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Replace (broken) --unrag with --ragged #1539

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merged 3 commits into from
Oct 11, 2023
Merged

Replace (broken) --unrag with --ragged #1539

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f74429e
Fix unrag always set to true in sleap-export
Oct 4, 2023
2f7a1ad
Replace unrag with ragged
Oct 10, 2023
8d1de19
Fix typos
roomrys Oct 10, 2023
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6 changes: 3 additions & 3 deletions docs/guides/cli.md
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Original file line number Diff line number Diff line change
Expand Up @@ -99,9 +99,9 @@ optional arguments:
-e [EXPORT_PATH], --export_path [EXPORT_PATH]
Path to output directory where the frozen model will be exported to.
Defaults to a folder named 'exported_model'.
-u, --unrag UNRAG
Convert ragged tensors into regular tensors with NaN padding.
Defaults to True.
-r, --ragged RAGGED
Keep tensors ragged if present. If ommited, convert
ragged tensors into regular tensors with NaN padding.
-n, --max_instances MAX_INSTANCES
Limit maximum number of instances in multi-instance models.
Not available for ID models. Defaults to None.
Expand Down
12 changes: 6 additions & 6 deletions sleap/nn/inference.py
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Expand Up @@ -4939,7 +4939,7 @@ def export_cli(args: Optional[list] = None):
export_model(
args.models,
args.export_path,
unrag_outputs=args.unrag,
unrag_outputs=(not args.ragged),
max_instances=args.max_instances,
)

Expand Down Expand Up @@ -4971,13 +4971,13 @@ def _make_export_cli_parser() -> argparse.ArgumentParser:
),
)
parser.add_argument(
"-u",
"--unrag",
"-r",
"--ragged",
action="store_true",
default=True,
default=False,
help=(
"Convert ragged tensors into regular tensors with NaN padding. "
"Defaults to True."
"Keep tensors ragged if present. If ommited, convert ragged tensors"
" into regular tensors with NaN padding."
),
)
parser.add_argument(
Expand Down
40 changes: 26 additions & 14 deletions tests/nn/test_inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,6 +50,7 @@
_make_tracker_from_cli,
main as sleap_track,
export_cli as sleap_export,
_make_export_cli_parser,
)
from sleap.nn.tracking import (
MatchedFrameInstance,
Expand Down Expand Up @@ -925,7 +926,7 @@ def test_load_model(resize_input_shape, model_fixture_name, request):
predictor = load_model(model_path, resize_input_layer=resize_input_shape)

# Determine predictor type
for (fname, mname, ptype, ishape) in fname_mname_ptype_ishape:
for fname, mname, ptype, ishape in fname_mname_ptype_ishape:
if fname in model_fixture_name:
expected_model_name = mname
expected_predictor_type = ptype
Expand Down Expand Up @@ -966,7 +967,6 @@ def test_topdown_multi_size_inference(
def test_ensure_numpy(
min_centroid_model_path, min_centered_instance_model_path, min_labels_slp
):

model = load_model([min_centroid_model_path, min_centered_instance_model_path])

# each frame has same number of instances
Expand Down Expand Up @@ -1037,7 +1037,6 @@ def test_ensure_numpy(


def test_centroid_inference():

xv, yv = make_grid_vectors(image_height=12, image_width=12, output_stride=1)
points = tf.cast([[[1.75, 2.75]], [[3.75, 4.75]], [[5.75, 6.75]]], tf.float32)
cms = tf.expand_dims(make_multi_confmaps(points, xv, yv, sigma=1.5), axis=0)
Expand Down Expand Up @@ -1093,7 +1092,6 @@ def test_centroid_inference():


def export_frozen_graph(model, preds, output_path):

tensors = {}

for key, val in preds.items():
Expand All @@ -1120,7 +1118,6 @@ def export_frozen_graph(model, preds, output_path):
info = json.load(json_file)

for tensor_info in info["frozen_model_inputs"] + info["frozen_model_outputs"]:

saved_name = (
tensor_info.split("Tensor(")[1].split(", shape")[0].replace('"', "")
)
Expand All @@ -1137,7 +1134,6 @@ def export_frozen_graph(model, preds, output_path):


def test_single_instance_save(min_single_instance_robot_model_path, tmp_path):

single_instance_model = tf.keras.models.load_model(
min_single_instance_robot_model_path + "/best_model.h5", compile=False
)
Expand All @@ -1152,7 +1148,6 @@ def test_single_instance_save(min_single_instance_robot_model_path, tmp_path):


def test_centroid_save(min_centroid_model_path, tmp_path):

centroid_model = tf.keras.models.load_model(
min_centroid_model_path + "/best_model.h5", compile=False
)
Expand All @@ -1171,7 +1166,6 @@ def test_centroid_save(min_centroid_model_path, tmp_path):
def test_topdown_save(
min_centroid_model_path, min_centered_instance_model_path, min_labels_slp, tmp_path
):

centroid_model = tf.keras.models.load_model(
min_centroid_model_path + "/best_model.h5", compile=False
)
Expand All @@ -1195,7 +1189,6 @@ def test_topdown_save(
def test_topdown_id_save(
min_centroid_model_path, min_topdown_multiclass_model_path, min_labels_slp, tmp_path
):

centroid_model = tf.keras.models.load_model(
min_centroid_model_path + "/best_model.h5", compile=False
)
Expand All @@ -1217,7 +1210,6 @@ def test_topdown_id_save(


def test_single_instance_predictor_save(min_single_instance_robot_model_path, tmp_path):

# directly initialize predictor
predictor = SingleInstancePredictor.from_trained_models(
min_single_instance_robot_model_path, resize_input_layer=False
Expand Down Expand Up @@ -1254,10 +1246,33 @@ def test_single_instance_predictor_save(min_single_instance_robot_model_path, tm
)


def test_make_export_cli():
models_path = r"psuedo/models/path"
export_path = r"psuedo/test/path"
max_instances = 5

parser = _make_export_cli_parser()

# Test default values
args = None
args, _ = parser.parse_known_args(args=args)
assert args.models is None
assert args.export_path == "exported_model"
assert not args.ragged
assert args.max_instances is None

# Test all arguments
cmd = f"-m {models_path} -e {export_path} -r -n {max_instances}"
args, _ = parser.parse_known_args(args=cmd.split())
assert args.models == [models_path]
assert args.export_path == export_path
assert args.ragged
assert args.max_instances == max_instances


def test_topdown_predictor_save(
min_centroid_model_path, min_centered_instance_model_path, tmp_path
):

# directly initialize predictor
predictor = TopDownPredictor.from_trained_models(
centroid_model_path=min_centroid_model_path,
Expand Down Expand Up @@ -1300,7 +1315,6 @@ def test_topdown_predictor_save(
def test_topdown_id_predictor_save(
min_centroid_model_path, min_topdown_multiclass_model_path, tmp_path
):

# directly initialize predictor
predictor = TopDownMultiClassPredictor.from_trained_models(
centroid_model_path=min_centroid_model_path,
Expand Down Expand Up @@ -1478,7 +1492,6 @@ def test_flow_tracker(centered_pair_predictions: Labels, tmpdir):
# Run tracking on subset of frames using psuedo-implementation of
# sleap.nn.tracking.run_tracker
for lf in frames[:20]:

# Clear the tracks
for inst in lf.instances:
inst.track = None
Expand Down Expand Up @@ -1522,7 +1535,6 @@ def test_max_tracks_matching_queue(
frames = sorted(labels.labeled_frames, key=lambda lf: lf.frame_idx)

for lf in frames[:20]:

# Clear the tracks
for inst in lf.instances:
inst.track = None
Expand Down
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