def main(config):
"""
Main code for training a classification/seg/classification+seg model.
Args:
config (dict): dictionary read from a yaml file
i.e. script/configs/train.yml
Returns:
None
"""
# setting up the train/val split with filenames
seed = config["io_params"]["split_seed"]
seed_everything(seed)
exp = TrainSegExperiment2D(config)
output_key = "logits"
print(f"Seed: {seed}")
runner = SupervisedRunner(output_key=output_key)
runner.train(model=exp.model,
criterion=exp.criterion,
optimizer=exp.opt,
scheduler=exp.lr_scheduler,
loaders=exp.loaders,
callbacks=exp.cb_list,
**config["runner_params"])
# Not saving plots if plot_params not specified in config
if config.get("plot_params"):
figs = plot_metrics(logdir=config["runner_params"]["logdir"],
metrics=config["plot_params"]["metrics"])
save_figs(figs, save_dir=config["plot_params"]["save_dir"])
def main(config):
"""
Main code for training a classification model.
Args:
config (dict): dictionary read from a yaml file
i.e. experiments/finetune_classification.yml
Returns:
None
"""
# setting up the train/val split with filenames
seed = config["io_params"]["split_seed"]
seed_everything(seed)
dim = len(config["predict_3D_params"]["patch_size"])
mode = config["mode"].lower()
assert mode in ["classification", "segmentation"], \
"The `mode` must be one of ['classification', 'segmentation']."
if mode == "classification":
raise NotImplementedError
elif mode == "segmentation":
if dim == 2:
exp = SegmentationInferenceExperiment2D(config)
elif dim == 3:
exp = SegmentationInferenceExperiment(config)
print(f"Seed: {seed}\nMode: {mode}")
pred = Predictor(out_dir=config["out_dir"],
checkpoint_path=config["checkpoint_path"],
model=exp.model,
test_loader=exp.loaders["test"],
pred_3D_params=config["predict_3D_params"],
pseudo_3D=config.get("pseudo_3D"))
pred.run_3D_predictions()
def main(config):
"""
Main code for training a classification model.
Args:
config (dict): dictionary read from a yaml file
i.e. experiments/finetune_classification.yml
Returns:
None
"""
# setting up the train/val split with filenames
seed = config["io_params"]["split_seed"]
seed_everything(seed)
mode = config["mode"].lower()
assert mode in ["classification", "segmentation", "both"], \
"The `mode` must be one of ['classification', 'segmentation', 'both']."
if mode == "classification":
raise NotImplementedError
elif mode == "segmentation":
if config["dim"] == 2:
exp = TrainSegExperiment2D(config)
elif config["dim"] == 3:
exp = TrainSegExperiment(config)
output_key = "logits"
elif mode == "both":
if config["dim"] == 2:
exp = TrainClfSegExperiment2D(config)
elif config["dim"] == 3:
exp = TrainClfSegExperiment3D(config)
output_key = ["seg_logits", "clf_logits"]
print(f"Seed: {seed}\nMode: {mode}")
runner = SupervisedRunner(output_key=output_key)
runner.train(model=exp.model,
criterion=exp.criterion,
optimizer=exp.opt,
scheduler=exp.lr_scheduler,
loaders=exp.loaders,
callbacks=exp.cb_list,
**config["runner_params"])
# Not saving plots if plot_params not specified in config
if not config.get("plot_params"):
figs = plot_metrics(logdir=config["runner_params"]["logdir"],
metrics=config["plot_params"]["metrics"])
save_figs(figs, save_dir=config["plot_params"]["save_dir"])
def main(config, out_path):
"""
Main code for training a classification model.
Args:
config (dict): dictionary read from a yaml file
i.e. experiments/finetune_classification.yml
Returns:
None
"""
# setting up the train/val split with filenames
seed = config["io_params"]["split_seed"]
seed_everything(seed)
exp = SegmentationInferenceExperiment2D(config)
print(f"Seed: {seed}")
test_ids = exp.test_dset.im_ids
if not os.path.isdir(out_path):
os.mkdir(out_path)
print(f"Created {out_path}")
print(f"Copying {len(test_ids)} test files to a {out_path}")
copy_files(test_ids, out_path)
def main(config):
"""
Main code for training a classification model.
Args:
config (dict): dictionary read from a yaml file
i.e. experiments/finetune_classification.yml
Returns:
None
"""
# setting up the train/val split with filenames
seed = config["io_params"]["split_seed"]
seed_everything(seed)
exp = SegmentationInferenceExperiment2D(config)
print(f"Seed: {seed}")
pred = General3DPredictor(out_dir=config["out_dir"],
model=exp.model,
test_loader=exp.loaders["test"],
pred_3D_params=config["predict_3D_params"],
pseudo_3D=config.get("pseudo_3D"))
pred.run_3D_predictions()