def get_tpp_phocnet(self, word_image_lmdb_path, phoc_lmdb_path, phoc_size, tpp_levels=5,
generate_deploy=False):
'''
Returns a NetSpec definition of the TPP-PHOCNet. The definition can then be transformed
into a protobuffer message by casting it into a str.
'''
n = NetSpec()
# Data
self.set_phocnet_data(n=n, generate_deploy=generate_deploy,
word_image_lmdb_path=word_image_lmdb_path,
phoc_lmdb_path=phoc_lmdb_path)
# Conv Part
self.set_phocnet_conv_body(n=n, relu_in_place=True)
# FC Part
n.tpp5 = L.TPP(n.relu4_3, tpp_param=dict(pool=P.TPP.MAX, pyramid_layer=range(1, tpp_levels + 1), engine=self.spp_engine))
n.fc6, n.relu6, n.drop6 = self.fc_relu(bottom=n.tpp5, layer_size=4096,
dropout_ratio=0.5, relu_in_place=True)
n.fc7, n.relu7, n.drop7 = self.fc_relu(bottom=n.drop6, layer_size=4096,
dropout_ratio=0.5, relu_in_place=True)
n.fc8 = L.InnerProduct(n.drop7, num_output=phoc_size,
weight_filler=dict(type=self.initialization),
bias_filler=dict(type='constant'))
n.sigmoid = L.Sigmoid(n.fc8, include=dict(phase=self.phase_test))
# output part
if not generate_deploy:
n.silence = L.Silence(n.sigmoid, ntop=0, include=dict(phase=self.phase_test))
n.loss = L.SigmoidCrossEntropyLoss(n.fc8, n.phocs)
return n.to_proto()
