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script.py
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47 lines (41 loc) · 1.56 KB
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from second.pytorch.train import train, evaluate
from google.protobuf import text_format
from second.protos import pipeline_pb2
from pathlib import Path
from second.utils import config_tool
def train_multi_rpn_layer_num():
config_path = "./configs/car.lite.config"
model_root = Path.home() / "second_test" # don't forget to change this.
config = pipeline_pb2.TrainEvalPipelineConfig()
with open(config_path, "r") as f:
proto_str = f.read()
text_format.Merge(proto_str, config)
input_cfg = config.eval_input_reader
model_cfg = config.model.second
layer_nums = [2, 4, 7, 9]
for l in layer_nums:
model_dir = str(model_root / f"car_lite_L{l}")
model_cfg.rpn.layer_nums[:] = [l]
train(config, model_dir)
def eval_multi_threshold():
config_path = "./configs/car.fhd.config"
ckpt_name = "/path/to/your/model_ckpt" # don't forget to change this.
assert "/path/to/your" not in ckpt_name
config = pipeline_pb2.TrainEvalPipelineConfig()
with open(config_path, "r") as f:
proto_str = f.read()
text_format.Merge(proto_str, config)
model_cfg = config.model.second
threshs = [0.3]
for thresh in threshs:
model_cfg.nms_score_threshold = thresh
# don't forget to change this.
result_path = Path.home() / f"second_test_eval_{thresh:.2f}"
evaluate(
config,
result_path=result_path,
ckpt_path=str(ckpt_name),
batch_size=1,
measure_time=True)
if __name__ == "__main__":
eval_multi_threshold()