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464 lines
18 KiB
Plaintext
464 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "5a13ad6b-56c9-4381-b376-1765f6dd7553",
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"metadata": {
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"source": [
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"# Импортирование библиотек"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "7311cb4a-5bf3-4268-b431-43eea10e9ed6",
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"metadata": {
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"cuda\n"
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]
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},
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{
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"ename": "error",
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"evalue": "OpenCV(4.10.0) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\highgui\\src\\window.cpp:1295: error: (-2:Unspecified error) The function is not implemented. Rebuild the library with Windows, GTK+ 2.x or Cocoa support. If you are on Ubuntu or Debian, install libgtk2.0-dev and pkg-config, then re-run cmake or configure script in function 'cvDestroyAllWindows'\n",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31merror\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[1], line 37\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[38;5;28mprint\u001b[39m(device)\n\u001b[0;32m 36\u001b[0m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mempty_cache()\n\u001b[1;32m---> 37\u001b[0m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdestroyAllWindows\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 38\u001b[0m gc\u001b[38;5;241m.\u001b[39mcollect()\n",
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"\u001b[1;31merror\u001b[0m: OpenCV(4.10.0) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\highgui\\src\\window.cpp:1295: error: (-2:Unspecified error) The function is not implemented. Rebuild the library with Windows, GTK+ 2.x or Cocoa support. If you are on Ubuntu or Debian, install libgtk2.0-dev and pkg-config, then re-run cmake or configure script in function 'cvDestroyAllWindows'\n"
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]
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}
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],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"from torch.utils.data import Dataset, DataLoader\n",
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"from torch import default_generator, randperm\n",
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"from torch.utils.data.dataset import Subset\n",
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"import torchvision.transforms as transforms\n",
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"from torchvision.io import read_image\n",
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"from importlib import import_module\n",
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"import matplotlib.pyplot as plt\n",
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"from torchvision import models\n",
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"import torch, torchvision\n",
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"from pathlib import Path\n",
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"from PIL import Image\n",
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"import torch.nn as nn\n",
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"from tqdm import tqdm\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib\n",
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"import os, shutil\n",
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"import mlconfig\n",
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"import random\n",
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"import shutil\n",
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"import timeit\n",
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"import copy\n",
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"import time\n",
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"import cv2\n",
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"import csv\n",
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"import sys\n",
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"import io\n",
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"import gc\n",
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"\n",
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"plt.rcParams[\"savefig.bbox\"] = 'tight'\n",
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"torch.manual_seed(1)\n",
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"#matplotlib.use('Agg')\n",
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"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
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"print(device)\n",
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"torch.cuda.empty_cache()\n",
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"cv2.destroyAllWindows()\n",
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"gc.collect()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "384de097-82c6-41f5-bda9-b2f54bc99593",
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"metadata": {
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"source": [
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"# Подготовка и обучение детектирование"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "46e4dc99-6994-4fee-a32e-f3983bd991bd",
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"metadata": {},
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"outputs": [],
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"source": [
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"def prepare_and_learning_detection(num_classes, num_samples, path_dataset, model_name, config_name, model):\n",
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" num_samples_per_class = num_samples // num_classes\n",
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"\n",
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" #----------Создаём папку для сохранения результатов обучения--------------\n",
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" \n",
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" ind = 1\n",
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" while True:\n",
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" if os.path.exists(\"models/\" + model_name + str(ind)):\n",
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" ind += 1\n",
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" else:\n",
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" os.mkdir(\"models/\" + model_name + str(ind))\n",
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" path_res = \"models/\" + model_name + str(ind) + '/'\n",
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" break\n",
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" \n",
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" #----------Создаём файл dataset.csv для обучения--------------\n",
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" \n",
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" pd_columns = ['file_name']\n",
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" df = pd.DataFrame(columns=pd_columns)\n",
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" \n",
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" subdirs = os.listdir(path_dataset)\n",
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" for subdir in subdirs:\n",
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" files = os.listdir(path_dataset + subdir + '/')\n",
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" num_samples_per_class = min(num_samples_per_class, len(files))\n",
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" for subdir in subdirs:\n",
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" files = os.listdir(path_dataset + subdir + '/')\n",
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" random.shuffle(files)\n",
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" files_to_process = files[:num_samples_per_class]\n",
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" for file in files_to_process:\n",
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" row = pd.DataFrame({pd_columns[0]: [str(path_dataset + subdir + '/' + file)]})\n",
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" df = pd.concat([df, row], ignore_index=True)\n",
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" \n",
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" df.to_csv(path_res + 'dataset.csv', index=False)\n",
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" \n",
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" #----------Импортируем параметры для обучения--------------\n",
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" \n",
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" def load_function(attr):\n",
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" module_, func = attr.rsplit('.', maxsplit=1)\n",
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" return getattr(import_module(module_), func)\n",
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" \n",
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" config = mlconfig.load('config_' + config_name + '.yaml')\n",
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" \n",
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" #----------Создаём класс датасета--------------\n",
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" \n",
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" class MyDataset(Dataset):\n",
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" def __init__(self, path_dataset, csv_file):\n",
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" data=[]\n",
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" with open(path_dataset + csv_file, newline='') as csvfile:\n",
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" reader = csv.reader(csvfile, delimiter=' ', quotechar='|')\n",
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" for row in list(reader)[1:]:\n",
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" row = str(row)\n",
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" data.append(row[2: len(row)-2])\n",
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" self.sig_filenames = data\n",
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" self.path_dataset = path_dataset\n",
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" \n",
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" def __len__(self):\n",
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" return len(self.sig_filenames)\n",
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" \n",
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" def __getitem__(self, idx):\n",
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" data_file = np.asarray(np.load(self.sig_filenames[idx], 'r+'), dtype=np.float32)\n",
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" if 'drone' in list(self.sig_filenames[idx].split('/')):\n",
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" label = torch.tensor(0)\n",
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" if 'noise' in list(self.sig_filenames[idx].split('/')):\n",
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" label = torch.tensor(1)\n",
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" return data_file, label\n",
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" \n",
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" #----------Создаём датасет--------------\n",
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" \n",
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" dataset = MyDataset(path_dataset=path_res, csv_file='dataset.csv')\n",
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" train_set, valid_set = torch.utils.data.random_split(dataset, [0.7, 0.3], generator=torch.Generator().manual_seed(42))\n",
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" batch_size = config.batch_size\n",
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" train_dataloader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, drop_last=True)\n",
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" valid_dataloader = torch.utils.data.DataLoader(valid_set, batch_size=batch_size, shuffle=True, drop_last=True)\n",
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" \n",
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" dataloaders = {}\n",
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" dataloaders['train'] = train_dataloader\n",
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" dataloaders['val'] = valid_dataloader\n",
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" dataset_sizes = {}\n",
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" dataset_sizes['train'] = len(train_set)\n",
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" dataset_sizes['val'] = len(valid_set)\n",
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"\n",
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" #----------Обучаем модель--------------\n",
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"\n",
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" val_loss = []\n",
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" val_acc = []\n",
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" train_loss = []\n",
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" train_acc = []\n",
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" epochs = config.epoch\n",
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" \n",
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" best_acc = 0.0\n",
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" best_model = copy.deepcopy(model.state_dict())\n",
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" limit = config.limit\n",
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" epoch_limit = epochs\n",
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" \n",
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" start = timeit.default_timer()\n",
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" for epoch in range(1, epochs+1):\n",
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" print(f\"Epoch : {epoch}\\n\")\n",
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" dataloader = None\n",
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" \n",
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" for phase in ['train', 'val']:\n",
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" running_loss = 0.0\n",
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" running_corrects = 0\n",
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" \n",
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" for (img, label) in tqdm(dataloaders[phase]):\n",
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" img, label = img.to(device), label.to(device)\n",
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" optimizer.zero_grad()\n",
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" \n",
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" with torch.set_grad_enabled(phase == 'train'):\n",
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" output = model(img)\n",
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" _, pred = torch.max(output.data, 1)\n",
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" loss = criterion(output, label)\n",
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" if phase=='train' :\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" \n",
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" running_loss += loss.item() * img.size(0)\n",
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" running_corrects += torch.sum(pred == label.data)\n",
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" \n",
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" epoch_loss = running_loss / dataset_sizes[phase]\n",
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" epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
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" \n",
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" print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))\n",
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" \n",
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" if phase=='train' :\n",
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" train_loss.append(epoch_loss)\n",
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" train_acc.append(epoch_acc)\n",
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" else :\n",
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" val_loss.append(epoch_loss)\n",
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" val_acc.append(epoch_acc)\n",
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" if val_acc[-1] > best_acc :\n",
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" ind_limit = 0\n",
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" best_acc = val_acc[-1]\n",
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" best_model = copy.deepcopy(model.state_dict())\n",
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" torch.save(best_model, path_res + model_name + '.pth')\n",
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" else:\n",
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" ind_limit += 1\n",
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" \n",
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" if ind_limit >= limit:\n",
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" break\n",
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" \n",
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" if ind_limit >= limit:\n",
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" epoch_limit = epoch\n",
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" break\n",
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" \n",
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" print()\n",
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" \n",
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" end = timeit.default_timer()\n",
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" print(f\"Total time elapsed = {end - start} seconds\")\n",
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" epoch_limit += 1\n",
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" \n",
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" #----------Вывод графиков и сохранение результатов обучения--------------\n",
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" \n",
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" train_acc = np.asarray(list(map(lambda x: x.item(), train_acc)))\n",
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" val_acc = np.asarray(list(map(lambda x: x.item(), val_acc)))\n",
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" \n",
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" np.save(path_res+'train_acc.npy', train_acc)\n",
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" np.save(path_res+'val_acc.npy', val_acc)\n",
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" np.save(path_res+'train_loss.npy', train_loss)\n",
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" np.save(path_res+'val_loss.npy', val_loss)\n",
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" \n",
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" plt.figure()\n",
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" plt.plot(range(1,epoch_limit), train_loss, color='blue')\n",
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" plt.plot(range(1,epoch_limit), val_loss, color='red')\n",
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" plt.xlabel('Epoch')\n",
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" plt.ylabel('Loss')\n",
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" plt.title('Loss Curve')\n",
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" plt.legend(['Train Loss', 'Validation Loss'])\n",
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" plt.show()\n",
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" plt.clf()\n",
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" plt.cla()\n",
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" plt.close()\n",
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" \n",
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" plt.figure()\n",
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" plt.plot(range(1,epoch_limit), train_acc, color='blue')\n",
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" plt.plot(range(1,epoch_limit), val_acc, color='red')\n",
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" plt.xlabel('Epoch')\n",
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" plt.ylabel('Accuracy')\n",
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" plt.title('Accuracy Curve')\n",
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" plt.legend(['Train Accuracy', 'Validation Accuracy'])\n",
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" plt.show()\n",
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" \n",
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" plt.clf()\n",
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" plt.cla()\n",
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" plt.close()\n",
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" torch.cuda.empty_cache()\n",
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" cv2.destroyAllWindows()\n",
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" del model\n",
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" gc.collect()\n",
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"\n",
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" return path_res, model_name"
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]
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},
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{
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"cell_type": "markdown",
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"id": "93c136ee",
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"metadata": {},
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"source": [
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"### Ensemble"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "52e8d4c5",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
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" warnings.warn(\n",
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"C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
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" warnings.warn(msg)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch : 1\n",
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"\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|████████████████████████████████████████████████████████████████████████████████| 437/437 [23:49<00:00, 3.27s/it]\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"train Loss: 0.0884 Acc: 0.9634\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|████████████████████████████████████████████████████████████████████████████████| 187/187 [09:15<00:00, 2.97s/it]\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"val Loss: 0.0342 Acc: 0.9873\n",
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"\n",
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"Epoch : 2\n",
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"\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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" 84%|██████████████████████████████████████████████████████████████████▊ | 365/437 [19:28<04:18, 3.59s/it]"
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]
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}
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],
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"source": [
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"#----------Инициализируем модель и параметры обучения--------------\n",
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"\n",
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"torch.cuda.empty_cache()\n",
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"cv2.destroyAllWindows()\n",
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"gc.collect()\n",
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"\n",
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"num_classes = 3\n",
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"config_name = \"ensemble\"\n",
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" \n",
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"def load_function(attr):\n",
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" module_, func = attr.rsplit('.', maxsplit=1)\n",
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" return getattr(import_module(module_), func)\n",
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" \n",
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"config = mlconfig.load('config_' + config_name + '.yaml')\n",
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"\n",
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"model1 = models.resnet18(pretrained=False)\n",
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"model2 = models.resnet50(pretrained=False)\n",
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"model3 = models.resnet101(pretrained=False)\n",
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"\n",
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"num_classes = 2\n",
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"\n",
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"model1.fc = nn.Linear(model1.fc.in_features, num_classes)\n",
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"model2.fc = nn.Linear(model2.fc.in_features, num_classes)\n",
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"model3.fc = nn.Linear(model3.fc.in_features, num_classes)\n",
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"\n",
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"class Ensemble(nn.Module):\n",
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" def __init__(self, model1, model2, model3):\n",
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" super(Ensemble, self).__init__()\n",
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" self.model1 = model1\n",
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" self.model2 = model2\n",
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" self.model3 = model3\n",
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" self.fc = nn.Linear(3 * num_classes, num_classes)\n",
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"\n",
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" def forward(self, x):\n",
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" x1 = self.model1(x)\n",
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" x2 = self.model2(x)\n",
|
|
" x3 = self.model3(x)\n",
|
|
" x = torch.cat((x1, x2, x3), dim=1)\n",
|
|
" x = self.fc(x)\n",
|
|
" return x\n",
|
|
"\n",
|
|
"model = Ensemble(model1, model2, model3)\n",
|
|
"\n",
|
|
"optimizer = load_function(config.optimizer.name)(model.parameters(), lr=config.optimizer.lr)\n",
|
|
"criterion = load_function(config.loss_function.name)()\n",
|
|
"scheduler = load_function(config.scheduler.name)(optimizer, step_size=config.scheduler.step_size, gamma=config.scheduler.gamma)\n",
|
|
"\n",
|
|
"if device != 'cpu':\n",
|
|
" model = model.to(device)\n",
|
|
"\n",
|
|
"#----------Создания датасета и обучение модели--------------\n",
|
|
"\n",
|
|
"path_res, model_name = prepare_and_learning_detection(num_classes = num_classes, num_samples = 10000, path_dataset = \"//192.168.1.64/data/DATASETS/2.4/2.4_learning/\", \n",
|
|
" model_name = config_name+\"_2.4_\", config_name = config_name, model=model)\n",
|
|
"\n",
|
|
"\n",
|
|
"torch.cuda.empty_cache()\n",
|
|
"cv2.destroyAllWindows()\n",
|
|
"del model\n",
|
|
"gc.collect()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "57d18676",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"celltoolbar": "Отсутствует",
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.11.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|