{ "cells": [ { "cell_type": "code", "execution_count": 2, "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": 3, "metadata": {}, "outputs": [], "source": [ "# setting path to results folder\n", "root_path = '../../out/pretrained/adept/loci_unlooped/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", "tf = pd.DataFrame()\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, 'surprise', 'statistics',)\n", " with open(os.path.join(path, 'trialframe.csv'), 'rb') as f:\n", " tf_temp = pd.read_csv(f, index_col=0)\n", " tf_temp['net'] = net\n", " tf = pd.concat([tf,tf_temp])\n", "\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['vanishing'] = sf['vanishing'].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", "tf['control'] = [('control' in set) for set in tf['set']]\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": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tracking Error when visible: M: 12.4 , STD: 14.8, Count: 2144\n", "Tracking Error when occluded: M: 7.66 , STD: 4.17, Count: 550\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']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculate Succesfull Trackings (TE)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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setevalmodetracked_postracked_negtracked_pos_protracked_neg_pro
0createdown_controlopen4500.0740740.925926
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" ], "text/plain": [ " set evalmode tracked_pos tracked_neg tracked_pos_pro \\\n", "0 createdown_control open 4 50 0.074074 \n", "\n", " tracked_neg_pro \n", "0 0.925926 " ] }, "execution_count": 5, "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 & sf.bound & sf.inimage].groupby(grouping_factors).max()\n", "ff.rename(columns = {'frame':'last_visible'}, inplace = True)\n", "sf = sf.merge(ff[['last_visible']], on=grouping_factors, how='left')\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", "sf = sf.merge(ff[['first_visible']], on=grouping_factors, how='left')\n", "\n", "# add dummy variable to sf\n", "sf['last_visible'] = (sf['last_visible'] == sf['frame'])\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']== 1) & ~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": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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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": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idf1idpidrrecallprecisionnum_unique_objectsmostly_trackedpartially_trackedmostly_lostnum_false_positivesnum_missesnum_switchesnum_fragmentationsmotamotpnum_transfernum_ascendnum_migrate
set
createdown_control0.7490710.7570390.7412700.8744130.89301486.048.037.01.0513.0615.057.051.00.7580150.03741423.034.00.0
createdown_surprise0.8029530.8139230.7922740.8682710.89199733.017.016.00.0166.0208.011.013.00.7561750.0362937.04.00.0
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" ], "text/plain": [ " idf1 idp idr recall precision \\\n", "set \n", "createdown_control 0.749071 0.757039 0.741270 0.874413 0.893014 \n", "createdown_surprise 0.802953 0.813923 0.792274 0.868271 0.891997 \n", "\n", " num_unique_objects mostly_tracked partially_tracked \\\n", "set \n", "createdown_control 86.0 48.0 37.0 \n", "createdown_surprise 33.0 17.0 16.0 \n", "\n", " mostly_lost num_false_positives num_misses \\\n", "set \n", "createdown_control 1.0 513.0 615.0 \n", "createdown_surprise 0.0 166.0 208.0 \n", "\n", " num_switches num_fragmentations mota motp \\\n", "set \n", "createdown_control 57.0 51.0 0.758015 0.037414 \n", "createdown_surprise 11.0 13.0 0.756175 0.036293 \n", "\n", " num_transfer num_ascend num_migrate \n", "set \n", "createdown_control 23.0 34.0 0.0 \n", "createdown_surprise 7.0 4.0 0.0 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "af[af.index == 'OVERALL'].groupby(['set']).mean()" ] } ], "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 }