023-10-29 04:33:38+00:00 | 2k |
serengil/LightPHE | lightphe/models/Tensor.py | [
{
"identifier": "Homomorphic",
"path": "lightphe/models/Homomorphic.py",
"snippet": "class Homomorphic(ABC):\n keys: dict\n plaintext_modulo: int\n ciphertext_modulo: int\n\n @abstractmethod\n def generate_keys(self, key_size: int, s: Optional[int] = None) -> dict:\n pass\n\n @a... | from typing import Union, List
from lightphe.models.Homomorphic import Homomorphic
from lightphe.commons import phe_utils | 674 |
# pylint: disable=too-few-public-methods, no-else-return
class Fraction:
"""
Class to store fractional values
"""
def __init__(
self,
dividend: Union[int, tuple, list],
abs_dividend: Union[int, tuple, list],
divisor: Union[int, tuple, list],
sign: int = 1,
... |
# pylint: disable=too-few-public-methods, no-else-return
class Fraction:
"""
Class to store fractional values
"""
def __init__(
self,
dividend: Union[int, tuple, list],
abs_dividend: Union[int, tuple, list],
divisor: Union[int, tuple, list],
sign: int = 1,
... | def __init__(self, fractions: List[Fraction], cs: Homomorphic): | 0 | 2023-10-28 14:57:59+00:00 | 2k |
DataCanvasIO/LMS | lms/runtime/prune/llm_pruner/LLMPruner/peft/utils/save_and_load.py | [
{
"identifier": "PeftType",
"path": "lms/runtime/prune/llm_pruner/LLMPruner/peft/utils/config.py",
"snippet": "class PeftType(str, enum.Enum):\n PROMPT_TUNING = \"PROMPT_TUNING\"\n P_TUNING = \"P_TUNING\"\n PREFIX_TUNING = \"PREFIX_TUNING\"\n LORA = \"LORA\"\n ADALORA = \"ADALORA\""
},
... | from .config import PeftType, PromptLearningConfig | 732 | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | if config.peft_type in (PeftType.LORA, PeftType.ADALORA): | 0 | 2023-10-30 10:50:32+00:00 | 2k |
imhotep/hass-unifi-access | custom_components/unifi_access/hub.py | [
{
"identifier": "DEVICE_NOTIFICATIONS_URL",
"path": "custom_components/unifi_access/const.py",
"snippet": "DEVICE_NOTIFICATIONS_URL = \"/api/v1/developer/devices/notifications\""
},
{
"identifier": "DOOR_UNLOCK_URL",
"path": "custom_components/unifi_access/const.py",
"snippet": "DOOR_UNL... | import asyncio
import json
import logging
import ssl
import urllib3
import websocket
from datetime import timedelta
from threading import Thread
from urllib.parse import urlparse
from requests import request
from requests.exceptions import ConnectionError as ConnError, SSLError
from homeassistant.core import HomeAssist... | 1,326 | """Unifi Access Hub.
This module interacts with the Unifi Access API server.
"""
_LOGGER = logging.getLogger(__name__)
class ApiAuthError(Exception):
"""Raised when we can't authenticate with the API Token."""
class ApiError(Exception):
"""Raised when we have some trouble using the API."""
class Unif... | """Unifi Access Hub.
This module interacts with the Unifi Access API server.
"""
_LOGGER = logging.getLogger(__name__)
class ApiAuthError(Exception):
"""Raised when we can't authenticate with the API Token."""
class ApiError(Exception):
"""Raised when we have some trouble using the API."""
class Unif... | data = self._make_http_request(f"{self.host}{DOORS_URL}") | 2 | 2023-10-27 20:34:27+00:00 | 2k |
aws-samples/amazon-bedrock-serverless-prompt-chaining | stacks/trip_planner_stack.py | [
{
"identifier": "get_lambda_bundling_options",
"path": "stacks/util.py",
"snippet": "def get_lambda_bundling_options():\n return lambda_python.BundlingOptions(\n asset_excludes=[\".venv\", \".mypy_cache\", \"__pycache__\"],\n command_hooks=CommandHooks(),\n )"
},
{
"identifie... | from aws_cdk import (
Duration,
Stack,
RemovalPolicy,
aws_lambda as lambda_,
aws_lambda_python_alpha as lambda_python,
aws_s3 as s3,
aws_ssm as ssm,
aws_stepfunctions as sfn,
aws_stepfunctions_tasks as tasks,
)
from constructs import Construct
from .util import (
get_lambda_bundl... | 692 |
class TripPlannerStack(Stack):
def __init__(self, scope: Construct, construct_id: str, **kwargs) -> None:
super().__init__(scope, construct_id, **kwargs)
# Agent #1: suggest places to stay
|
class TripPlannerStack(Stack):
def __init__(self, scope: Construct, construct_id: str, **kwargs) -> None:
super().__init__(scope, construct_id, **kwargs)
# Agent #1: suggest places to stay | hotels_job = get_claude_instant_invoke_chain( | 1 | 2023-10-26 22:17:30+00:00 | 2k |
pengsongyou/lseg_feature_extraction | modules/models/lseg_blocks.py | [
{
"identifier": "_make_pretrained_clip_vitl16_384",
"path": "modules/models/lseg_vit.py",
"snippet": "def _make_pretrained_clip_vitl16_384(\n pretrained, use_readout=\"ignore\", hooks=None, enable_attention_hooks=False\n):\n clip_pretrained, _ = clip.load(\"ViT-B/32\", device='cuda', jit=False)\n ... | import torch
import torch.nn as nn
from .lseg_vit import (
_make_pretrained_clip_vitl16_384,
_make_pretrained_clip_vitb32_384,
_make_pretrained_clipRN50x16_vitl16_384,
forward_vit,
) | 1,199 |
def _make_encoder(
backbone,
features,
use_pretrained=True,
groups=1,
expand=False,
exportable=True,
hooks=None,
use_vit_only=False,
use_readout="ignore",
enable_attention_hooks=False,
):
if backbone == "clip_vitl16_384":
|
def _make_encoder(
backbone,
features,
use_pretrained=True,
groups=1,
expand=False,
exportable=True,
hooks=None,
use_vit_only=False,
use_readout="ignore",
enable_attention_hooks=False,
):
if backbone == "clip_vitl16_384": | clip_pretrained, pretrained = _make_pretrained_clip_vitl16_384( | 0 | 2023-10-27 15:40:36+00:00 | 2k |
chenran-li/RQL-release | rl_zoo3/plots/plot_train.py | [
{
"identifier": "LoadMonitorResultsError",
"path": "stable_baselines3/common/monitor.py",
"snippet": "class LoadMonitorResultsError(Exception):\n \"\"\"\n Raised when loading the monitor log fails.\n \"\"\"\n\n pass"
},
{
"identifier": "load_results",
"path": "stable_baselines3/c... | import argparse
import os
import numpy as np
import seaborn
from matplotlib import pyplot as plt
from stable_baselines3.common.monitor import LoadMonitorResultsError, load_results
from stable_baselines3.common.results_plotter import X_EPISODES, X_TIMESTEPS, X_WALLTIME, ts2xy, window_func | 1,247 | """
Plot training reward/success rate
"""
# Activate seaborn
seaborn.set()
def plot_train():
parser = argparse.ArgumentParser("Gather results, plot training reward/success")
parser.add_argument("-a", "--algo", help="Algorithm to include", type=str, required=True)
parser.add_argument("-e", "--env", help=... | """
Plot training reward/success rate
"""
# Activate seaborn
seaborn.set()
def plot_train():
parser = argparse.ArgumentParser("Gather results, plot training reward/success")
parser.add_argument("-a", "--algo", help="Algorithm to include", type=str, required=True)
parser.add_argument("-e", "--env", help=... | "steps": X_TIMESTEPS, | 3 | 2023-10-28 01:09:21+00:00 | 2k |
AmgdGocha/DriveFS-Sleuth | drivefs_sleuth/tasks.py | [
{
"identifier": "copy_file",
"path": "drivefs_sleuth/utils.py",
"snippet": "def copy_file(file_path, dest_filename, recovery_path=''):\n if not recovery_path:\n recovery_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'recovered_items')\n\n if not os.path.exists(recovery_pat... | import os
import csv
from jinja2 import Environment
from jinja2 import FileSystemLoader
from drivefs_sleuth.utils import copy_file
from drivefs_sleuth.utils import lookup_account_id
from drivefs_sleuth.utils import get_properties_list
from drivefs_sleuth.utils import get_account_properties
from drivefs_sleuth.utils imp... | 1,505 |
def get_accounts(drivefs_path):
accounts = {}
experiments_ids = get_experiment_account_ids(drivefs_path)
profiles = get_available_profiles(drivefs_path)
available_accounts = set(experiments_ids + profiles)
for account_id in available_accounts:
accounts[account_id] = {
'email... |
def get_accounts(drivefs_path):
accounts = {}
experiments_ids = get_experiment_account_ids(drivefs_path)
profiles = get_available_profiles(drivefs_path)
available_accounts = set(experiments_ids + profiles)
for account_id in available_accounts:
accounts[account_id] = {
'email... | for prop in get_properties_list(os.path.join(setup.get_drivefs_path(), account.get_account_id())): | 2 | 2023-10-29 11:05:04+00:00 | 2k |
zyang1580/CoLLM | minigpt4/datasets/builders/rec_base_dataset_builder.py | [
{
"identifier": "is_dist_avail_and_initialized",
"path": "minigpt4/common/dist_utils.py",
"snippet": "def is_dist_avail_and_initialized():\n if not dist.is_available():\n return False\n if not dist.is_initialized():\n return False\n return True"
},
{
"identifier": "is_main... | import logging
import os
import shutil
import warnings
import torch.distributed as dist
import minigpt4.common.utils as utils
from omegaconf import OmegaConf
from torchvision.datasets.utils import download_url
from minigpt4.common.dist_utils import is_dist_avail_and_initialized, is_main_process
from minigpt4.common.reg... | 799 | """
This file is from
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
class RecBaseDatasetBuilder:
train_dataset_cls, eval_dataset_cls... | """
This file is from
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
class RecBaseDatasetBuilder:
train_dataset_cls, eval_dataset_cls... | if is_main_process(): | 1 | 2023-10-29 12:47:25+00:00 | 2k |
naver/bq-nco | learning/op/traj_learner.py | [
{
"identifier": "decode",
"path": "learning/op/decoding.py",
"snippet": "def decode(node_coords: Tensor, node_values: Tensor, upper_bounds: Tensor, dist_matrices: Tensor, net: Module,\n beam_size: int, knns: int) -> Tensor:\n if beam_size == 1:\n tours, collected_rewards = greedy_dec... | import time
import torch
from torch import nn
from learning.op.decoding import decode
from utils.misc import do_lr_decay, EpochMetrics | 791 | """
BQ-NCO
Copyright (c) 2023-present NAVER Corp.
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license
"""
DEBUG_NUM_BATCHES = 3
class TrajectoryLearner:
def __init__(self, args, net, module, device, data_iterator, optimizer=None, checkpointer=None):
# same supervisor is used for training a... | """
BQ-NCO
Copyright (c) 2023-present NAVER Corp.
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license
"""
DEBUG_NUM_BATCHES = 3
class TrajectoryLearner:
def __init__(self, args, net, module, device, data_iterator, optimizer=None, checkpointer=None):
# same supervisor is used for training a... | epoch_metrics_train = EpochMetrics() | 2 | 2023-10-27 09:08:45+00:00 | 2k |
coder-pig/YuQueBackups | app.py | [
{
"identifier": "init_token",
"path": "yuque_doc_backups.py",
"snippet": "def is_dir_existed(file_path, mkdir=True):\ndef write_text_to_file(content, file_path, mode=\"w+\"):\ndef scan_file_list_by_suffix(file_dir=os.getcwd(), suffix=\"\"):\n def __init__(self, repo_id, repo_type, repo_slug, repo_nam... | from yuque_doc_backups import init_token, fetch_user_id, fetch_repo_list, fetch_toc_list, doc_count
from yeque_md_to_local import search_all_file, md_to_local, pic_url_path_record_list, download_pic
import asyncio
import time | 685 | # -*- coding: utf-8 -*-
# !/usr/bin/env python
"""
-------------------------------------------------
File : app.py
Author : CoderPig
date : 2023-10-26 14:57
Desc : 语雀备份脚本-入口
-------------------------------------------------
"""
if __name__ == '__main__':
yq_token = input("请输入你的语雀Token：")
... | # -*- coding: utf-8 -*-
# !/usr/bin/env python
"""
-------------------------------------------------
File : app.py
Author : CoderPig
date : 2023-10-26 14:57
Desc : 语雀备份脚本-入口
-------------------------------------------------
"""
if __name__ == '__main__':
yq_token = input("请输入你的语雀Token：")
... | md_to_local(yq_doc_file_list) | 1 | 2023-10-26 08:35:04+00:00 | 2k |
tobagin/whakarere | whakarere/pages/whatsapp.py | [
{
"identifier": "ChatItem",
"path": "whakarere/types/chat.py",
"snippet": "class ChatItem(GObject.Object):\n chat_id = GObject.Property(type=str)\n chat_name = GObject.Property(type=str)\n chat_picture = GObject.Property(type=Gdk.Texture)\n last_message_body = GObject.Property(type=str)\n ... | import gi
import base64, requests, threading
from whakarere.types.chat import ChatItem
from whakarere.widgets.titlebar import WindowTitlebarWidget
from whakarere.widgets.main_menu import MainMenuButtonWidget
from gi.repository import Gtk, Adw, GLib, Gio, GdkPixbuf, Pango, Gdk, GObject
from datetime import datetime | 1,319 |
gi.require_version("Gtk", "4.0")
gi.require_version("Adw", "1")
gi.require_version("GdkPixbuf", "2.0")
class WhatsappMessengerPage(Adw.NavigationPage):
def __init__(self, app_manager, session_id):
super().__init__()
self.set_title("Whakarere")
self.app_manager = app_manager
self.se... |
gi.require_version("Gtk", "4.0")
gi.require_version("Adw", "1")
gi.require_version("GdkPixbuf", "2.0")
class WhatsappMessengerPage(Adw.NavigationPage):
def __init__(self, app_manager, session_id):
super().__init__()
self.set_title("Whakarere")
self.app_manager = app_manager
self.se... | self.window_titlebar_widget = WindowTitlebarWidget() | 1 | 2023-10-29 15:46:50+00:00 | 2k |
Agricultural-Robotics-Bonn/pagnerf | loss/lin_assignment_things.py | [
{
"identifier": "centers_from_3d_points_with_ids",
"path": "utils/outlier_rejection.py",
"snippet": "def centers_from_3d_points_with_ids(points):\n # points: [N,[x,y,z,ID]]\n # return: [I,[x,y,z,ID]]\n # K: number of unique IDs\n # [K,[x,y,z,ID]]: centers of the points with t... | import numpy as np
import torch
import scipy
import torch.nn.functional as F
from torch import nn
from utils.outlier_rejection import centers_from_3d_points_with_ids, add_position_id_range_cost | 1,485 | # from panoptic lifting implementation
#
#https://github.com/nihalsid/panoptic-lifting/blob/7af7a3e8477ead8e57f699a240d993e3bc21ee42/trainer/train_panopli_tensorf.py#L195-L206
class LinAssignmentThingsLoss(nn.Module):
def __init__(self, outlier_rejection=False, min_distance=0.2, max_distance=0.5, *args, **kwargs... | # from panoptic lifting implementation
#
#https://github.com/nihalsid/panoptic-lifting/blob/7af7a3e8477ead8e57f699a240d993e3bc21ee42/trainer/train_panopli_tensorf.py#L195-L206
class LinAssignmentThingsLoss(nn.Module):
def __init__(self, outlier_rejection=False, min_distance=0.2, max_distance=0.5, *args, **kwargs... | cost_matrix = add_position_id_range_cost(cost_matrix, current_inst_centers) | 1 | 2023-10-30 16:14:39+00:00 | 2k |
John-WL/sd-webui-inpaint-difference | lib_inpaint_difference/webui_hijacks.py | [
{
"identifier": "DifferenceGlobals",
"path": "lib_inpaint_difference/globals.py",
"snippet": "class DifferenceGlobals:\n tab_index = None\n\n base_image = None\n altered_image = None\n generated_mask = None\n\n is_extension_enabled = opts.data.get('inpaint_difference_enabled', True)\n ... | import gradio as gr
from modules import img2img, ui_loadsave
from lib_inpaint_difference.globals import DifferenceGlobals
from lib_inpaint_difference.one_time_callable import one_time_callable
from lib_inpaint_difference.img2img_tab_extender import Img2imgTabExtender | 1,429 |
@one_time_callable
def hijack_img2img_processing():
original_img2img_processing = img2img.img2img
def hijack_func(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch,
init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint,
... |
@one_time_callable
def hijack_img2img_processing():
original_img2img_processing = img2img.img2img
def hijack_func(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch,
init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint,
... | if mode == DifferenceGlobals.tab_index: | 0 | 2023-10-30 16:17:34+00:00 | 2k |
BIT-DA/Annotator | tools/utils/train_utils.py | [
{
"identifier": "common_utils",
"path": "tools/utils/common/common_utils.py",
"snippet": "def check_numpy_to_torch(x):\ndef limit_period(val, offset=0.5, period=np.pi):\ndef drop_info_with_name(info, name):\ndef rotate_points_along_z(points, angle):\ndef mask_points_by_range(points, limit_range):\ndef g... | import glob
import os
import torch
import tqdm
import time
import pcdet
from torch.nn.utils import clip_grad_norm_
from tools.utils.common import common_utils, commu_utils | 756 |
def train_one_epoch(model, optimizer, train_loader, model_func, lr_scheduler, accumulated_iter, optim_cfg,
rank, tbar, total_it_each_epoch, dataloader_iter, tb_log=None, leave_pbar=False):
if total_it_each_epoch == len(train_loader):
dataloader_iter = iter(train_loader)
if rank =... |
def train_one_epoch(model, optimizer, train_loader, model_func, lr_scheduler, accumulated_iter, optim_cfg,
rank, tbar, total_it_each_epoch, dataloader_iter, tb_log=None, leave_pbar=False):
if total_it_each_epoch == len(train_loader):
dataloader_iter = iter(train_loader)
if rank =... | avg_data_time = commu_utils.average_reduce_value(cur_data_time) | 1 | 2023-10-31 08:11:57+00:00 | 2k |
hl123-123/yiyan-ppt | gradio_test.py | [
{
"identifier": "yiyan_api",
"path": "yiyan.py",
"snippet": "def yiyan_api(message,access_token,use4=False):\n if use4:\n url = \"https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro?access_token=\" + access_token\n else:\n url = \"https://aip.baidubce.co... | import gradio as gr
import gradio as gr
import os
import time
import random
import structure_article
import shutil
from yiyan import yiyan_api, get_access_token
from mdtree import tree2ppt
from PIL import Image | 953 | # def image_mod():
# return Image.open("pptx_static/static/img.png")
def save_knowledge_func(task_name,knowledge_content,mode,sub_num):
time1= time.time()
sub_num = int(sub_num)
rand_seed = str(random.randint(0,10000000000000000000000000000000000000000000000000000))
character_a = "你是一个精通各方面知识的人"
... | # def image_mod():
# return Image.open("pptx_static/static/img.png")
def save_knowledge_func(task_name,knowledge_content,mode,sub_num):
time1= time.time()
sub_num = int(sub_num)
rand_seed = str(random.randint(0,10000000000000000000000000000000000000000000000000000))
character_a = "你是一个精通各方面知识的人"
... | tree2ppt.Tree2PPT(content,"./my_ppt_mode/"+str(int(mode)),save_path=save_path) | 2 | 2023-10-29 15:10:06+00:00 | 2k |
thoddnn/open-datagen | opendatagen/anonymizer.py | [
{
"identifier": "OpenAIChatModel",
"path": "opendatagen/model.py",
"snippet": "class OpenAIChatModel(BaseModel):\n\n name:str = \"gpt-3.5-turbo-1106\"\n system_prompt:Optional[str] = \"No verbose.\"\n max_tokens:Optional[int] = 256\n temperature:Optional[List[float]] = [1]\n json_mode:Opt... | import re
import spacy
from opendatagen.model import OpenAIChatModel, ModelName
from opendatagen.utils import load_file | 1,575 |
class Anonymizer:
NER_PLACEHOLDER = {
"PERSON": "{person}",
"ORG": "{organization}",
"GPE": "{location}",
"DATE": "{date}",
"TIME": "{time}",
"NORP": "{group}",
"FAC": "{facility}",
"LOC": "{location}",
"PRODUCT": "{product}",
"EVENT"... |
class Anonymizer:
NER_PLACEHOLDER = {
"PERSON": "{person}",
"ORG": "{organization}",
"GPE": "{location}",
"DATE": "{date}",
"TIME": "{time}",
"NORP": "{group}",
"FAC": "{facility}",
"LOC": "{location}",
"PRODUCT": "{product}",
"EVENT"... | def __init__(self, completion_model:OpenAIChatModel): | 0 | 2023-10-27 17:38:37+00:00 | 2k |
HAMNET-AI/PDFTriage | src/routers.py | [
{
"identifier": "fetch_figure",
"path": "src/triage.py",
"snippet": "def fetch_figure(query):\n query_prompt = f\"What contents mentioned in the figure of this pdf\"\n path = query_engine.query(query_prompt).metadata['json_path_response_str'].replace(\"&&\", \"&\")\n jsonpath_expression = parse... | from llama_index.tools import ToolMetadata
from llama_index.selectors.llm_selectors import LLMSingleSelector
from .triage import fetch_figure, fetch_pages, fetch_sections, fetch_table, retrieve | 884 |
def router(query):
choices = [
ToolMetadata(description="Get the text contained in the pages listed", name="fetch_pages"),
ToolMetadata(description="Get the text contained in the section listed", name="fetch_sections"),
ToolMetadata(description="Get the text contained in the figure caption... |
def router(query):
choices = [
ToolMetadata(description="Get the text contained in the pages listed", name="fetch_pages"),
ToolMetadata(description="Get the text contained in the section listed", name="fetch_sections"),
ToolMetadata(description="Get the text contained in the figure caption... | content = fetch_pages(query=query) | 1 | 2023-10-30 14:36:23+00:00 | 2k |
zhanggang001/HEDNet | pcdet/models/backbones_3d/vfe/dynamic_voxel_vfe.py | [
{
"identifier": "VFETemplate",
"path": "pcdet/models/backbones_3d/vfe/vfe_template.py",
"snippet": "class VFETemplate(nn.Module):\n def __init__(self, model_cfg, **kwargs):\n super().__init__()\n self.model_cfg = model_cfg\n\n def get_output_feature_dim(self):\n raise NotImple... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_scatter
from .vfe_template import VFETemplate
from .dynamic_pillar_vfe import PFNLayerV2 | 746 |
try:
except Exception as e:
# Incase someone doesn't want to use dynamic pillar vfe and hasn't installed torch_scatter
pass
class DynamicVoxelVFE(VFETemplate):
def __init__(self, model_cfg, num_point_features, voxel_size, grid_size, point_cloud_range, **kwargs):
super().__init__(model_cfg=model_... |
try:
except Exception as e:
# Incase someone doesn't want to use dynamic pillar vfe and hasn't installed torch_scatter
pass
class DynamicVoxelVFE(VFETemplate):
def __init__(self, model_cfg, num_point_features, voxel_size, grid_size, point_cloud_range, **kwargs):
super().__init__(model_cfg=model_... | PFNLayerV2(in_filters, out_filters, self.use_norm, last_layer=(i >= len(num_filters) - 2)) | 1 | 2023-10-25 02:57:35+00:00 | 2k |
deepsearch-ai/deepsearch | deepsearchai/tests/sources/test_local.py | [
{
"identifier": "MEDIA_TYPE",
"path": "deepsearchai/enums.py",
"snippet": "class MEDIA_TYPE(Enum):\n UNKNOWN = -1\n IMAGE = 1\n TEXT = 2\n AUDIO = 3\n VIDEO = 4"
},
{
"identifier": "DataSource",
"path": "deepsearchai/sources/data_source.py",
"snippet": "class DataSource(En... | import unittest
import mock.mock
from unittest import mock
from unittest.mock import patch
from deepsearchai.enums import MEDIA_TYPE
from deepsearchai.sources.data_source import DataSource
from deepsearchai.sources.local import LocalDataSource | 862 |
class LocalDataSourceTest(unittest.TestCase):
def setUp(self):
self.local_data_source = LocalDataSource()
@patch("os.walk")
@patch("PIL.Image.open")
def test_add_data_image_directory_with_no_existing_files(
self, mock_image_file, mock_listdir
):
embedding_models_config =... |
class LocalDataSourceTest(unittest.TestCase):
def setUp(self):
self.local_data_source = LocalDataSource()
@patch("os.walk")
@patch("PIL.Image.open")
def test_add_data_image_directory_with_no_existing_files(
self, mock_image_file, mock_listdir
):
embedding_models_config =... | DataSource.LOCAL, | 1 | 2023-10-27 06:46:22+00:00 | 2k |
jerpint/RAGTheDocs | app.py | [
{
"identifier": "embed_documents",
"path": "embed_docs.py",
"snippet": "def embed_documents(homepage_url, save_directory, target_version=None):\n # adds https:// and trailing slash\n homepage_url = sanitize_url(homepage_url)\n\n # Crawl the website using scrapy\n run_spider(\n homepag... | import os
import gradio as gr
import pandas as pd
import cfg
from typing import Optional, Tuple
from buster.completers import Completion
from embed_docs import embed_documents
from cfg import setup_buster | 922 |
# from embed_docs import embed_rtd_website
# from rtd_scraper.scrape_rtd import scrape_rtd
# Typehint for chatbot history
ChatHistory = list[list[Optional[str], Optional[str]]]
# Because this is a one-click deploy app, we will be relying on env. variables being set
openai_api_key = os.getenv("OPENAI_API_KEY") # M... |
# from embed_docs import embed_rtd_website
# from rtd_scraper.scrape_rtd import scrape_rtd
# Typehint for chatbot history
ChatHistory = list[list[Optional[str], Optional[str]]]
# Because this is a one-click deploy app, we will be relying on env. variables being set
openai_api_key = os.getenv("OPENAI_API_KEY") # M... | embed_documents( | 0 | 2023-10-31 03:36:43+00:00 | 2k |
Paulo-Lopes-Estevao/ci-generator | cigen/core/github/nodejs_action.py | [
{
"identifier": "Steps",
"path": "cigen/core/github/github_action.py",
"snippet": "class Steps:\n def __init__(self, steps: list[dict]) -> None:\n self.steps = steps\n\n def to_dict(self) -> list[dict]:\n return self.steps\n\n def add(self, step: dict) -> None:\n self.steps... | from abc import ABC, abstractmethod
from cigen.core.github.github_action import Steps, Action | 792 | from __future__ import annotations
class NodejsActionBuilder(ABC):
@property
@abstractmethod
def build(self) -> Action:
pass
@property
@abstractmethod
def build_steps(self) -> NodejsActionSteps:
pass
@abstractmethod
def base(self) -> None:
pass
@abstra... | from __future__ import annotations
class NodejsActionBuilder(ABC):
@property
@abstractmethod
def build(self) -> Action:
pass
@property
@abstractmethod
def build_steps(self) -> NodejsActionSteps:
pass
@abstractmethod
def base(self) -> None:
pass
@abstra... | self.step = Steps([]) | 0 | 2023-10-31 03:36:36+00:00 | 2k |
TheCompAce/ShellSpeak | modules/llm.py | [
{
"identifier": "get_token_count",
"path": "modules/utils.py",
"snippet": "def get_token_count(text, token_adjust=1):\n # Define the maximum length for a text chunk\n max_length = 1000000\n\n # Initialize the total token count\n total_token_count = 0\n\n # Split the text into chunks of up... | from enum import Enum
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from concurrent.futures import ThreadPoolExecutor
from modules.utils import get_token_count
from modules.responseCache import ResponseCache
import json
import sqlite3
import os
import torch
import transformers
import requests
i... | 1,430 |
transformers.logging.set_verbosity_error()
executor = ThreadPoolExecutor()
class ModelTypes(Enum):
OpenAI = "OpenAI"
OpenAI4 = "OpenAI4"
Mistral = "Mistral"
StableBeluga7B = "StableBeluga7B"
Zephyr7bAlpha = "Zephyr7bAlpha"
Zephyr7bBeta = "Zephyr7bBeta"
Falcon7BInst = "Falcon7BInst"
c... |
transformers.logging.set_verbosity_error()
executor = ThreadPoolExecutor()
class ModelTypes(Enum):
OpenAI = "OpenAI"
OpenAI4 = "OpenAI4"
Mistral = "Mistral"
StableBeluga7B = "StableBeluga7B"
Zephyr7bAlpha = "Zephyr7bAlpha"
Zephyr7bBeta = "Zephyr7bBeta"
Falcon7BInst = "Falcon7BInst"
c... | token_ct = max_tokens - int(get_token_count(system_prompt + "\n" + user_prompt) + 20) | 0 | 2023-10-31 23:35:19+00:00 | 2k |
qym7/SparseDiff | sparse_diffusion/models/transconv_layer.py | [
{
"identifier": "SparseXtoy",
"path": "sparse_diffusion/models/layers.py",
"snippet": "class SparseXtoy(nn.Module):\n def __init__(self, dx, dy):\n \"\"\"Map node features to global features\"\"\"\n super().__init__()\n self.lin = nn.Linear(4 * dx, dy)\n\n def forward(self, X,... | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Union
from torch import Tensor
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.typing import Adj, OptTensor, Size
from torch_geome... | 1,359 |
class TransformerConv(MessagePassing):
r"""The graph transformer operator from the `"Masked Label Prediction:
Unified Message Passing Model for Semi-Supervised Classification"
<https://arxiv.org/abs/2009.03509>`_ paper
.. math::
\mathbf{x}^{\prime}_i = \mathbf{W}_1 \mathbf{x}_i +
\s... |
class TransformerConv(MessagePassing):
r"""The graph transformer operator from the `"Masked Label Prediction:
Unified Message Passing Model for Semi-Supervised Classification"
<https://arxiv.org/abs/2009.03509>`_ paper
.. math::
\mathbf{x}^{\prime}_i = \mathbf{W}_1 \mathbf{x}_i +
\s... | self.e_y = SparseEtoy(de, dy) | 1 | 2023-10-30 12:12:16+00:00 | 2k |
ZhangLin-PKU/FedFTG | train.py | [
{
"identifier": "util_dataset",
"path": "utils/util_dataset.py",
"snippet": "COLOR_MAP = ['red', 'green', 'blue', 'black', 'brown', 'purple', 'yellow', 'pink', 'cyan', 'gray']\r\nclass DatasetObject:\r\nclass Dataset(torch.utils.data.Dataset):\r\nclass DatasetFromDir(data.Dataset):\r\n def __init__(s... | from utils import util_dataset, util_parser
from models import model_choose_fn
from methods import FedAvg, FedProx, SCAFFOLD, MOON, FedDyn
from methods import FedFTG, FedProxGAN, SCAFFOLDGAN, MOONGAN, FedDynGAN
import torch
import os
import random
import numpy as np
import matplotlib.pyplot as plt
| 1,537 |
def run(conf):
print('Init-------------------------')
root_path = os.getcwd()
# print(root_path)
if root_path.endswith('scripts'):
root_path = os.path.dirname(root_path)
conf['savepath'] = os.path.join(root_path, conf['savepath'].strip())
print('Data and results save path is... |
def run(conf):
print('Init-------------------------')
root_path = os.getcwd()
# print(root_path)
if root_path.endswith('scripts'):
root_path = os.path.dirname(root_path)
conf['savepath'] = os.path.join(root_path, conf['savepath'].strip())
print('Data and results save path is... | data_obj = util_dataset.DatasetObject(dataset=conf['dataset'],
| 0 | 2023-10-26 03:35:17+00:00 | 2k |
Shou-Hsu/Report.ai | summarize.py | [
{
"identifier": "convert_json",
"path": "utils.py",
"snippet": "def convert_json(txt:str, item_list:list) -> str:\n txt = txt.replace('\\n', '').replace('#', '')\n\n output = dict()\n for i in range(len(item_list)):\n start = txt.lower().find(item_list[i].lower() + ':')\n\n if i !... | from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from utils import convert_json, get_items
from langchain.docstore.document import Document
from langchain.prompts import PromptTemplate
from langchain.chains.llm import LLMChain
from tqdm import tqdm
from utils import llm
from lang... | 669 |
class generate_summary():
def __init__(self, file_name:str, original_language:str, translated_language:str, chunk_size:int, output_dir:str) -> None:
self.file_name = file_name
self.chunk_size = chunk_size
self.original_language = original_language
self.translated_language = transla... |
class generate_summary():
def __init__(self, file_name:str, original_language:str, translated_language:str, chunk_size:int, output_dir:str) -> None:
self.file_name = file_name
self.chunk_size = chunk_size
self.original_language = original_language
self.translated_language = transla... | item_list, items, item_format = get_items('general') | 1 | 2023-10-30 12:29:20+00:00 | 2k |
Thinksy-app/thinksy | app/review_ops.py | [
{
"identifier": "SYSTEM_TEXT",
"path": "app/env.py",
"snippet": "SYSTEM_TEXT = os.environ.get(\"OPENAI_SYSTEM_TEXT\", DEFAULT_SYSTEM_TEXT)"
},
{
"identifier": "fetch_channel_messages",
"path": "app/slack_ops.py",
"snippet": "def fetch_channel_messages(\n client: WebClient,\n user: ... | from datetime import datetime
from slack_sdk import WebClient
from app.env import (
SYSTEM_TEXT,
)
from app.slack_ops import fetch_channel_messages, filter_non_membership_and_join
from app.openai_ops import make_synchronous_openai_call
import json
import re | 1,105 | """
Business logic writing the reviews
"""
def generate_review(context, user: str, web_client: WebClient, selected_conversations, start_date, end_date, logger):
"""
Generates the review based on the user's criteria
Parameters:
user (str): The user ID from Slack
slack_enc_team_id (str): ... | """
Business logic writing the reviews
"""
def generate_review(context, user: str, web_client: WebClient, selected_conversations, start_date, end_date, logger):
"""
Generates the review based on the user's criteria
Parameters:
user (str): The user ID from Slack
slack_enc_team_id (str): ... | "content": SYSTEM_TEXT | 0 | 2023-10-26 23:47:28+00:00 | 2k |
CrystalWindSnake/nicegui-toolkit | __tests/test_componentStore.py | [
{
"identifier": "ComponentStore",
"path": "niceguiToolkit/layout/componentStore.py",
"snippet": "class ComponentStore:\n def __init__(self) -> None:\n self.cpMapper: Dict[_TNiceguiComponentId, ComponentInfo] = {}\n self._styles_records: Set[_TNiceguiComponentId] = set()\n self._c... | from niceguiToolkit.layout.componentStore import ComponentStore
from niceguiToolkit.utils import astCore
from .utils import get_data_file | 1,464 |
def test_create_new_style_call():
mock_code_file = get_data_file("code1.py")
exp_file = get_data_file("code1_exp.txt")
store = ComponentStore()
|
def test_create_new_style_call():
mock_code_file = get_data_file("code1.py")
exp_file = get_data_file("code1_exp.txt")
store = ComponentStore()
| entry_info = astCore._T_entry_point_info( | 1 | 2023-10-27 13:50:03+00:00 | 2k |
EnVision-Research/Defect_Spectrum | models/stylegan/mapper.py | [
{
"identifier": "EqualLinear",
"path": "models/stylegan/modules.py",
"snippet": "class EqualLinear(nn.Module):\r\n def __init__(\r\n self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None\r\n ):\r\n super().__init__()\r\n\r\n self.weight = nn.Parameter(torch.... | from abc import abstractmethod
from torch import nn
from models.stylegan.modules import EqualLinear, PixelNorm
import torch | 1,149 |
STYLESPACE_DIMENSIONS = [512 for _ in range(15)] + [256, 256, 256] + [128, 128, 128] + [64, 64, 64] + [32, 32]
class ConcatSquashLinear(nn.Module):
def __init__(self, dim_in, dim_out, dim_ctx):
super(ConcatSquashLinear, self).__init__()
self._layer = EqualLinear(dim_in, dim_out, lr_mul=0.01, acti... |
STYLESPACE_DIMENSIONS = [512 for _ in range(15)] + [256, 256, 256] + [128, 128, 128] + [64, 64, 64] + [32, 32]
class ConcatSquashLinear(nn.Module):
def __init__(self, dim_in, dim_out, dim_ctx):
super(ConcatSquashLinear, self).__init__()
self._layer = EqualLinear(dim_in, dim_out, lr_mul=0.01, acti... | layers = [PixelNorm()] | 1 | 2023-10-26 10:28:26+00:00 | 2k |
ORI-Muchim/BEGANSing | main.py | [
{
"identifier": "update_text_file_in_yaml",
"path": "main_util.py",
"snippet": "def update_text_file_in_yaml(yaml_path):\n yaml = YAML()\n yaml.preserve_quotes = True\n try:\n with open(yaml_path, 'r', encoding='utf-8') as file:\n data = yaml.load(file)\n\n current_text... | import os
import sys
import shutil
import argparse
from main_util import update_text_file_in_yaml, find_index_files
from get_models import get_model | 1,003 |
if len(sys.argv) < 4:
print("Usage: python main.py <model_name> <song_name> <f0_up_key> [--audiosr]")
sys.exit(1)
# Init
model_name = sys.argv[1]
song_name = sys.argv[2]
f0_up_key = int(sys.argv[3]) # transpose value
input_path = f"../samples/latest_G_{song_name}.wav"
output_path = f"../samples/latest_G_{son... |
if len(sys.argv) < 4:
print("Usage: python main.py <model_name> <song_name> <f0_up_key> [--audiosr]")
sys.exit(1)
# Init
model_name = sys.argv[1]
song_name = sys.argv[2]
f0_up_key = int(sys.argv[3]) # transpose value
input_path = f"../samples/latest_G_{song_name}.wav"
output_path = f"../samples/latest_G_{son... | get_model() | 2 | 2023-10-29 09:32:19+00:00 | 2k |
Charl-AI/stochastic-caching | run_benchmark.py | [
{
"identifier": "DummyDataset",
"path": "benchmark/dataset.py",
"snippet": "class DummyDataset(Dataset):\n def __init__(self, data_dir: str, cache_limit_gib: int):\n \"\"\"PyTorch dataset for dummy data.\n No cache is used if cache_limit_gib is 0.\"\"\"\n self.data_dir = data_dir... | import argparse
import os
import pandas as pd
import torch
from torch.utils.data import DataLoader
from benchmark.dataset import DummyDataset
from benchmark.trainer import train | 889 |
parser = argparse.ArgumentParser()
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--data-dir", type=str, default="/data2/dummy_data")
parser.add_argument("--cache-limit-gib", type=int, default=0)
parser.add_argument("--batch-size", type=int, default=256)
parser.add_argument("--num-workers",... |
parser = argparse.ArgumentParser()
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--data-dir", type=str, default="/data2/dummy_data")
parser.add_argument("--cache-limit-gib", type=int, default=0)
parser.add_argument("--batch-size", type=int, default=256)
parser.add_argument("--num-workers",... | dataset = DummyDataset(args.data_dir, args.cache_limit_gib) | 0 | 2023-10-27 09:33:43+00:00 | 2k |
hugoycj/light-hloc | lighthloc/pipeline.py | [
{
"identifier": "extract_features",
"path": "lighthloc/extract_features.py",
"snippet": "def resize_image(image, size, interp):\n def __init__(self, root, conf, paths=None):\n def __getitem__(self, idx):\n def __len__(self):\ndef main(conf: Dict,\n image_dir: Path,\n export_dir:... | from lighthloc import extract_features, match_features, reconstruction
from lighthloc.associators import pairs_from_retrieval, pairs_from_exhaustive, pairs_from_sequance
from pathlib import Path
import click | 1,377 | # To install hloc, see: https://github.com/cvg/Hierarchical-retrivalization
mapper_confs = {
'default' : {},
'fast' : {'ba_global_max_num_iterations': 20, "ba_global_max_refinements":1,
"ba_global_points_freq":200000}
}
@click.command()
@click.option('--data', type=str, help='Path to data direc... | # To install hloc, see: https://github.com/cvg/Hierarchical-retrivalization
mapper_confs = {
'default' : {},
'fast' : {'ba_global_max_num_iterations': 20, "ba_global_max_refinements":1,
"ba_global_points_freq":200000}
}
@click.command()
@click.option('--data', type=str, help='Path to data direc... | matcher_conf = match_features.confs[matcher_type] | 1 | 2023-10-27 01:20:50+00:00 | 2k |
KUNLP/XAI_EvidenceExtraction | src/functions/utils.py | [
{
"identifier": "SquadV1Processor",
"path": "src/functions/processor_sent.py",
"snippet": "class SquadV1Processor(SquadProcessor):\r\n train_file = \"train-v1.1.json\"\r\n dev_file = \"dev-v1.1.json\"\r"
},
{
"identifier": "squad_convert_examples_to_features",
"path": "src/functions/pr... | import logging
import random
import torch
import numpy as np
import os
from src.functions.processor_sent import (
SquadV1Processor,
squad_convert_examples_to_features
)
| 1,277 |
def init_logger():
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(arg... |
def init_logger():
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(arg... | processor = SquadV1Processor()
| 0 | 2023-10-25 07:03:47+00:00 | 2k |
joenghl/HYPO | hypo/algo/hypo_bc.py | [
{
"identifier": "PPOPolicy",
"path": "hypo/network/policy.py",
"snippet": "class PPOPolicy(nn.Module):\n\n def __init__(self, state_shape, action_shape, hidden_units=(64, 64),\n hidden_activation=nn.Tanh()):\n super().__init__()\n\n self.net = build_mlp(\n inp... | from torch import nn
from torch.optim import Adam
from hypo.network import PPOPolicy
from .base import Algorithm
import torch | 768 |
class HBC(Algorithm):
def __init__(self, buffer_exp, state_shape, action_shape, device, seed, logger, gamma=0.995,
log_interval=1e3, lr_actor=3e-4, batch_size=64, units_actor=(64, 64), **kwargs):
super().__init__(state_shape, action_shape, device, seed, logger, gamma)
self.buffer... |
class HBC(Algorithm):
def __init__(self, buffer_exp, state_shape, action_shape, device, seed, logger, gamma=0.995,
log_interval=1e3, lr_actor=3e-4, batch_size=64, units_actor=(64, 64), **kwargs):
super().__init__(state_shape, action_shape, device, seed, logger, gamma)
self.buffer... | self.actor = PPOPolicy( | 0 | 2023-10-27 10:37:44+00:00 | 2k |
jmcruvellier/little_monkey | custom_components/little_monkey/sensor.py | [
{
"identifier": "EcojokoEntity",
"path": "custom_components/little_monkey/entity.py",
"snippet": "class EcojokoEntity(CoordinatorEntity):\n \"\"\"EcojokoEntity class.\"\"\"\n\n _attr_attribution = ATTRIBUTION\n\n def __init__(self, coordinator, device_name, firmware_version):\n \"\"\"Ini... | from homeassistant.components.sensor import (
SensorStateClass,
SensorDeviceClass,
)
from homeassistant.const import UnitOfPower, UnitOfEnergy, UnitOfTemperature, PERCENTAGE, CONF_NAME
from custom_components.little_monkey.entity import EcojokoEntity, EcojokoSensor
from .const import (
DOMAIN,
CONF_USE_H... | 1,325 | """Sensor platform for mon_ecojoko."""
from __future__ import annotations
async def async_setup_entry(hass, config_entry, async_add_entities):
"""Set up the custom component sensors."""
# Fetch data or configure your sensors here
coordinator = hass.data[DOMAIN][config_entry.entry_id]
# Create the ma... | """Sensor platform for mon_ecojoko."""
from __future__ import annotations
async def async_setup_entry(hass, config_entry, async_add_entities):
"""Set up the custom component sensors."""
# Fetch data or configure your sensors here
coordinator = hass.data[DOMAIN][config_entry.entry_id]
# Create the ma... | main_device.add_child_entity(EcojokoSensor( | 1 | 2023-10-29 21:03:13+00:00 | 2k |
stanleylsx/text_embedding | engines/predict.py | [
{
"identifier": "configure",
"path": "config.py",
"snippet": ""
},
{
"identifier": "Model",
"path": "engines/model.py",
"snippet": "class Model(torch.nn.Module):\n def __init__(self):\n super(Model, self).__init__()\n self.model_type = configure['model_type']\n se... | from config import configure
from engines.model import Model
from engines.utils.metrics import MyModel
from torch.utils.data import DataLoader
from mteb import MTEB
import pandas as pd
import torch
import os | 837 | # -*- coding: utf-8 -*-
# @Time : 2023/10/27 22:05
# @Author : lishouxian
# @Email : gzlishouxian@gmail.com
# @File : predict.py
# @Software: VSCode
class Predictor:
def __init__(self, data_manage, device, logger):
self.logger = logger
self.data_manage = data_manage
self.device = device
... | # -*- coding: utf-8 -*-
# @Time : 2023/10/27 22:05
# @Author : lishouxian
# @Email : gzlishouxian@gmail.com
# @File : predict.py
# @Software: VSCode
class Predictor:
def __init__(self, data_manage, device, logger):
self.logger = logger
self.data_manage = data_manage
self.device = device
... | self.model = Model().to(device) | 1 | 2023-10-27 07:47:02+00:00 | 2k |
akekic/causal-component-analysis | data_generator/mixing_function.py | [
{
"identifier": "leaky_tanh",
"path": "data_generator/utils.py",
"snippet": "def leaky_tanh(x: Tensor, alpha: float = 1.0, beta: float = 0.1) -> Tensor:\n return torch.tanh(alpha * x) + beta * x"
},
{
"identifier": "sample_invertible_matrix",
"path": "data_generator/utils.py",
"snippe... | from abc import ABC
from pathlib import Path
from torch import Tensor
from .utils import leaky_tanh, sample_invertible_matrix
import pandas as pd
import torch | 1,048 | """
def __init__(self, latent_dim: int, observation_dim: int) -> None:
self.latent_dim = latent_dim
self.observation_dim = observation_dim
def __call__(self, v: Tensor) -> Tensor:
"""
Apply the mixing function to the latent variables.
Parameters
----------
... |
class MixingFunction(ABC):
"""
Base class for mixing functions.
The mixing function is the function that maps from the latent space to the observation space.
Parameters
----------
latent_dim: int
Dimension of the latent space.
observation_dim: int
Dimension of the obser... | nonlinearities.append(leaky_tanh) | 0 | 2023-10-25 09:25:26+00:00 | 2k |
facebookresearch/verde | src/generate/export.py | [
{
"identifier": "to_cuda",
"path": "src/utils.py",
"snippet": "def to_cuda(*args):\n \"\"\"\n Move tensors to CUDA.\n \"\"\"\n if not CUDA:\n return args\n return [None if x is None else x.cuda() for x in args]"
},
{
"identifier": "timeout",
"path": "src/utils.py",
... | import os
import io
import sys
import ast
import time
import numpy as np
import torch
from logging import getLogger
from collections import OrderedDict
from torch import nn
from ..utils import to_cuda, timeout, TimeoutError | 1,067 | # 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.
logger = getLogger()
class Generator(object):
def __init__(self, params, gen):
"""
Initialize t... | # 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.
logger = getLogger()
class Generator(object):
def __init__(self, params, gen):
"""
Initialize t... | except TimeoutError: | 2 | 2023-10-30 17:53:57+00:00 | 2k |
Paiman-Rasoli/flatway | src/tests/test_flatten.py | [
{
"identifier": "mock_list_with_deep_one",
"path": "src/tests/fixtures.py",
"snippet": "@pytest.fixture\ndef mock_list_with_deep_one():\n return [1, 2, 3, 4, 5, 6, 7, 8, [9, 10, 11], 12]"
},
{
"identifier": "mock_list_with_deep_five",
"path": "src/tests/fixtures.py",
"snippet": "@pyte... | from flatway.flatten import flatten, flattenDict
from .fixtures import (mock_list_with_deep_one, mock_list_with_deep_five,
mock_tuple_with_deep_one, mock_tuple_with_deep_five,
mock_dictionary_deep_one, mock_dictionary_deep_three) | 646 |
def test_flatten_of_list_with_deep_one(mock_list_with_deep_one):
result = flatten(mock_list_with_deep_one)
expect = [x for x in range(1, 13)]
assert result == expect
assert isinstance(result, list)
|
def test_flatten_of_list_with_deep_one(mock_list_with_deep_one):
result = flatten(mock_list_with_deep_one)
expect = [x for x in range(1, 13)]
assert result == expect
assert isinstance(result, list)
| def test_flatten_of_list_with_deep_five(mock_list_with_deep_five): | 1 | 2023-10-25 20:47:36+00:00 | 2k |
Muhammadali-Akbarov/aiogram-bot-template | aiogram_bot_template/db/db_api/storages/postgres/storage.py | [
{
"identifier": "MultipleQueryResults",
"path": "aiogram_bot_template/db/db_api/storages/basestorage/storage.py",
"snippet": "class MultipleQueryResults:\n def __init__(self, results: list[typing.Mapping[str, Any]]):\n self._data: list[dict[str, Any]] = [{**i} for i in results]\n\n @propert... | import time
import asyncpg
import structlog
from typing import Any, Optional, TypeVar
from ..basestorage.storage import MultipleQueryResults, RawConnection, SingleQueryResult | 901 |
T = TypeVar("T")
class PostgresConnection(RawConnection):
def __init__(
self,
connection_poll: asyncpg.Pool,
logger: structlog.typing.FilteringBoundLogger,
):
self._pool = connection_poll
self._logger = logger
async def _fetch(
self,
sql: str,
... |
T = TypeVar("T")
class PostgresConnection(RawConnection):
def __init__(
self,
connection_poll: asyncpg.Pool,
logger: structlog.typing.FilteringBoundLogger,
):
self._pool = connection_poll
self._logger = logger
async def _fetch(
self,
sql: str,
... | ) -> SingleQueryResult: | 2 | 2023-10-28 19:44:58+00:00 | 2k |
Doubling-Open-Source/git_calculator | src/calculators/throughput_calculator.py | [
{
"identifier": "git_log",
"path": "src/git_ir.py",
"snippet": "def git_log():\n def to_obj(line):\n parts = line.split('|', 5)\n parts[3] = parts[3].split() # Multiple parents\n return git_obj.commit(*parts)\n res = [\n to_obj(line)\n for line in git_run('log',... | from datetime import datetime
from src.git_ir import git_log, format_git_logs_as_string
from collections import defaultdict
from io import StringIO
from subprocess import run as sp_run
import logging | 880 |
# Logging configuration
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
)
def extract_commits_and_authors(logs):
"""
Extract commits and their authors from git logs.
Args:
logs (list): List of commit logs.
Returns:
dict... |
# Logging configuration
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
)
def extract_commits_and_authors(logs):
"""
Extract commits and their authors from git logs.
Args:
logs (list): List of commit logs.
Returns:
dict... | logging.debug('Logs: %s', format_git_logs_as_string(logs)) | 1 | 2023-10-28 13:43:03+00:00 | 2k |
sisl/SceneInformer | sceneinformer/model/encoder.py | [
{
"identifier": "MLPPointEncoder",
"path": "sceneinformer/model/utils.py",
"snippet": "class MLPPointEncoder(nn.Module):\n def __init__(self, config):\n super(MLPPointEncoder, self).__init__()\n self.config = config\n in_dim = config['in_dim'] * 11\n out_dim = config['out_... | import torch
import torch.nn as nn
import torch.nn.functional as F
import lightning.pytorch as pl
from sceneinformer.model.utils import MLPPointEncoder, PointEncoder, count_parameters | 861 |
class Encoder(pl.LightningModule):
def __init__(self, config: dict) -> None:
super(Encoder, self).__init__()
self.config = config
self.hidden_dim = config['d_model']
if 'point_enc' in config.keys():
if config['point_enc'] == 'mlp':
self.veh_encoder = ... |
class Encoder(pl.LightningModule):
def __init__(self, config: dict) -> None:
super(Encoder, self).__init__()
self.config = config
self.hidden_dim = config['d_model']
if 'point_enc' in config.keys():
if config['point_enc'] == 'mlp':
self.veh_encoder = ... | self.veh_encoder = PointEncoder(config['vehicle_encoder']) | 1 | 2023-10-31 08:08:26+00:00 | 2k |
LFhase/GALA | drugood/models/algorithms/groupdro.py | [
{
"identifier": "BaseAlgorithm",
"path": "drugood/models/algorithms/base.py",
"snippet": "class BaseAlgorithm(BaseModule, metaclass=ABCMeta):\n def __init__(self, init_cfg=None):\n super(BaseAlgorithm, self).__init__(init_cfg)\n\n @abstractmethod\n def forward_train(self, input, group, *... | import torch
import torch_scatter
from drugood.models.algorithms.base import BaseAlgorithm
from ..builder import MODELS, build_tasker | 970 | # Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
@MODELS.register_module()
class GroupDRO(BaseAlgorithm):
"""
Group distributionally robust optimization.
Original paper:
@inproceedings{sagawa2019distributionally,
title={Distributionally robust neural networ... | # Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
@MODELS.register_module()
class GroupDRO(BaseAlgorithm):
"""
Group distributionally robust optimization.
Original paper:
@inproceedings{sagawa2019distributionally,
title={Distributionally robust neural networ... | self.tasker = build_tasker(tasker) | 2 | 2023-10-30 16:57:56+00:00 | 2k |
Graph-and-Geometric-Learning/D4Explainer | main.py | [
{
"identifier": "feature_dict",
"path": "constants.py",
"snippet": ""
},
{
"identifier": "get_datasets",
"path": "utils/dataset.py",
"snippet": "def get_datasets(name, root=\"data/\"):\n \"\"\"\n Get preloaded datasets by name\n :param name: name of the dataset\n :param root:... | import argparse
import torch
from torch_geometric.loader import DataLoader
from constants import feature_dict, task_type, dataset_choices
from explainers import *
from gnns import *
from utils.dataset import get_datasets | 1,234 |
def parse_args():
parser = argparse.ArgumentParser(description="Train explainers")
parser.add_argument("--cuda", type=int, default=0, help="GPU device.")
parser.add_argument("--root", type=str, default="results/", help="Result directory.")
parser.add_argument("--dataset", type=str, default="Tree_Cycl... |
def parse_args():
parser = argparse.ArgumentParser(description="Train explainers")
parser.add_argument("--cuda", type=int, default=0, help="GPU device.")
parser.add_argument("--root", type=str, default="results/", help="Result directory.")
parser.add_argument("--dataset", type=str, default="Tree_Cycl... | args.task = task_type[args.dataset] | 0 | 2023-10-28 19:58:40+00:00 | 2k |
p4p1/havoc-reporter | reporter.py | [
{
"identifier": "html_panel_mitre",
"path": "html_source/mitre.py",
"snippet": ""
},
{
"identifier": "html_panel_vulns",
"path": "html_source/vulnerabilities.py",
"snippet": ""
},
{
"identifier": "network_vulns",
"path": "vulns/network_vulnerabilities.py",
"snippet": ""
... | import havocui
import webbrowser
import os, sys, html, json
from html_source.mitre import html_panel_mitre
from html_source.vulnerabilities import html_panel_vulns
from mitre.tactics import *
from vulns.network_vulnerabilities import network_vulns
from vulns.active_directory import active_directory_vulns
from... | 1,298 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Made by papi
# Created on: Wen 25 Oct 2023
# reporter.py
# Description:
# A havoc extention to provide examples for different vulnerabilities that can
# be tested on the infected networks and on the infected machines.
# Usage:
# To use this script save it on y... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Made by papi
# Created on: Wen 25 Oct 2023
# reporter.py
# Description:
# A havoc extention to provide examples for different vulnerabilities that can
# be tested on the infected networks and on the infected machines.
# Usage:
# To use this script save it on y... | tree_display_vulns.setPanel(html_panel_vulns % (title, image, mitre, desc, html.escape(command), external_data))
| 1 | 2023-10-25 10:39:20+00:00 | 2k |
amazon-science/adaptive-in-context-learning | MetaICL/utils/download.py | [
{
"identifier": "all_settings",
"path": "MetaICL/utils/utils.py",
"snippet": "def get_checkpoint_id(key):\ndef download_file(_id, dest):"
},
{
"identifier": "download_file",
"path": "MetaICL/utils/utils.py",
"snippet": "def download_file(_id, dest):\n if os.path.exists(dest):\n ... | import os
import json
import argparse
import subprocess
from .utils import all_settings, all_methods
from .utils import download_file, get_checkpoint_id | 970 |
'''
script for downloading preprocessed data and trained checkpoints
'''
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoints", default=False, action="store_true")
parser.add_argument("--demo_data", default=False, action="store_true")
parser.add_argument("--target_... |
'''
script for downloading preprocessed data and trained checkpoints
'''
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoints", default=False, action="store_true")
parser.add_argument("--demo_data", default=False, action="store_true")
parser.add_argument("--target_... | _, _, _id = get_checkpoint_id(method + "/" + setting) | 2 | 2023-10-30 16:34:21+00:00 | 2k |
endo-yuki-t/MAG | ldm/models/diffusion/ddim.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 cv2
import matplotlib.pyplot as plt
import numpy as np
import math
from tqdm import tqdm
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
from ldm.diffusion_utils import denoising_step
from einops import rearrange, repe... | 1,445 | """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 | 2023-10-27 06:56:37+00:00 | 2k |
LibreTranslate/LexiLang | lexilang/utils.py | [
{
"identifier": "get_supported_languages",
"path": "lexilang/languages.py",
"snippet": "def get_supported_languages():\n return {\n 'afrikaans': 'af', \n 'albanian': 'sq', \n 'arabic': 'ar', \n 'bengali': 'bn', \n 'bulgarian': 'bg', \n 'catalan': 'ca', \n ... | import os
import pickle
from .languages import get_supported_languages, tokenize | 647 |
root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
def compile_data():
print("Compiling database...")
words = {}
langs = get_supported_languages()
for name in langs:
code = langs[name]
with open(os.path.join(root_dir, "dictionaries", f"{name}.txt"), "r", encodin... |
root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
def compile_data():
print("Compiling database...")
words = {}
langs = get_supported_languages()
for name in langs:
code = langs[name]
with open(os.path.join(root_dir, "dictionaries", f"{name}.txt"), "r", encodin... | tokens = tokenize(code, l.strip()) | 1 | 2023-10-30 13:43:19+00:00 | 2k |
alexeichhorn/typegpt | typegpt/parser.py | [
{
"identifier": "LLMOutputFieldMissing",
"path": "typegpt/exceptions.py",
"snippet": "class LLMOutputFieldMissing(LLMParseException):\n ..."
},
{
"identifier": "LLMOutputFieldWrongType",
"path": "typegpt/exceptions.py",
"snippet": "class LLMOutputFieldWrongType(LLMParseException):\n ... | import re
from typing import TYPE_CHECKING, Generic, TypeVar
from .exceptions import LLMOutputFieldMissing, LLMOutputFieldWrongType
from .fields import LLMArrayOutputInfo, LLMFieldInfo, LLMOutputInfo, LLMArrayElementOutputInfo
from .utils.utils import symmetric_strip
from .utils.type_checker import if_response_... | 781 | from __future__ import annotations
_Output = TypeVar("_Output", bound="BaseLLMResponse | BaseLLMArrayElement")
class Parser(Generic[_Output]):
def __init__(self, output_type: type[_Output]):
self.output_type = output_type
self.fields = self.output_type.__fields__.values()
def _regex_for_fi... | from __future__ import annotations
_Output = TypeVar("_Output", bound="BaseLLMResponse | BaseLLMArrayElement")
class Parser(Generic[_Output]):
def __init__(self, output_type: type[_Output]):
self.output_type = output_type
self.fields = self.output_type.__fields__.values()
def _regex_for_fi... | if isinstance(field.info, LLMOutputInfo) or isinstance(field.info, LLMArrayElementOutputInfo): | 4 | 2023-10-25 22:17:27+00:00 | 2k |
andriioreshk1118/python-storage-main | tests/system/test_transfer_manager.py | [
{
"identifier": "transfer_manager",
"path": "google/cloud/storage/transfer_manager.py",
"snippet": "TM_DEFAULT_CHUNK_SIZE = 32 * 1024 * 1024\nDEFAULT_MAX_WORKERS = 8\nMAX_CRC32C_ZERO_ARRAY_SIZE = 4 * 1024 * 1024\nMETADATA_HEADER_TRANSLATION = {\n \"cacheControl\": \"Cache-Control\",\n \"contentDis... | import tempfile
import os
import pytest
import datetime
import gzip
from google.cloud.storage import transfer_manager
from google.cloud.storage._helpers import _base64_md5hash
from google.api_core import exceptions
from google.cloud._helpers import UTC | 1,412 | # coding=utf-8
# Copyright 2022 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | # coding=utf-8
# Copyright 2022 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | results = transfer_manager.upload_many( | 0 | 2023-10-31 10:36:21+00:00 | 2k |
TopGuru777/badsecrets | badsecrets/modules/aspnet_viewstate.py | [
{
"identifier": "unpad",
"path": "badsecrets/helpers.py",
"snippet": "def unpad(s):\n return s[: -ord(s[len(s) - 1 :])]"
},
{
"identifier": "sp800_108_derivekey",
"path": "badsecrets/helpers.py",
"snippet": "def sp800_108_derivekey(key, label, context, keyLengthInBits):\n lblcnt = ... | import re
import hmac
import struct
import base64
import hashlib
import binascii
from Crypto.Cipher import AES
from Crypto.Cipher import DES
from Crypto.Cipher import DES3
from viewstate import ViewState
from contextlib import suppress
from urllib.parse import urlsplit, urlparse
from badsecrets.helpers import unpad, sp... | 1,372 |
class ASPNET_Viewstate(BadsecretsBase):
check_secret_args = 3
identify_regex = generic_base64_regex
description = {"product": "ASP.NET Viewstate", "secret": "ASP.NET MachineKey", "severity": "CRITICAL"}
def carve_regex(self):
return re.compile(
r"<input.+__VIEWSTATE\"\svalue=\"(.+... |
class ASPNET_Viewstate(BadsecretsBase):
check_secret_args = 3
identify_regex = generic_base64_regex
description = {"product": "ASP.NET Viewstate", "secret": "ASP.NET MachineKey", "severity": "CRITICAL"}
def carve_regex(self):
return re.compile(
r"<input.+__VIEWSTATE\"\svalue=\"(.+... | decrypt = unpad(decrypted_raw) | 0 | 2023-10-30 12:52:39+00:00 | 2k |
asprenger/ray_vllm_inference | tests/prompt_format_test.py | [
{
"identifier": "Message",
"path": "ray_vllm_inference/prompt_format.py",
"snippet": "class Message(BaseModel):\n role: Literal[\"system\", \"assistant\", \"user\"]\n content: str\n\n def __str__(self):\n return self.content"
},
{
"identifier": "Prompt",
"path": "ray_vllm_inf... | import unittest
import pytest
from pydantic import ValidationError
from ray_vllm_inference.prompt_format import Message, Prompt, PromptFormat | 1,231 | # Adapted from:
# https://github.com/ray-project/ray-llm/blob/master/rayllm/common/models.py
class PromptFormatCases(unittest.TestCase):
def test_prompt_format_with_prompt_obj(self):
prompt_format = PromptFormat(
system="[system] {instruction} [/system] ",
assistant="[assistant] {... | # Adapted from:
# https://github.com/ray-project/ray-llm/blob/master/rayllm/common/models.py
class PromptFormatCases(unittest.TestCase):
def test_prompt_format_with_prompt_obj(self):
prompt_format = PromptFormat(
system="[system] {instruction} [/system] ",
assistant="[assistant] {... | messages = [Message(role="user", content="hello1")] | 0 | 2023-10-28 23:17:59+00:00 | 2k |
fu-feng/GRL | algos/ppo.py | [
{
"identifier": "Actor",
"path": "algos/network.py",
"snippet": "class Actor(Network):\n def __init__(self, layer_num, input_dim, output_dim, hidden_dim, activation_function = torch.tanh,last_activation_mu = None, last_activation_std = None, is_actor=True):\n super(Actor, self).__init__(layer_... | from algos.network import Actor, Critic
from utils.utils import ReplayBuffer, make_mini_batch, convert_to_tensor
import torch
import torch.nn as nn
import torch.optim as optim | 976 |
class PPO(nn.Module):
def __init__(self, device, state_dim, action_dim, args):
super(PPO,self).__init__()
self.args = args
|
class PPO(nn.Module):
def __init__(self, device, state_dim, action_dim, args):
super(PPO,self).__init__()
self.args = args
| self.data = ReplayBuffer(action_prob_exist = True, max_size = self.args.traj_length, state_dim = state_dim, num_action = action_dim) | 2 | 2023-10-27 07:39:01+00:00 | 2k |
CoderMungan/Otel | OtelIcerik/forms.py | [
{
"identifier": "OtelOda",
"path": "OtelIcerik/models.py",
"snippet": "class OtelOda(models.Model):\n otel = models.ForeignKey(OtelYonetim, verbose_name=(\"Otel Adı\"), on_delete=models.CASCADE)\n odaNumarasi = models.CharField((\"Oda Numarası\"), max_length=5)\n odaTipi = models.CharField((\"O... | from django import forms
from .models import OtelOda, KonukBilgileri, KonukCheckInveCheckOut | 957 |
class UpdateOtelOdaForm(forms.ModelForm):
class Meta:
model = OtelOda
fields = ["odaNumarasi","odaTipi","odaTemizMi","odaArizaliMi","odaBosMu","odaProblemi",]
class UpdateMusteriDetay(forms.ModelForm):
class Meta:
|
class UpdateOtelOdaForm(forms.ModelForm):
class Meta:
model = OtelOda
fields = ["odaNumarasi","odaTipi","odaTemizMi","odaArizaliMi","odaBosMu","odaProblemi",]
class UpdateMusteriDetay(forms.ModelForm):
class Meta: | model = KonukBilgileri | 1 | 2023-10-26 02:42:23+00:00 | 2k |
lukas-clarke/pyEight | pyeight/eight.py | [
{
"identifier": "Token",
"path": "pyeight/structs.py",
"snippet": "class Token:\n bearer_token: str\n expiration: float\n main_id: str"
},
{
"identifier": "User",
"path": "pyeight/structs.py",
"snippet": "class User:\n def __init__(self,\n user_name: str,\n ... | import asyncio
import time
import httpx
import atexit
import logging
from aiohttp.client import ClientError, ClientSession, ClientTimeout
from pyeight.constants import *
from pyeight.structs import Token, User | 793 |
_LOGGER = logging.getLogger(__name__)
CLIENT_TIMEOUT = ClientTimeout(total=DEFAULT_TIMEOUT)
class EightSleep():
def __init__(
self,
email: str,
password: str,
client_id: str,
client_secret: str):
self.email = email
self.password = passwor... |
_LOGGER = logging.getLogger(__name__)
CLIENT_TIMEOUT = ClientTimeout(total=DEFAULT_TIMEOUT)
class EightSleep():
def __init__(
self,
email: str,
password: str,
client_id: str,
client_secret: str):
self.email = email
self.password = passwor... | async def _get_auth(self) -> Token: | 0 | 2023-10-26 21:11:20+00:00 | 2k |
loliverhennigh/PhantomGaze | phantomgaze/render/camera.py | [
{
"identifier": "normalize",
"path": "phantomgaze/utils/math.py",
"snippet": "@cuda.jit(device=True)\ndef normalize(vector):\n \"\"\"Normalize a vector.\n\n Parameters\n ----------\n vector : tuple\n The vector to normalize.\n\n Returns\n -------\n tuple\n The normaliz... | import math
import numba
from numba import cuda
from phantomgaze.utils.math import normalize, dot, cross | 773 | # Render functions for volumes
@cuda.jit(device=True)
def calculate_ray_direction(
x,
y,
img_shape,
camera_position,
camera_focal,
camera_up):
"""
Calculate the direction of a ray from the camera to the image plane.
Parameters
----------
x : int
... | # Render functions for volumes
@cuda.jit(device=True)
def calculate_ray_direction(
x,
y,
img_shape,
camera_position,
camera_focal,
camera_up):
"""
Calculate the direction of a ray from the camera to the image plane.
Parameters
----------
x : int
... | forward = normalize(forward) | 0 | 2023-10-26 23:53:16+00:00 | 2k |
Khushiyant/dockerpulse | dockerpulse/lgbert/bert_pytorch/trainer/pretrain.py | [
{
"identifier": "BERT",
"path": "dockerpulse/lgbert/bert_pytorch/model/bert.py",
"snippet": "class BERT(nn.Module):\r\n \"\"\"\r\n BERT model : Bidirectional Encoder Representations from Transformers.\r\n \"\"\"\r\n\r\n def __init__(self, vocab_size, max_len=512, hidden=768, n_layers=12,\r\n... | import torch
import torch.nn as nn
import time
import tqdm
import numpy as np
import pandas as pd
from torch.optim import Adam
from torch.utils.data import DataLoader
from ..model import BERTLog, BERT
from .optim_schedule import ScheduledOptim
| 1,287 |
class BERTTrainer:
"""
BERTTrainer make the pretrained BERT model with two LM training method.
1. Masked Language Model : 3.3.1 Task #1: Masked LM
2. Next Sentence prediction : 3.3.2 Task #2: Next Sentence Prediction
please check the details on README.md with simple example.
... |
class BERTTrainer:
"""
BERTTrainer make the pretrained BERT model with two LM training method.
1. Masked Language Model : 3.3.1 Task #1: Masked LM
2. Next Sentence prediction : 3.3.2 Task #2: Next Sentence Prediction
please check the details on README.md with simple example.
... | def __init__(self, bert: BERT, vocab_size: int,
| 0 | 2023-10-29 09:52:36+00:00 | 2k |
audiodude/rainfall | rainfall/blueprint/site.py | [
{
"identifier": "db",
"path": "rainfall/db.py",
"snippet": "class Base(DeclarativeBase):"
},
{
"identifier": "with_current_user",
"path": "rainfall/decorators.py",
"snippet": "def with_current_user(f):\n '''\n Retrieves the current user from the session, performs some checks, and then\... | from uuid import UUID
from rainfall.db import db
from rainfall.decorators import with_current_user, with_current_site
from rainfall.models.site import Site
import flask | 944 |
site = flask.Blueprint('site', __name__)
@site.route('/site', methods=['POST'])
@with_current_user
def create_site(user):
if not user.is_welcomed:
return flask.jsonify(status=400,
error='User has not yet been welcomed'), 400
data = flask.request.get_json()
if data is None:
r... |
site = flask.Blueprint('site', __name__)
@site.route('/site', methods=['POST'])
@with_current_user
def create_site(user):
if not user.is_welcomed:
return flask.jsonify(status=400,
error='User has not yet been welcomed'), 400
data = flask.request.get_json()
if data is None:
r... | @with_current_site | 2 | 2023-10-30 04:43:03+00:00 | 2k |
LasticXYZ/price-simulation | tests/test_poly.py | [
{
"identifier": "Linear",
"path": "poly.py",
"snippet": "class Linear:\n @staticmethod\n def leadin_factor_at(when, factor = 1):\n \"\"\"\n Factor represents the slope of the linear function\n Factor is not a parameter that is originally used in the `broker pallet code`.\n\n ... | import unittest
from poly import Linear, Exponential | 653 |
class TestLinearNoPanic(unittest.TestCase):
def test_linear_no_panic(self):
for limit in range(10):
for target in range(1, 10):
for sold in range(limit + 1):
price = Linear.adapt_price(sold, target, limit)
if sold > target:
... |
class TestLinearNoPanic(unittest.TestCase):
def test_linear_no_panic(self):
for limit in range(10):
for target in range(1, 10):
for sold in range(limit + 1):
price = Linear.adapt_price(sold, target, limit)
if sold > target:
... | price = Exponential.adapt_price(sold, target, limit) | 1 | 2023-10-30 12:49:00+00:00 | 2k |
dangeng/flowmag | test_time_adapt.py | [
{
"identifier": "TestTimeAdaptDataset",
"path": "dataset.py",
"snippet": "class TestTimeAdaptDataset(Dataset):\n def __init__(self, root, mode='first', length=None):\n '''\n args:\n root: (string) path to directory of frames\n mode: ['first', 'random'] how to sampl... | from tqdm import tqdm
from torch.optim import Adam
from torch.utils.data import DataLoader
from dataset import TestTimeAdaptDataset
from myutils import AverageMeter
import torch
import matplotlib.pyplot as plt | 1,430 |
def test_time_adapt(model, frames_dir, num_epochs=5, mode='first', device=0, inference_fn=None, inference_freq=1, alpha=None, save_dir=None, dataset_length=None):
'''
params:
model: (nn.Module) model with checkpoint already loaded
frames_dir: (string) path to directory of frames for test tim... |
def test_time_adapt(model, frames_dir, num_epochs=5, mode='first', device=0, inference_fn=None, inference_freq=1, alpha=None, save_dir=None, dataset_length=None):
'''
params:
model: (nn.Module) model with checkpoint already loaded
frames_dir: (string) path to directory of frames for test tim... | meter_loss = AverageMeter('loss') | 1 | 2023-10-27 05:23:08+00:00 | 2k |
warner-benjamin/optimi | optimi/adam.py | [
{
"identifier": "MIN_TORCH_2_1",
"path": "optimi/utils.py",
"snippet": "MIN_TORCH_2_1 = parse(torch.__version__) >= parse(\"2.1\")"
},
{
"identifier": "debias_beta",
"path": "optimi/utils.py",
"snippet": "def debias_beta(beta: float, step: int) -> float:\n \"\"\"Applies the Adam-style... | from typing import Any, Callable, Iterable
from warnings import warn
from torch import Tensor
from torch.optim.optimizer import Optimizer, _default_to_fused_or_foreach
from torch.utils._foreach_utils import _group_tensors_by_device_and_dtype
from optimi.utils import MIN_TORCH_2_1, debias_beta
import torch | 1,021 | # Copyright (c) 2023 Benjamin Warner
# SPDX-License-Identifier: MIT
# Based on PyTorch Optimizers
# PyTorch - PyTorch BSD-style license - Copyright (c) 2013-present PyTorch contributors
# Kahan summation inspired by Torch Distributed Experimental's `AnyPrecisionAdamW`
# torchdistX - BSD 3-Clause License - Copyright (... | # Copyright (c) 2023 Benjamin Warner
# SPDX-License-Identifier: MIT
# Based on PyTorch Optimizers
# PyTorch - PyTorch BSD-style license - Copyright (c) 2013-present PyTorch contributors
# Kahan summation inspired by Torch Distributed Experimental's `AnyPrecisionAdamW`
# torchdistX - BSD 3-Clause License - Copyright (... | if not MIN_TORCH_2_1: | 0 | 2023-10-25 00:51:05+00:00 | 2k |
Subsets and Splits
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Identifies repositories that have complete performance data across all seven code complexity levels, revealing consistent benchmarking patterns across different code sizes.
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Identifies repositories that contain all 7 distinct quality levels (2k through 32k), revealing complete datasets that might be useful for comprehensive analysis.