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DroneDetector
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Automatica_1_v2
Automatica-3
Ufa
neptune
main
automatica-1
main_2
new_read_pipe
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from '98f6fbdbdc'
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DroneDetector/train_scripts/Training_models_1.2.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "5a13ad6b-56c9-4381-b376-1765f6dd7553",
"metadata": {
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"# Импортирование библиотек"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7311cb4a-5bf3-4268-b431-43eea10e9ed6",
"metadata": {
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/sibscience-4/from_ssh/DroneDetector/.venv-train/lib/python3.12/site-packages/matplotlib/projections/__init__.py:63: UserWarning: Unable to import Axes3D. This may be due to multiple versions of Matplotlib being installed (e.g. as a system package and as a pip package). As a result, the 3D projection is not available.\n",
" warnings.warn(\"Unable to import Axes3D. This may be due to multiple versions of \"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"cuda\n"
]
},
{
"data": {
"text/plain": [
"191"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from torch.utils.data import Dataset, DataLoader\n",
"from torch import default_generator, randperm\n",
"from torch.utils.data.dataset import Subset\n",
"import torchvision.transforms as transforms\n",
"from torchvision.io import read_image\n",
"from importlib import import_module\n",
"import matplotlib.pyplot as plt\n",
"from torchvision import models\n",
"import torch, torchvision\n",
"from pathlib import Path\n",
"from PIL import Image\n",
"import torch.nn as nn\n",
"from tqdm import tqdm\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib\n",
"import os, shutil\n",
"import mlconfig\n",
"import random\n",
"import shutil\n",
"import timeit\n",
"import copy\n",
"import time\n",
"import cv2\n",
"import csv\n",
"import sys\n",
"import io\n",
"import gc\n",
"\n",
"plt.rcParams[\"savefig.bbox\"] = 'tight'\n",
"torch.manual_seed(1)\n",
"#matplotlib.use('Agg')\n",
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"print(device)\n",
"torch.cuda.empty_cache()\n",
"cv2.destroyAllWindows()\n",
"gc.collect()"
]
},
{
"cell_type": "markdown",
"id": "384de097-82c6-41f5-bda9-b2f54bc99593",
"metadata": {
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"# Подготовка и обучение детектирование"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "46e4dc99-6994-4fee-a32e-f3983bd991bd",
"metadata": {},
"outputs": [],
"source": [
"def prepare_and_learning_detection(num_classes, num_samples, path_dataset, model_name, config_name, model,selected_freq):\n",
" num_samples_per_class = num_samples // num_classes\n",
"\n",
"\n",
" #----------Создаём папку для сохранения результатов обучения--------------\n",
" os.makedirs(\"models\", exist_ok=True)\n",
" ind = 1\n",
" while True:\n",
" if os.path.exists(\"models/\" + model_name + str(ind)):\n",
" ind += 1\n",
" else:\n",
" os.mkdir(\"models/\" + model_name + str(ind))\n",
" path_res = \"models/\" + model_name + str(ind) + '/'\n",
" break\n",
" \n",
" #----------Создаём файл dataset.csv для обучения--------------\n",
" \n",
" pd_columns = ['file_name']\n",
" df = pd.DataFrame(columns=pd_columns)\n",
" \n",
" subdirs = os.listdir(path_dataset)\n",
" print(subdirs)\n",
" \n",
" for subdir in subdirs:\n",
" freq_dir = os.path.join(path_dataset, subdir, str(selected_freq))\n",
" if not os.path.isdir(freq_dir):\n",
" continue\n",
"\n",
" files = [\n",
" f for f in os.listdir(freq_dir)\n",
" if os.path.isfile(os.path.join(freq_dir, f)) and f.endswith('.npy')\n",
" ]\n",
" num_samples_per_class = min(num_samples_per_class, len(files))\n",
"\n",
" for subdir in subdirs:\n",
" freq_dir = os.path.join(path_dataset, subdir, str(selected_freq))\n",
" if not os.path.isdir(freq_dir):\n",
" continue\n",
"\n",
" files = [\n",
" f for f in os.listdir(freq_dir)\n",
" if os.path.isfile(os.path.join(freq_dir, f)) and f.endswith('.npy')\n",
" ]\n",
" random.shuffle(files)\n",
" files_to_process = files[:num_samples_per_class]\n",
"\n",
" for file in files_to_process:\n",
" row = pd.DataFrame({\n",
" pd_columns[0]: [str(os.path.join(freq_dir, file))]\n",
" })\n",
" df = pd.concat([df, row], ignore_index=True)\n",
"\n",
" dataset_csv_path = os.path.join(path_res, 'dataset.csv')\n",
" df.to_csv(dataset_csv_path, index=False)\n",
"\n",
" if not os.path.exists(dataset_csv_path):\n",
" raise RuntimeError(f'dataset.csv was not created: {dataset_csv_path}')\n",
" #----------Импортируем параметры для обучения--------------\n",
" \n",
" def load_function(attr):\n",
" module_, func = attr.rsplit('.', maxsplit=1)\n",
" return getattr(import_module(module_), func)\n",
" \n",
" config = mlconfig.load('config_' + config_name + '.yaml')\n",
" \n",
" #----------Создаём класс датасета--------------\n",
" \n",
" class MyDataset(Dataset):\n",
" def __init__(self, path_dataset, csv_file):\n",
" data=[]\n",
" with open(os.path.join(path_dataset, csv_file), newline='') as csvfile:\n",
" reader = csv.reader(csvfile, delimiter=' ', quotechar='|')\n",
" for row in list(reader)[1:]:\n",
" row = str(row)\n",
" data.append(row[2: len(row)-2])\n",
"\n",
" self.sig_filenames = data\n",
" self.path_dataset = path_dataset\n",
" \n",
" def __len__(self):\n",
" return len(self.sig_filenames)\n",
" \n",
" def __getitem__(self, idx):\n",
" base = os.path.splitext(self.sig_filenames[idx])[0]\n",
"\n",
" #raise(base)\n",
"\n",
" exit(1)\n",
" image_real = np.asarray(cv2.split(cv2.imread(base + '_real.png')), dtype=np.float32)\n",
" image_imag = np.asarray(cv2.split(cv2.imread(base + '_imag.png')), dtype=np.float32)\n",
" image_spec = np.asarray(cv2.split(cv2.imread(base + '_spec.png')), dtype=np.float32)\n",
"\n",
" if 'drone' in list(self.sig_filenames[idx].split('/')):\n",
" label = torch.tensor(0)\n",
" if 'noise' in list(self.sig_filenames[idx].split('/')):\n",
" exit(1)\n",
" label = torch.tensor(1)\n",
" return image_real, image_imag, image_spec, label\n",
" \n",
" #----------Создаём датасет--------------\n",
" exit(1)\n",
" dataset = MyDataset(path_dataset=path_res, csv_file='dataset.csv')\n",
" train_set, valid_set = torch.utils.data.random_split(dataset, [0.7, 0.3], generator=torch.Generator().manual_seed(42))\n",
" batch_size = config.batch_size\n",
" train_dataloader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, drop_last=True)\n",
" valid_dataloader = torch.utils.data.DataLoader(valid_set, batch_size=batch_size, shuffle=True, drop_last=True)\n",
" \n",
" dataloaders = {}\n",
" dataloaders['train'] = train_dataloader\n",
" dataloaders['val'] = valid_dataloader\n",
" dataset_sizes = {}\n",
" dataset_sizes['train'] = len(train_set)\n",
" dataset_sizes['val'] = len(valid_set)\n",
"\n",
" #----------Обучаем модель--------------\n",
"\n",
" val_loss = []\n",
" val_acc = []\n",
" train_loss = []\n",
" train_acc = []\n",
" epochs = config.epoch\n",
" \n",
" best_acc = 0.0\n",
" best_model = copy.deepcopy(model.state_dict())\n",
" limit = config.limit\n",
" epoch_limit = epochs\n",
" \n",
" start = timeit.default_timer()\n",
" for epoch in range(1, epochs+1):\n",
" print(f\"Epoch : {epoch}\\n\")\n",
" dataloader = None\n",
" \n",
" for phase in ['train', 'val']:\n",
" running_loss = 0.0\n",
" running_corrects = 0\n",
" \n",
" for (img1, img2, img3, label) in tqdm(dataloaders[phase]):\n",
" img1, img2, img3, label = img1.to(device), img2.to(device), img3.to(device), label.to(device)\n",
" optimizer.zero_grad()\n",
" \n",
" with torch.set_grad_enabled(phase == 'train'):\n",
" output = model([img1, img2, img3])\n",
" _, pred = torch.max(output.data, 1)\n",
" loss = criterion(output, label)\n",
" if phase=='train' :\n",
" loss.backward()\n",
" optimizer.step()\n",
" \n",
" running_loss += loss.item() * 3 * img1.size(0)\n",
" running_corrects += torch.sum(pred == label.data)\n",
" \n",
" epoch_loss = running_loss / dataset_sizes[phase]\n",
" epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
" \n",
" print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))\n",
" \n",
" if phase=='train' :\n",
" train_loss.append(epoch_loss)\n",
" train_acc.append(epoch_acc)\n",
" else :\n",
" val_loss.append(epoch_loss)\n",
" val_acc.append(epoch_acc)\n",
" if val_acc[-1] > best_acc :\n",
" ind_limit = 0\n",
" best_acc = val_acc[-1]\n",
" best_model = copy.deepcopy(model.state_dict())\n",
" torch.save(best_model, path_res + model_name + '.pth')\n",
" else:\n",
" ind_limit += 1\n",
" \n",
" if ind_limit >= limit:\n",
" break\n",
" \n",
" if ind_limit >= limit:\n",
" epoch_limit = epoch\n",
" break\n",
" \n",
" print()\n",
" \n",
" end = timeit.default_timer()\n",
" print(f\"Total time elapsed = {end - start} seconds\")\n",
" epoch_limit += 1\n",
" \n",
" #----------Вывод графиков и сохранение результатов обучения--------------\n",
" \n",
" train_acc = np.asarray(list(map(lambda x: x.item(), train_acc)))\n",
" val_acc = np.asarray(list(map(lambda x: x.item(), val_acc)))\n",
" \n",
" np.save(path_res+'train_acc.npy', train_acc)\n",
" np.save(path_res+'val_acc.npy', val_acc)\n",
" np.save(path_res+'train_loss.npy', train_loss)\n",
" np.save(path_res+'val_loss.npy', val_loss)\n",
" \n",
" plt.figure()\n",
" plt.plot(range(1,epoch_limit), train_loss, color='blue')\n",
" plt.plot(range(1,epoch_limit), val_loss, color='red')\n",
" plt.xlabel('Epoch')\n",
" plt.ylabel('Loss')\n",
" plt.title('Loss Curve')\n",
" plt.legend(['Train Loss', 'Validation Loss'])\n",
" plt.show()\n",
" plt.clf()\n",
" plt.cla()\n",
" plt.close()\n",
" \n",
" plt.figure()\n",
" plt.plot(range(1,epoch_limit), train_acc, color='blue')\n",
" plt.plot(range(1,epoch_limit), val_acc, color='red')\n",
" plt.xlabel('Epoch')\n",
" plt.ylabel('Accuracy')\n",
" plt.title('Accuracy Curve')\n",
" plt.legend(['Train Accuracy', 'Validation Accuracy'])\n",
" plt.show()\n",
" \n",
" plt.clf()\n",
" plt.cla()\n",
" plt.close()\n",
" torch.cuda.empty_cache()\n",
" cv2.destroyAllWindows()\n",
" del model\n",
" gc.collect()\n",
"\n",
" return path_res, model_name"
]
},
{
"cell_type": "markdown",
"id": "93c136ee",
"metadata": {},
"source": [
"### Ensemble"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "52e8d4c5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['noise', 'drone']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_3754538/2835334388.py:103: UserWarning: Length of split at index 0 is 0. This might result in an empty dataset.\n",
" train_set, valid_set = torch.utils.data.random_split(dataset, [0.7, 0.3], generator=torch.Generator().manual_seed(42))\n",
"/tmp/ipykernel_3754538/2835334388.py:103: UserWarning: Length of split at index 1 is 0. This might result in an empty dataset.\n",
" train_set, valid_set = torch.utils.data.random_split(dataset, [0.7, 0.3], generator=torch.Generator().manual_seed(42))\n"
]
},
{
"ename": "ValueError",
"evalue": "num_samples should be a positive integer value, but got num_samples=0",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mValueError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[13]\u001b[39m\u001b[32m, line 49\u001b[39m\n\u001b[32m 45\u001b[39m model = model.to(device)\n\u001b[32m 46\u001b[39m \n\u001b[32m 47\u001b[39m \u001b[38;5;66;03m#----------Создания датасета и обучение модели--------------\u001b[39;00m\n\u001b[32m 48\u001b[39m \n\u001b[32m---> \u001b[39m\u001b[32m49\u001b[39m path_res, model_name = prepare_and_learning_detection(num_classes = num_classes, num_samples = 10000, path_dataset = \"/mnt/data/Dataset_img\", \n\u001b[32m 50\u001b[39m selected_freq=\u001b[32m2400\u001b[39m,model_name = config_name+\u001b[33m\"_2.4_jpg_\"\u001b[39m, config_name = config_name, model=model)\n\u001b[32m 51\u001b[39m \n\u001b[32m 52\u001b[39m \n",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 105\u001b[39m, in \u001b[36mprepare_and_learning_detection\u001b[39m\u001b[34m(num_classes, num_samples, path_dataset, model_name, config_name, model, selected_freq)\u001b[39m\n\u001b[32m 101\u001b[39m exit(\u001b[32m1\u001b[39m)\n\u001b[32m 102\u001b[39m dataset = MyDataset(path_dataset=path_res, csv_file=\u001b[33m'dataset.csv'\u001b[39m)\n\u001b[32m 103\u001b[39m train_set, valid_set = torch.utils.data.random_split(dataset, [\u001b[32m0.7\u001b[39m, \u001b[32m0.3\u001b[39m], generator=torch.Generator().manual_seed(\u001b[32m42\u001b[39m))\n\u001b[32m 104\u001b[39m batch_size = config.batch_size\n\u001b[32m--> \u001b[39m\u001b[32m105\u001b[39m train_dataloader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=\u001b[38;5;28;01mTrue\u001b[39;00m, drop_last=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m 106\u001b[39m valid_dataloader = torch.utils.data.DataLoader(valid_set, batch_size=batch_size, shuffle=\u001b[38;5;28;01mTrue\u001b[39;00m, drop_last=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m 107\u001b[39m \n\u001b[32m 108\u001b[39m dataloaders = {}\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/from_ssh/DroneDetector/.venv-train/lib/python3.12/site-packages/torch/utils/data/dataloader.py:394\u001b[39m, in \u001b[36mDataLoader.__init__\u001b[39m\u001b[34m(self, dataset, batch_size, shuffle, sampler, batch_sampler, num_workers, collate_fn, pin_memory, drop_last, timeout, worker_init_fn, multiprocessing_context, generator, prefetch_factor, persistent_workers, pin_memory_device, in_order)\u001b[39m\n\u001b[32m 392\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# map-style\u001b[39;00m\n\u001b[32m 393\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m shuffle:\n\u001b[32m--> \u001b[39m\u001b[32m394\u001b[39m sampler = \u001b[30;43mRandomSampler\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mdataset\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mgenerator\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mgenerator\u001b[39;49m\u001b[30;43m)\u001b[39;49m \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[32m 395\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 396\u001b[39m sampler = SequentialSampler(dataset) \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/from_ssh/DroneDetector/.venv-train/lib/python3.12/site-packages/torch/utils/data/sampler.py:149\u001b[39m, in \u001b[36mRandomSampler.__init__\u001b[39m\u001b[34m(self, data_source, replacement, num_samples, generator)\u001b[39m\n\u001b[32m 144\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m 145\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mreplacement should be a boolean value, but got replacement=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.replacement\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 146\u001b[39m )\n\u001b[32m 148\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m.num_samples, \u001b[38;5;28mint\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m.num_samples <= \u001b[32m0\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m149\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 150\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mnum_samples should be a positive integer value, but got num_samples=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.num_samples\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 151\u001b[39m )\n",
"\u001b[31mValueError\u001b[39m: num_samples should be a positive integer value, but got num_samples=0"
]
}
],
"source": [
"#----------Инициализируем модель и параметры обучения--------------\n",
"\n",
"torch.cuda.empty_cache()\n",
"cv2.destroyAllWindows()\n",
"gc.collect()\n",
"\n",
"num_classes = 2\n",
"config_name = \"ensemble\"\n",
" \n",
"def load_function(attr):\n",
" module_, func = attr.rsplit('.', maxsplit=1)\n",
" return getattr(import_module(module_), func)\n",
" \n",
"config = mlconfig.load('config_' + config_name + '.yaml')\n",
"\n",
"model1 = models.resnet18(pretrained=False)\n",
"model2 = models.resnet50(pretrained=False)\n",
"\n",
"num_classes = 2\n",
"\n",
"model1.fc = nn.Linear(model1.fc.in_features, num_classes)\n",
"model2.fc = nn.Linear(model2.fc.in_features, num_classes)\n",
"\n",
"class Ensemble(nn.Module):\n",
" def __init__(self, model1, model2):\n",
" super(Ensemble, self).__init__()\n",
" self.model1 = model1\n",
" self.model2 = model2\n",
" self.fc = nn.Linear(2 * num_classes, num_classes)\n",
"\n",
" def forward(self, x):\n",
" x1 = self.model1(x[0])\n",
" x2 = self.model2(x[1])\n",
" x = torch.cat((x1, x2), dim=1)\n",
" x = self.fc(x)\n",
" return x\n",
"\n",
"model = Ensemble(model1, model2)\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 = \"/mnt/data/Dataset_img\", \n",
" selected_freq=2400,model_name = config_name+\"_2.4_jpg_\", 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": "4234ee26",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Отсутствует",
"kernelspec": {
"display_name": ".venv-train",
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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