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)