Testing script and data needed to run it for NeurIPS 2024 submission 18634

<p>Dependencies:<br>pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 (get specific command for your environment from Pytorch website. If you can't use CUDA, you must edit the code to use the CPU.)<br>pip install timm<br>pip install opencv-python<br>pip install pillow<br>pip install joblib<br>pip install scipy<br>pip install scikit-learn</p> <p>Please make the following edits to run the script. Also, either move the models folder (https://zenodo.org/records/11245477) or change the directory.<br>Also, add a folder to the directory with the testing script called "Results"</p> <p>The testing script also includes the models for directionality, another statistic we attempted to predict. We did not report the values for directionality in our paper, so feel free to comment lines 2175-2253 and lines 2296-2308 to speed up the computation for the statistics presented in the paper. Due to time constraints, this version also doesn't include the class-modifying arguments for Brownian and straight-trajectory motion. These will be included in the testing script on the official release of DeepTrakStat on Github.&nbsp;<br>line 485: sim_directory='ground_truth_trajectories/'<br>line 486: tmate_directory='tmate_trajectories/'</p> <p>lines 509-525:<br>directories = [<br>'simulated_imagery/1000part_16xspeed_heterogeneous/','simulated_imagery/1000part_32xspeed_heterogeneous/',<br>'simulated_imagery/1000part_4xspeed_heterogeneous/',<br>'simulated_imagery/2000part_16xspeed_heterogeneous/','simulated_imagery/2000part_32xspeed_heterogeneous/', ## Group 1<br>'simulated_imagery/500part_16xspeed_heterogeneous/','simulated_imagery/500part_32xspeed_heterogeneous/',<br>'simulated_imagery/500part_4xspeed_heterogeneous/',<br>'simulated_imagery/sim111_brown/', 'simulated_imagery/sim112_brown/',<br>'simulated_imagery/sim113_brown/','simulated_imagery/sim114_brown/', &nbsp;# Group 2<br>'simulated_imagery/sim115_brown/','simulated_imagery/sim119_brown/','simulated_imagery/sim120_brown/',<br>'simulated_imagery/sim2201/', 'simulated_imagery/sim2210/', 'simulated_imagery/sim2215/',<br>'simulated_imagery/sim2220/','simulated_imagery/sim2230/','simulated_imagery/sim2235/','simulated_imagery/sim2240/', # Group 3<br>'simulated_imagery/sim2241/','simulated_imagery/sim2242/','simulated_imagery/sim2243/','simulated_imagery/sim2244/', #Group 4<br>'simulated_imagery/sim2251/','simulated_imagery/sim2252/','simulated_imagery/sim2253/',<br>'simulated_imagery/sim2254/','simulated_imagery/sim2255/','simulated_imagery/sim2256/',<br>'simulated_imagery/sim2257/','simulated_imagery/sim2258/','simulated_imagery/sim2259/','simulated_imagery/sim2260/', # Group 5<br>'simulated_imagery/test1/','simulated_imagery/test2/','simulated_imagery/test5/','simulated_imagery/test8/' #Group 6<br>]</p> <p>line 606:<br>&nbsp; &nbsp; &nbsp; &nbsp; sorted_frames = sorted(files, key=lambda x: int(x[4:-4]))</p>