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|
{
"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": [],
"source": [
"# setting path to results folder\n",
"root_path = '../../../out/pretrained/adept_ablations/recon'\n",
"\n",
"# list all folders in root path that don't stat with a dot\n",
"nets = ['recon']\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, 'results')\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": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tracking Error when visible: M: 2.95 , STD: 4.8, Count: 1686\n",
"Tracking Error when occluded: M: 2.54 , STD: 2.52, Count: 477\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": 4,
"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>set</th>\n",
" <th>evalmode</th>\n",
" <th>tracked_pos</th>\n",
" <th>tracked_neg</th>\n",
" <th>tracked_pos_pro</th>\n",
" <th>tracked_neg_pro</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>control</td>\n",
" <td>open</td>\n",
" <td>37</td>\n",
" <td>9</td>\n",
" <td>0.804348</td>\n",
" <td>0.195652</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" set evalmode tracked_pos tracked_neg tracked_pos_pro \\\n",
"0 control open 37 9 0.804348 \n",
"\n",
" tracked_neg_pro \n",
"0 0.195652 "
]
},
"execution_count": 4,
"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": 5,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"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": 6,
"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>idf1</th>\n",
" <th>idp</th>\n",
" <th>idr</th>\n",
" <th>recall</th>\n",
" <th>precision</th>\n",
" <th>num_unique_objects</th>\n",
" <th>mostly_tracked</th>\n",
" <th>partially_tracked</th>\n",
" <th>mostly_lost</th>\n",
" <th>num_false_positives</th>\n",
" <th>num_misses</th>\n",
" <th>num_switches</th>\n",
" <th>num_fragmentations</th>\n",
" <th>mota</th>\n",
" <th>motp</th>\n",
" <th>num_transfer</th>\n",
" <th>num_ascend</th>\n",
" <th>num_migrate</th>\n",
" </tr>\n",
" <tr>\n",
" <th>set</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>control</th>\n",
" <td>0.754576</td>\n",
" <td>0.919577</td>\n",
" <td>0.639779</td>\n",
" <td>0.662855</td>\n",
" <td>0.952744</td>\n",
" <td>86.0</td>\n",
" <td>38.0</td>\n",
" <td>18.0</td>\n",
" <td>30.0</td>\n",
" <td>161.0</td>\n",
" <td>1651.0</td>\n",
" <td>13.0</td>\n",
" <td>28.0</td>\n",
" <td>0.627323</td>\n",
" <td>0.037094</td>\n",
" <td>0.0</td>\n",
" <td>12.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>surprise</th>\n",
" <td>0.790401</td>\n",
" <td>0.968750</td>\n",
" <td>0.667511</td>\n",
" <td>0.667511</td>\n",
" <td>0.968750</td>\n",
" <td>33.0</td>\n",
" <td>13.0</td>\n",
" <td>8.0</td>\n",
" <td>12.0</td>\n",
" <td>34.0</td>\n",
" <td>525.0</td>\n",
" <td>0.0</td>\n",
" <td>11.0</td>\n",
" <td>0.645978</td>\n",
" <td>0.034979</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" idf1 idp idr recall precision \\\n",
"set \n",
"control 0.754576 0.919577 0.639779 0.662855 0.952744 \n",
"surprise 0.790401 0.968750 0.667511 0.667511 0.968750 \n",
"\n",
" num_unique_objects mostly_tracked partially_tracked mostly_lost \\\n",
"set \n",
"control 86.0 38.0 18.0 30.0 \n",
"surprise 33.0 13.0 8.0 12.0 \n",
"\n",
" num_false_positives num_misses num_switches num_fragmentations \\\n",
"set \n",
"control 161.0 1651.0 13.0 28.0 \n",
"surprise 34.0 525.0 0.0 11.0 \n",
"\n",
" mota motp num_transfer num_ascend num_migrate \n",
"set \n",
"control 0.627323 0.037094 0.0 12.0 0.0 \n",
"surprise 0.645978 0.034979 0.0 0.0 0.0 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"af[af.index == 'OVERALL'].groupby(['set']).mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Gate Openings"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Percept gate openings when visible: M: 0.223 , STD: 0.303, Count: 1686\n",
"Percept gate openings when occluded: M: 0.00995 , STD: 0.0471, Count: 477\n"
]
}
],
"source": [
"grouping = (sf.inimage & sf.bound & ~sf.occluder & sf.control)\n",
"temp = sf[grouping & sf.visible]\n",
"print(f'Percept gate openings when visible:' + get_stats(temp['alpha_pos'] + temp['alpha_ges']))\n",
"temp = sf[grouping & ~sf.visible]\n",
"print(f'Percept gate openings when occluded:' + get_stats(temp['alpha_pos'] + temp['alpha_ges']))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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
}
|
