def create_homographynet_mobilenet_v2(weights_file=None, input_shape=(128, 128, 2), num_pts=4, alpha=1.0,
pooling=None, **kwargs):
"""
Use fully convolutional structure to support any input shape
"""
base_model = mobilenet_v2.MobileNetV2(input_shape=input_shape, include_top=False, weights=None, alpha=alpha)
# The output shape just before the pooling and dense layers is: (4, 4, 1024)
x = base_model.output
if pooling == 'max':
x = GlobalMaxPool2D(name='global_max_pool2d')(x) # to support any input shape
elif pooling == 'avg':
x = GlobalAveragePooling2D(name='global_avg_pool2d')(x) # to support any input shape
else:
x = Flatten(name='flatten')(x)
x = Dropout(rate=0.5, name='dropout')(x)
x = Dense(2*num_pts, name='preds')(x)
model = Model(inputs=base_model.input, outputs=x, name='homographynet_mobilenet_v2')
if weights_file is not None:
model.load_weights(weights_file)
return model
def MobileNetV2(*args, **kwargs):
return mobilenet_v2.MobileNetV2(*args, **kwargs)
