id int64 0 458k | file_name stringlengths 4 119 | file_path stringlengths 14 227 | content stringlengths 24 9.96M | size int64 24 9.96M | language stringclasses 1
value | extension stringclasses 14
values | total_lines int64 1 219k | avg_line_length float64 2.52 4.63M | max_line_length int64 5 9.91M | alphanum_fraction float64 0 1 | repo_name stringlengths 7 101 | repo_stars int64 100 139k | repo_forks int64 0 26.4k | repo_open_issues int64 0 2.27k | repo_license stringclasses 12
values | repo_extraction_date stringclasses 433
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,283,600 | frame.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/datasets/frame.py | #!/usr/bin/env python3
"""
Copyright 2022 James Hong, Haotian Zhang, Matthew Fisher, Michael Gharbi,
Kayvon Fatahalian
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above ... | 56,812 | Python | .py | 1,327 | 31.962321 | 222 | 0.565224 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,601 | json.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/datasets/json.py | from torch.utils.data import Dataset
import numpy as np
import random
import os
from tqdm import tqdm
import torch
import logging
import json
from .utils import (
feats2clip,
getChunks_anchors,
getTimestampTargets,
oneHotToShifts,
)
K_V2 = torch.FloatTensor(
[
[
-100,
... | 17,877 | Python | .py | 416 | 29.050481 | 153 | 0.526098 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,602 | download_configtask.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/apis/download_configtask.py | import urllib.request
import os
from tqdm import tqdm
class MyProgressBar:
def __init__(self, filename):
self.pbar = None
self.filename = filename
def __call__(self, block_num, block_size, total_size):
if not self.pbar:
self.pbar = tqdm(total=total_size, unit="iB", unit_sc... | 10,307 | Python | .py | 233 | 26.042918 | 115 | 0.4615 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,603 | inference.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/apis/inference/inference.py | import logging
import os
from oslactionspotting.apis.inference.utils import infer_and_process_predictions_e2e
from oslactionspotting.core.utils.lightning import CustomProgressBar
from oslactionspotting.datasets.builder import build_dataloader
import pytorch_lightning as pl
class Inferer:
def __init__(self, cfg, ... | 3,616 | Python | .py | 97 | 29.072165 | 84 | 0.632437 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,604 | utils.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/apis/inference/utils.py | """
Copyright 2022 James Hong, Haotian Zhang, Matthew Fisher, Michael Gharbi,
Kayvon Fatahalian
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
... | 18,340 | Python | .py | 462 | 29.943723 | 128 | 0.572159 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,605 | builder.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/apis/inference/builder.py | from .inference import Inferer, infer_E2E, infer_JSON, infer_SN
def build_inferer(cfg, model, default_args=None):
"""Build a inferer from config dict.
Args:
cfg (dict): Config dict. It should at least contain the key "type".
model: The model that will be used to infer.
default_args (d... | 956 | Python | .py | 20 | 40.9 | 79 | 0.676344 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,606 | builder.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/apis/visualize/builder.py | from .visualizer import Visualizer
def build_visualizer(cfg, Default_args=None):
"""Build a visualizer from config dict.
Args:
cfg (dict): Config dict.
default_args (dict | None, optional): Default initialization arguments.
Default: None.
Returns:
inferer: The constru... | 397 | Python | .py | 12 | 26.75 | 79 | 0.67979 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,607 | visualizer.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/apis/visualize/visualizer.py | import os
import cv2
import json
from oslactionspotting.core.utils.io import load_json
def check_config(cfg):
# assert cfg.dataset.test.path is not None and os.path.isfilecfg.dataset.test.path.
return
class Visualizer:
"""Visualier class used to visualize a video along with its predictions using opencv... | 4,186 | Python | .py | 81 | 39.481481 | 147 | 0.603973 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,608 | utils.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/apis/evaluate/utils.py | """
Copyright 2022 James Hong, Haotian Zhang, Matthew Fisher, Michael Gharbi,
Kayvon Fatahalian
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
... | 28,074 | Python | .py | 619 | 36.615509 | 168 | 0.627173 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,609 | builder.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/apis/evaluate/builder.py | from .evaluate import Evaluator, evaluate_pred_JSON, evaluate_pred_SN, evaluate_pred_E2E
def build_evaluator(cfg, default_args=None):
"""Build a evaluator from config dict.
Args:
cfg (dict): Config dict. It should at least contain the key "type".
default_args (dict | None, optional): Default ... | 938 | Python | .py | 19 | 42.631579 | 88 | 0.698031 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,610 | evaluate.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/apis/evaluate/evaluate.py | from tabulate import tabulate
from oslactionspotting.apis.evaluate.utils import (
compute_performances_mAP,
get_closest_action_index,
label2vector,
non_maximum_supression,
predictions2vector,
store_eval_files_json,
)
from oslactionspotting.core.utils.io import load_gz_json, load_text, load_json
... | 9,296 | Python | .py | 221 | 32.59276 | 210 | 0.609851 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,611 | trainer.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/core/trainer.py | """
Copyright 2022 James Hong, Haotian Zhang, Matthew Fisher, Michael Gharbi,
Kayvon Fatahalian
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
... | 13,599 | Python | .py | 308 | 31.37013 | 234 | 0.568742 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,612 | io.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/core/utils/io.py | """
Copyright 2022 James Hong, Haotian Zhang, Matthew Fisher, Michael Gharbi,
Kayvon Fatahalian
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
... | 7,316 | Python | .py | 182 | 32.016484 | 126 | 0.631497 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,613 | default_args.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/core/utils/default_args.py | def get_default_args_data_train_e2e_dali(cfg):
return {
"classes": cfg.classes,
"train": True,
"acc_grad_iter": cfg.training.acc_grad_iter,
"num_epochs": cfg.training.num_epochs,
"repartitions": cfg.repartitions,
}
def get_default_args_data_valid_e2e_dali(cfg):
retu... | 3,356 | Python | .py | 86 | 30.209302 | 85 | 0.604436 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,614 | lightning.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/core/utils/lightning.py | import pytorch_lightning as pl
from pytorch_lightning.callbacks.progress import TQDMProgressBar
import logging
class CustomProgressBar(TQDMProgressBar):
"""Override the custom progress bar used by pytorch lightning to change some attributes."""
def get_metrics(self, trainer, pl_module):
"""Override t... | 2,027 | Python | .py | 41 | 40.560976 | 95 | 0.654684 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,615 | nll.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/core/loss/nll.py |
import torch
class NLLLoss(torch.nn.Module):
"""Negative Log LikeLihood Loss."""
def __init__(self):
super(NLLLoss, self).__init__()
def forward(self, labels, output):
"""Forward function.
Args:
labels (torch.Tensor): The ground truth labels.
output (tor... | 607 | Python | .py | 18 | 24.333333 | 83 | 0.558419 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,616 | ce.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/core/loss/ce.py | import torch
import torch.nn.functional as F
class CELoss(torch.nn.Module):
"""Cross Entropy Loss."""
def __init__(self):
super(CELoss, self).__init__()
def forward(self, labels, output, **ce_kwargs):
"""Forward function.
Args:
labels (torch.Tensor): The ground truth... | 632 | Python | .py | 17 | 28.529412 | 67 | 0.612479 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,617 | builder.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/core/loss/builder.py | from oslactionspotting.core.loss.ce import CELoss
from .nll import NLLLoss
from .calf import ContextAwareLoss, SpottingLoss
from .combine import Combined2x
def build_criterion(cfg, default_args=None):
"""Build a criterion from config dict.
Args:
cfg (dict): Config dict. It should at least contain the... | 1,258 | Python | .py | 35 | 28.628571 | 79 | 0.640394 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,618 | combine.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/core/loss/combine.py | import torch
####################################################################################################################################################
# Combined loss function
#################################################################################################################################... | 1,193 | Python | .py | 29 | 33.206897 | 148 | 0.414422 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,619 | calf.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/core/loss/calf.py | import torch
####################################################################################################################################################
# Context-aware loss function
############################################################################################################################... | 7,960 | Python | .py | 205 | 28.087805 | 148 | 0.452999 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,620 | builder.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/core/optimizer/builder.py | import torch
def build_optimizer(parameters, cfg, default_args=None):
"""Build a optimizer from config dict.
Args:
cfg (dict): Config dict. It should at least contain the key "type".
default_args (dict | None, optional): Default initialization arguments.
Default: None.
Return... | 825 | Python | .py | 25 | 24.16 | 79 | 0.594221 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,621 | builder.py | OpenSportsLab_OSL-ActionSpotting/oslactionspotting/core/scheduler/builder.py | """
Copyright 2022 James Hong, Haotian Zhang, Matthew Fisher, Michael Gharbi,
Kayvon Fatahalian
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
... | 3,070 | Python | .py | 64 | 41.203125 | 83 | 0.716383 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,622 | test_simple.py | OpenSportsLab_OSL-ActionSpotting/tests/test_simple.py | import pytest
def test_simple():
assert 1 + 1 == 2 | 57 | Python | .py | 3 | 16 | 21 | 0.653846 | OpenSportsLab/OSL-ActionSpotting | 8 | 1 | 2 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,623 | feature.py | gh203322_image-search-by-image/feature.py | import torch
import torch.nn as nn
from torchvision import models, transforms
from torch.autograd import Variable
import cv2
import numpy as np
from sklearn.decomposition import PCA
# 图片缩放大小
IMG_SCALE_SIZE_W = 224
IMG_SCALE_SIZE_H = 224
# 加载特征提取模型
FE_MODEL = models.resnet50(pretrained=True) # 导入ResNet50的预训练模型
"""
... | 2,123 | Python | .py | 56 | 26.160714 | 79 | 0.704225 | gh203322/image-search-by-image | 8 | 1 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,624 | setting.py | gh203322_image-search-by-image/setting.py | # -*- coding: UTF-8 -*-
import os
# 程序配置
# 应用启动端口
API_PORT = os.environ.get('API_PORT')
if not API_PORT:
API_PORT = 7000
TOKEN = os.environ.get('TOKEN')
# 向量数据库地址（必要配置参数）
MILVUS_HOST = os.environ.get('MILVUS_HOST')
# 向量数据库端口
MILVUS_PORT = os.environ.get('MILVUS_PORT')
if not MILVUS_PORT:
MILVUS_PORT = 11088... | 1,073 | Python | .py | 32 | 26.15625 | 68 | 0.717314 | gh203322/image-search-by-image | 8 | 1 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,625 | api.py | gh203322_image-search-by-image/api.py | # -*- coding: UTF-8 -*-
from fastapi import FastAPI, File, UploadFile, Form, Header
from fastapi.responses import JSONResponse
from typing import Union
from typing import Optional
from typing_extensions import Annotated
from pydantic import BaseModel
import base64
import setting as ST
import milvus as MV
import featur... | 11,430 | Python | .py | 287 | 27.581882 | 103 | 0.624718 | gh203322/image-search-by-image | 8 | 1 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,626 | init.py | gh203322_image-search-by-image/init.py | # -*- coding: UTF-8 -*-
import os
import shutil
import setting as ST
import utils as UT
import milvus as MV
import feature as FT
"""
方法：初始化默认目录里面的图片，如果存在则导入向量数据库
参数：
"""
def run():
# 创建向量数据库
MV.create_milvus_collection(MV.EB_TB_NAME, MV.EB_DB_DIM)
# 如果初始化目录有图片则初始化图片
if ST.IMAGE_INC_PATH:
inc... | 1,704 | Python | .py | 41 | 22.731707 | 78 | 0.511236 | gh203322/image-search-by-image | 8 | 1 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,627 | utils.py | gh203322_image-search-by-image/utils.py | import os
import hashlib
import requests
import cv2
# 获取路径下所有的文件
def get_files_by_suffix(path, suffix_list):
file_list = []
for root, dirs, files in os.walk(path):
for filename in files:
ext = os.path.splitext(filename)[1]
if ext in suffix_list:
file_path = os.p... | 3,356 | Python | .py | 90 | 22.788889 | 85 | 0.603702 | gh203322/image-search-by-image | 8 | 1 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,628 | milvus.py | gh203322_image-search-by-image/milvus.py | # -*- coding: UTF-8 -*-
from pymilvus import (
connections,
utility,
FieldSchema,
CollectionSchema,
DataType,
Collection,
)
import setting as ST
# 图片向量库名称(表名)
EB_TB_NAME = 'images'
# 图片向量库维度
EB_DB_DIM = 2048 #Milvus 支持的最大向量维度是32768，一般情况为了检索性能建议在1024-2048
# 全局 Milvus 连接对象
connections.connect... | 6,341 | Python | .py | 192 | 21.526042 | 116 | 0.553705 | gh203322/image-search-by-image | 8 | 1 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,629 | main.py | gh203322_image-search-by-image/main.py | # -*- coding: UTF-8 -*-
import uvicorn
import api
import init
import setting as ST
import utils as UT
import feature as FT
app = api.app
# 主入口
if __name__ == '__main__':
# 初始化图库和导入增量图片
init.run()
uvicorn.run("main:app", host="0.0.0.0", port=ST.API_PORT, reload=True) | 311 | Python | .py | 13 | 19.538462 | 74 | 0.676692 | gh203322/image-search-by-image | 8 | 1 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,630 | daam.py | ltdrdata_ComfyUI-Impact-Exp/daam.py | from torch import nn
class AttentionLogger:
def __init__(self, heat_maps):
self.heat_maps = heat_maps
def __call__(self, q, k, v, extra_options):
org_dtype = q.dtype
with torch.autocast(device_type=self.device, dtype=self.dtype):
out = optimized_attention(q, k, v, extra_op... | 1,879 | Python | .py | 45 | 32.755556 | 86 | 0.58052 | ltdrdata/ComfyUI-Impact-Exp | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,631 | __init__.py | ltdrdata_ComfyUI-Impact-Exp/__init__.py | import importlib
import os
node_list = [
"flex",
"daam"
]
NODE_CLASS_MAPPINGS = {}
NODE_DISPLAY_NAME_MAPPINGS = {}
for module_name in node_list:
imported_module = importlib.import_module(".{}".format(module_name), __name__)
NODE_CLASS_MAPPINGS = {**NODE_CLASS_MAPPINGS, **imported_module.NODE_CLASS_M... | 544 | Python | .py | 14 | 36.071429 | 109 | 0.712381 | ltdrdata/ComfyUI-Impact-Exp | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,632 | flex.py | ltdrdata_ComfyUI-Impact-Exp/flex.py | import inspect
class FlexTest:
@classmethod
def INPUT_TYPES(s):
flex_inputs = {}
stack = inspect.stack()
if stack[1].function == 'get_input_data':
# bypass validation
for x in range(0, 20):
flex_inputs[f"input{x}"] = (["aaa", "bbb", "ccc"],)
... | 810 | Python | .py | 28 | 20.178571 | 78 | 0.479948 | ltdrdata/ComfyUI-Impact-Exp | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,633 | test.py | miniTalDev_yolov5-lip-detection/test.py | import argparse
import glob
import json
import os
import shutil
from pathlib import Path
import numpy as np
import torch
import yaml
from tqdm import tqdm
from models.experimental import attempt_load
from utils.datasets import create_dataloader
from utils.general import (
coco80_to_coco91_class, check_dataset, ch... | 13,588 | Python | .py | 255 | 42 | 119 | 0.561983 | miniTalDev/yolov5-lip-detection | 8 | 0 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,634 | sotabench.py | miniTalDev_yolov5-lip-detection/sotabench.py | import argparse
import glob
import os
import shutil
from pathlib import Path
import numpy as np
import torch
import yaml
from sotabencheval.object_detection import COCOEvaluator
from sotabencheval.utils import is_server
from tqdm import tqdm
from models.experimental import attempt_load
from utils.datasets import crea... | 14,274 | Python | .py | 277 | 42.021661 | 120 | 0.552119 | miniTalDev/yolov5-lip-detection | 8 | 0 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,635 | tutorial.ipynb | miniTalDev_yolov5-lip-detection/tutorial.ipynb | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "YOLOv5 Tutorial",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"a... | 3,276,585 | Python | .py | 758 | 4,313.667546 | 1,056,923 | 0.945577 | miniTalDev/yolov5-lip-detection | 8 | 0 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,636 | train.py | miniTalDev_yolov5-lip-detection/train.py | import argparse
import logging
import os
import random
import shutil
import time
from pathlib import Path
from warnings import warn
import math
import numpy as np
import torch.distributed as dist
import torch.nn.functional as F
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import torch.ut... | 28,259 | Python | .py | 476 | 47.783613 | 120 | 0.570264 | miniTalDev/yolov5-lip-detection | 8 | 0 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,637 | detect.py | miniTalDev_yolov5-lip-detection/detect.py | import argparse
import os
import shutil
import time
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import (
check_img_size, non_max_sup... | 7,787 | Python | .py | 147 | 42.37415 | 119 | 0.584132 | miniTalDev/yolov5-lip-detection | 8 | 0 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,638 | hubconf.py | miniTalDev_yolov5-lip-detection/hubconf.py | """File for accessing YOLOv5 via PyTorch Hub https://pytorch.org/hub/
Usage:
import torch
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, channels=3, classes=80)
"""
dependencies = ['torch', 'yaml']
import os
import torch
from models.yolo import Model
from utils.google_utils import ... | 3,475 | Python | .py | 75 | 39.613333 | 116 | 0.681778 | miniTalDev/yolov5-lip-detection | 8 | 0 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,639 | export.py | miniTalDev_yolov5-lip-detection/models/export.py | """Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
Usage:
$ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
"""
import argparse
import sys
import time
sys.path.append('./') # to run '$ python *.py' files in subdirectories
import torch
import to... | 3,729 | Python | .py | 77 | 42.090909 | 117 | 0.646217 | miniTalDev/yolov5-lip-detection | 8 | 0 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,640 | yolo.py | miniTalDev_yolov5-lip-detection/models/yolo.py | import argparse
import logging
import sys
from copy import deepcopy
from pathlib import Path
import math
sys.path.append('./') # to run '$ python *.py' files in subdirectories
logger = logging.getLogger(__name__)
import torch
import torch.nn as nn
from models.common import Conv, Bottleneck, SPP, DWConv, Focus, Bot... | 12,350 | Python | .py | 241 | 40.970954 | 116 | 0.535223 | miniTalDev/yolov5-lip-detection | 8 | 0 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,641 | experimental.py | miniTalDev_yolov5-lip-detection/models/experimental.py | # This file contains experimental modules
import numpy as np
import torch
import torch.nn as nn
from models.common import Conv, DWConv
from utils.google_utils import attempt_download
class CrossConv(nn.Module):
# Cross Convolution Downsample
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
... | 5,565 | Python | .py | 118 | 38.457627 | 117 | 0.562546 | miniTalDev/yolov5-lip-detection | 8 | 0 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,642 | common.py | miniTalDev_yolov5-lip-detection/models/common.py | # This file contains modules common to various models
import math
import numpy as np
import torch
import torch.nn as nn
from utils.datasets import letterbox
from utils.general import non_max_suppression, make_divisible, scale_coords
def autopad(k, p=None): # kernel, padding
# Pad to 'same'
if p is None:
... | 7,226 | Python | .py | 144 | 42.861111 | 117 | 0.588636 | miniTalDev/yolov5-lip-detection | 8 | 0 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,643 | test.py | miniTalDev_yolov5-lip-detection/models/mine/test.py | from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import time
import cv2
CLASSES = ["background", "aeroplane", "bicycle", "bird"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
# load our serialized model from disk
print("[INFO] loading ... | 1,098 | Python | .py | 31 | 34.290323 | 68 | 0.726246 | miniTalDev/yolov5-lip-detection | 8 | 0 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,644 | open.py | miniTalDev_yolov5-lip-detection/models/mine/open.py | import onnx
import cv2
# onnx_model = onnx.load( 'best.onnx')
# onnx.checker.check_model(onnx_model)
model = cv2.dnn.readNetFromONNX('best.onnx')
| 148 | Python | .py | 5 | 28.2 | 44 | 0.765957 | miniTalDev/yolov5-lip-detection | 8 | 0 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,645 | general.py | miniTalDev_yolov5-lip-detection/utils/general.py | import glob
import logging
import os
import platform
import random
import re
import shutil
import subprocess
import time
from contextlib import contextmanager
from copy import copy
from pathlib import Path
import cv2
import math
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import torch
import t... | 54,154 | Python | .py | 1,066 | 42.772045 | 120 | 0.574799 | miniTalDev/yolov5-lip-detection | 8 | 0 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,646 | datasets.py | miniTalDev_yolov5-lip-detection/utils/datasets.py | import glob
import os
import random
import shutil
import time
from pathlib import Path
from threading import Thread
import cv2
import math
import numpy as np
import torch
from PIL import Image, ExifTags
from torch.utils.data import Dataset
from tqdm import tqdm
from utils.general import xyxy2xywh, xywh2xyxy, torch_di... | 39,107 | Python | .py | 778 | 38.922879 | 238 | 0.533499 | miniTalDev/yolov5-lip-detection | 8 | 0 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,647 | torch_utils.py | miniTalDev_yolov5-lip-detection/utils/torch_utils.py | import logging
import os
import time
from copy import deepcopy
import math
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torchvision
logger = logging.getLogger(__name__)
def init_torch_seeds(seed=0):
torch.manual_seed(seed)
# Speed-reproducib... | 8,861 | Python | .py | 182 | 40.89011 | 119 | 0.624652 | miniTalDev/yolov5-lip-detection | 8 | 0 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,648 | google_utils.py | miniTalDev_yolov5-lip-detection/utils/google_utils.py | # This file contains google utils: https://cloud.google.com/storage/docs/reference/libraries
# pip install --upgrade google-cloud-storage
# from google.cloud import storage
import os
import platform
import subprocess
import time
from pathlib import Path
import torch
def gsutil_getsize(url=''):
# gs://bucket/fil... | 4,971 | Python | .py | 103 | 42.533981 | 118 | 0.641163 | miniTalDev/yolov5-lip-detection | 8 | 0 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,649 | activations.py | miniTalDev_yolov5-lip-detection/utils/activations.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# Swish https://arxiv.org/pdf/1905.02244.pdf ---------------------------------------------------------------------------
class Swish(nn.Module): #
@staticmethod
def forward(x):
return x * torch.sigmoid(x)
class Hardswish(nn.Module):... | 2,176 | Python | .py | 53 | 33.622642 | 120 | 0.536087 | miniTalDev/yolov5-lip-detection | 8 | 0 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,650 | run.py | esl-epfl_sz-validation-framework/3-runSzDetection/run.py | import argparse
from pathlib import Path
import subprocess
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Run dockerized sz detection on all EEG files"
)
parser.add_argument("input", help="Path to root of the EDF EEG files.")
args = parser.parse_args()
for edf in... | 829 | Python | .py | 26 | 21.5 | 76 | 0.510101 | esl-epfl/sz-validation-framework | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,651 | __main__.py | esl-epfl_sz-validation-framework/3-runSzDetection/docker-szDetection/algo/src/algo/__main__.py | if __name__ == "__main__":
import argparse
from algo.sz_detection import szDetection
parser = argparse.ArgumentParser(
description="Run a seizure detection algorithm on an EDF file and store computed seizure annotations to a TSV."
)
parser.add_argument("input", help="Path to the input EDF E... | 486 | Python | .py | 10 | 43.4 | 119 | 0.71097 | esl-epfl/sz-validation-framework | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,652 | sz_detection.py | esl-epfl_sz-validation-framework/3-runSzDetection/docker-szDetection/algo/src/algo/sz_detection.py | from pathlib import Path
import numpy as np
import pyedflib
import epilepsy2bids.annotations
from epilepsy2bids.eeg import Eeg
import timescoring.annotations
import timescoring.scoring
def szDetection(edfFile: str, outFile: str):
"""Run a seizure detection algorithm on an EDF file.
The algorithm implemente... | 2,140 | Python | .py | 52 | 34.807692 | 83 | 0.698749 | esl-epfl/sz-validation-framework | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,653 | __main__.py | esl-epfl_sz-validation-framework/4-evaluate/src/evaluate/__main__.py | if __name__ == "__main__":
import argparse
from evaluate.evaluate import evaluate
parser = argparse.ArgumentParser(
description="Evaluation code to compare annotations from a seizure detection algorithm to ground truth annotations."
)
parser.add_argument("ref", help="Path to the root folder... | 527 | Python | .py | 10 | 47.6 | 124 | 0.734496 | esl-epfl/sz-validation-framework | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,654 | evaluate.py | esl-epfl_sz-validation-framework/4-evaluate/src/evaluate/evaluate.py | from pathlib import Path
import numpy as np
import pandas as pd
from epilepsy2bids.annotations import Annotations, SeizureType
from timescoring import annotations, scoring
FS = 256
def toMask(annotations):
mask = np.zeros(int(annotations.events[0]["recordingDuration"] * FS))
for event in annotations.events... | 3,667 | Python | .py | 101 | 28.742574 | 86 | 0.5819 | esl-epfl/sz-validation-framework | 8 | 1 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,655 | __init__.py | XTxiaoting14332_nonebot-plugin-muteme/nonebot_plugin_muteme/__init__.py | from nonebot.adapters.onebot.v11 import Message, MessageSegment
from nonebot.adapters.onebot.v11 import Bot, Event, GroupMessageEvent, GROUP_ADMIN, GROUP_OWNER
from nonebot.permission import SUPERUSER
import nonebot
import random, re
import datetime
from nonebot.plugin import PluginMetadata, on_regex
from nonebot.exce... | 1,869 | Python | .py | 45 | 35.088889 | 95 | 0.697376 | XTxiaoting14332/nonebot-plugin-muteme | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,656 | Plotter.py | joaquinbarroso_UVeh-Vis/Plotter.py | #!/usr/bin/python3
import matplotlib.pyplot as plt
import math as math
import numpy as np
def lotz(osc,nm,dev):
sup=1.3062974e8*(2/math.pi)**0.5*osc*(dev/1e14)
inf=4*((1/i)-(1/nm))**2+(dev/1e7)**2
global ABSn
ABSn=sup/inf
return ABSn
def gau(osc,nm,dev):
exp=-1*(((1/i)-(1/nm)... | 3,887 | Python | .py | 103 | 26.893204 | 122 | 0.508628 | joaquinbarroso/UVeh-Vis | 8 | 2 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,657 | Main.py | EstebanZxx_X-Tools/Main.py | import os,time,zlib,base64
from time import sleep
from sys import stdin, stdout, stderr
from argparse import ArgumentParser
from pathlib import Path
from base64 import b64decode
from Crypto.Protocol.KDF import PBKDF2
from Crypto.Cipher import AES
from Crypto.Hash import SHA256
from Crypto.Util.Padding import unpad
from... | 4,851 | Python | .py | 121 | 33.214876 | 113 | 0.632865 | EstebanZxx/X-Tools | 8 | 5 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,658 | XTools.py | EstebanZxx_X-Tools/XTools.py | import os
black="\033[0;30m"
red="\033[0;31m"
green="\033[0;32m"
yellow="\033[0;33m"
blue="\033[0;34m"
purple="\033[0;35m"
cyan="\033[0;36m"
white="\033[0;37m"
XTools = """
██╗░░██╗  ░░░░░░  ████████╗░█████╗░░█████╗░██╗░â–... | 2,977 | Python | .py | 62 | 42.935484 | 177 | 0.487348 | EstebanZxx/X-Tools | 8 | 5 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,659 | NM.py | EstebanZxx_X-Tools/X/NM.py | import base64
from Crypto.Cipher import AES
import json
import os
import glob
Ezxx = ("This Script Generated By : EstebanZxx\nBeta Test Module : V1\n=====================================\nResult Decrypt : ")
def decrypt_aes_ecb_128(ciphertext, key):
cipher = AES.new(key, AES.MODE_ECB)
plaintext = cipher.decry... | 1,120 | Python | .py | 28 | 35.428571 | 129 | 0.66946 | EstebanZxx/X-Tools | 8 | 5 | 1 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,660 | demo.py | Leaf-0010_cell_detection/demo.py | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'demo.ui'
#
# Created by: PyQt5 UI code generator 5.15.9
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again. Do not edit this file unless you know what you are doing.
from PyQt5 import QtCor... | 61,370 | Python | .py | 1,457 | 36.594372 | 339 | 0.694404 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,661 | test.py | Leaf-0010_cell_detection/test.py | import glob
import sys
from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QPushButton, QVBoxLayout, QWidget, QFileDialog
from PyQt5.QtGui import QPixmap
from PyQt5.QtCore import Qt
from detect import *
class MainWindow(QMainWindow):
def __init__(self):
super(MainWindow, self).__init_... | 3,188 | Python | .py | 69 | 33.565217 | 110 | 0.64549 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,662 | export.py | Leaf-0010_cell_detection/export.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
Format | `export.py --include` | Model
--- | --- | ---
PyTorch ... | 29,985 | Python | .py | 524 | 46.71374 | 151 | 0.593423 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,663 | tutorial.ipynb | Leaf-0010_cell_detection/tutorial.ipynb | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "YOLOv5 Tutorial",
"provenance": [],
"collapsed_sections": [],
"machine_shape": "hm",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
... | 57,230 | Python | .py | 1,115 | 41.055605 | 849 | 0.492899 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,664 | val.py | Leaf-0010_cell_detection/val.py | # YOLOv5 üöÄ by Ultralytics, GPL-3.0 license
"""
Validate a trained YOLOv5 model accuracy on a custom dataset
Usage:
$ python path/to/val.py --weights yolov5s.pt --data coco128.yaml --img 640
Usage - formats:
$ python path/to/val.py --weights yolov5s.pt # PyTorch
... | 19,460 | Python | .py | 346 | 46.034682 | 122 | 0.587276 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,665 | train.py | Leaf-0010_cell_detection/train.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Train a YOLOv5 model on a custom dataset.
Models and datasets download automatically from the latest YOLOv5 release.
Models: https://github.com/ultralytics/yolov5/tree/master/models
Datasets: https://github.com/ultralytics/yolov5/tree/master/data
Tutorial: https://gith... | 34,919 | Python | .py | 591 | 46.993232 | 120 | 0.569401 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,666 | data_process_core.py | Leaf-0010_cell_detection/data_process_core.py | import cv2
import numpy as np
# 读取二值图像
image = cv2.imread('1.png', cv2.IMREAD_GRAYSCALE)
# 确保成功读取图像
if image is not None:
# 二值化图像
ret, binary_image = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)
# 查找轮廓
contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_S... | 1,074 | Python | .py | 28 | 27.571429 | 93 | 0.638732 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,667 | detect.py | Leaf-0010_cell_detection/detect.py | # YOLOv5 üöÄ by Ultralytics, GPL-3.0 license
"""
Run inference on images, videos, directories, streams, etc.
Usage - sources:
$ python path/to/detect.py --weights yolov5s.pt --source 0 # webcam
img.jpg # image
... | 13,628 | Python | .py | 230 | 46.782609 | 120 | 0.567355 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,668 | main.py | Leaf-0010_cell_detection/main.py | import csv
import sys
from datetime import datetime
import pandas as pd
import pymysql
import glob
import os
import random
from PIL import Image
from PyQt5.QtCore import QTimer, QDate, Qt
from PyQt5.QtCore import QDateTime
from PyQt5.QtGui import QStandardItemModel, QStandardItem
from PyQt5.QtWidgets i... | 33,143 | Python | .py | 690 | 31.607246 | 192 | 0.56441 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,669 | hubconf.py | Leaf-0010_cell_detection/hubconf.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/
Usage:
import torch
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
model = torch.hub.load('ultralytics/yolov5:master', 'custom', 'path/to/yolov5s.onnx') # file from branch
"""
import ... | 6,396 | Python | .py | 106 | 53.641509 | 109 | 0.69936 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,670 | data_precess_malaria.py | Leaf-0010_cell_detection/data_precess_malaria.py | import pandas as pd
import json
import os
def convert_to_yolov5_format(data, image_width, image_height):
yolov5_labels = []
label_mapping = {
'gametocyte': 0,
'trophozoite': 1,
'leukocyte': 2,
'difficult': 3,
'red blood cell': 4,
'schizont': 5,
... | 2,054 | Python | .py | 51 | 28.901961 | 84 | 0.576986 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,671 | yolo.py | Leaf-0010_cell_detection/models/yolo.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
YOLO-specific modules
Usage:
$ python path/to/models/yolo.py --cfg yolov5s.yaml
"""
import argparse
import os
import platform
import sys
from copy import deepcopy
from pathlib import Path
FILE = Path(__file__).resolve()
ROOT = FILE.parents[1] # YOLOv5 root direc... | 15,354 | Python | .py | 295 | 42.345763 | 120 | 0.552544 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,672 | experimental.py | Leaf-0010_cell_detection/models/experimental.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Experimental modules
"""
import math
import numpy as np
import torch
import torch.nn as nn
from models.common import Conv
from utils.downloads import attempt_download
class Sum(nn.Module):
# Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
d... | 4,146 | Python | .py | 87 | 39.563218 | 116 | 0.591044 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,673 | common.py | Leaf-0010_cell_detection/models/common.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Common modules
"""
import json
import math
import platform
import warnings
from collections import OrderedDict, namedtuple
from copy import copy
from pathlib import Path
import cv2
import numpy as np
import pandas as pd
import requests
import torch
import torch.nn as ... | 35,329 | Python | .py | 638 | 44.595611 | 119 | 0.563066 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,674 | tf.py | Leaf-0010_cell_detection/models/tf.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
TensorFlow, Keras and TFLite versions of YOLOv5
Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127
Usage:
$ python models/tf.py --weights yolov5s.pt
Export:
$ python path/to/export.py --weights yolov5s.pt --include sa... | 25,502 | Python | .py | 482 | 41.925311 | 120 | 0.559732 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,675 | common.cpython-39.pyc | Leaf-0010_cell_detection/models/__pycache__/common.cpython-39.pyc | a
2ªdŠ ã @ sÊ d Z ddlZddlZddlZddlZddlmZmZ ddlmZ ddl m
Z
ddl Z ddl Z
ddlZddlZddlZddlmZ ddlZddlmZ ddlmZ ddlmZmZ dd l m Z m Z m Z m Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z' dd
l(m)Z)m*Z*m+Z+ dd l,m-Z-m.Z. d@d d
„Z/... | 32,327 | Python | .py | 185 | 173.691892 | 2,204 | 0.362132 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,676 | tf.cpython-37.pyc | Leaf-0010_cell_detection/models/__pycache__/tf.cpython-37.pyc | B
,Åb�c ã @ sà d Z ddlZddlZddlmZ ddlmZ eeƒ ¡ Z e j
d Z e e ƒej
kr`ej
e e ƒ¡ ddlZddlZddlZddlmZ ddlmZ ddlmZmZmZmZm Z m Z m Z m Z m Z m!Z!m"Z"m#Z#m$Z$ ddl%m&Z&m'Z' dd l(m)Z) dd
l*m+Z+ dd l,m-Z-m.Z.m/Z/ G d d
„ d
ej0... | 25,429 | Python | .py | 167 | 151.11976 | 1,078 | 0.378805 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,677 | yolo.cpython-37.pyc | Leaf-0010_cell_detection/models/__pycache__/yolo.cpython-37.pyc | B
.ªdú; ã @ sÀ d Z ddlZddlZddlZddlZddlmZ ddlmZ ee ƒ
¡ Z e j d Z
ee
ƒejkrpej ee
ƒ¡ e ¡ dkr’eej e
e ¡ ¡ƒZ
ddlT ddlT ddlmZ dd lmZmZmZm Z m Z dd
l m Z dd l m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z' y ddl(Z(W n e)k
�r ... | 13,058 | Python | .py | 121 | 106.735537 | 924 | 0.395361 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,678 | common.cpython-37.pyc | Leaf-0010_cell_detection/models/__pycache__/common.cpython-37.pyc | B
0ªdŠ ã @ sÊ d Z ddlZddlZddlZddlZddlmZmZ ddlmZ ddl m
Z
ddl Z ddl Z
ddlZddlZddlZddlmZ ddlZddlmZ ddlmZ ddlmZmZ dd l m Z m Z m Z m Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z' dd
l(m)Z)m*Z*m+Z+ dd l,m-Z-m.Z. d@d d
„Z/G d... | 33,022 | Python | .py | 187 | 175.540107 | 2,462 | 0.363625 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,679 | experimental.cpython-37.pyc | Leaf-0010_cell_detection/models/__pycache__/experimental.cpython-37.pyc | B
.ªd2 ã @ s„ d Z ddlZddlZddlZddlmZ ddlmZ ddl m
Z
G dd„ dej ƒZ G dd„ dej ƒZ
G d d
„ d
ejƒZdd d
„ZdS )z
Experimental modules
é N)ÚConv)Úattempt_downloadc s& e Zd Zd‡ fdd„ Zdd„ Z‡ ZS )ÚSumFc ... | 4,901 | Python | .py | 35 | 138.914286 | 1,200 | 0.401479 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,680 | general.py | Leaf-0010_cell_detection/utils/general.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
General utils
"""
import contextlib
import glob
import inspect
import logging
import math
import os
import platform
import random
import re
import shutil
import signal
import threading
import time
import urllib
from datetime import datetime
from itertools import repeat... | 41,842 | Python | .py | 820 | 43.465854 | 121 | 0.600039 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,681 | dataloaders.py | Leaf-0010_cell_detection/utils/dataloaders.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Dataloaders and dataset utils
"""
import glob
import hashlib
import json
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import Pool, ThreadPool
from pathlib import Path
from threading import Thread
f... | 47,683 | Python | .py | 942 | 38.53397 | 120 | 0.540514 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,682 | torch_utils.py | Leaf-0010_cell_detection/utils/torch_utils.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
PyTorch utils
"""
import math
import os
import platform
import subprocess
import time
import warnings
from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path
import torch
import torch.distributed as dist
import torch.nn as nn
import to... | 13,717 | Python | .py | 262 | 43.683206 | 120 | 0.60682 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,683 | autoanchor.py | Leaf-0010_cell_detection/utils/autoanchor.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
AutoAnchor utils
"""
import random
import numpy as np
import torch
import yaml
from tqdm import tqdm
from utils.general import LOGGER, colorstr, emojis
PREFIX = colorstr('AutoAnchor: ')
def check_anchor_order(m):
# Check anchor order against stride order for Y... | 7,443 | Python | .py | 144 | 44.0625 | 119 | 0.598928 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,684 | augmentations.py | Leaf-0010_cell_detection/utils/augmentations.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Image augmentation functions
"""
import math
import random
import cv2
import numpy as np
from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box
from utils.metrics import bbox_ioa
class Albumentations:
# YOLOv5 Albumentations c... | 11,910 | Python | .py | 230 | 42.982609 | 118 | 0.565543 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,685 | plots.py | Leaf-0010_cell_detection/utils/plots.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Plotting utils
"""
import math
import os
from copy import copy
from pathlib import Path
from urllib.error import URLError
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
import torch
from PIL imp... | 21,021 | Python | .py | 424 | 40.799528 | 120 | 0.574567 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,686 | metrics.py | Leaf-0010_cell_detection/utils/metrics.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Model validation metrics
"""
import math
import warnings
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import torch
def fitness(x):
# Model fitness as a weighted combination of metrics
w = [0.0, 0.0, 0.1, 0.9] # weights for [P,... | 14,361 | Python | .py | 290 | 41.096552 | 120 | 0.567471 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,687 | downloads.py | Leaf-0010_cell_detection/utils/downloads.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Download utils
"""
import logging
import os
import platform
import subprocess
import time
import urllib
from pathlib import Path
from zipfile import ZipFile
import requests
import torch
def is_url(url):
# Check if online file exists
try:
r = urllib.r... | 7,111 | Python | .py | 152 | 40.111842 | 120 | 0.62527 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,688 | __init__.py | Leaf-0010_cell_detection/utils/__init__.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
utils/initialization
"""
def notebook_init(verbose=True):
# Check system software and hardware
print('Checking setup...')
import os
import shutil
from utils.general import check_requirements, emojis, is_colab
from utils.torch_ut... | 1,093 | Python | .py | 28 | 32.178571 | 114 | 0.635844 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,689 | benchmarks.py | Leaf-0010_cell_detection/utils/benchmarks.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Run YOLOv5 benchmarks on all supported export formats
Format | `export.py --include` | Model
--- | --- | ---
PyTorch | - | yolov5s.pt
TorchScr... | 6,325 | Python | .py | 127 | 43.661417 | 120 | 0.589121 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,690 | loss.py | Leaf-0010_cell_detection/utils/loss.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Loss functions
"""
import torch
import torch.nn as nn
from utils.metrics import bbox_iou
from utils.torch_utils import de_parallel
def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
# return positive, negative lab... | 9,919 | Python | .py | 192 | 40.567708 | 119 | 0.543624 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,691 | activations.py | Leaf-0010_cell_detection/utils/activations.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Activation functions
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class SiLU(nn.Module):
# SiLU activation https://arxiv.org/pdf/1606.08415.pdf
@staticmethod
def forward(x):
return x * torch.sigmoid(x)
class Hardswish(n... | 3,449 | Python | .py | 80 | 36.275 | 106 | 0.615362 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,692 | autobatch.py | Leaf-0010_cell_detection/utils/autobatch.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Auto-batch utils
"""
from copy import deepcopy
import numpy as np
import torch
from utils.general import LOGGER, colorstr, emojis
from utils.torch_utils import profile
def check_train_batch_size(model, imgsz=640, amp=True):
# Check YOLOv5 training batch size
... | 2,600 | Python | .py | 54 | 43 | 121 | 0.656669 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,693 | callbacks.py | Leaf-0010_cell_detection/utils/callbacks.py | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Callback utils
"""
class Callbacks:
""""
Handles all registered callbacks for YOLOv5 Hooks
"""
def __init__(self):
# Define the available callbacks
self._callbacks = {
'on_pretrain_routine_start': [],
'on_pretra... | 2,402 | Python | .py | 60 | 30.183333 | 97 | 0.559417 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,694 | metrics.cpython-37.pyc | Leaf-0010_cell_detection/utils/__pycache__/metrics.cpython-37.pyc | B
%ªd8 ã @ sÆ d Z ddlZddlZddlmZ ddlmZ ddlZ ddl
Z
dd„ Z d'dd„Z d(d
d„Z
dd„ ZG dd„ dƒZd)dd„Zdd„ Zdd„ Zd*dd „Zd d „ Zed ƒd fd d!„Zed"ƒd d#d$fd%d&„ZdS )+z
Model validation metrics
é N)ÚPathc C s* ddddg... | 11,387 | Python | .py | 118 | 93.288136 | 496 | 0.40213 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,695 | __init__.cpython-37.pyc | Leaf-0010_cell_detection/utils/__pycache__/__init__.cpython-37.pyc | B
*ªdE ã @ s d Z ddd„ZdS )z
utils/initialization
Tc C sğ t dƒ dd l}dd l}ddlm}m}m} ddlm} |dƒ dd l }ddl
m } |ƒ rh|j ddd � | rÌd
} |
¡ j}
| d ¡\} } }
| ¡ d | ¡ › d
|
| d›d| |
| d›d | | d›d� }nd}... | 1,065 | Python | .py | 11 | 95.727273 | 441 | 0.480569 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,696 | augmentations.cpython-37.pyc | Leaf-0010_cell_detection/utils/__pycache__/augmentations.cpython-37.pyc | B
)ªd†. ã @ s´ d Z ddlZddlZddlZddlZddlmZmZm Z m
Z
m Z ddl m
Z
G dd„ dƒZd'dd „Zd(d d
„Zdd„ Zd)dd„Zd*dd„Zd+d d „Zd,d d „Zd d!„ Zd-d%d&„ZdS ).z
Image augmentation functions
é N)ÚLOGGERÚ
check_versionÚcolorstrÚresample_segmentsÚ se... | 9,107 | Python | .py | 91 | 98.989011 | 902 | 0.373184 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,697 | autoanchor.cpython-37.pyc | Leaf-0010_cell_detection/utils/__pycache__/autoanchor.cpython-37.pyc | B
)ªd ã @ sl d Z ddlZddlZddlZddlZddlmZ ddlmZm Z m
Z
e dƒZ dd„ Z dd
d „Z
ddd„ZdS )z
AutoAnchor utils
é N)Útqdm)ÚLOGGERÚcolorstrÚemojisz AutoAnchor: c C sz | j d¡ d¡ d¡}|d |d }| jd | jd }|rv|... | 6,550 | Python | .py | 57 | 111.578947 | 612 | 0.442527 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,698 | downloads.cpython-39.pyc | Leaf-0010_cell_detection/utils/__pycache__/downloads.cpython-39.pyc | a
(ªdÇ ã @ sš d Z ddlZddlZddlZddlZddlZddlZddlmZ ddl m
Z
ddl Z ddl Z dd„ Z
ddd „Zdd d „Zddd„Zddd„Zd dd„ZdS ) z
Download utils
é N)ÚPath)ÚZipFilec C s8 zt j | ¡}| ¡ dkW S t jjy2 Y... | 5,045 | Python | .py | 51 | 97.882353 | 562 | 0.463063 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |
2,283,699 | callbacks.cpython-37.pyc | Leaf-0010_cell_detection/utils/__pycache__/callbacks.cpython-37.pyc | B
,Åbb ã @ s d Z G dd„ dƒZdS )z
Callback utils
c @ s4 e Zd ZdZdd„ Zd dd„Zd
dd „Zd
d „ ZdS )Ú Callbacksz<"
Handles all registered callbacks for YOLOv5 Hooks
c C s8 g g g g g g g g g g g g g g g g g g g dœ| _ d| _d S )... | 2,508 | Python | .py | 31 | 74.354839 | 351 | 0.543469 | Leaf-0010/cell_detection | 8 | 0 | 0 | GPL-3.0 | 9/5/2024, 10:47:27 PM (Europe/Amsterdam) |