{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn as sns\n", "import warnings\n", "import scipy.stats as stats\n", "import os\n", "\n", "warnings.simplefilter(action='ignore', category=FutureWarning)\n", "pd.options.mode.chained_assignment = None \n", "plt.style.use('ggplot')\n", "sns.color_palette(\"Paired\");\n", "sns.set_theme();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Loading" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: '../../out/pretrained/adept/gswm/results/'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[2], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m root_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../../out/pretrained/adept/gswm/results/\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# list all folders in root path that don't stat with a dot\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m nets \u001b[38;5;241m=\u001b[39m [f \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlistdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroot_path\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m f\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m)]\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# read pickle file\u001b[39;00m\n\u001b[1;32m 8\u001b[0m sf \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame()\n", "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../../out/pretrained/adept/gswm/results/'" ] } ], "source": [ "# setting path to results folder\n", "root_path = '../../out/pretrained/adept/gswm/results/'\n", "\n", "# list all folders in root path that don't stat with a dot\n", "nets = [f for f in os.listdir(root_path) if not f.startswith('.')]\n", "\n", "# read pickle file\n", "sf = pd.DataFrame()\n", "af = pd.DataFrame()\n", "\n", "# load statistics files from nets\n", "for net in nets:\n", " #path = os.path.join(root_path, net, 'control', 'statistics',)\n", " path = os.path.join(root_path, 'statistics',)\n", " with open(os.path.join(path, 'slotframe.csv'), 'rb') as f:\n", " sf_temp = pd.read_csv(f, index_col=0)\n", " sf_temp['net'] = net\n", " sf = pd.concat([sf,sf_temp])\n", "\n", " with open(os.path.join(path, 'accframe.csv'), 'rb') as f:\n", " af_temp = pd.read_csv(f, index_col=0)\n", " af_temp['net'] = net\n", " af = pd.concat([af,af_temp])\n", "\n", "# cast variables\n", "sf['visible'] = sf['visible'].astype(bool)\n", "sf['bound'] = sf['bound'].astype(bool)\n", "sf['occluder'] = sf['occluder'].astype(bool)\n", "sf['inimage'] = sf['inimage'].astype(bool)\n", "sf['alpha_pos'] = 1-sf['alpha_pos']\n", "sf['alpha_ges'] = 1-sf['alpha_ges']\n", "\n", "# scale to percentage\n", "sf['TE'] = sf['TE'] * 100\n", "\n", "# add surprise as dummy code\n", "sf['control'] = [('control' in set) for set in sf['set']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculate Tracking Error (TE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tracking Error when visible: M: 30.3 , STD: 14.1, Count: 507\n", "Tracking Error when occluded: M: 25.8 , STD: 16.1, Count: 15\n", "Tracking Error Overall: M: 30.2 , STD: 14.1, Count: 522\n" ] } ], "source": [ "grouping = (sf.inimage & sf.bound & ~sf.occluder & sf.control)\n", "\n", "def get_stats(col):\n", " return f' M: {col.mean():.3} , STD: {col.std():.3}, Count: {col.count()}'\n", "\n", "# When Visible\n", "temp = sf[grouping & sf.visible]\n", "print(f'Tracking Error when visible:' + get_stats(temp['TE']))\n", "\n", "# When Occluded\n", "temp = sf[grouping & ~sf.visible]\n", "print(f'Tracking Error when occluded:' + get_stats(temp['TE']))\n", "\n", "# When Overall\n", "temp = sf[grouping]\n", "print(f'Tracking Error Overall:' + get_stats(temp['TE']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculate Succesfull Trackings (TE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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setevalmodetracked_postracked_negtracked_pos_protracked_neg_pro
0controlcontrol1190.050.95
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" ], "text/plain": [ " set evalmode tracked_pos tracked_neg tracked_pos_pro \\\n", "0 control control 1 19 0.05 \n", "\n", " tracked_neg_pro \n", "0 0.95 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# succesfull trackings: In the last visible moment of the target, the slot was less than 10% away from the target\n", "# determine last visible frame numeric\n", "grouping_factors = ['net','set','evalmode','scene','slot']\n", "ff = sf[sf.visible].groupby(grouping_factors).max()\n", "ff.rename(columns = {'frame':'last_visible'}, inplace = True)\n", "ff = ff[['last_visible']]\n", "\n", "# add dummy variable to sf\n", "sf = sf.merge(ff, on=grouping_factors, how='left')\n", "sf['last_visible'] = (sf['last_visible'] == sf['frame'])\n", "\n", "# same for first bound frame\n", "ff = sf[sf.visible & sf.bound & sf.inimage].groupby(grouping_factors).min()\n", "ff.rename(columns = {'frame':'first_visible'}, inplace = True)\n", "ff = ff[['first_visible']]\n", "\n", "# add dummy variable to sf\n", "sf = sf.merge(ff, on=grouping_factors, how='left')\n", "\n", "# extract the trials where the target was last visible and threshold the TE\n", "ff = sf[sf['last_visible']] \n", "ff['tracked_pos'] = (ff['TE'] < 10)\n", "ff['tracked_neg'] = (ff['TE'] >= 10)\n", "\n", "# fill NaN with 0\n", "sf = sf.merge(ff[grouping_factors + ['tracked_pos', 'tracked_neg']], on=grouping_factors, how='left')\n", "sf['tracked_pos'].fillna(False, inplace=True)\n", "sf['tracked_neg'].fillna(False, inplace=True)\n", "\n", "# Aggreagte over all scenes\n", "temp = sf[(sf['frame']== 15) & ~sf.occluder & sf.control & (sf.first_visible < 20)]\n", "temp = temp.groupby(['set', 'evalmode']).sum()\n", "temp = temp[['tracked_pos', 'tracked_neg']]\n", "temp = temp.reset_index()\n", "\n", "temp['tracked_pos_pro'] = temp['tracked_pos'] / (temp['tracked_pos'] + temp['tracked_neg'])\n", "temp['tracked_neg_pro'] = temp['tracked_neg'] / (temp['tracked_pos'] + temp['tracked_neg'])\n", "\n", "temp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Mostly Tracked stats" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "temp = af[af.index == 'OVERALL']\n", "temp['mostly_tracked'] = temp['mostly_tracked'] / temp['num_unique_objects']\n", "temp['partially_tracked'] = temp['partially_tracked'] / temp['num_unique_objects']\n", "temp['mostly_lost'] = temp['mostly_lost'] / temp['num_unique_objects']\n", "g = temp[['mostly_tracked', 'partially_tracked', 'mostly_lost','set']].set_index(['set']).groupby(['set']).mean().plot(kind='bar', stacked=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# MOTA " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " idf1 idp idr recall precision num_unique_objects \\\n", "set \n", "control 0.54837 0.487418 0.626745 0.70247 0.546309 86.0 \n", "\n", " mostly_tracked partially_tracked mostly_lost num_false_positives \\\n", "set \n", "control 35.0 44.0 7.0 4345.0 \n", "\n", " num_misses num_switches num_fragmentations mota motp \\\n", "set \n", "control 2216.0 240.0 73.0 0.086869 0.07704 \n", "\n", " num_transfer num_ascend num_migrate \n", "set \n", "control 119.0 55.0 19.0 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "af[af.index == 'OVERALL'].groupby(['set']).mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "loci23", "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": "3.9.16" } }, "nbformat": 4, "nbformat_minor": 2 }