def training():
c = get_config()
dataset_name = 'Task04_Hippocampus'
# dataset_name = 'Task02_Heart'
# download_dataset(dest_path=c.data_root_dir, dataset=dataset_name, id=c.google_drive_id)
# c.do_load_checkpoint = True
# c.checkpoint_dir = c.base_dir + '/20190801-_unet_experiment' + '/checkpoint/checkpoint_current'
# c.checkpoint_file = "checkpoint_last.pth.tar"
if not exists(os.path.join(os.path.join(c.data_root_dir, dataset_name), 'preprocessed')):
print('Preprocessing data. [STARTED]')
preprocess_data(root_dir=os.path.join(c.data_root_dir, dataset_name))
create_splits(output_dir=c.split_dir, image_dir=c.data_dir)
print('Preprocessing data. [DONE]')
else:
print('The data has already been preprocessed. It will not be preprocessed again. Delete the folder to enforce it.')
exp = UNetExperiment(config=c, name='unet_experiment', n_epochs=c.n_epochs,
seed=42, append_rnd_to_name=c.append_rnd_string) # visdomlogger_kwargs={"auto_start": c.start_visdom}
exp.run()
exp.run_test(setup=False)
def testing():
c = get_config()
c.do_load_checkpoint = True
#c.checkpoint_dir = c.base_dir + '/20190424-020641_unet_experiment' + '/checkpoint/checkpoint_current' # dice_cost train
# c.checkpoint_dir = c.base_dir + '/20190424-234657_unet_experiment' + '/checkpoint/checkpoint_last' # SDG
c.checkpoint_dir = c.base_dir + '/20190906-085449_unet_experiment' + '/checkpoint/checkpoint_current'
# c.checkpoint_file = "checkpoint_last.pth.tar"
# c.cross_vali_index = valiIndex
cross_vali_result_all_dir = os.path.join(c.base_dir, c.dataset_name
+ '_' + str(
c.batch_size) + c.cross_vali_result_all_dir + datetime.datetime.now().strftime("_%Y%m%d-%H%M%S"))
if not os.path.exists(cross_vali_result_all_dir):
os.makedirs(cross_vali_result_all_dir)
print('Created' + cross_vali_result_all_dir + '...')
c.base_dir = cross_vali_result_all_dir
c.cross_vali_result_all_dir = os.path.join(cross_vali_result_all_dir, "results")
os.makedirs(c.cross_vali_result_all_dir)
exp = UNetExperiment(config=c, name='unet_test', n_epochs=c.n_epochs,
seed=42, globs=globals())
exp.run_test(setup=True)
c = get_config()
#download_dataset(dest_path=c.data_root_dir, dataset=c.dataset_name, id=c.google_drive_id)
if not exists(
os.path.join(os.path.join(c.data_root_dir, c.dataset_name),
'preprocessed')):
print('Preprocessing data. [STARTED]')
#preprocess_data(root_dir=os.path.join(c.data_root_dir, c.dataset_name), y_shape=c.patch_size, z_shape=c.patch_size)
#create_splits(output_dir=c.split_dir, image_dir=c.data_dir)
print('Preprocessing data. [DONE]')
else:
print(
'The data has already been preprocessed. It will not be preprocessed again. Delete the folder to enforce it.'
)
exp = UNetExperiment(
config=c,
name=c.name,
n_epochs=c.n_epochs,
seed=42,
append_rnd_to_name=c.append_rnd_string,
globs=globals(),
# visdomlogger_kwargs={"auto_start": c.start_visdom},
loggers={"visdom": ("visdom", {
"auto_start": c.start_visdom
})})
exp.run()
exp.run_test(setup=False)
#__solution
# We are splitting 60:20:20 for train: val and test for the dataset
split = {
'train': keys[0:int(max(keys) * 0.6)],
'val': keys[int(max(keys) * 0.6):int(max(keys) * 0.8)],
'test': keys[int(max(keys) * 0.8):max(keys)]
}
# Set up and run experiment
# TASK: Class UNetExperiment has missing pieces. Go to the file and fill them in
#__solution in UNetExperiment
exp = UNetExperiment(c, split, data)
# You could free up memory by deleting the dataset
# as it has been copied into loaders
# del dataset
# run training
exp.run()
# prep and run testing
# TASK: Test method is not complete. Go to the method and complete it
results_json = exp.run_test()
results_json["config"] = vars(c)
with open(os.path.join(exp.out_dir, "results.json"), 'w') as out_file:
json.dump(results_json, out_file, indent=2, separators=(',', ': '))
def run_on_dataset():
#models = os.listdir('./models/')
models = glob('./output_experiment/*/checkpoint/checkpoint_last.pth.tar')
metric_list = [
"Dice", "Hausdorff Distance", "Hausdorff Distance 95",
"Avg. Symmetric Surface Distance", "Avg. Surface Distance"
]
summary = pd.DataFrame(columns=['modelname'] + metric_list)
summary_mean = pd.DataFrame(columns=['modelname'] + metric_list)
for model_name in models:
# Load data
print('starting with ' + model_name)
exp = UNetExperiment(config=c,
name=c.name,
n_epochs=c.n_epochs,
seed=42,
append_rnd_to_name=c.append_rnd_string,
globs=globals())
exp.setup()
exp.test_data_loader.do_reshuffle = False
# Load checkpoint
checkpoint = torch.load(model_name)
exp.model.load_state_dict(checkpoint['model'])
# exp.model.eval() # done in UNetExperiment
exp.run_test(setup=False)
#TODO get metrics
# select interesting ones, add to pandas dataframe
json_scores = exp.scores
result_dict = json_scores[
"results"] #{"all": results, "mean": results_mean}
im_scores = result_dict['all']
model_res = {'modelname': model_name}
print('adding results to summary..')
for nr, im in enumerate(im_scores):
for cl in range(c.num_classes):
for m in metric_list:
model_res['class_' + str(cl) + '_imagenr_' + str(nr) +
'_' + m] = im[str(cl)][m]
summary = summary.append(model_res, ignore_index=True)
im_mean_scores = result_dict['mean']
model_res_mean = {'modelname': model_name}
for cl in range(c.num_classes):
for m in metric_list:
model_res_mean['class_' + str(cl) + '_' +
m] = im_mean_scores[str(cl)][m]
summary_mean = summary_mean.append(model_res_mean, ignore_index=True)
summary.to_csv('./output_experiment/summary_ims' + c.dataset_name +
'.csv')
summary_mean.to_csv('./output_experiment/summary_mean_' +
c.dataset_name + '.csv')
print('..summary updated')