def check_remote(target_edgetpu=False):
tflite_model_path = get_tflite_model_path(target_edgetpu)
# inference via tflite interpreter python apis
interpreter = init_interpreter(tflite_model_path, target_edgetpu)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
input_shape = input_details[0]["shape"]
tflite_input = np.array(np.random.random_sample(input_shape),
dtype=np.uint8)
interpreter.set_tensor(input_details[0]["index"], tflite_input)
interpreter.invoke()
tflite_output = interpreter.get_tensor(output_details[0]["index"])
# inference via remote tvm tflite runtime
server = rpc.Server("localhost")
remote = rpc.connect(server.host, server.port)
ctx = remote.cpu(0)
with open(tflite_model_path, "rb") as model_fin:
runtime = tflite_runtime.create(model_fin.read(), ctx)
runtime.set_input(0, tvm.nd.array(tflite_input, ctx))
runtime.invoke()
out = runtime.get_output(0)
np.testing.assert_equal(out.asnumpy(), tflite_output)
def check_remote():
tflite_fname = "model.tflite"
tflite_model = create_tflite_model()
temp = util.tempdir()
tflite_model_path = temp.relpath(tflite_fname)
open(tflite_model_path, 'wb').write(tflite_model)
# inference via tflite interpreter python apis
interpreter = tflite.Interpreter(model_path=tflite_model_path)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
input_shape = input_details[0]['shape']
tflite_input = np.array(np.random.random_sample(input_shape),
dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], tflite_input)
interpreter.invoke()
tflite_output = interpreter.get_tensor(output_details[0]['index'])
# inference via remote tvm tflite runtime
server = rpc.Server("localhost")
remote = rpc.connect(server.host, server.port)
ctx = remote.cpu(0)
a = remote.upload(tflite_model_path)
with open(tflite_model_path, 'rb') as model_fin:
runtime = tflite_runtime.create(model_fin.read(), remote.cpu(0))
runtime.set_input(0, tvm.nd.array(tflite_input, remote.cpu(0)))
runtime.invoke()
out = runtime.get_output(0)
np.testing.assert_equal(out.asnumpy(), tflite_output)
def check_remote(server):
remote = rpc.connect(server.host, server.port)
a = remote.upload(tflite_model_path)
with open(tflite_model_path, "rb") as model_fin:
runtime = tflite_runtime.create(model_fin.read(), remote.cpu(0))
runtime.set_input(0, tvm.nd.array(tflite_input, remote.cpu(0)))
runtime.invoke()
out = runtime.get_output(0)
np.testing.assert_equal(out.numpy(), tflite_output)
def test_remote():
if not tvm.runtime.enabled("tflite"):
print("skip because tflite runtime is not enabled...")
return
if not tvm.get_global_func("tvm.tflite_runtime.create", True):
print("skip because tflite runtime is not enabled...")
return
try:
import tensorflow as tf
except ImportError:
print("skip because tensorflow not installed...")
return
tflite_fname = "model.tflite"
tflite_model = _create_tflite_model()
temp = utils.tempdir()
tflite_model_path = temp.relpath(tflite_fname)
open(tflite_model_path, "wb").write(tflite_model)
# inference via tflite interpreter python apis
interpreter = tf.lite.Interpreter(model_path=tflite_model_path)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
input_shape = input_details[0]["shape"]
tflite_input = np.array(np.random.random_sample(input_shape),
dtype=np.float32)
interpreter.set_tensor(input_details[0]["index"], tflite_input)
interpreter.invoke()
tflite_output = interpreter.get_tensor(output_details[0]["index"])
# inference via remote tvm tflite runtime
server = rpc.Server("localhost")
remote = rpc.connect(server.host, server.port)
ctx = remote.cpu(0)
a = remote.upload(tflite_model_path)
with open(tflite_model_path, "rb") as model_fin:
runtime = tflite_runtime.create(model_fin.read(), remote.cpu(0))
runtime.set_input(0, tvm.nd.array(tflite_input, remote.cpu(0)))
runtime.invoke()
out = runtime.get_output(0)
np.testing.assert_equal(out.asnumpy(), tflite_output)
server.terminate()
def test_local():
if not tvm.runtime.enabled("tflite"):
print("skip because tflite runtime is not enabled...")
return
if not tvm.get_global_func("tvm.tflite_runtime.create", True):
print("skip because tflite runtime is not enabled...")
return
try:
import tensorflow as tf
except ImportError:
print('skip because tensorflow not installed...')
return
tflite_fname = "model.tflite"
tflite_model = _create_tflite_model()
temp = util.tempdir()
tflite_model_path = temp.relpath(tflite_fname)
open(tflite_model_path, 'wb').write(tflite_model)
# inference via tflite interpreter python apis
interpreter = tf.lite.Interpreter(model_path=tflite_model_path)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
input_shape = input_details[0]['shape']
tflite_input = np.array(np.random.random_sample(input_shape), dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], tflite_input)
interpreter.invoke()
tflite_output = interpreter.get_tensor(output_details[0]['index'])
# inference via tvm tflite runtime
with open(tflite_model_path, 'rb') as model_fin:
runtime = tflite_runtime.create(model_fin.read(), tvm.cpu(0))
runtime.set_input(0, tvm.nd.array(tflite_input))
runtime.invoke()
out = runtime.get_output(0)
np.testing.assert_equal(out.asnumpy(), tflite_output)
