def main(datadir, limit, threads, segment_length, cachedir, minimal_confidence, fold, model, evalgt): # dataset = eval(dataset)(datadir, cachedir=cachedir, default_min_conf=minimal_confidence) # dataset.download() # dataset.prepare() dataset = Mot16(datadir, cachedir=cachedir, default_min_conf=minimal_confidence, fold=fold) dataset.logdir += '_' + model model = eval(model)() prep_training_graphs(dataset, cachedir, limit=limit, threads=threads, segment_length_s=segment_length) prep_minimal_graph_diffs(dataset, model, threads=threads) prep_eval_graphs(dataset, model, threads=threads) if evalgt: dataset.logdir += '_gt' prep_eval_gt_tracks(dataset, model, 'eval', split_on_no_edge=False) else: train_graphres_minimal(dataset, model) prep_eval_tracks(dataset, model, 'eval', threads=1) res, res_int = eval_prepped_tracks(dataset, 'eval') open(os.path.join(dataset.logdir, "eval_results.txt"), "w").write(res) open(os.path.join(dataset.logdir, "eval_results_int.txt"), "w").write(res_int)
def main(dataset, datadir, threads, segment_length, cachedir, minimal_confidence, fold, max_connect, max_worse_eval_epochs, epochs, too_short_track, logdir_prefix): opts = dict(cachedir=cachedir, default_min_conf=minimal_confidence) if fold is not None: opts['fold'] = fold dataset = eval(dataset)(datadir, **opts) dataset.cachedir = cachedir logdir = logdir_prefix + '/' + dataset.logdir find_minimal_graph_diff.too_short_track = too_short_track find_minimal_graph_diff.long_track = too_short_track * 2 skipped_ggd_types = set(ggd_types) for add in ggd_types_order: skipped_ggd_types.remove(add) dataset.logdir = logdir + "_added_" + add print(dataset.logdir) if os.path.exists(dataset.logdir): continue prep_training_graphs(dataset, cachedir, limit_train_amount=0.1, threads=threads, segment_length_s=segment_length, worker_params=dict(max_connect=max_connect)) model = NNModelGraphresPerConnection() prep_minimal_graph_diffs(dataset, model, threads=threads, skipped_ggd_types=skipped_ggd_types) prep_eval_graphs(dataset, model, threads=threads) train_graphres_minimal(dataset, model, epochs=epochs, max_worse_eval_epochs=max_worse_eval_epochs) prep_eval_tracks(dataset, model, 'eval', threads=1) res, res_int = eval_prepped_tracks(dataset, 'eval') open(os.path.join(dataset.logdir, "eval_results.txt"), "w").write(res) open(os.path.join(dataset.logdir, "eval_results_int.txt"), "w").write(res_int)
def main(dataset, datadir, limit, threads, segment_length, cachedir, minimal_confidence, fold, max_connect, no_train, resume, max_worse_eval_epochs, epochs, too_short_track): opts = dict(cachedir=cachedir, default_min_conf=minimal_confidence) if fold is not None: opts['fold'] = fold dataset = eval(dataset)(datadir, **opts) dataset.download() dataset.prepare() find_minimal_graph_diff.too_short_track = too_short_track find_minimal_graph_diff.long_track = too_short_track * 2 prep_training_graphs(dataset, cachedir, limit=limit, threads=threads, segment_length_s=segment_length, worker_params=dict(max_connect=max_connect)) model = NNModelGraphresPerConnection() prep_minimal_graph_diffs(dataset, model, threads=threads) prep_eval_graphs(dataset, model, threads=threads) if not no_train: train_graphres_minimal(dataset, model, epochs=epochs, resume=resume, max_worse_eval_epochs=max_worse_eval_epochs) prep_eval_tracks(dataset, model, 'eval', threads=1) res, res_int = eval_prepped_tracks(dataset, 'eval') open(os.path.join(dataset.logdir, "eval_results.txt"), "w").write(res) open(os.path.join(dataset.logdir, "eval_results_int.txt"), "w").write(res_int) res, res_int = eval_prepped_tracks_joined(dataset, 'eval') open(os.path.join(dataset.logdir, "eval_results_joined.txt"), "w").write(res) open(os.path.join(dataset.logdir, "eval_results_joined_int.txt"), "w").write(res_int) prep_eval_tracks(dataset, model, 'test', threads=1) dataset.prepare_submition()
def main(threads, cachedir, train_amounts, itterations, logdir_prefix): max_extra = 3 dataset = Duke("data") dataset.cachedir = cachedir logdir = logdir_prefix + '/' + dataset.logdir global_skip = {"LongConnectionOrder", "LongFalsePositiveTrack"} for train_amount in map(float, train_amounts.split(',')): for itt in range(int(itterations)): t0 = time() prep_training_graphs(dataset, cachedir, limit_train_amount=train_amount, threads=threads, seed=hash(logdir) + itt) model = NNModelGraphresPerConnection() prep_minimal_graph_diffs(dataset, model, threads=threads, skipped_ggd_types=global_skip) prep_eval_graphs(dataset, model, threads=threads) t1 = time() dataset.logdir = logdir + "_%8.6f_%.2d" % (train_amount, itt) train_graphres_minimal(dataset, model, epochs=1000, max_worse_eval_epochs=max_extra, train_amount=train_amount) t2 = time() fn = sorted(glob("%s/snapshot_???.pyt" % dataset.logdir))[-max_extra - 1] prep_eval_tracks(dataset, model, 'eval', threads=1, snapshot=fn) res, res_int = eval_prepped_tracks(dataset, 'eval') open(os.path.join(dataset.logdir, "eval_results.txt"), "w").write(res) open(os.path.join(dataset.logdir, "eval_results_int.txt"), "w").write(res_int) t3 = time() open(os.path.join(dataset.logdir, "timeing.txt"), "w").write(repr((t0, t1, t2, t3))) prep_eval_gt_tracks(dataset, NNModelGraphresPerConnection) res, res_int = eval_prepped_tracks(dataset, 'eval') open(os.path.join(dataset.cachedir, "eval_gt_results.txt"), "w").write(res) open(os.path.join(dataset.cachedir, "eval_gt_results_int.txt"), "w").write(res_int)
def main(datadir): dataset = Duke(datadir) model = NNModelGraphresPerConnection() prep_eval_graphs(dataset, NNModelGraphresPerConnection(), parts=["train"]) # train_frossard(dataset, "cachedir/logdir_fossard", model, resume_from="cachedir/logdir/model_0001.pyt", epochs=10) train_frossard(dataset, "cachedir/logdir_fossard", model, resume_from="cachedir/logdir/snapshot_009.pyt", save_every=10, epochs=1) prep_eval_tracks(dataset, "cachedir/logdir_fossard", model, 'eval', threads=1) res, res_int = eval_prepped_tracks(dataset, 'eval') open("cachedir/logdir_fossard/eval_results.txt", "w").write(res) open("cachedir/logdir_fossard/eval_results_int.txt", "w").write(res_int)
import re from glob import glob from os import stat from random import shuffle import torch from ggdtrack.duke_dataset import Duke from ggdtrack.eval import eval_hamming, ConnectionBatch, prep_eval_graphs from ggdtrack.model import NNModelGraphresPerConnection from ggdtrack.utils import save_json dataset = Duke("data") model = NNModelGraphresPerConnection() prep_eval_graphs(dataset, NNModelGraphresPerConnection(), parts=["train"]) models = glob("cachedir/logdir/model*") shuffle(models) hammings = [] for fn in models: model.load_state_dict(torch.load(fn)) hamming = eval_hamming(dataset, None, model) print(hamming) t = stat(fn).st_ctime_ns / 1e9 hammings.append((t, hamming)) save_json(hammings, "cachedir/logdir/hammings.json")