def basic_model_properties(self, cf, variable_input_size):
# Define the input size, loss and metrics
if cf.dataset.class_mode == 'categorical':
if K.image_dim_ordering() == 'th':
in_shape = (cf.dataset.n_channels, cf.target_size_train[0],
cf.target_size_train[1])
else:
in_shape = (cf.target_size_train[0], cf.target_size_train[1],
cf.dataset.n_channels)
loss = 'categorical_crossentropy'
metrics = ['accuracy']
elif cf.dataset.class_mode == 'detection':
if 'yolo' in cf.model_name:
in_shape = (cf.dataset.n_channels, cf.target_size_train[0],
cf.target_size_train[1])
loss = YOLOLoss(in_shape, cf.dataset.n_classes,
cf.dataset.priors)
metrics = [
YOLOMetrics(in_shape, cf.dataset.n_classes,
cf.dataset.priors)
]
elif cf.model_name == 'ssd':
in_shape = (cf.target_size_train[0], cf.target_size_train[1],
cf.dataset.n_channels)
loss = SSDLoss(cf.dataset.n_classes,
neg_pos_ratio=2.0).compute_loss
metrics = [] # TODO: There is no metrics for the ssd model
else:
raise ValueError(
'Uknown "' + cf.model_name + '" name for the ' +
cf.dataset.class_mode + ' problem type.'
'Only is implemented for: {yolo, tiny-yolo, ssd}')
elif cf.dataset.class_mode == 'segmentation':
if K.image_dim_ordering() == 'th':
if variable_input_size:
in_shape = (cf.dataset.n_channels, None, None)
else:
in_shape = (cf.dataset.n_channels, cf.target_size_train[0],
cf.target_size_train[1])
else:
if variable_input_size:
in_shape = (None, None, cf.dataset.n_channels)
else:
in_shape = (cf.target_size_train[0],
cf.target_size_train[1], cf.dataset.n_channels)
loss = cce_flatt(cf.dataset.void_class, cf.dataset.cb_weights)
metrics = [IoU(cf.dataset.n_classes, cf.dataset.void_class)]
# if cf.model_name == 'fcn8':
# loss = cce_flatt(cf.dataset.void_class, cf.dataset.cb_weights)
# metrics = [IoU(cf.dataset.n_classes, cf.dataset.void_class)]
#
# elif 'segnet' in cf.model_name:
# loss = 'categorical_crossentropy'
# metrics = []
#
# else:
# raise ValueError('Uknown "'+cf.model_name+'" name for the '+cf.dataset.class_mode+' problem type.'
# 'Only is implemented for: {fc8, segnet}')
else:
raise ValueError('Unknown problem type')
return in_shape, loss, metrics
def basic_model_properties(self, cf, variable_input_size):
# Define the input size, loss and metrics
if cf.dataset.class_mode == 'categorical':
if K.image_dim_ordering() == 'th':
in_shape = (cf.dataset.n_channels, cf.target_size_train[0],
cf.target_size_train[1])
else:
in_shape = (cf.target_size_train[0], cf.target_size_train[1],
cf.dataset.n_channels)
loss = 'categorical_crossentropy'
metrics = ['accuracy']
elif cf.dataset.class_mode == 'detection':
in_shape = (cf.dataset.n_channels, cf.target_size_train[0],
cf.target_size_train[1])
# TODO detection : check model, different detection nets may have different losses and metrics
if cf.model_name == 'retinanet':
loss = YOLOLoss(in_shape, cf.dataset.n_classes,
cf.dataset.priors)
metrics = [
YOLOMetrics(in_shape,
cf.dataset.n_classes,
cf.dataset.priors,
name='avg_recall'),
YOLOMetrics(in_shape,
cf.dataset.n_classes,
cf.dataset.priors,
name='avg_iou')
]
else:
loss = YOLOLoss(in_shape, cf.dataset.n_classes,
cf.dataset.priors)
metrics = [
YOLOMetrics(in_shape,
cf.dataset.n_classes,
cf.dataset.priors,
name='avg_recall'),
YOLOMetrics(in_shape,
cf.dataset.n_classes,
cf.dataset.priors,
name='avg_iou')
]
elif cf.dataset.class_mode == 'segmentation':
if K.image_dim_ordering() == 'th':
if variable_input_size:
in_shape = (cf.dataset.n_channels, None, None)
else:
in_shape = (cf.dataset.n_channels, cf.target_size_train[0],
cf.target_size_train[1])
else:
if variable_input_size:
in_shape = (None, None, cf.dataset.n_channels)
else:
in_shape = (cf.target_size_train[0],
cf.target_size_train[1], cf.dataset.n_channels)
#loss = cce_flatt(cf.dataset.void_class, cf.dataset.cb_weights)
#metrics = [IoU(cf.dataset.n_classes, cf.dataset.void_class)]
loss = 'categorical_crossentropy'
metrics = ['accuracy', jaccard_coef]
else:
raise ValueError('Unknown problem type')
return in_shape, loss, metrics