Удалил лишние скрипты и бинарники
Sergey Revyakin
1 month ago
parent
339b2b1615
commit
d6feece926
@ -1,284 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
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"metadata": {},
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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:0\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<contextlib.ExitStack at 0x272800ef510>"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import time\n",
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"import io\n",
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"import cv2\n",
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"import copy\n",
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"import os\n",
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"from tqdm import tqdm\n",
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"import torch.nn as nn\n",
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"import torch\n",
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"import torchvision\n",
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"from torch.utils.data import Dataset\n",
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"from torch import default_generator, randperm\n",
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"from PIL import Image\n",
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"#from torch._utils import _accumulate\n",
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"import csv\n",
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"from torch.utils.data.dataset import Subset\n",
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"from scipy import ndimage\n",
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"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
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"print(device)\n",
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"batch_size = 16\n",
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"momentum=0.9\n",
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"lr = 1e-3\n",
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"import random\n",
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"sub_sample = 0.5\n",
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"import matplotlib\n",
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"import gc\n",
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"import torchsig.utils as u\n",
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"import torchsig.transforms.transforms as T\n",
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"matplotlib.use('Agg')\n",
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"import matplotlib as mpl\n",
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"mpl.rcParams['agg.path.chunksize'] = 256*256\n",
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"plt.ioff()"
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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": "4848b066-2e09-4c1c-b8fa-8e3fa84d907a",
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"metadata": {},
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"outputs": [],
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"source": [
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"s = T.Spectrogram(nperseg=1024)"
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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": 3,
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"id": "9267fbe1",
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"metadata": {},
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"outputs": [],
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"source": [
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"def sig2pic_with_spec(path_to_data, filename, specT=None,figsize=(16,16), dpi=16, resize = None):\n",
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" try:\n",
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" if specT is None:\n",
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" specT = T.Spectrogram(nperseg=1024)\n",
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" with open(path_to_data + filename, 'rb') as file:\n",
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" tmp = np.frombuffer(file.read(), dtype=np.complex64)\n",
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" signal = tmp\n",
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" spectr = np.array(specT(signal)['data']['samples'][:,:figsize[0] * dpi])\n",
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" mag = np.abs(signal)\n",
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" real = signal.real\n",
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"\n",
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" fig2 = plt.figure(figsize = figsize)\n",
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" plt.axes(ylim=(-1, 1))\n",
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"\n",
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" plt.plot(real, color='black')\n",
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" plt.gca().set_axis_off()\n",
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" plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
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" plt.margins(0,0)\n",
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" buf2 = io.BytesIO()\n",
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" fig2.savefig(buf2, format=\"png\", dpi=dpi)\n",
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" buf2.seek(0)\n",
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" img_arr2 = np.frombuffer(buf2.getvalue(), dtype=np.uint8)\n",
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" buf2.close()\n",
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" img2 = cv2.imdecode(img_arr2, 1)\n",
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" img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\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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" plt.close(fig2)\n",
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"\n",
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" fig3 = plt.figure(figsize = figsize)\n",
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" plt.axes(ylim=(-1, 1))\n",
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"\n",
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" plt.plot(mag, color='black')\n",
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" plt.gca().set_axis_off()\n",
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" plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
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" plt.margins(0,0)\n",
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" buf3 = io.BytesIO()\n",
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" fig3.savefig(buf3, format=\"png\", dpi=dpi)\n",
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" buf3.seek(0)\n",
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" img_arr3 = np.frombuffer(buf3.getvalue(), dtype=np.uint8)\n",
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" buf3.close()\n",
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" img3 = cv2.imdecode(img_arr3, 1)\n",
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" img3 = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY)\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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" plt.close(fig3)\n",
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"\n",
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" if resize != None:\n",
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" resized_real = cv2.resize(img2, resize)\n",
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" resized_mag = cv2.resize(img3, resize)\n",
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" resized_spectr = cv2.resize(spectr, resize)\n",
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" img = np.asarray([resized_real, resized_mag, resized_spectr], dtype=np.float32)\n",
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" return img\n",
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" img = np.asarray([img2, img3, spectr], dtype=np.float32)\n",
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" return img\n",
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" except Exception as e:\n",
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" print(str(e))\n",
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" return None"
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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": 4,
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"id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
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"metadata": {},
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"outputs": [],
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"source": [
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"path_to_binaries = '//192.168.11.63/data/DATASETS/Energomash/1200'\n",
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"path_to_pictures = '//192.168.11.63/data/DATASETS/Energomash/1200_jpg'"
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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": 5,
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"id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
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"metadata": {},
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"outputs": [],
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"source": [
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"from tqdm import tqdm"
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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": 6,
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"id": "6f226f86-5d72-4573-8af6-750128b70263",
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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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" 0%| | 0/963 [00:00<?, ?it/s]C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\scipy\\signal\\_spectral_py.py:1936: RuntimeWarning: overflow encountered in multiply\n",
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" result = np.conjugate(result) * result\n",
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"C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\scipy\\signal\\_spectral_py.py:1938: RuntimeWarning: invalid value encountered in multiply\n",
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" result *= scale\n",
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"100%|████████████████████████████████████████████████████████████████████████████████| 963/963 [17:40<00:00, 1.10s/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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"Dir: drone finished!\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%|████████████████████████████████████████████████████████████████████████████████| 963/963 [51:41<00:00, 3.22s/it]"
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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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"Dir: noise finished!\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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"\n"
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]
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}
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],
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"source": [
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"size = (256,256)\n",
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"if not os.path.exists(path_to_pictures):\n",
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" os.mkdir(path_to_pictures)\n",
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"for subdir in os.listdir(path_to_binaries):\n",
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" filepath = path_to_binaries + '/' + subdir + '/'\n",
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" if not os.path.exists(path_to_pictures +'/' + subdir):\n",
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" os.mkdir(path_to_pictures + '/' + subdir)\n",
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" files = os.listdir(filepath)\n",
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" for file in tqdm(files):\n",
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" savepath = path_to_pictures +'/' + subdir + '/' + file + '.npy'\n",
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" savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '_real' + '.png' \n",
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" savepath_imag_png = path_to_pictures +'/' + subdir + '/' + file + '_imag' + '.png' \n",
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" savepath_spec_png = path_to_pictures +'/' + subdir + '/' + file + '_spec' + '.png'\n",
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" if not os.path.exists(savepath):\n",
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" img = sig2pic_with_spec(path_to_data=filepath, filename=file, specT=s, resize = size)\n",
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" gc.collect()\n",
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" try:\n",
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" \n",
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" plt.imshow(img[0])\n",
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" plt.savefig(savepath_real_png)\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.imshow(img[1])\n",
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" plt.savefig(savepath_imag_png)\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.imshow(img[2])\n",
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" plt.savefig(savepath_spec_png)\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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" np.save(savepath, img)\n",
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" \n",
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" except Exception:\n",
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" continue\n",
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" print('Dir: ', subdir , ' finished!')"
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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": "6f4bf849",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -1,270 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
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"metadata": {},
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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:0\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<contextlib.ExitStack at 0x25775eabcd0>"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import time\n",
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"import io\n",
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"import cv2\n",
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"import copy\n",
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"import os\n",
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"from tqdm import tqdm\n",
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"import torch.nn as nn\n",
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"import torch\n",
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"import torchvision\n",
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"from torch.utils.data import Dataset\n",
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"from torch import default_generator, randperm\n",
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"from PIL import Image\n",
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"#from torch._utils import _accumulate\n",
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"import csv\n",
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"from torch.utils.data.dataset import Subset\n",
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"from scipy import ndimage\n",
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"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
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"print(device)\n",
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"batch_size = 16\n",
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"momentum=0.9\n",
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"lr = 1e-3\n",
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"import random\n",
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"sub_sample = 0.5\n",
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"import matplotlib\n",
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"import gc\n",
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"import torchsig.utils as u\n",
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"import torchsig.transforms.transforms as T\n",
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"matplotlib.use('Agg')\n",
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"import matplotlib as mpl\n",
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"mpl.rcParams['agg.path.chunksize'] = 256*256\n",
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"plt.ioff()"
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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": "4848b066-2e09-4c1c-b8fa-8e3fa84d907a",
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"metadata": {},
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"outputs": [],
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"source": [
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"s = T.Spectrogram(nperseg=256)"
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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": 3,
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"id": "9267fbe1",
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"metadata": {},
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"outputs": [],
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"source": [
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"def sig2pic_with_spec(path_to_data, filename, specT=None,figsize=(16,16), dpi=16, resize = None):\n",
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" try:\n",
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" if specT is None:\n",
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" specT = T.Spectrogram(nperseg=256)\n",
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" with open(path_to_data + filename, 'rb') as file:\n",
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" tmp = np.frombuffer(file.read(), dtype=np.complex64)\n",
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" signal = tmp\n",
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" spectr = np.array(specT(signal)['data']['samples'][:,:figsize[0] * dpi])\n",
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" mag = np.abs(signal)\n",
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" real = signal.real\n",
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"\n",
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" fig2 = plt.figure(figsize = figsize)\n",
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" plt.axes(ylim=(-1, 1))\n",
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"\n",
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" plt.plot(real, color='black')\n",
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" plt.gca().set_axis_off()\n",
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" plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
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" plt.margins(0,0)\n",
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" buf2 = io.BytesIO()\n",
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" fig2.savefig(buf2, format=\"png\", dpi=dpi)\n",
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" buf2.seek(0)\n",
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" img_arr2 = np.frombuffer(buf2.getvalue(), dtype=np.uint8)\n",
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" buf2.close()\n",
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" img2 = cv2.imdecode(img_arr2, 1)\n",
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" img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\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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" plt.close(fig2)\n",
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"\n",
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" fig3 = plt.figure(figsize = figsize)\n",
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" plt.axes(ylim=(-1, 1))\n",
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"\n",
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" plt.plot(mag, color='black')\n",
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" plt.gca().set_axis_off()\n",
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" plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
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" plt.margins(0,0)\n",
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" buf3 = io.BytesIO()\n",
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" fig3.savefig(buf3, format=\"png\", dpi=dpi)\n",
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" buf3.seek(0)\n",
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" img_arr3 = np.frombuffer(buf3.getvalue(), dtype=np.uint8)\n",
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" buf3.close()\n",
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" img3 = cv2.imdecode(img_arr3, 1)\n",
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" img3 = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY)\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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" plt.close(fig3)\n",
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"\n",
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" if resize != None:\n",
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" resized_real = cv2.resize(img2, resize)\n",
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" resized_mag = cv2.resize(img3, resize)\n",
|
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" resized_spectr = cv2.resize(spectr, resize)\n",
|
||||
" img = np.asarray([resized_real, resized_mag, resized_spectr], dtype=np.float32)\n",
|
||||
" return img\n",
|
||||
" img = np.asarray([img2, img3, spectr], dtype=np.float32)\n",
|
||||
" return img\n",
|
||||
" except Exception as e:\n",
|
||||
" print(str(e))\n",
|
||||
" return None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"path_to_binaries = '//192.168.11.63/data/DATASETS/Energomash/2400'\n",
|
||||
"path_to_pictures = '//192.168.11.63/data/DATASETS/Energomash/2400_jpg'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from tqdm import tqdm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "6f226f86-5d72-4573-8af6-750128b70263",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 0%| | 0/965 [00:00<?, ?it/s]C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\scipy\\signal\\_spectral_py.py:1936: RuntimeWarning: overflow encountered in multiply\n",
|
||||
" result = np.conjugate(result) * result\n",
|
||||
"C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\scipy\\signal\\_spectral_py.py:1938: RuntimeWarning: invalid value encountered in multiply\n",
|
||||
" result *= scale\n",
|
||||
"100%|████████████████████████████████████████████████████████████████████████████████| 965/965 [28:11<00:00, 1.75s/it]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Dir: noise finished!\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"size = (256,256)\n",
|
||||
"if not os.path.exists(path_to_pictures):\n",
|
||||
" os.mkdir(path_to_pictures)\n",
|
||||
"for subdir in os.listdir(path_to_binaries):\n",
|
||||
" filepath = path_to_binaries + '/' + subdir + '/'\n",
|
||||
" if not os.path.exists(path_to_pictures +'/' + subdir):\n",
|
||||
" os.mkdir(path_to_pictures + '/' + subdir)\n",
|
||||
" files = os.listdir(filepath)\n",
|
||||
" for file in tqdm(files):\n",
|
||||
" savepath = path_to_pictures +'/' + subdir + '/' + file + '.npy'\n",
|
||||
" savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '_real' + '.png' \n",
|
||||
" savepath_imag_png = path_to_pictures +'/' + subdir + '/' + file + '_imag' + '.png' \n",
|
||||
" savepath_spec_png = path_to_pictures +'/' + subdir + '/' + file + '_spec' + '.png'\n",
|
||||
" if not os.path.exists(savepath):\n",
|
||||
" img = sig2pic_with_spec(path_to_data=filepath, filename=file, specT=s, resize = size)\n",
|
||||
" gc.collect()\n",
|
||||
" try:\n",
|
||||
" \n",
|
||||
" plt.imshow(img[0])\n",
|
||||
" plt.savefig(savepath_real_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" \n",
|
||||
" plt.imshow(img[1])\n",
|
||||
" plt.savefig(savepath_imag_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
"\n",
|
||||
" plt.imshow(img[2])\n",
|
||||
" plt.savefig(savepath_spec_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" \n",
|
||||
" np.save(savepath, img)\n",
|
||||
" \n",
|
||||
" except Exception:\n",
|
||||
" continue\n",
|
||||
" print('Dir: ', subdir , ' finished!')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "106b1add",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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
|
||||
}
|
||||
@ -1,270 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"cuda:0\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<contextlib.ExitStack at 0x25775eabcd0>"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import time\n",
|
||||
"import io\n",
|
||||
"import cv2\n",
|
||||
"import copy\n",
|
||||
"import os\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"import torch.nn as nn\n",
|
||||
"import torch\n",
|
||||
"import torchvision\n",
|
||||
"from torch.utils.data import Dataset\n",
|
||||
"from torch import default_generator, randperm\n",
|
||||
"from PIL import Image\n",
|
||||
"#from torch._utils import _accumulate\n",
|
||||
"import csv\n",
|
||||
"from torch.utils.data.dataset import Subset\n",
|
||||
"from scipy import ndimage\n",
|
||||
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
|
||||
"print(device)\n",
|
||||
"batch_size = 16\n",
|
||||
"momentum=0.9\n",
|
||||
"lr = 1e-3\n",
|
||||
"import random\n",
|
||||
"sub_sample = 0.5\n",
|
||||
"import matplotlib\n",
|
||||
"import gc\n",
|
||||
"import torchsig.utils as u\n",
|
||||
"import torchsig.transforms.transforms as T\n",
|
||||
"matplotlib.use('Agg')\n",
|
||||
"import matplotlib as mpl\n",
|
||||
"mpl.rcParams['agg.path.chunksize'] = 256*256\n",
|
||||
"plt.ioff()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "4848b066-2e09-4c1c-b8fa-8e3fa84d907a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"s = T.Spectrogram(nperseg=256)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "9267fbe1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def sig2pic_with_spec(path_to_data, filename, specT=None,figsize=(16,16), dpi=16, resize = None):\n",
|
||||
" try:\n",
|
||||
" if specT is None:\n",
|
||||
" specT = T.Spectrogram(nperseg=256)\n",
|
||||
" with open(path_to_data + filename, 'rb') as file:\n",
|
||||
" tmp = np.frombuffer(file.read(), dtype=np.complex64)\n",
|
||||
" signal = tmp\n",
|
||||
" spectr = np.array(specT(signal)['data']['samples'][:,:figsize[0] * dpi])\n",
|
||||
" mag = np.abs(signal)\n",
|
||||
" real = signal.real\n",
|
||||
"\n",
|
||||
" fig2 = plt.figure(figsize = figsize)\n",
|
||||
" plt.axes(ylim=(-1, 1))\n",
|
||||
"\n",
|
||||
" plt.plot(real, color='black')\n",
|
||||
" plt.gca().set_axis_off()\n",
|
||||
" plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
|
||||
" plt.margins(0,0)\n",
|
||||
" buf2 = io.BytesIO()\n",
|
||||
" fig2.savefig(buf2, format=\"png\", dpi=dpi)\n",
|
||||
" buf2.seek(0)\n",
|
||||
" img_arr2 = np.frombuffer(buf2.getvalue(), dtype=np.uint8)\n",
|
||||
" buf2.close()\n",
|
||||
" img2 = cv2.imdecode(img_arr2, 1)\n",
|
||||
" img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" plt.close(fig2)\n",
|
||||
"\n",
|
||||
" fig3 = plt.figure(figsize = figsize)\n",
|
||||
" plt.axes(ylim=(-1, 1))\n",
|
||||
"\n",
|
||||
" plt.plot(mag, color='black')\n",
|
||||
" plt.gca().set_axis_off()\n",
|
||||
" plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
|
||||
" plt.margins(0,0)\n",
|
||||
" buf3 = io.BytesIO()\n",
|
||||
" fig3.savefig(buf3, format=\"png\", dpi=dpi)\n",
|
||||
" buf3.seek(0)\n",
|
||||
" img_arr3 = np.frombuffer(buf3.getvalue(), dtype=np.uint8)\n",
|
||||
" buf3.close()\n",
|
||||
" img3 = cv2.imdecode(img_arr3, 1)\n",
|
||||
" img3 = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" plt.close(fig3)\n",
|
||||
"\n",
|
||||
" if resize != None:\n",
|
||||
" resized_real = cv2.resize(img2, resize)\n",
|
||||
" resized_mag = cv2.resize(img3, resize)\n",
|
||||
" resized_spectr = cv2.resize(spectr, resize)\n",
|
||||
" img = np.asarray([resized_real, resized_mag, resized_spectr], dtype=np.float32)\n",
|
||||
" return img\n",
|
||||
" img = np.asarray([img2, img3, spectr], dtype=np.float32)\n",
|
||||
" return img\n",
|
||||
" except Exception as e:\n",
|
||||
" print(str(e))\n",
|
||||
" return None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"path_to_binaries = '//192.168.11.63/data/DATASETS/Energomash/2400'\n",
|
||||
"path_to_pictures = '//192.168.11.63/data/DATASETS/Energomash/2400_jpg'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from tqdm import tqdm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "6f226f86-5d72-4573-8af6-750128b70263",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 0%| | 0/965 [00:00<?, ?it/s]C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\scipy\\signal\\_spectral_py.py:1936: RuntimeWarning: overflow encountered in multiply\n",
|
||||
" result = np.conjugate(result) * result\n",
|
||||
"C:\\Users\\snytk\\miniconda3\\envs\\python311\\Lib\\site-packages\\scipy\\signal\\_spectral_py.py:1938: RuntimeWarning: invalid value encountered in multiply\n",
|
||||
" result *= scale\n",
|
||||
"100%|████████████████████████████████████████████████████████████████████████████████| 965/965 [28:11<00:00, 1.75s/it]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Dir: noise finished!\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"size = (256,256)\n",
|
||||
"if not os.path.exists(path_to_pictures):\n",
|
||||
" os.mkdir(path_to_pictures)\n",
|
||||
"for subdir in os.listdir(path_to_binaries):\n",
|
||||
" filepath = path_to_binaries + '/' + subdir + '/'\n",
|
||||
" if not os.path.exists(path_to_pictures +'/' + subdir):\n",
|
||||
" os.mkdir(path_to_pictures + '/' + subdir)\n",
|
||||
" files = os.listdir(filepath)\n",
|
||||
" for file in tqdm(files):\n",
|
||||
" savepath = path_to_pictures +'/' + subdir + '/' + file + '.npy'\n",
|
||||
" savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '_real' + '.png' \n",
|
||||
" savepath_imag_png = path_to_pictures +'/' + subdir + '/' + file + '_imag' + '.png' \n",
|
||||
" savepath_spec_png = path_to_pictures +'/' + subdir + '/' + file + '_spec' + '.png'\n",
|
||||
" if not os.path.exists(savepath):\n",
|
||||
" img = sig2pic_with_spec(path_to_data=filepath, filename=file, specT=s, resize = size)\n",
|
||||
" gc.collect()\n",
|
||||
" try:\n",
|
||||
" \n",
|
||||
" plt.imshow(img[0])\n",
|
||||
" plt.savefig(savepath_real_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" \n",
|
||||
" plt.imshow(img[1])\n",
|
||||
" plt.savefig(savepath_imag_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
"\n",
|
||||
" plt.imshow(img[2])\n",
|
||||
" plt.savefig(savepath_spec_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" \n",
|
||||
" np.save(savepath, img)\n",
|
||||
" \n",
|
||||
" except Exception:\n",
|
||||
" continue\n",
|
||||
" print('Dir: ', subdir , ' finished!')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "106b1add",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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
|
||||
}
|
||||
@ -1,428 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"cuda:0\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<contextlib.ExitStack at 0x73285e4f6300>"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import time\n",
|
||||
"import io\n",
|
||||
"import cv2\n",
|
||||
"import copy\n",
|
||||
"import os\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"import torch.nn as nn\n",
|
||||
"import torch\n",
|
||||
"import torchvision\n",
|
||||
"from torch.utils.data import Dataset\n",
|
||||
"from torch import default_generator, randperm\n",
|
||||
"from PIL import Image\n",
|
||||
"#from torch._utils import _accumulate\n",
|
||||
"import csv\n",
|
||||
"from torch.utils.data.dataset import Subset\n",
|
||||
"from scipy import ndimage\n",
|
||||
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
|
||||
"print(device)\n",
|
||||
"batch_size = 16\n",
|
||||
"momentum=0.9\n",
|
||||
"lr = 1e-3\n",
|
||||
"import random\n",
|
||||
"sub_sample = 0.5\n",
|
||||
"import matplotlib\n",
|
||||
"import gc\n",
|
||||
"import torchsig.utils as u\n",
|
||||
"import torchsig.transforms.transforms as T\n",
|
||||
"matplotlib.use('Agg')\n",
|
||||
"import matplotlib as mpl\n",
|
||||
"mpl.rcParams['agg.path.chunksize'] = 256*256\n",
|
||||
"plt.ioff()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "4848b066-2e09-4c1c-b8fa-8e3fa84d907a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"s = T.Spectrogram(nperseg=256)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "9267fbe1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def sig2pic_with_spec(path_to_data, filename, specT=None, figsize=(16,16), dpi=16, resize = None):\n",
|
||||
" def standartize_signal(signal):\n",
|
||||
" mean = np.mean(signal)\n",
|
||||
" std = np.std(signal)\n",
|
||||
" standardized_signal = (signal - mean) / std\n",
|
||||
" return standardized_signal\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" if specT is None:\n",
|
||||
" specT = T.Spectrogram(nperseg=256)\n",
|
||||
" with open(path_to_data + filename, 'rb') as file:\n",
|
||||
" tmp = np.frombuffer(file.read(), dtype=np.complex64)\n",
|
||||
" signal = tmp\n",
|
||||
" print(len(signal))\n",
|
||||
" spectr = np.array(specT(signal)['data']['samples'][:,:figsize[0] * dpi])\n",
|
||||
" mag = np.abs(signal)\n",
|
||||
" mag = standartize_signal(mag)\n",
|
||||
" real = signal.real\n",
|
||||
" real = standartize_signal(real)\n",
|
||||
"\n",
|
||||
" fig2 = plt.figure(figsize = figsize)\n",
|
||||
" plt.axes(ylim=(-1, 1))\n",
|
||||
"\n",
|
||||
" plt.plot(real, color='black')\n",
|
||||
" plt.gca().set_axis_off()\n",
|
||||
" plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
|
||||
" plt.margins(0,0)\n",
|
||||
" buf2 = io.BytesIO()\n",
|
||||
" fig2.savefig(buf2, format=\"png\", dpi=dpi)\n",
|
||||
" buf2.seek(0)\n",
|
||||
" img_arr2 = np.frombuffer(buf2.getvalue(), dtype=np.uint8)\n",
|
||||
" buf2.close()\n",
|
||||
" img2 = cv2.imdecode(img_arr2, 1)\n",
|
||||
" img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" plt.close(fig2)\n",
|
||||
"\n",
|
||||
" fig3 = plt.figure(figsize = figsize)\n",
|
||||
" plt.axes(ylim=(-1, 1))\n",
|
||||
"\n",
|
||||
" plt.plot(mag, color='black')\n",
|
||||
" plt.gca().set_axis_off()\n",
|
||||
" plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
|
||||
" plt.margins(0,0)\n",
|
||||
" buf3 = io.BytesIO()\n",
|
||||
" fig3.savefig(buf3, format=\"png\", dpi=dpi)\n",
|
||||
" buf3.seek(0)\n",
|
||||
" img_arr3 = np.frombuffer(buf3.getvalue(), dtype=np.uint8)\n",
|
||||
" buf3.close()\n",
|
||||
" img3 = cv2.imdecode(img_arr3, 1)\n",
|
||||
" img3 = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" plt.close(fig3)\n",
|
||||
"\n",
|
||||
" if resize != None:\n",
|
||||
" resized_real = cv2.resize(img2, resize)\n",
|
||||
" resized_mag = cv2.resize(img3, resize)\n",
|
||||
" resized_spectr = cv2.resize(spectr, resize)\n",
|
||||
" img = np.asarray([resized_real, resized_mag, resized_spectr], dtype=np.float32)\n",
|
||||
" return img\n",
|
||||
" img = np.asarray([img2, img3, spectr], dtype=np.float32)\n",
|
||||
" return img\n",
|
||||
" except Exception as e:\n",
|
||||
" print(str(e))\n",
|
||||
" return None\n",
|
||||
"\n",
|
||||
"def plot_signal_and_magnitude(path_to_data, filename, filename_signal):\n",
|
||||
" def remove_outliers(signal, threshold):\n",
|
||||
" filtered_signal = np.where(np.abs(signal) <= threshold, signal, np.nan)\n",
|
||||
" return np.nan_to_num(filtered_signal)\n",
|
||||
" \n",
|
||||
" def standartize_signal(signal):\n",
|
||||
" mean = np.mean(signal)\n",
|
||||
" std = np.std(signal)\n",
|
||||
" standardized_signal = (signal - mean) / std\n",
|
||||
" return standardized_signal\n",
|
||||
" \n",
|
||||
" with open(path_to_data + filename, 'rb') as file:\n",
|
||||
" signal = np.frombuffer(file.read(), dtype=np.complex64)\n",
|
||||
" print(max(np.real(signal)))\n",
|
||||
" print(signal[:100])\n",
|
||||
" plt.figure(figsize=(12, 6))\n",
|
||||
" plt.subplot(2, 1, 1)\n",
|
||||
" plt.plot(remove_outliers(standartize_signal(np.real(signal)),1)[10000:], label='Real Part')\n",
|
||||
" plt.plot(remove_outliers(standartize_signal(np.imag(signal)),1)[10000:], label='Imaginary Part')\n",
|
||||
" plt.title('QAM Signal')\n",
|
||||
" plt.legend()\n",
|
||||
" plt.subplot(2, 1, 2)\n",
|
||||
" plt.plot(np.abs(signal), label='Magnitude')\n",
|
||||
" plt.title('Magnitude of QAM Signal')\n",
|
||||
" plt.legend()\n",
|
||||
" plt.tight_layout()\n",
|
||||
" plt.savefig(filename_signal)\n",
|
||||
" plt.close()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"path_to_binaries = '/home/sibscience/Datasets/915_9K'\n",
|
||||
"path_to_pictures = '/home/sibscience/Datasets/915_9K_jpg'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from tqdm import tqdm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "6a5f4c51",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "FileNotFoundError",
|
||||
"evalue": "[Errno 2] No such file or directory: '/home/sibscience/Datasets/915_9K_jpg'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
||||
"\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)",
|
||||
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m size = (\u001b[32m256\u001b[39m,\u001b[32m256\u001b[39m)\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os.path.exists(path_to_pictures):\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[43mos\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmkdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_to_pictures\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m subdir \u001b[38;5;129;01min\u001b[39;00m os.listdir(path_to_binaries):\n\u001b[32m 5\u001b[39m filepath = path_to_binaries + \u001b[33m'\u001b[39m\u001b[33m/\u001b[39m\u001b[33m'\u001b[39m + subdir + \u001b[33m'\u001b[39m\u001b[33m/\u001b[39m\u001b[33m'\u001b[39m\n",
|
||||
"\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: '/home/sibscience/Datasets/915_9K_jpg'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"size = (256,256)\n",
|
||||
"if not os.path.exists(path_to_pictures):\n",
|
||||
" os.mkdir(path_to_pictures)\n",
|
||||
"for subdir in os.listdir(path_to_binaries):\n",
|
||||
" filepath = path_to_binaries + '/' + subdir + '/'\n",
|
||||
" if not os.path.exists(path_to_pictures +'/' + subdir):\n",
|
||||
" os.mkdir(path_to_pictures + '/' + subdir)\n",
|
||||
" files = os.listdir(filepath)\n",
|
||||
" for file in tqdm(files):\n",
|
||||
" savepath = path_to_pictures +'/' + subdir + '/' + file + '.npy'\n",
|
||||
" savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '.png' \n",
|
||||
" if not os.path.exists(savepath):\n",
|
||||
" img = plot_signal_and_magnitude(path_to_data=filepath, filename=file, filename_signal= savepath_real_png)\n",
|
||||
" gc.collect()\n",
|
||||
" print('Dir: ', subdir , ' finished!')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6f226f86-5d72-4573-8af6-750128b70263",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\r\n",
|
||||
" 0%| | 0/565 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"800016\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\r\n",
|
||||
" 28%|█████████████████████▉ | 157/565 [00:01<00:02, 145.94it/s]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"800016\n",
|
||||
"800016\n",
|
||||
"800016\n",
|
||||
"800016\n",
|
||||
"800016\n",
|
||||
"800016\n",
|
||||
"800016\n",
|
||||
"800016\n",
|
||||
"800016\n",
|
||||
"800016\n",
|
||||
"800016\n",
|
||||
"800016\n",
|
||||
"800016\n",
|
||||
"800016\n",
|
||||
"800016\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\r\n",
|
||||
" 30%|████████████████████████▎ | 172/565 [00:15<00:46, 8.38it/s]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"800016\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 31%|████████████████████████▍ | 173/565 [00:16<00:37, 10.59it/s]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"800016\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "KeyboardInterrupt",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[10], line 15\u001b[0m\n\u001b[0;32m 13\u001b[0m savepath_spec_png \u001b[38;5;241m=\u001b[39m path_to_pictures \u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m subdir \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m file \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_spec\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.png\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 14\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(savepath):\n\u001b[1;32m---> 15\u001b[0m img \u001b[38;5;241m=\u001b[39m \u001b[43msig2pic_with_spec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_to_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilepath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mspecT\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresize\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 16\u001b[0m gc\u001b[38;5;241m.\u001b[39mcollect()\n\u001b[0;32m 17\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
|
||||
"Cell \u001b[1;32mIn[7], line 29\u001b[0m, in \u001b[0;36msig2pic_with_spec\u001b[1;34m(path_to_data, filename, specT, figsize, dpi, resize)\u001b[0m\n\u001b[0;32m 27\u001b[0m plt\u001b[38;5;241m.\u001b[39mmargins(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m 28\u001b[0m buf2 \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mBytesIO()\n\u001b[1;32m---> 29\u001b[0m \u001b[43mfig2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msavefig\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuf2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpng\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 30\u001b[0m buf2\u001b[38;5;241m.\u001b[39mseek(\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m 31\u001b[0m img_arr2 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mfrombuffer(buf2\u001b[38;5;241m.\u001b[39mgetvalue(), dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39muint8)\n",
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"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\figure.py:3390\u001b[0m, in \u001b[0;36mFigure.savefig\u001b[1;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[0;32m 3388\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes:\n\u001b[0;32m 3389\u001b[0m _recursively_make_axes_transparent(stack, ax)\n\u001b[1;32m-> 3390\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
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"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\backend_bases.py:2193\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m 2189\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 2190\u001b[0m \u001b[38;5;66;03m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[0;32m 2191\u001b[0m \u001b[38;5;66;03m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[0;32m 2192\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[1;32m-> 2193\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mprint_method\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2194\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2195\u001b[0m \u001b[43m \u001b[49m\u001b[43mfacecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfacecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2196\u001b[0m \u001b[43m \u001b[49m\u001b[43medgecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2197\u001b[0m \u001b[43m \u001b[49m\u001b[43morientation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morientation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2198\u001b[0m \u001b[43m \u001b[49m\u001b[43mbbox_inches_restore\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_bbox_inches_restore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2199\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2200\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 2201\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;129;01mand\u001b[39;00m restore_bbox:\n",
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"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\backend_bases.py:2043\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method.<locals>.<lambda>\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 2039\u001b[0m optional_kws \u001b[38;5;241m=\u001b[39m { \u001b[38;5;66;03m# Passed by print_figure for other renderers.\u001b[39;00m\n\u001b[0;32m 2040\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfacecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medgecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morientation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 2041\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbbox_inches_restore\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[0;32m 2042\u001b[0m skip \u001b[38;5;241m=\u001b[39m optional_kws \u001b[38;5;241m-\u001b[39m {\u001b[38;5;241m*\u001b[39minspect\u001b[38;5;241m.\u001b[39msignature(meth)\u001b[38;5;241m.\u001b[39mparameters}\n\u001b[1;32m-> 2043\u001b[0m print_method \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mwraps(meth)(\u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: \u001b[43mmeth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2044\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mskip\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m 2045\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# Let third-parties do as they see fit.\u001b[39;00m\n\u001b[0;32m 2046\u001b[0m print_method \u001b[38;5;241m=\u001b[39m meth\n",
|
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"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:497\u001b[0m, in \u001b[0;36mFigureCanvasAgg.print_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[0;32m 450\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprint_png\u001b[39m(\u001b[38;5;28mself\u001b[39m, filename_or_obj, \u001b[38;5;241m*\u001b[39m, metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, pil_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m 451\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 452\u001b[0m \u001b[38;5;124;03m Write the figure to a PNG file.\u001b[39;00m\n\u001b[0;32m 453\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 495\u001b[0m \u001b[38;5;124;03m *metadata*, including the default 'Software' key.\u001b[39;00m\n\u001b[0;32m 496\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 497\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_print_pil\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpng\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpil_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m)\u001b[49m\n",
|
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"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:445\u001b[0m, in \u001b[0;36mFigureCanvasAgg._print_pil\u001b[1;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[0;32m 440\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_print_pil\u001b[39m(\u001b[38;5;28mself\u001b[39m, filename_or_obj, fmt, pil_kwargs, metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m 441\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 442\u001b[0m \u001b[38;5;124;03m Draw the canvas, then save it using `.image.imsave` (to which\u001b[39;00m\n\u001b[0;32m 443\u001b[0m \u001b[38;5;124;03m *pil_kwargs* and *metadata* are forwarded).\u001b[39;00m\n\u001b[0;32m 444\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 445\u001b[0m \u001b[43mFigureCanvasAgg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 446\u001b[0m mpl\u001b[38;5;241m.\u001b[39mimage\u001b[38;5;241m.\u001b[39mimsave(\n\u001b[0;32m 447\u001b[0m filename_or_obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer_rgba(), \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39mfmt, origin\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mupper\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 448\u001b[0m dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mdpi, metadata\u001b[38;5;241m=\u001b[39mmetadata, pil_kwargs\u001b[38;5;241m=\u001b[39mpil_kwargs)\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:388\u001b[0m, in \u001b[0;36mFigureCanvasAgg.draw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 385\u001b[0m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[0;32m 386\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\u001b[38;5;241m.\u001b[39m_wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\n\u001b[0;32m 387\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[1;32m--> 388\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 389\u001b[0m \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[0;32m 390\u001b[0m \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[0;32m 391\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mdraw()\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m 93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[0;32m 94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m---> 95\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 96\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[0;32m 97\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
|
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"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\figure.py:3154\u001b[0m, in \u001b[0;36mFigure.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m 3151\u001b[0m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[0;32m 3153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m-> 3154\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 3155\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3157\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[0;32m 3158\u001b[0m sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[0;32m 131\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[1;32m--> 132\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 134\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[0;32m 135\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\axes\\_base.py:3070\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m 3067\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[0;32m 3068\u001b[0m _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[1;32m-> 3070\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 3071\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3073\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 3074\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[0;32m 131\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[1;32m--> 132\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 134\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[0;32m 135\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\lines.py:801\u001b[0m, in \u001b[0;36mLine2D.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m 798\u001b[0m gc\u001b[38;5;241m.\u001b[39mset_foreground(lc_rgba, isRGBA\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 800\u001b[0m gc\u001b[38;5;241m.\u001b[39mset_dashes(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dash_pattern)\n\u001b[1;32m--> 801\u001b[0m \u001b[43mrenderer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maffine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrozen\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 802\u001b[0m gc\u001b[38;5;241m.\u001b[39mrestore()\n\u001b[0;32m 804\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_marker \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_markersize \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:117\u001b[0m, in \u001b[0;36mRendererAgg.draw_path\u001b[1;34m(self, gc, path, transform, rgbFace)\u001b[0m\n\u001b[0;32m 115\u001b[0m p\u001b[38;5;241m.\u001b[39msimplify_threshold \u001b[38;5;241m=\u001b[39m path\u001b[38;5;241m.\u001b[39msimplify_threshold\n\u001b[0;32m 116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 117\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_renderer\u001b[38;5;241m.\u001b[39mdraw_path(gc, p, transform, rgbFace)\n\u001b[0;32m 118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOverflowError\u001b[39;00m:\n\u001b[0;32m 119\u001b[0m msg \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 120\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExceeded cell block limit in Agg.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 121\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease reduce the value of \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 127\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;241m.\u001b[39msimplify_threshold\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m on the input).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 128\u001b[0m )\n",
|
||||
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"size = (256,256)\n",
|
||||
"if not os.path.exists(path_to_pictures):\n",
|
||||
" os.mkdir(path_to_pictures)\n",
|
||||
"for subdir in os.listdir(path_to_binaries):\n",
|
||||
" filepath = path_to_binaries + '/' + subdir + '/'\n",
|
||||
" if not os.path.exists(path_to_pictures +'/' + subdir):\n",
|
||||
" os.mkdir(path_to_pictures + '/' + subdir)\n",
|
||||
" files = os.listdir(filepath)\n",
|
||||
" for file in tqdm(files):\n",
|
||||
" savepath = path_to_pictures +'/' + subdir + '/' + file + '.npy'\n",
|
||||
" savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '_real' + '.png' \n",
|
||||
" savepath_imag_png = path_to_pictures +'/' + subdir + '/' + file + '_imag' + '.png' \n",
|
||||
" savepath_spec_png = path_to_pictures +'/' + subdir + '/' + file + '_spec' + '.png'\n",
|
||||
" if not os.path.exists(savepath):\n",
|
||||
" img = sig2pic_with_spec(path_to_data=filepath, filename=file, specT=s, resize = size)\n",
|
||||
" gc.collect()\n",
|
||||
" try:\n",
|
||||
" \n",
|
||||
" plt.imshow(img[0])\n",
|
||||
" plt.savefig(savepath_real_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" \n",
|
||||
" plt.imshow(img[1])\n",
|
||||
" plt.savefig(savepath_imag_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
"\n",
|
||||
" plt.imshow(img[2])\n",
|
||||
" plt.savefig(savepath_spec_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" \n",
|
||||
" np.save(savepath, img)\n",
|
||||
" \n",
|
||||
" except Exception:\n",
|
||||
" continue\n",
|
||||
" print('Dir: ', subdir , ' finished!')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "58ff5fbd",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9f9ad366",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@ -1,222 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"cuda:0\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<contextlib.ExitStack at 0x249d580ff50>"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import time\n",
|
||||
"import io\n",
|
||||
"import cv2\n",
|
||||
"import copy\n",
|
||||
"import os\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"import torch.nn as nn\n",
|
||||
"import torch\n",
|
||||
"import torchvision\n",
|
||||
"from torch.utils.data import Dataset\n",
|
||||
"from torch import default_generator, randperm\n",
|
||||
"from PIL import Image\n",
|
||||
"#from torch._utils import _accumulate\n",
|
||||
"import csv\n",
|
||||
"from torch.utils.data.dataset import Subset\n",
|
||||
"from scipy import ndimage\n",
|
||||
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
|
||||
"print(device)\n",
|
||||
"batch_size = 16\n",
|
||||
"momentum=0.9\n",
|
||||
"lr = 1e-3\n",
|
||||
"import random\n",
|
||||
"sub_sample = 0.5\n",
|
||||
"import matplotlib\n",
|
||||
"import gc\n",
|
||||
"matplotlib.use('Agg')\n",
|
||||
"import matplotlib as mpl\n",
|
||||
"mpl.rcParams['agg.path.chunksize'] = 256*256\n",
|
||||
"plt.ioff()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "9267fbe1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def sig2pic_with_spec(path_to_data, filename, figsize=(16,16), dpi=16, resize = None):\n",
|
||||
" try:\n",
|
||||
" array = np.load(path_to_data+filename)\n",
|
||||
"\n",
|
||||
" if resize != None:\n",
|
||||
" resized_real = cv2.resize(array[0], resize)\n",
|
||||
" resized_mag = cv2.resize(array[1], resize)\n",
|
||||
" resized_spectr = cv2.resize(array[2], resize)\n",
|
||||
" img = np.asarray(array, dtype=np.float32)\n",
|
||||
" return img\n",
|
||||
" \n",
|
||||
" img = np.asarray(array, dtype=np.float32)\n",
|
||||
" return img\n",
|
||||
" \n",
|
||||
" except Exception as e:\n",
|
||||
" print(str(e))\n",
|
||||
" return None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"path_to_binaries = 'C:/Users/snytk/Lerning_NN_for_work/datasets/2.4_learning'\n",
|
||||
"path_to_pictures = 'C:/Users/snytk/Lerning_NN_for_work/datasets_jpg/2.4_jpg_learning'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from tqdm import tqdm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "6f226f86-5d72-4573-8af6-750128b70263",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|████████████████████████████████████████████████████████████████████████████| 8751/8751 [1:01:54<00:00, 2.36it/s]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Dir: drone finished!\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████████████████████████████████████████████████████████████████████| 11202/11202 [1:19:30<00:00, 2.35it/s]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Dir: noise finished!\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"size = (256,256)\n",
|
||||
"if not os.path.exists(path_to_pictures):\n",
|
||||
" os.mkdir(path_to_pictures)\n",
|
||||
"for subdir in os.listdir(path_to_binaries):\n",
|
||||
" filepath = path_to_binaries + '/' + subdir + '/'\n",
|
||||
" if not os.path.exists(path_to_pictures +'/' + subdir):\n",
|
||||
" os.mkdir(path_to_pictures + '/' + subdir)\n",
|
||||
" files = os.listdir(filepath)\n",
|
||||
" for file in tqdm(files):\n",
|
||||
" savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '_real' + '.jpg' \n",
|
||||
" savepath_imag_png = path_to_pictures +'/' + subdir + '/' + file + '_imag' + '.jpg' \n",
|
||||
" savepath_spec_png = path_to_pictures +'/' + subdir + '/' + file + '_spec' + '.jpg'\n",
|
||||
" if not os.path.exists(savepath_real_png):\n",
|
||||
" img = sig2pic_with_spec(path_to_data=filepath, filename=file, resize = size)\n",
|
||||
" gc.collect()\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" plt.imshow(img[0])\n",
|
||||
" plt.savefig(savepath_real_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" \n",
|
||||
" plt.imshow(img[1])\n",
|
||||
" plt.savefig(savepath_imag_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
"\n",
|
||||
" plt.imshow(img[2])\n",
|
||||
" plt.savefig(savepath_spec_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" \n",
|
||||
" except Exception:\n",
|
||||
" continue\n",
|
||||
" print('Dir: ', subdir , ' finished!')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cfbd309d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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
|
||||
}
|
||||
@ -1,360 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"cuda:0\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<contextlib.ExitStack at 0x20af0408250>"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import time\n",
|
||||
"import io\n",
|
||||
"import cv2\n",
|
||||
"import copy\n",
|
||||
"import os\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"import torch.nn as nn\n",
|
||||
"import torch\n",
|
||||
"import torchvision\n",
|
||||
"from torch.utils.data import Dataset\n",
|
||||
"from torch import default_generator, randperm\n",
|
||||
"from PIL import Image\n",
|
||||
"#from torch._utils import _accumulate\n",
|
||||
"import csv\n",
|
||||
"from torch.utils.data.dataset import Subset\n",
|
||||
"from scipy import ndimage\n",
|
||||
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
|
||||
"print(device)\n",
|
||||
"batch_size = 16\n",
|
||||
"momentum=0.9\n",
|
||||
"lr = 1e-3\n",
|
||||
"import random\n",
|
||||
"sub_sample = 0.5\n",
|
||||
"import matplotlib\n",
|
||||
"import gc\n",
|
||||
"matplotlib.use('Agg')\n",
|
||||
"import matplotlib as mpl\n",
|
||||
"mpl.rcParams['agg.path.chunksize'] = 256*256\n",
|
||||
"plt.ioff()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "9267fbe1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def sig2pic_with_spec(path_to_data, filename, figsize=(16,16), dpi=16, resize = None):\n",
|
||||
" try:\n",
|
||||
" array = np.load(path_to_data+filename)\n",
|
||||
"\n",
|
||||
" if resize != None:\n",
|
||||
" resized_real = cv2.resize(array[0], resize)\n",
|
||||
" resized_mag = cv2.resize(array[1], resize)\n",
|
||||
" resized_spectr = cv2.resize(array[2], resize)\n",
|
||||
" img = np.asarray(array, dtype=np.float32)\n",
|
||||
" return img\n",
|
||||
" \n",
|
||||
" img = np.asarray(array, dtype=np.float32)\n",
|
||||
" return img\n",
|
||||
" \n",
|
||||
" except Exception as e:\n",
|
||||
" print(str(e))\n",
|
||||
" return None\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
"\n",
|
||||
"def pre_func_ensemble(data=None, src ='', ind_inference=0):\n",
|
||||
" try:\n",
|
||||
" import matplotlib.pyplot as plt\n",
|
||||
" matplotlib.use('Agg')\n",
|
||||
" plt.ioff()\n",
|
||||
"\n",
|
||||
" figsize = (16, 16)\n",
|
||||
" dpi = 16\n",
|
||||
"\n",
|
||||
" signal = np.vectorize(complex)(data[0], data[1])\n",
|
||||
" #np.save(src + '_inference_2400_' + str(ind_inference) + '.npy', signal)\n",
|
||||
" spec = transform.Spectrogram(nperseg=256)\n",
|
||||
" spectr = np.array(spec(signal)[:,:figsize[0] * dpi])\n",
|
||||
" fig1 = plt.figure(figsize = figsize)\n",
|
||||
" plt.axes(ylim=(-1, 1))\n",
|
||||
" sigr = signal.real\n",
|
||||
" sigi = signal.imag\n",
|
||||
" \n",
|
||||
" plt.plot(sigr, color='black')\n",
|
||||
" plt.gca().set_axis_off()\n",
|
||||
" plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
|
||||
" plt.margins(0,0)\n",
|
||||
" buf1 = io.BytesIO()\n",
|
||||
" fig1.savefig(buf1, format=\"png\", dpi=dpi)\n",
|
||||
" buf1.seek(0)\n",
|
||||
" img_arr1 = np.frombuffer(buf1.getvalue(), dtype=np.uint8)\n",
|
||||
" buf1.close()\n",
|
||||
" img1 = cv2.imdecode(img_arr1, 1)\n",
|
||||
" img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" plt.close(fig1)\n",
|
||||
"\n",
|
||||
" fig2 = plt.figure(figsize = figsize)\n",
|
||||
" plt.axes(ylim=(-1, 1))\n",
|
||||
" sigr = signal.real\n",
|
||||
" sigi = signal.imag\n",
|
||||
" \n",
|
||||
" plt.plot(sigi, color='black')\n",
|
||||
" plt.gca().set_axis_off()\n",
|
||||
" plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
|
||||
" plt.margins(0,0)\n",
|
||||
" buf = io.BytesIO()\n",
|
||||
" fig2.savefig(buf, format=\"png\", dpi=dpi)\n",
|
||||
" buf.seek(0)\n",
|
||||
" img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8)\n",
|
||||
" buf.close()\n",
|
||||
" img = cv2.imdecode(img_arr, 1)\n",
|
||||
" img2 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" plt.close(fig2)\n",
|
||||
"\n",
|
||||
" img = np.array([img1, img2, spectr])\n",
|
||||
" \n",
|
||||
" cv2.destroyAllWindows()\n",
|
||||
" del signal\n",
|
||||
" del spec\n",
|
||||
" del spectr\n",
|
||||
" del img1\n",
|
||||
" del img2\n",
|
||||
" del sigr\n",
|
||||
" del sigi\n",
|
||||
" del buf\n",
|
||||
" del buf1\n",
|
||||
" del img_arr\n",
|
||||
" del img_arr1\n",
|
||||
" cv2.destroyAllWindows()\n",
|
||||
" gc.collect()\n",
|
||||
"\n",
|
||||
" print('Подготовка данных завершена')\n",
|
||||
" print()\n",
|
||||
" return img\n",
|
||||
"\n",
|
||||
" except Exception as e:\n",
|
||||
" print(str(e))\n",
|
||||
" return None\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def build_func_ensemble(file_model='', file_config='', num_classes=None):\n",
|
||||
" try:\n",
|
||||
" import matplotlib.pyplot as plt\n",
|
||||
" matplotlib.use('Agg')\n",
|
||||
" plt.ioff()\n",
|
||||
" torch.cuda.empty_cache()\n",
|
||||
" model1 = models.resnet18(pretrained=False)\n",
|
||||
" model2 = models.resnet50(pretrained=False)\n",
|
||||
" model3 = models.resnet101(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",
|
||||
" model3.fc = nn.Linear(model3.fc.in_features, num_classes)\n",
|
||||
"\n",
|
||||
" class Ensemble(nn.Module):\n",
|
||||
" def __init__(self, model1, model2, model3):\n",
|
||||
" super(Ensemble, self).__init__()\n",
|
||||
" self.model1 = model1\n",
|
||||
" self.model2 = model2\n",
|
||||
" self.model3 = model3\n",
|
||||
" self.fc = nn.Linear(3 * num_classes, num_classes)\n",
|
||||
"\n",
|
||||
" def forward(self, x):\n",
|
||||
" x1 = self.model1(x)\n",
|
||||
" 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",
|
||||
" device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
|
||||
" if device != 'cpu':\n",
|
||||
" model = model.to(device)\n",
|
||||
" model.load_state_dict(torch.load(file_model, map_location=device))\n",
|
||||
" model.eval()\n",
|
||||
"\n",
|
||||
" cv2.destroyAllWindows()\n",
|
||||
" del model1\n",
|
||||
" del model2\n",
|
||||
" del model3\n",
|
||||
" gc.collect()\n",
|
||||
"\n",
|
||||
" print('Инициализация модели завершена')\n",
|
||||
" print()\n",
|
||||
" return model\n",
|
||||
"\n",
|
||||
" except Exception as exc:\n",
|
||||
" print(str(exc))\n",
|
||||
" return None\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"path_to_binaries = 'C:/Users/snytk/Lerning_NN_for_work/datasets/1.2_learning'\n",
|
||||
"path_to_pictures = 'C:/Users/snytk/Lerning_NN_for_work/datasets_jpg/1.2_jpg_learning'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from tqdm import tqdm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "6f226f86-5d72-4573-8af6-750128b70263",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|████████████████████████████████████████████████████████████████████████████████| 963/963 [06:36<00:00, 2.43it/s]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Dir: drone finished!\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████████████████████████████████████████████████████████████████████████| 1724/1724 [11:41<00:00, 2.46it/s]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Dir: noise finished!\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"size = (256,256)\n",
|
||||
"if not os.path.exists(path_to_pictures):\n",
|
||||
" os.mkdir(path_to_pictures)\n",
|
||||
"for subdir in os.listdir(path_to_binaries):\n",
|
||||
" filepath = path_to_binaries + '/' + subdir + '/'\n",
|
||||
" if not os.path.exists(path_to_pictures +'/' + subdir):\n",
|
||||
" os.mkdir(path_to_pictures + '/' + subdir)\n",
|
||||
" files = os.listdir(filepath)\n",
|
||||
" for file in tqdm(files):\n",
|
||||
" savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '_real' + '.jpg' \n",
|
||||
" savepath_imag_png = path_to_pictures +'/' + subdir + '/' + file + '_imag' + '.jpg' \n",
|
||||
" savepath_spec_png = path_to_pictures +'/' + subdir + '/' + file + '_spec' + '.jpg'\n",
|
||||
" if not os.path.exists(savepath_real_png):\n",
|
||||
" img = sig2pic_with_spec(path_to_data=filepath, filename=file, resize = size)\n",
|
||||
" gc.collect()\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" plt.imshow(img[0])\n",
|
||||
" plt.savefig(savepath_real_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" \n",
|
||||
" plt.imshow(img[1])\n",
|
||||
" plt.savefig(savepath_imag_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
"\n",
|
||||
" plt.imshow(img[2])\n",
|
||||
" plt.savefig(savepath_spec_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" \n",
|
||||
" except Exception:\n",
|
||||
" continue\n",
|
||||
" print('Dir: ', subdir , ' finished!')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "871d7ab6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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
|
||||
}
|
||||
@ -1,194 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"cuda:0\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<contextlib.ExitStack at 0x249d580ff50>"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import time\n",
|
||||
"import io\n",
|
||||
"import cv2\n",
|
||||
"import copy\n",
|
||||
"import os\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"import torch.nn as nn\n",
|
||||
"import torch\n",
|
||||
"import torchvision\n",
|
||||
"from torch.utils.data import Dataset\n",
|
||||
"from torch import default_generator, randperm\n",
|
||||
"from PIL import Image\n",
|
||||
"#from torch._utils import _accumulate\n",
|
||||
"import csv\n",
|
||||
"from torch.utils.data.dataset import Subset\n",
|
||||
"from scipy import ndimage\n",
|
||||
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
|
||||
"print(device)\n",
|
||||
"batch_size = 16\n",
|
||||
"momentum=0.9\n",
|
||||
"lr = 1e-3\n",
|
||||
"import random\n",
|
||||
"sub_sample = 0.5\n",
|
||||
"import matplotlib\n",
|
||||
"import gc\n",
|
||||
"matplotlib.use('Agg')\n",
|
||||
"import matplotlib as mpl\n",
|
||||
"mpl.rcParams['agg.path.chunksize'] = 256*256\n",
|
||||
"plt.ioff()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "9267fbe1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def sig2pic_with_spec(path_to_data, filename, figsize=(16,16), dpi=16, resize = None):\n",
|
||||
" try:\n",
|
||||
" array = np.load(path_to_data+filename)\n",
|
||||
"\n",
|
||||
" if resize != None:\n",
|
||||
" resized_real = cv2.resize(array[0], resize)\n",
|
||||
" resized_mag = cv2.resize(array[1], resize)\n",
|
||||
" resized_spectr = cv2.resize(array[2], resize)\n",
|
||||
" img = np.asarray(array, dtype=np.float32)\n",
|
||||
" return img\n",
|
||||
" \n",
|
||||
" img = np.asarray(array, dtype=np.float32)\n",
|
||||
" return img\n",
|
||||
" \n",
|
||||
" except Exception as e:\n",
|
||||
" print(str(e))\n",
|
||||
" return None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"path_to_binaries = 'C:/Users/snytk/Lerning_NN_for_work/datasets/2.4_learning'\n",
|
||||
"path_to_pictures = 'C:/Users/snytk/Lerning_NN_for_work/datasets_jpg/2.4_jpg_learning'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from tqdm import tqdm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6f226f86-5d72-4573-8af6-750128b70263",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 2%|█▍ | 158/8751 [01:06<1:01:14, 2.34it/s]"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"size = (256,256)\n",
|
||||
"if not os.path.exists(path_to_pictures):\n",
|
||||
" os.mkdir(path_to_pictures)\n",
|
||||
"for subdir in os.listdir(path_to_binaries):\n",
|
||||
" filepath = path_to_binaries + '/' + subdir + '/'\n",
|
||||
" if not os.path.exists(path_to_pictures +'/' + subdir):\n",
|
||||
" os.mkdir(path_to_pictures + '/' + subdir)\n",
|
||||
" files = os.listdir(filepath)\n",
|
||||
" for file in tqdm(files):\n",
|
||||
" savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '_real' + '.jpg' \n",
|
||||
" savepath_imag_png = path_to_pictures +'/' + subdir + '/' + file + '_imag' + '.jpg' \n",
|
||||
" savepath_spec_png = path_to_pictures +'/' + subdir + '/' + file + '_spec' + '.jpg'\n",
|
||||
" if not os.path.exists(savepath_real_png):\n",
|
||||
" img = sig2pic_with_spec(path_to_data=filepath, filename=file, resize = size)\n",
|
||||
" gc.collect()\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" plt.imshow(img[0])\n",
|
||||
" plt.savefig(savepath_real_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" \n",
|
||||
" plt.imshow(img[1])\n",
|
||||
" plt.savefig(savepath_imag_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
"\n",
|
||||
" plt.imshow(img[2])\n",
|
||||
" plt.savefig(savepath_spec_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" \n",
|
||||
" except Exception:\n",
|
||||
" continue\n",
|
||||
" print('Dir: ', subdir , ' finished!')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "871d7ab6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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
|
||||
}
|
||||
@ -1,194 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"cuda:0\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<contextlib.ExitStack at 0x249d580ff50>"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import time\n",
|
||||
"import io\n",
|
||||
"import cv2\n",
|
||||
"import copy\n",
|
||||
"import os\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"import torch.nn as nn\n",
|
||||
"import torch\n",
|
||||
"import torchvision\n",
|
||||
"from torch.utils.data import Dataset\n",
|
||||
"from torch import default_generator, randperm\n",
|
||||
"from PIL import Image\n",
|
||||
"#from torch._utils import _accumulate\n",
|
||||
"import csv\n",
|
||||
"from torch.utils.data.dataset import Subset\n",
|
||||
"from scipy import ndimage\n",
|
||||
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
|
||||
"print(device)\n",
|
||||
"batch_size = 16\n",
|
||||
"momentum=0.9\n",
|
||||
"lr = 1e-3\n",
|
||||
"import random\n",
|
||||
"sub_sample = 0.5\n",
|
||||
"import matplotlib\n",
|
||||
"import gc\n",
|
||||
"matplotlib.use('Agg')\n",
|
||||
"import matplotlib as mpl\n",
|
||||
"mpl.rcParams['agg.path.chunksize'] = 256*256\n",
|
||||
"plt.ioff()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "9267fbe1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def sig2pic_with_spec(path_to_data, filename, figsize=(16,16), dpi=16, resize = None):\n",
|
||||
" try:\n",
|
||||
" array = np.load(path_to_data+filename)\n",
|
||||
"\n",
|
||||
" if resize != None:\n",
|
||||
" resized_real = cv2.resize(array[0], resize)\n",
|
||||
" resized_mag = cv2.resize(array[1], resize)\n",
|
||||
" resized_spectr = cv2.resize(array[2], resize)\n",
|
||||
" img = np.asarray(array, dtype=np.float32)\n",
|
||||
" return img\n",
|
||||
" \n",
|
||||
" img = np.asarray(array, dtype=np.float32)\n",
|
||||
" return img\n",
|
||||
" \n",
|
||||
" except Exception as e:\n",
|
||||
" print(str(e))\n",
|
||||
" return None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"path_to_binaries = 'C:/Users/snytk/Lerning_NN_for_work/datasets/2.4_learning'\n",
|
||||
"path_to_pictures = 'C:/Users/snytk/Lerning_NN_for_work/datasets_jpg/2.4_jpg_learning'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from tqdm import tqdm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6f226f86-5d72-4573-8af6-750128b70263",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 2%|█▍ | 158/8751 [01:06<1:01:14, 2.34it/s]"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"size = (256,256)\n",
|
||||
"if not os.path.exists(path_to_pictures):\n",
|
||||
" os.mkdir(path_to_pictures)\n",
|
||||
"for subdir in os.listdir(path_to_binaries):\n",
|
||||
" filepath = path_to_binaries + '/' + subdir + '/'\n",
|
||||
" if not os.path.exists(path_to_pictures +'/' + subdir):\n",
|
||||
" os.mkdir(path_to_pictures + '/' + subdir)\n",
|
||||
" files = os.listdir(filepath)\n",
|
||||
" for file in tqdm(files):\n",
|
||||
" savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '_real' + '.jpg' \n",
|
||||
" savepath_imag_png = path_to_pictures +'/' + subdir + '/' + file + '_imag' + '.jpg' \n",
|
||||
" savepath_spec_png = path_to_pictures +'/' + subdir + '/' + file + '_spec' + '.jpg'\n",
|
||||
" if not os.path.exists(savepath_real_png):\n",
|
||||
" img = sig2pic_with_spec(path_to_data=filepath, filename=file, resize = size)\n",
|
||||
" gc.collect()\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" plt.imshow(img[0])\n",
|
||||
" plt.savefig(savepath_real_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" \n",
|
||||
" plt.imshow(img[1])\n",
|
||||
" plt.savefig(savepath_imag_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
"\n",
|
||||
" plt.imshow(img[2])\n",
|
||||
" plt.savefig(savepath_spec_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" \n",
|
||||
" except Exception:\n",
|
||||
" continue\n",
|
||||
" print('Dir: ', subdir , ' finished!')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "871d7ab6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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
|
||||
}
|
||||
@ -1,236 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"cuda:0\n",
|
||||
"['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<contextlib.ExitStack at 0x27e4cbae550>"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import time\n",
|
||||
"import io\n",
|
||||
"import cv2\n",
|
||||
"import copy\n",
|
||||
"import os\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"import torch.nn as nn\n",
|
||||
"import torch\n",
|
||||
"import torchvision\n",
|
||||
"from torch.utils.data import Dataset\n",
|
||||
"from torch import default_generator, randperm\n",
|
||||
"from PIL import Image\n",
|
||||
"#from torch._utils import _accumulate\n",
|
||||
"import csv\n",
|
||||
"from torch.utils.data.dataset import Subset\n",
|
||||
"from scipy import ndimage\n",
|
||||
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
|
||||
"print(device)\n",
|
||||
"batch_size = 16\n",
|
||||
"momentum=0.9\n",
|
||||
"lr = 1e-3\n",
|
||||
"import random\n",
|
||||
"sub_sample = 0.5\n",
|
||||
"import matplotlib\n",
|
||||
"import gc\n",
|
||||
"matplotlib.use('Agg')\n",
|
||||
"import matplotlib as mpl\n",
|
||||
"mpl.rcParams['agg.path.chunksize'] = 256*256\n",
|
||||
"#plt.style.use('mplstyle')\n",
|
||||
"plt.style.use('ggplot')\n",
|
||||
"plt.grid(None)\n",
|
||||
"plt.rcParams[\"axes.grid\"] = False\n",
|
||||
"print(plt.style.available)\n",
|
||||
"plt.ioff()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "9267fbe1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def sig2pic_with_spec(path_to_data, filename, figsize=(16,16), dpi=16, resize = None):\n",
|
||||
" try:\n",
|
||||
" array = np.load(path_to_data+filename)\n",
|
||||
"\n",
|
||||
" if resize != None:\n",
|
||||
" resized_real = cv2.resize(array[0], resize)\n",
|
||||
" resized_mag = cv2.resize(array[1], resize)\n",
|
||||
" resized_spectr = cv2.resize(array[2], resize)\n",
|
||||
" img = np.asarray(array, dtype=np.float32)\n",
|
||||
" return img\n",
|
||||
" \n",
|
||||
" img = np.asarray(array, dtype=np.float32)\n",
|
||||
" return img\n",
|
||||
" \n",
|
||||
" except Exception as e:\n",
|
||||
" print(str(e))\n",
|
||||
" return None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"path_to_binaries = 'C:/Users/snytk/Lerning_NN_for_work/datasets/915_learning'\n",
|
||||
"path_to_pictures = 'C:/Users/snytk/Lerning_NN_for_work/datasets_jpg/915_jpg_learning'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from tqdm import tqdm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "6f226f86-5d72-4573-8af6-750128b70263",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|████████████████████████████████████████████████████████████████████████████████| 856/856 [10:50<00:00, 1.32it/s]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Dir: drone finished!\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|████████████████████████████████████████████████████████████████████████████████| 627/627 [08:40<00:00, 1.20it/s]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Dir: noise finished!\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"size = (256,256)\n",
|
||||
"if not os.path.exists(path_to_pictures):\n",
|
||||
" os.mkdir(path_to_pictures)\n",
|
||||
"for subdir in os.listdir(path_to_binaries):\n",
|
||||
" filepath = path_to_binaries + '/' + subdir + '/'\n",
|
||||
" if not os.path.exists(path_to_pictures +'/' + subdir):\n",
|
||||
" os.mkdir(path_to_pictures + '/' + subdir)\n",
|
||||
" files = os.listdir(filepath)\n",
|
||||
" for file in tqdm(files):\n",
|
||||
" savepath_real_png = path_to_pictures +'/' + subdir + '/' + file + '_real' + '.jpg' \n",
|
||||
" savepath_imag_png = path_to_pictures +'/' + subdir + '/' + file + '_imag' + '.jpg' \n",
|
||||
" savepath_spec_png = path_to_pictures +'/' + subdir + '/' + file + '_spec' + '.jpg'\n",
|
||||
" if not os.path.exists(savepath_real_png):\n",
|
||||
" img = sig2pic_with_spec(path_to_data=filepath, filename=file, resize = size)\n",
|
||||
" gc.collect()\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" plt.imshow(img[0])\n",
|
||||
" plt.savefig(savepath_real_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" \n",
|
||||
" plt.imshow(img[1])\n",
|
||||
" plt.savefig(savepath_imag_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
"\n",
|
||||
" plt.imshow(img[2])\n",
|
||||
" plt.savefig(savepath_spec_png)\n",
|
||||
" plt.clf()\n",
|
||||
" plt.cla()\n",
|
||||
" plt.close()\n",
|
||||
" \n",
|
||||
" except Exception:\n",
|
||||
" continue\n",
|
||||
" print('Dir: ', subdir , ' finished!')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "871d7ab6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "e080bb07",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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
|
||||
}
|
||||
@ -1,7 +0,0 @@
|
||||
Для ящика на выставки:
|
||||
aleksandr@192.168.3.85 19751975
|
||||
aleksandr@192.168.3.86 19751975
|
||||
|
||||
Для ящика на Липецк:
|
||||
aleksandr@192.168.3.85 19751975
|
||||
aleksandr@192.168.3.86 19751975
|
||||
@ -1,55 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a89c0273",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import torch\n",
|
||||
"import torchvision\n",
|
||||
"\n",
|
||||
"def convert_pth_to_pt(pth_path, pt_path, model_class):\n",
|
||||
" state_dict = torch.load(pth_path)\n",
|
||||
" model = model_class()\n",
|
||||
" model.load_state_dict(state_dict)\n",
|
||||
" torch.save(model, pt_path)\n",
|
||||
" print(f'Model saved to {pt_path}')\n",
|
||||
"\n",
|
||||
"class ModelClass(torch.nn.Module):\n",
|
||||
" def __init__(self):\n",
|
||||
" super(ModelClass, self).__init__()\n",
|
||||
"\n",
|
||||
" def forward(self, x):\n",
|
||||
" pass\n",
|
||||
"\n",
|
||||
"pth_path = 'model.pth'\n",
|
||||
"pt_path = 'model.pt'\n",
|
||||
"\n",
|
||||
"convert_pth_to_pt(pth_path, pt_path, ModelClass)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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": "3.11.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -1,463 +0,0 @@
|
||||
{
|
||||
"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": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"cuda\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "error",
|
||||
"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",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31merror\u001b[0m Traceback (most recent call last)",
|
||||
"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",
|
||||
"\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"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": 2,
|
||||
"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):\n",
|
||||
" num_samples_per_class = num_samples // num_classes\n",
|
||||
"\n",
|
||||
" #----------Создаём папку для сохранения результатов обучения--------------\n",
|
||||
" \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",
|
||||
" for subdir in subdirs:\n",
|
||||
" files = os.listdir(path_dataset + subdir + '/')\n",
|
||||
" num_samples_per_class = min(num_samples_per_class, len(files))\n",
|
||||
" for subdir in subdirs:\n",
|
||||
" files = os.listdir(path_dataset + subdir + '/')\n",
|
||||
" random.shuffle(files)\n",
|
||||
" files_to_process = files[:num_samples_per_class]\n",
|
||||
" for file in files_to_process:\n",
|
||||
" row = pd.DataFrame({pd_columns[0]: [str(path_dataset + subdir + '/' + file)]})\n",
|
||||
" df = pd.concat([df, row], ignore_index=True)\n",
|
||||
" \n",
|
||||
" df.to_csv(path_res + 'dataset.csv', index=False)\n",
|
||||
" \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(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",
|
||||
" 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",
|
||||
" data_file = np.asarray(np.load(self.sig_filenames[idx], 'r+'), dtype=np.float32)\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",
|
||||
" label = torch.tensor(1)\n",
|
||||
" return data_file, label\n",
|
||||
" \n",
|
||||
" #----------Создаём датасет--------------\n",
|
||||
" \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 (img, label) in tqdm(dataloaders[phase]):\n",
|
||||
" img, label = img.to(device), label.to(device)\n",
|
||||
" optimizer.zero_grad()\n",
|
||||
" \n",
|
||||
" with torch.set_grad_enabled(phase == 'train'):\n",
|
||||
" output = model(img)\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() * img.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": null,
|
||||
"id": "52e8d4c5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"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",
|
||||
" warnings.warn(\n",
|
||||
"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",
|
||||
" warnings.warn(msg)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch : 1\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|████████████████████████████████████████████████████████████████████████████████| 437/437 [23:49<00:00, 3.27s/it]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"train Loss: 0.0884 Acc: 0.9634\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|████████████████████████████████████████████████████████████████████████████████| 187/187 [09:15<00:00, 2.97s/it]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"val Loss: 0.0342 Acc: 0.9873\n",
|
||||
"\n",
|
||||
"Epoch : 2\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 84%|██████████████████████████████████████████████████████████████████▊ | 365/437 [19:28<04:18, 3.59s/it]"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#----------Инициализируем модель и параметры обучения--------------\n",
|
||||
"\n",
|
||||
"torch.cuda.empty_cache()\n",
|
||||
"cv2.destroyAllWindows()\n",
|
||||
"gc.collect()\n",
|
||||
"\n",
|
||||
"num_classes = 3\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",
|
||||
"model3 = models.resnet101(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",
|
||||
"model3.fc = nn.Linear(model3.fc.in_features, num_classes)\n",
|
||||
"\n",
|
||||
"class Ensemble(nn.Module):\n",
|
||||
" def __init__(self, model1, model2, model3):\n",
|
||||
" super(Ensemble, self).__init__()\n",
|
||||
" self.model1 = model1\n",
|
||||
" self.model2 = model2\n",
|
||||
" self.model3 = model3\n",
|
||||
" self.fc = nn.Linear(3 * num_classes, num_classes)\n",
|
||||
"\n",
|
||||
" def forward(self, x):\n",
|
||||
" x1 = self.model1(x)\n",
|
||||
" 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
|
||||
}
|
||||
@ -1,465 +0,0 @@
|
||||
{
|
||||
"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": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"cuda\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0"
|
||||
]
|
||||
},
|
||||
"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": 2,
|
||||
"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):\n",
|
||||
" num_samples_per_class = num_samples // num_classes\n",
|
||||
"\n",
|
||||
" #----------Создаём папку для сохранения результатов обучения--------------\n",
|
||||
" \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",
|
||||
" for subdir in subdirs:\n",
|
||||
" files = os.listdir(path_dataset + subdir + '/')\n",
|
||||
" num_samples_per_class = min(num_samples_per_class, len(files))\n",
|
||||
" for subdir in subdirs:\n",
|
||||
" files = os.listdir(path_dataset + subdir + '/')\n",
|
||||
" random.shuffle(files)\n",
|
||||
" files_to_process = files[:num_samples_per_class]\n",
|
||||
" for file in files_to_process:\n",
|
||||
" row = pd.DataFrame({pd_columns[0]: [str(path_dataset + subdir + '/' + file)]})\n",
|
||||
" df = pd.concat([df, row], ignore_index=True)\n",
|
||||
" \n",
|
||||
" df.to_csv(path_res + 'dataset.csv', index=False)\n",
|
||||
" \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(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",
|
||||
" 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",
|
||||
" image_real = np.asarray(cv2.split(cv2.imread(self.sig_filenames[idx][:-8]+'real.jpg')), dtype=np.float32)\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",
|
||||
" label = torch.tensor(1)\n",
|
||||
" return image_real, label\n",
|
||||
" \n",
|
||||
" #----------Создаём датасет--------------\n",
|
||||
" \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 (img, label) in tqdm(dataloaders[phase]):\n",
|
||||
" img, label = img.to(device), label.to(device)\n",
|
||||
" optimizer.zero_grad()\n",
|
||||
" \n",
|
||||
" with torch.set_grad_enabled(phase == 'train'):\n",
|
||||
" output = model(img)\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() * img.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": 10,
|
||||
"id": "52e8d4c5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"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",
|
||||
" warnings.warn(\n",
|
||||
"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=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
|
||||
" warnings.warn(msg)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch : 1\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 0%| | 0/337 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"tensor([], device='cuda:0', size=(4, 0))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "RuntimeError",
|
||||
"evalue": "Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [4, 0]",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[10], line 42\u001b[0m\n\u001b[0;32m 38\u001b[0m model \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m 40\u001b[0m \u001b[38;5;66;03m#----------Создания датасета и обучение модели--------------\u001b[39;00m\n\u001b[1;32m---> 42\u001b[0m path_res, model_name \u001b[38;5;241m=\u001b[39m \u001b[43mprepare_and_learning_detection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_classes\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnum_classes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_samples\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m20000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath_dataset\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m//192.168.11.63/data/DATASETS/Energomash/2400_learning/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 43\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m_2.4_jpg_\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 46\u001b[0m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mempty_cache()\n\u001b[0;32m 47\u001b[0m cv2\u001b[38;5;241m.\u001b[39mdestroyAllWindows()\n",
|
||||
"Cell \u001b[1;32mIn[2], line 108\u001b[0m, in \u001b[0;36mprepare_and_learning_detection\u001b[1;34m(num_classes, num_samples, path_dataset, model_name, config_name, model)\u001b[0m\n\u001b[0;32m 105\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m 107\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(phase \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m--> 108\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 109\u001b[0m _, pred \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mmax(output\u001b[38;5;241m.\u001b[39mdata, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 110\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(output, label)\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1551\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1560\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1561\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1562\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1565\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
|
||||
"Cell \u001b[1;32mIn[10], line 28\u001b[0m, in \u001b[0;36mModel.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m 27\u001b[0m \u001b[38;5;28mprint\u001b[39m(x)\n\u001b[1;32m---> 28\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 29\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1551\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1560\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1561\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1562\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1565\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torchvision\\models\\resnet.py:285\u001b[0m, in \u001b[0;36mResNet.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 284\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m--> 285\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torchvision\\models\\resnet.py:268\u001b[0m, in \u001b[0;36mResNet._forward_impl\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 266\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_forward_impl\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m 267\u001b[0m \u001b[38;5;66;03m# See note [TorchScript super()]\u001b[39;00m\n\u001b[1;32m--> 268\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 269\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbn1(x)\n\u001b[0;32m 270\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrelu(x)\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1551\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1560\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1561\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1562\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1565\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\conv.py:458\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 457\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m--> 458\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_conv_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\conv.py:454\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[1;34m(self, input, weight, bias)\u001b[0m\n\u001b[0;32m 450\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m 451\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv2d(F\u001b[38;5;241m.\u001b[39mpad(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode),\n\u001b[0;32m 452\u001b[0m weight, bias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[0;32m 453\u001b[0m _pair(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n\u001b[1;32m--> 454\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv2d\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 455\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"\u001b[1;31mRuntimeError\u001b[0m: Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [4, 0]"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#----------Инициализируем модель и параметры обучения--------------\n",
|
||||
"\n",
|
||||
"torch.cuda.empty_cache()\n",
|
||||
"cv2.destroyAllWindows()\n",
|
||||
"gc.collect()\n",
|
||||
"\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",
|
||||
"model = models.resnet18(pretrained=True)\n",
|
||||
"\n",
|
||||
"num_classes = 2\n",
|
||||
"\n",
|
||||
"model.fc = nn.Linear(model.fc.in_features, num_classes)\n",
|
||||
"\n",
|
||||
"class Model(nn.Module):\n",
|
||||
" def __init__(self, model):\n",
|
||||
" super(Model, self).__init__()\n",
|
||||
" self.model = model\n",
|
||||
"\n",
|
||||
" def forward(self, x):\n",
|
||||
" print(x)\n",
|
||||
" x = self.model(x)\n",
|
||||
" return x\n",
|
||||
"\n",
|
||||
"model = Model(model)\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 = 20000, path_dataset = \"//192.168.11.63/data/DATASETS/Energomash/2400_learning/\", \n",
|
||||
" 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": "57d18676",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c10afb29",
|
||||
"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
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
@ -1,465 +0,0 @@
|
||||
{
|
||||
"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": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"cuda\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0"
|
||||
]
|
||||
},
|
||||
"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": 2,
|
||||
"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):\n",
|
||||
" num_samples_per_class = num_samples // num_classes\n",
|
||||
"\n",
|
||||
" #----------Создаём папку для сохранения результатов обучения--------------\n",
|
||||
" \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",
|
||||
" for subdir in subdirs:\n",
|
||||
" files = os.listdir(path_dataset + subdir + '/')\n",
|
||||
" num_samples_per_class = min(num_samples_per_class, len(files))\n",
|
||||
" for subdir in subdirs:\n",
|
||||
" files = os.listdir(path_dataset + subdir + '/')\n",
|
||||
" random.shuffle(files)\n",
|
||||
" files_to_process = files[:num_samples_per_class]\n",
|
||||
" for file in files_to_process:\n",
|
||||
" row = pd.DataFrame({pd_columns[0]: [str(path_dataset + subdir + '/' + file)]})\n",
|
||||
" df = pd.concat([df, row], ignore_index=True)\n",
|
||||
" \n",
|
||||
" df.to_csv(path_res + 'dataset.csv', index=False)\n",
|
||||
" \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(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",
|
||||
" 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",
|
||||
" image_real = np.asarray(cv2.split(cv2.imread(self.sig_filenames[idx][:-8]+'real.jpg')), dtype=np.float32)\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",
|
||||
" label = torch.tensor(1)\n",
|
||||
" return image_real, label\n",
|
||||
" \n",
|
||||
" #----------Создаём датасет--------------\n",
|
||||
" \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 (img, label) in tqdm(dataloaders[phase]):\n",
|
||||
" img, label = img.to(device), label.to(device)\n",
|
||||
" optimizer.zero_grad()\n",
|
||||
" \n",
|
||||
" with torch.set_grad_enabled(phase == 'train'):\n",
|
||||
" output = model(img)\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() * img.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": 10,
|
||||
"id": "52e8d4c5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"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",
|
||||
" warnings.warn(\n",
|
||||
"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=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
|
||||
" warnings.warn(msg)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch : 1\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 0%| | 0/337 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"tensor([], device='cuda:0', size=(4, 0))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "RuntimeError",
|
||||
"evalue": "Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [4, 0]",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[10], line 42\u001b[0m\n\u001b[0;32m 38\u001b[0m model \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m 40\u001b[0m \u001b[38;5;66;03m#----------Создания датасета и обучение модели--------------\u001b[39;00m\n\u001b[1;32m---> 42\u001b[0m path_res, model_name \u001b[38;5;241m=\u001b[39m \u001b[43mprepare_and_learning_detection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_classes\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnum_classes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_samples\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m20000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath_dataset\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m//192.168.11.63/data/DATASETS/Energomash/2400_learning/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 43\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m_2.4_jpg_\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 46\u001b[0m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mempty_cache()\n\u001b[0;32m 47\u001b[0m cv2\u001b[38;5;241m.\u001b[39mdestroyAllWindows()\n",
|
||||
"Cell \u001b[1;32mIn[2], line 108\u001b[0m, in \u001b[0;36mprepare_and_learning_detection\u001b[1;34m(num_classes, num_samples, path_dataset, model_name, config_name, model)\u001b[0m\n\u001b[0;32m 105\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m 107\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(phase \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m--> 108\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 109\u001b[0m _, pred \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mmax(output\u001b[38;5;241m.\u001b[39mdata, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 110\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(output, label)\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1551\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1560\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1561\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1562\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1565\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
|
||||
"Cell \u001b[1;32mIn[10], line 28\u001b[0m, in \u001b[0;36mModel.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m 27\u001b[0m \u001b[38;5;28mprint\u001b[39m(x)\n\u001b[1;32m---> 28\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 29\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1551\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1560\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1561\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1562\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1565\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torchvision\\models\\resnet.py:285\u001b[0m, in \u001b[0;36mResNet.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 284\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m--> 285\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torchvision\\models\\resnet.py:268\u001b[0m, in \u001b[0;36mResNet._forward_impl\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 266\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_forward_impl\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m 267\u001b[0m \u001b[38;5;66;03m# See note [TorchScript super()]\u001b[39;00m\n\u001b[1;32m--> 268\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 269\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbn1(x)\n\u001b[0;32m 270\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrelu(x)\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1551\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1560\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1561\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1562\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1565\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\conv.py:458\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 457\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m--> 458\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_conv_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[1;32m~\\miniconda3\\envs\\python311\\Lib\\site-packages\\torch\\nn\\modules\\conv.py:454\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[1;34m(self, input, weight, bias)\u001b[0m\n\u001b[0;32m 450\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m 451\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv2d(F\u001b[38;5;241m.\u001b[39mpad(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode),\n\u001b[0;32m 452\u001b[0m weight, bias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[0;32m 453\u001b[0m _pair(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n\u001b[1;32m--> 454\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv2d\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 455\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"\u001b[1;31mRuntimeError\u001b[0m: Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [4, 0]"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#----------Инициализируем модель и параметры обучения--------------\n",
|
||||
"\n",
|
||||
"torch.cuda.empty_cache()\n",
|
||||
"cv2.destroyAllWindows()\n",
|
||||
"gc.collect()\n",
|
||||
"\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",
|
||||
"model = models.resnet18(pretrained=True)\n",
|
||||
"\n",
|
||||
"num_classes = 2\n",
|
||||
"\n",
|
||||
"model.fc = nn.Linear(model.fc.in_features, num_classes)\n",
|
||||
"\n",
|
||||
"class Model(nn.Module):\n",
|
||||
" def __init__(self, model):\n",
|
||||
" super(Model, self).__init__()\n",
|
||||
" self.model = model\n",
|
||||
"\n",
|
||||
" def forward(self, x):\n",
|
||||
" print(x)\n",
|
||||
" x = self.model(x)\n",
|
||||
" return x\n",
|
||||
"\n",
|
||||
"model = Model(model)\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 = 20000, path_dataset = \"//192.168.11.63/data/DATASETS/Energomash/2400_learning/\", \n",
|
||||
" 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": "57d18676",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c10afb29",
|
||||
"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
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@ -1,503 +0,0 @@
|
||||
{
|
||||
"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": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"cuda\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"12"
|
||||
]
|
||||
},
|
||||
"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": 2,
|
||||
"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):\n",
|
||||
" num_samples_per_class = num_samples // num_classes\n",
|
||||
"\n",
|
||||
" #----------Создаём папку для сохранения результатов обучения--------------\n",
|
||||
" \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",
|
||||
" for subdir in subdirs:\n",
|
||||
" files = os.listdir(path_dataset + subdir + '/')\n",
|
||||
" num_samples_per_class = min(num_samples_per_class, len(files))\n",
|
||||
" for subdir in subdirs:\n",
|
||||
" files = os.listdir(path_dataset + subdir + '/')\n",
|
||||
" random.shuffle(files)\n",
|
||||
" files_to_process = files[:num_samples_per_class]\n",
|
||||
" for file in files_to_process:\n",
|
||||
" row = pd.DataFrame({pd_columns[0]: [str(path_dataset + subdir + '/' + file)]})\n",
|
||||
" df = pd.concat([df, row], ignore_index=True)\n",
|
||||
" \n",
|
||||
" df.to_csv(path_res + 'dataset.csv', index=False)\n",
|
||||
" \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(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",
|
||||
" 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",
|
||||
" image_real = np.asarray(cv2.split(cv2.imread(self.sig_filenames[idx][:-8]+'real.jpg')), dtype=np.float32)\n",
|
||||
" image_imag = np.asarray(cv2.split(cv2.imread(self.sig_filenames[idx][:-8]+'imag.jpg')), dtype=np.float32)\n",
|
||||
" image_spec = np.asarray(cv2.split(cv2.imread(self.sig_filenames[idx][:-8]+'spec.jpg')), dtype=np.float32)\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",
|
||||
" label = torch.tensor(1)\n",
|
||||
" return image_real, image_imag, image_spec, label\n",
|
||||
" \n",
|
||||
" #----------Создаём датасет--------------\n",
|
||||
" \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": null,
|
||||
"id": "52e8d4c5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"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",
|
||||
" warnings.warn(\n",
|
||||
"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",
|
||||
" warnings.warn(msg)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch : 1\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████████████████████████████████████████████████████████████████████████| 658/658 [1:00:26<00:00, 5.51s/it]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"train Loss: 0.6663 Acc: 0.9241\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|████████████████████████████████████████████████████████████████████████████████| 282/282 [02:45<00:00, 1.71it/s]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"val Loss: 0.4023 Acc: 0.9557\n",
|
||||
"\n",
|
||||
"Epoch : 2\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|████████████████████████████████████████████████████████████████████████████████| 658/658 [43:11<00:00, 3.94s/it]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"train Loss: 0.4096 Acc: 0.9514\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|████████████████████████████████████████████████████████████████████████████████| 282/282 [00:47<00:00, 5.98it/s]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"val Loss: 0.3390 Acc: 0.9574\n",
|
||||
"\n",
|
||||
"Epoch : 3\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 8%|██████▍ | 52/658 [04:26<51:43, 5.12s/it]"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#----------Инициализируем модель и параметры обучения--------------\n",
|
||||
"\n",
|
||||
"torch.cuda.empty_cache()\n",
|
||||
"cv2.destroyAllWindows()\n",
|
||||
"gc.collect()\n",
|
||||
"\n",
|
||||
"num_classes = 3\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",
|
||||
"model3 = models.resnet101(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",
|
||||
"model3.fc = nn.Linear(model3.fc.in_features, num_classes)\n",
|
||||
"\n",
|
||||
"class Ensemble(nn.Module):\n",
|
||||
" def __init__(self, model1, model2, model3):\n",
|
||||
" super(Ensemble, self).__init__()\n",
|
||||
" self.model1 = model1\n",
|
||||
" self.model2 = model2\n",
|
||||
" self.model3 = model3\n",
|
||||
" self.fc = nn.Linear(3 * num_classes, num_classes)\n",
|
||||
"\n",
|
||||
" def forward(self, x):\n",
|
||||
" x1 = self.model1(x[0])\n",
|
||||
" x2 = self.model2(x[1])\n",
|
||||
" x3 = self.model3(x[2])\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 = 5000, path_dataset = \"C:/Users/snytk/Lerning_NN_for_work/datasets_jpg/915_jpg_learning/\", \n",
|
||||
" model_name = config_name+\"_915_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": "57d18676",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "eab69324",
|
||||
"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
|
||||
}
|
||||
@ -1,964 +0,0 @@
|
||||
file_name
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1261.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_333.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_53.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_933.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_81.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_193.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_419.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1440.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_501.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_475.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_981.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_837.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_880.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_150.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1802.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_363.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_360.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_161.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_199.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_43.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1275.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_54.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_833.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_459.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1814.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_338.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_451.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_414.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_968.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_328.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1243.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_64.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_8.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1826.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_444.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_842.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_779.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_847.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1250.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_903.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_455.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_406.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_778.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1341.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_751.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_44.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_511.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_743.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_888.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_497.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1805.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1821.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_194.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_875.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1279.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_25.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_366.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_885.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_912.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_473.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_828.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_740.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_492.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1455.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_97.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1357.npy.npy
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_354.npy.npy
|
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|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_368.npy.npy
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|
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|
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|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_797.npy.npy
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|
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|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_371.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_407.npy.npy
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|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1255.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_824.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_194.npy.npy
|
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|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_923.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1408.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_915.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1428.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1310.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_833.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_157.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_457.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_13.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_57.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_886.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1446.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_116.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_972.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_321.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_156.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1372.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_314.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_839.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_416.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_12.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_928.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_313.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_372.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_438.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_104.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_419.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_425.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_137.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1426.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_477.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1291.npy.npy
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_449.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1321.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_88.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_842.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_857.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_53.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1270.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_374.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_837.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_405.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_779.npy.npy
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|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_852.npy.npy
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|
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|
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|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1778.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1360.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_957.npy.npy
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|
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|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_136.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1825.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_803.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_510.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_150.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1789.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_982.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1290.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_73.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_911.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1417.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_741.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_437.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_900.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_888.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_36.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_400.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_92.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1232.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1285.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1422.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_942.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1281.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_159.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1364.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1421.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1282.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_761.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1244.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_758.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_75.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1827.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_103.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_764.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_5.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_3.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1246.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_334.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_429.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_796.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1264.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_509.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1237.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1297.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_142.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1374.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1355.npy.npy
|
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/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1314.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_33.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1828.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_135.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_514.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_121.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_149.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1345.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1805.npy.npy
|
||||
/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_743.npy.npy
|
||||
|
@ -1,58 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "537ea5d0-1d6c-423e-9417-171b70a76c66",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"path = '//192.168.11.63/data/DATASETS/Energomash/915_learning'\n",
|
||||
"pd_columns = ['file_name']\n",
|
||||
"df = pd.DataFrame(columns=pd_columns)\n",
|
||||
"\n",
|
||||
"p = 0\n",
|
||||
"for i in os.walk(path):\n",
|
||||
" p+=1\n",
|
||||
" if p != 1:\n",
|
||||
" for j in i[2]:\n",
|
||||
" row = pd.DataFrame({pd_columns[0]: [str(str(i[0]) + '\\\\' + str(j)).replace('\\\\', '/')]})\n",
|
||||
" df = pd.concat([df, row], ignore_index=True)\n",
|
||||
"\n",
|
||||
"df.to_csv(path + '\\dataset.csv', index=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8f6e1ff8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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
|
||||
}
|
||||
Binary file not shown.
@ -1,91 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "689613d2",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"80.58750000000002\n",
|
||||
"(array([8.97705408, 4.95 , 0. ]), array([-8.97705408, 4.95 , 0. ]))\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import numpy \n",
|
||||
"from math import sqrt as square\n",
|
||||
"from numpy import sqrt, dot, cross \n",
|
||||
"from numpy.linalg import norm \n",
|
||||
"\n",
|
||||
"#rssi = [rssi, max_rssi, min_rssi, gamma]\n",
|
||||
"\n",
|
||||
"def dist(rssi):\n",
|
||||
" rssi = list(map(float, rssi))\n",
|
||||
" return square(abs(rssi[0]-rssi[1]))*rssi[3]/square(abs(rssi[0]-rssi[2]))\n",
|
||||
"\n",
|
||||
"def sol(x1,x2,x3,rssi1,rssi2,rssi3):\n",
|
||||
" r1 = dist(rssi1)\n",
|
||||
" r2 = dist(rssi2)\n",
|
||||
" r3 = dist(rssi3)\n",
|
||||
" x1=numpy.array(x1)\n",
|
||||
" x2=numpy.array(x2)\n",
|
||||
" x3=numpy.array(x3)\n",
|
||||
" temp1 = x2-x1 \n",
|
||||
" e_x = temp1/norm(temp1) \n",
|
||||
" temp2 = x3-x1 \n",
|
||||
" i = dot(e_x,temp2) \n",
|
||||
" temp3 = temp2 - i*e_x \n",
|
||||
" e_y = temp3/norm(temp3) \n",
|
||||
" e_z = cross(e_x,e_y) \n",
|
||||
" d = norm(x2-x1) \n",
|
||||
" j = dot(e_y,temp2) \n",
|
||||
" x = (r1*r1 - r2*r2 + d*d) / (2*d) \n",
|
||||
" y = (r1*r1 - r3*r3 -2*i*x + i*i + j*j) / (2*j) \n",
|
||||
" temp4 = r1*r1 - x*x - y*y \n",
|
||||
" print(temp4)\n",
|
||||
" if temp4<0: \n",
|
||||
" return \"Нет пересечения!\"\n",
|
||||
" z = sqrt(temp4) \n",
|
||||
" p_12_a = x1 + x*e_x + y*e_y + z*e_z \n",
|
||||
" p_12_b = x1 + x*e_x + y*e_y - z*e_z \n",
|
||||
" return p_12_a,p_12_b\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"print(sol([0,0,1],[0,0,-1],[0,10,0],[50,100,0,10.3],[50,100,0,10.3],[50,100,0,10.3]))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "0a68f35a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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
|
||||
}
|
||||
@ -1,57 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f18afe4b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy \n",
|
||||
"from math import sqrt as square\n",
|
||||
"from numpy import sqrt, dot, cross \n",
|
||||
"from numpy.linalg import norm \n",
|
||||
"\n",
|
||||
"#rssi = [rssi, max_rssi, min_rssi, gamma]\n",
|
||||
"\n",
|
||||
"def dist(rssi):\n",
|
||||
" rssi = list(map(float, rssi))\n",
|
||||
" return square(abs(rssi[0]-rssi[1]))*rssi[3]/square(abs(rssi[0]-rssi[2]))\n",
|
||||
"\n",
|
||||
"def sol(x1,x2,x3,rssi1,rssi2,rssi3):\n",
|
||||
" r1 = dist(rssi1)\n",
|
||||
" r2 = dist(rssi2)\n",
|
||||
" r3 = dist(rssi3)\n",
|
||||
" x1=numpy.array(x1)\n",
|
||||
" x2=numpy.array(x2)\n",
|
||||
" x3=numpy.array(x3)\n",
|
||||
" \n",
|
||||
" return sector.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"print(sol([0,0,1],[0,0,-1],[0,10,0],[50,100,0,10.3],[50,100,0,10.3],[50,100,0,10.3]))"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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
|
||||
}
|
||||
Loading…
Reference in New Issue