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def testTFGraph(self):
_ = get_tf_graph()
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
path = f'{time.time()}.pb'
save_model(sess, path, output=['output'])
model = load_model(path)
os.remove(path)
con = self.get_client()
con.modelset(
'tfmodel', Backend.tf, Device.cpu, model,
input=['input'], output=['output'])
con.tensorset('a', Tensor.scalar(DType.float, 2))
con.modelrun('tfmodel', ['a'], 'c')
tensor = con.tensorget('c')
self.assertEqual([13], tensor.value)
def init(config):
host = config['server'].split(':')[0]
port = config['server'].split(':')[1]
init.con = rai.Client(host=host, port=port)
graph = raimodel.Model.load(config['modelpath'])
inputs = ['images']
outputs = ['output']
init.con.modelset(
'graph', rai.Backend.tf, rai.Device.cpu, graph,
input=inputs, output=outputs)
image, init.img_class = get_one_image()
init.image = rai.BlobTensor.from_numpy(image)
import numpy as np
from redisai import Client, DType, Device, Backend
import ml2rt
client = Client()
client.tensorset('x', [2, 3], dtype=DType.float)
t = client.tensorget('x')
print(t.value)
model = ml2rt.load_model('test/testdata/graph.pb')
tensor1 = np.array([2, 3], dtype=np.float)
client.tensorset('a', tensor1)
client.tensorset('b', (12, 10), dtype=np.float)
client.modelset('m', Backend.tf,
Device.cpu,
inputs=['a', 'b'],
outputs='mul',
data=model)
client.modelrun('m', ['a', 'b'], ['mul'])
print(client.tensorget('mul'))
# Try with a script
script = ml2rt.load_script('test/testdata/script.txt')
client.scriptset('ket', Device.cpu, script)
client.scriptrun('ket', 'bar', inputs=['a', 'b'], outputs='c')