def _switch_to_new_mode(self):
"""
helper function to switch to the new debug mode
(and saving the previous one in ``self._mode``)
"""
prev_mode = get_current_debug_mode()
set_debug_mode(self._mode)
self._mode = prev_mode
import torch
import os
from tqdm import tqdm
from batchgenerators.transforms import RangeTransform, Compose
from delira.training.backends import convert_torch_to_numpy
from functools import partial
from delira.data_loading import DataManager, SequentialSampler
from delira_unet import UNetTorch
from delira import set_debug_mode
if __name__ == "__main__":
checkpoint_path = ""
data_path = ""
save_path = ""
set_debug_mode(True)
transforms = Compose([
CopyTransform("data", "data_orig"),
# HistogramEqualization(),
RangeTransform((-1, 1)),
# AddGridTransform(),
])
img_size = (1024, 256)
thresh = 0.5
print("Load Model")
torch.jit.loat(checkpoint_path)
model.eval()
SingleBoneDataset,
DelayedCosineAnnealingLRCallback,
DistanceLoss,
FocalLossWithLogits,
ContourTransform,
)
from batchgenerators.transforms import (
RangeTransform,
Compose,
ZeroMeanUnitVarianceTransform,
)
from torchtools.optim import RangerLars
import json
import torch
set_debug_mode(False)
data_path = ""
config_path = ""
save_path = "/tmp/ContourUnet"
bone_label = None
data_path = os.path.expanduser(data_path)
save_path = os.path.expanduser(save_path)
num_epochs = 100
with open(config_path, "r") as f:
config = DeliraConfig(**json.load(f))
def run_experiment(cp: str, test=True) -> str:
"""
Run classification experiment on patches
Imports moved inside because of logging setups
Parameters
----------
ch : str
path to config file
test : bool
test best model on test set
Returns
-------
str
path to experiment folder
"""
# setup config
ch = ConfigHandlerPyTorchDelira(cp)
ch = feature_map_params(ch)
if 'mixed_precision' not in ch or ch['mixed_precision'] is None:
ch['mixed_precision'] = True
if 'debug_delira' in ch and ch['debug_delira'] is not None:
delira.set_debug_mode(ch['debug_delira'])
print("Debug mode active: settings n_process_augmentation to 1!")
ch['augment.n_process'] = 1
dset_keys = ['train', 'val', 'test']
losses = {'class_ce': torch.nn.CrossEntropyLoss()}
train_metrics = {}
val_metrics = {'CE': metric_wrapper_pytorch(torch.nn.CrossEntropyLoss())}
test_metrics = {'CE': metric_wrapper_pytorch(torch.nn.CrossEntropyLoss())}
#########################
# Setup Parameters #
#########################
params_dict = ch.get_params(losses=losses,
train_metrics=train_metrics,
val_metrics=val_metrics,
add_self=ch['add_config_to_params'])
params = Parameters(**params_dict)
#################
# Setup IO #
#################
# setup io
load_sample = load_pickle
load_fn = LoadPatches(load_fn=load_sample,
patch_size=ch['patch_size'],
**ch['data.load_patch'])
datasets = {}
for key in dset_keys:
p = os.path.join(ch["data.path"], str(key))
datasets[key] = BaseExtendCacheDataset(p,
load_fn=load_fn,
**ch['data.kwargs'])
#############################
# Setup Transformations #
#############################
base_transforms = []
base_transforms.append(PopKeys("mapping"))
train_transforms = []
if ch['augment.mode']:
logger.info("Training augmentation enabled.")
train_transforms.append(
SpatialTransform(patch_size=ch['patch_size'],
**ch['augment.kwargs']))
train_transforms.append(MirrorTransform(axes=(0, 1)))
process = ch['augment.n_process'] if 'augment.n_process' in ch else 1
#########################
# Setup Datamanagers #
#########################
datamanagers = {}
for key in dset_keys:
if key == 'train':
trafos = base_transforms + train_transforms
sampler = WeightedPrevalenceRandomSampler
else:
trafos = base_transforms
sampler = SequentialSampler
datamanagers[key] = BaseDataManager(
data=datasets[key],
batch_size=params.nested_get('batch_size'),
n_process_augmentation=process,
transforms=Compose(trafos),
sampler_cls=sampler,
)
#############################
# Initialize Experiment #
#############################
experiment = \
PyTorchExperiment(
params=params,
model_cls=ClassNetwork,
name=ch['exp.name'],
save_path=ch['exp.dir'],
optim_builder=create_optims_default_pytorch,
trainer_cls=PyTorchNetworkTrainer,
mixed_precision=ch['mixed_precision'],
mixed_precision_kwargs={'verbose': False},
key_mapping={"input_batch": "data"},
**ch['exp.kwargs'],
)
# save configurations
ch.dump(os.path.join(experiment.save_path, 'config.json'))
#################
# Training #
#################
model = experiment.run(datamanagers['train'],
datamanagers['val'],
save_path_exp=experiment.save_path,
ch=ch,
metric_keys={'val_CE': ['pred', 'label']},
val_freq=1,
verbose=True)
################
# Testing #
################
if test and datamanagers['test'] is not None:
# metrics and metric_keys are used differently than in original
# Delira implementation in order to support Evaluator
# see mscl.training.predictor
preds = experiment.test(
network=model,
test_data=datamanagers['test'],
metrics=test_metrics,
metric_keys={'CE': ['pred', 'label']},
verbose=True,
)
softmax_fn = metric_wrapper_pytorch(
partial(torch.nn.functional.softmax, dim=1))
preds = softmax_fn(preds[0]['pred'])
labels = [d['label'] for d in datasets['test']]
fpr, tpr, thresholds = roc_curve(labels, preds[:, 1])
roc_auc = auc(fpr, tpr)
plt.plot(fpr, tpr, label='ROC (AUC = %0.2f)' % roc_auc)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.savefig(os.path.join(experiment.save_path, 'test_roc.pdf'))
plt.close()
preds = experiment.test(
network=model,
test_data=datamanagers['val'],
metrics=test_metrics,
metric_keys={'CE': ['pred', 'label']},
verbose=True,
)
preds = softmax_fn(preds[0]['pred'])
labels = [d['label'] for d in datasets['val']]
fpr, tpr, thresholds = roc_curve(labels, preds[:, 1])
roc_auc = auc(fpr, tpr)
plt.plot(fpr, tpr, label='ROC (AUC = %0.2f)' % roc_auc)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.savefig(os.path.join(experiment.save_path, 'best_val_roc.pdf'))
plt.close()
return experiment.save_path