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Fix errors in model_training.py #39

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Fix errors in model_training.py #39

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10 changes: 5 additions & 5 deletions pathflowai/model_training.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,7 +81,7 @@ def return_transformer(training_opts):
transform_opts=dict(patch_size = training_opts['patch_resize'], mean=norm_dict['mean'], std=norm_dict['std'], resize=True, transform_platform=training_opts['transform_platform'] if not training_opts['segmentation'] else 'albumentations', user_transforms=training_opts['user_transforms'])

transformers = get_data_transforms(**transform_opts)
return dataset_df,dataset_opts,transformers
return dataset_df,dataset_opts,transformers,transform_opts

def return_datasets(training_opts,dataset_df,transformers):
datasets= {set: DynamicImageDataset(dataset_df, set, training_opts['patch_info_file'], transformers, training_opts['input_dir'], training_opts['target_names'], training_opts['pos_annotation_class'], segmentation=training_opts['segmentation'], patch_size=training_opts['patch_size'], fix_names=training_opts['fix_names'], other_annotations=training_opts['other_annotations'], target_segmentation_class=training_opts['target_segmentation_class'][0] if set=='train' else -1, target_threshold=training_opts['target_threshold'][0], oversampling_factor=training_opts['oversampling_factor'][0] if set=='train' else 1, n_segmentation_classes=training_opts['num_targets'],gdl=training_opts['loss_fn']=='gdl',mt_bce=training_opts['mt_bce'], classify_annotations=training_opts['classify_annotations'],dilation_jitter=training_opts['dilation_jitter'] if set == 'train' else {}) for set in ['train','val','test']}
Expand Down Expand Up @@ -124,7 +124,7 @@ def return_datasets(training_opts,dataset_df,transformers):

if training_opts['external_test_db'] and training_opts['external_test_dir']:
datasets['test'].update_dataset(input_dir=training_opts['external_test_dir'],new_db=training_opts['external_test_db'],prediction_basename=training_opts['prediction_basename'])
return datasets,training_opts,transform_opts
return datasets,training_opts

#@pysnooper.snoop('train_model.log')
def train_model_(training_opts):
Expand All @@ -141,14 +141,14 @@ def train_model_(training_opts):

model = return_model(training_opts)

dataset_df,dataset_opts,transformers=return_transformer(training_opts)
dataset_df,dataset_opts,transformers,transform_opts=return_transformer(training_opts)

if training_opts['extract_embedding'] and training_opts['npy_file']:
dataset=NPYDataset(training_opts['patch_info_file'],training_opts['patch_size'],training_opts['npy_file'],transformers["test"])
dataset.embed(model,training_opts['batch_size'],training_opts['prediction_output_dir'])
exit()

datasets,training_opts,transform_opts=return_datasets(training_opts,dataset_df,transformers)
datasets,training_opts=return_datasets(training_opts,dataset_df,transformers)

if training_opts['num_training_images_epoch']>0:
num_train_batches = min(training_opts['num_training_images_epoch'],len(datasets['train']))//training_opts['batch_size']
Expand All @@ -162,7 +162,7 @@ def train_model_(training_opts):

if training_opts['run_test']: run_test(dataloaders['train'])

model_trainer_opts=return_trainer_opts(model,training_opts,dataloders,num_train_batches)
model_trainer_opts=return_trainer_opts(model,training_opts,dataloaders,num_train_batches)

if not training_opts['predict']:

Expand Down