{ "cells": [ { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "from models.yolo import *\n", "PATH_WEIGHT = './models/best.pt'\n", "PATH = '/root/Public/pretrained/best.pt'\n", "\n", "net = Model('/root/helmet_det/yolov7-main/cfg/training/yolov7.yaml').to('cuda')\n", "state_dict = torch.load(PATH, map_location='cuda')['model'].state_dict()\n", "net.load_state_dict(state_dict, strict=False)\n", "\n", "a = torch.load(PATH_WEIGHT, map_location='cuda')['model']\n", "#print(a)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([[[2, 0, 4],\n", " [1, 3, 2],\n", " [1, 2, 3]],\n", "\n", " [[4, 4, 0],\n", " [2, 2, 0],\n", " [1, 2, 0]],\n", "\n", " [[4, 1, 0],\n", " [4, 0, 2],\n", " [4, 4, 1]]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torch\n", "from utils.torch_utils import select_device\n", "\n", "select_device" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([3, 3, 3]) 4\n", "tensor([[[4, 1, 0],\n", " [4, 0, 2],\n", " [4, 4, 1]],\n", "\n", " [[4, 4, 0],\n", " [2, 2, 0],\n", " [1, 2, 0]],\n", "\n", " [[2, 0, 4],\n", " [1, 3, 2],\n", " [1, 2, 3]]])\n" ] } ], "source": [ "a = a[[2, 1, 0], :, :]\n", "print(a.shape, 4)\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>a</th>\n", " <th>b</th>\n", " <th>c</th>\n", " <th>d</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>46</td>\n", " <td>37</td>\n", " <td>22</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>19</td>\n", " <td>38</td>\n", " <td>11</td>\n", " <td>10</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>15</td>\n", " <td>22</td>\n", " <td>40</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>5</td>\n", " <td>31</td>\n", " <td>34</td>\n", " <td>45</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>46</td>\n", " <td>47</td>\n", " <td>2</td>\n", " <td>46</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " a b c d\n", "0 46 37 22 8\n", "1 19 38 11 10\n", "2 15 22 40 30\n", "3 5 31 34 45\n", "4 46 47 2 46" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "df = pd.DataFrame(np.random.randint(0, 50, size=(5000000, 4)), columns=('a','b','c','d'))\n", "df.shape\n", "# (5000000, 5)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "451.1593863964081\n" ] } ], "source": [ "import time \n", "start = time.time() # iterrows for idx, row in df.iterrows() \n", "for idx, row in df.iterrows():\n", " if row.a == 0 : \n", " df.at[idx, 'e' ] = row.d \n", " elif ( row.a <= 25 ) and (row.a > 0 ): \n", " df.at[idx, 'e' ] = (row.b)-(row.c) \n", " else : \n", " df.at[idx, 'e' ] = row.b + row.c \n", "end = time.time()\n", "print (end - start) ### 걸린 시간: 177초\n", "\n", " \n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.16010785102844238\n" ] } ], "source": [ "# using vectorization \n", "\n", "start = time.time()\n", "df['e'] = df['b'] + df['c']\n", "df.loc[df['a'] <= 25, 'e'] = df['b'] -df['c']\n", "df.loc[df['a']==0, 'e'] = df['d']\n", "end = time.time()\n", "print(end - start)\n", "## 0.28007707595825195 sec" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n", "B\n", "C\n" ] } ], "source": [ "import time\n", "\n", "def yield_abc():\n", " for ch in \"ABC\":\n", " time.sleep(1)\n", " yield ch\n", "\n", "for ch in yield_abc():\n", " print(ch)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "False and device.type != 'cpu' \n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "False and True" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3.8.13 ('base')", "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": "3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:04:10) \n[GCC 10.3.0]" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe" } } }, "nbformat": 4, "nbformat_minor": 2 }