del d5, e4
d3 = F.relu(self.bnd3(self.dc3(d4)))
del d4
d2 = F.relu(self.bnd2(self.dc2(F.concat([e2, d3]))))
del d3, e2
d1 = F.relu(self.bnd1(self.dc1(d2)))
del d2
d0 = self.dc0(F.concat([e0, d1]))
return d0
google_net = GoogLeNet()
paintschainer = UNET()
chainer.serializers.load_npz('google_net.net', google_net)
chainer.serializers.load_npz('paintschainer.net', paintschainer)
session = K.get_session()
EPS = 1e-12
lr = 1e-6
beta1 = 0.5
with tf.variable_scope("generator"):
base_generator = load_model('base_generator.net')
sketch_ref_input_448 = tf.placeholder(dtype=tf.float32, shape=(None, None, None, 1))
local_hint_input_448 = tf.placeholder(dtype=tf.float32, shape=(None, None, None, 3))
hint_s57c64_0 = tf.placeholder(dtype=tf.float32, shape=(None, 64))
hint_s29c192_0 = tf.placeholder(dtype=tf.float32, shape=(None, 192))
hint_s29c256_0 = tf.placeholder(dtype=tf.float32, shape=(None, 256))
hint_s29c320_0 = tf.placeholder(dtype=tf.float32, shape=(None, 320))
hint_s15c576_0 = tf.placeholder(dtype=tf.float32, shape=(None, 576))
hint_s15c576_1 = tf.placeholder(dtype=tf.float32, shape=(None, 576))
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
loaded_model.load_weights(model_weights_filepath)
#loaded_model = load_model('/home/neeraj/keras_jobs/pattern_biased_polka_new/final_pattern_biased_polka_new_inception_v3.h5')
print("loaded model from disk")
if os.path.isdir("./export"):
shutil.rmtree("./export")
model = loaded_model
# export model
export_path = "export/pattern/1"
builder = saved_model_builder.SavedModelBuilder(export_path)
print(model.input)
print(model.output)
signature = predict_signature_def(inputs={'images': model.input},
outputs={'scores': model.output})
with K.get_session() as sess:
builder.add_meta_graph_and_variables(
sess=sess,
tags=[tag_constants.SERVING],
signature_def_map={'predict': signature})
builder.save()
