from keras.models import Model
from keras.layers import Dense, Input, Dropout, Reshape, merge
from keras.applications import ResNet50
def SDPN(summary=True):
"""
Create and return Semantic-aware Dense Prediction Network.
Parameters
----------
summary : bool
If True, network summary is printed to stout.
Returns
-------
model : keras Model
Model of SDPN
"""
input_coords = Input(shape=(4,))
input_crop = Input(shape=(3, 224, 224))
# extract feature from image crop
resnet = ResNet50(include_top=False, weights='imagenet')
for layer in resnet.layers: # set resnet as non-trainable
layer.trainable = False
crop_encoded = resnet(input_crop) # shape of `crop_encoded` is 2018x1x1
crop_encoded = Reshape(target_shape=(2048,))(crop_encoded)
# encode input coordinates
h = Dense(256, activation='relu')(input_coords)
h = Dropout(p=0.25)(h)
h = Dense(256, activation='relu')(h)
h = Dropout(p=0.25)(h)
h = Dense(256, activation='relu')(h)
# merge feature vectors from crop and coords
merged = merge([crop_encoded, h], mode='concat')
# decoding into output coordinates
h = Dense(1024, activation='relu')(merged)
h = Dropout(p=0.25)(h)
h = Dense(1024, activation='relu')(h)
h = Dropout(p=0.25)(h)
h = Dense(512, activation='relu')(h)
h = Dropout(p=0.25)(h)
h = Dense(256, activation='relu')(h)
h = Dropout(p=0.25)(h)
h = Dense(128, activation='relu')(h)
h = Dropout(p=0.25)(h)
output_coords = Dense(4, activation='tanh')(h)
model = Model(input=[input_coords, input_crop], output=output_coords)
if summary:
model.summary()
return model
