def predict(config_path,
model_path=None,
labels_json=None,
data_dir=None,
group=None):
configs = tools.read_configs(config_path)
model_path = tools.str2path(model_path or configs.get('model_path'))
data_dir = tools.str2path(data_dir or configs.get('data_dir'))
labels_json = tools.str2path(labels_json or configs.get('labels_json'))
group = group or configs.get('group')
model_builder = ModelBuilder(configs,
mode='predict',
model_path=model_path.as_posix())
model = model_builder.build()
pred_gen = DataGenerator(configs,
image_dir=data_dir,
labels_json=labels_json,
group=group,
mode='predict')
correct = 0
false = 0
for x, y, xnames in pred_gen.flow_from_labels():
predictions = model.predict(x, verbose=1)
pred_cls_ids = np.argmax(predictions, axis=1)
tp = np.sum(pred_cls_ids == y)
error = len(y) - tp
correct += tp
false += error
accuracy = (1 - false / correct) * 100
print(f"Correct: {correct}")
print(f'False: {false}')
print(f'Accuracy: {accuracy}')
def train(config_path,
train_dir=None,
val_dir=None,
output_dir=None,
train_labels_json=None,
val_labels_json=None,
group=None,
model_name=None,
model_suffix=None):
np.random.seed(42) # for reproducibility
logger = logging.getLogger('root')
configs = tools.read_configs(config_path)
train_dir = tools.str2path(train_dir or configs['train_dir'])
val_dir = tools.str2path(val_dir or configs['val_dir'])
train_labels_json = tools.str2path(train_labels_json
or configs['train_labels_json'])
val_labels_json = tools.str2path(val_labels_json
or configs['val_labels_json'])
output_dir = tools.str2path(output_dir or configs['output_dir'])
group = group or configs['group']
model_name = model_name or configs['model_name']
model_suffix = model_suffix or configs['model_suffix']
train_counts = configs.get('train_class_counts')
val_counts = configs.get('val_class_counts')
output_dir.mkdir(exist_ok=True)
model_out_name = f'{model_name}_{group}_{model_suffix}.h5'
model_path = output_dir / model_out_name
train_gen = DataGenerator(configs, train_dir, train_labels_json, 'train',
group, train_counts)
val_gen = DataGenerator(configs, val_dir, val_labels_json, 'val', group,
val_counts)
epochs = configs['epochs']
classes = configs['network_parameters']['classes']
loss = configs['loss']
optimizer = configs['optimizer']
model_builder = ModelBuilder(configs, 'train', model_name, model_path,
classes, loss, optimizer)
model = model_builder.build()
checkpoint = keras.callbacks.ModelCheckpoint(model_path.as_posix(),
monitor='loss',
verbose=1,
save_best_only=True,
save_weights_only=False,
mode='min')
logger.info(f"Training model {model_out_name} for {epochs} epochs")
logger.info(f'Class weights: {train_gen.class_weights}')
model.fit_generator(generator=train_gen.flow_generator,
steps_per_epoch=train_gen.steps_per_epoch,
epochs=epochs,
verbose=1,
class_weight=train_gen.class_weights,
callbacks=[checkpoint],
validation_data=val_gen.flow_generator,
validation_steps=val_gen.steps_per_epoch)
