from Models.erfh5_ConvModel import S80Deconv2ToDrySpotEff
from Pipeline.data_gather import get_filelist_within_folder_blacklisted
from Pipeline.data_loader_dryspot import DataloaderDryspots
from Trainer.ModelTrainer import ModelTrainer
from Trainer.evaluation import BinaryClassificationEvaluator
from Utils.eval_utils import run_eval_w_binary_classificator
if __name__ == "__main__":
dl = DataloaderDryspots(sensor_indizes=((1, 4), (1, 4)),
aux_info=True)
checkpoint_p = r.chkp_S80_to_ds_thres
adv_output_dir = checkpoint_p.parent / "advanced_eval"
m = ModelTrainer(
lambda: S80Deconv2ToDrySpotEff(pretrained="all",
checkpoint_path=checkpoint_p,
freeze_nlayers=9,
round_at=.8),
data_source_paths=r.get_data_paths_base_0(),
save_path=r.save_path,
dataset_split_path=r.dataset_split,
cache_path=r.cache_path,
batch_size=2048,
train_print_frequency=100,
epochs=1000,
num_workers=75,
num_validation_samples=131072,
num_test_samples=1048576,
data_processing_function=dl.get_sensor_bool_dryspot,
data_gather_function=get_filelist_within_folder_blacklisted,
loss_criterion=torch.nn.MSELoss(),
optimizer_function=lambda params: torch.optim.AdamW(params, lr=0.0001),
from Pipeline.data_loader_dryspot import DataloaderDryspots
from Trainer.ModelTrainer import ModelTrainer
from Trainer.evaluation import BinaryClassificationEvaluator
from Utils.training_utils import read_cmd_params
if __name__ == "__main__":
args = read_cmd_params()
dl = DataloaderDryspots(sensor_indizes=((1, 4), (1, 4)))
# def get_sampler(data_source):
# return RandomOverSampler(data_source, multiply_by=2)
m = ModelTrainer(
lambda: S80Deconv2ToDrySpotEff(pretrained="deconv_weights",
checkpoint_path=r.chkp_S80_to_ff2,
freeze_nlayers=9,
round_at=0.8),
data_source_paths=r.get_data_paths_base_0(),
save_path=r.save_path,
load_datasets_path=r.datasets_dryspots,
cache_path=r.cache_path,
batch_size=8192,
train_print_frequency=100,
epochs=1000,
num_workers=75,
num_validation_samples=131072,
num_test_samples=1048576,
data_processing_function=dl.get_sensor_bool_dryspot,
data_gather_function=get_filelist_within_folder_blacklisted,
loss_criterion=torch.nn.MSELoss(),
optimizer_function=lambda params: torch.optim.AdamW(params, lr=0.0001),
with 80 sensor data to Flowfront images.
Please add the path to the pretrained weights as parameter
checkpoint_path to the SensorDeconvToDryspotEfficient2 Model or use the command line option --checkpoint_path.
"""
args = read_cmd_params()
if args.demo is not None:
checkpoint_path = args.checkpoint_path
else:
checkpoint_path = "Use your own path to checkpoint."
dl = DataloaderDryspots(sensor_indizes=((1, 4), (1, 4)))
m = ModelTrainer(
lambda: S80Deconv2ToDrySpotEff(demo_mode=True
if args.demo is not None else False,
pretrained="deconv_weights",
checkpoint_path=Path(checkpoint_path),
freeze_nlayers=9),
data_source_paths=r.get_data_paths_base_0(),
save_path=r.save_path if args.demo is None else Path(args.demo),
dataset_split_path=r.dataset_split,
cache_path=r.cache_path,
batch_size=2048,
train_print_frequency=100,
epochs=1000,
num_workers=75,
num_validation_samples=131072,
num_test_samples=1048576,
data_processing_function=dl.get_sensor_bool_dryspot,
data_gather_function=get_filelist_within_folder_blacklisted,
loss_criterion=torch.nn.MSELoss(),
from Models.erfh5_ConvModel import S80Deconv2ToDrySpotEff
from Pipeline.data_gather import get_filelist_within_folder_blacklisted
from Pipeline.data_loader_dryspot import DataloaderDryspots
from Trainer.ModelTrainer import ModelTrainer
from Trainer.evaluation import BinaryClassificationEvaluator
from Utils.eval_utils import run_eval_w_binary_classificator
if __name__ == "__main__":
dl = DataloaderDryspots(sensor_indizes=((1, 4), (1, 4)),
aux_info=True)
checkpoint_p = r.chkp_S80_to_ds_no_thres
adv_output_dir = checkpoint_p.parent / "advanced_eval"
m = ModelTrainer(
lambda: S80Deconv2ToDrySpotEff(pretrained="deconv_weights",
checkpoint_path=checkpoint_p,
freeze_nlayers=9),
data_source_paths=r.get_data_paths_base_0(),
save_path=r.save_path,
dataset_split_path=r.dataset_split,
cache_path=r.cache_path,
batch_size=2048,
train_print_frequency=100,
epochs=1000,
num_workers=75,
num_validation_samples=131072,
num_test_samples=1048576,
data_processing_function=dl.get_sensor_bool_dryspot,
data_gather_function=get_filelist_within_folder_blacklisted,
loss_criterion=torch.nn.MSELoss(),
optimizer_function=lambda params: torch.optim.AdamW(params, lr=0.0001),