def SSD512_resnet(input_shape=(512, 512, 3), num_classes=21, softmax=True):
# TODO: it does not converge!
x = input_tensor = Input(shape=input_shape)
source_layers = ssd512_resnet_body(x)
# Add multibox head for classification and regression
num_priors = [4, 6, 6, 6, 6, 4, 4]
normalizations = [20, 20, 20, 20, 20, 20, 20]
output_tensor = multibox_head(source_layers, num_priors, num_classes,
normalizations, softmax)
model = Model(input_tensor, output_tensor)
model.num_classes = num_classes
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
# stay compatible with caffe models
model.aspect_ratios = [[1, 2, 1 / 2], [1, 2, 1 / 2, 3, 1 / 3],
[1, 2, 1 / 2, 3, 1 / 3], [1, 2, 1 / 2, 3, 1 / 3],
[1, 2, 3, 1 / 2, 1 / 3], [1, 2, 1 / 2],
[1, 2, 1 / 2]]
model.minmax_sizes = [(35, 76), (76, 153), (153, 230), (230, 307),
(307, 384), (384, 460), (460, 537)]
model.steps = [8, 16, 32, 64, 128, 256, 512]
model.special_ssd_boxes = True
return model
def SL512_resnet(input_shape=(512, 512, 3), activation='relu', softmax=True):
# body
x = input_tensor = Input(shape=input_shape)
source_layers = ssd512_resnet_body(x, activation=activation)
# Add multibox head for classification and regression
num_priors = [1, 1, 1, 1, 1, 1, 1]
normalizations = [20, 20, 20, 20, 20, 20, 20]
output_tensor = multibox_head(source_layers, num_priors, normalizations, softmax)
model = Model(input_tensor, output_tensor)
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
return model
