def testOptions(self):
delegate_a = interpreter_wrapper.load_delegate(self._delegate_file)
lib = delegate_a._library
self.assertEqual(lib.get_num_delegates_created(), 1)
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
self.assertEqual(lib.get_num_delegates_invoked(), 0)
self.assertEqual(lib.get_options_counter(), 0)
delegate_b = interpreter_wrapper.load_delegate(self._delegate_file,
options={
'unused': False,
'options_counter': 2
})
lib = delegate_b._library
self.assertEqual(lib.get_num_delegates_created(), 2)
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
self.assertEqual(lib.get_num_delegates_invoked(), 0)
self.assertEqual(lib.get_options_counter(), 2)
del delegate_a
del delegate_b
self.assertEqual(lib.get_num_delegates_created(), 2)
self.assertEqual(lib.get_num_delegates_destroyed(), 2)
self.assertEqual(lib.get_num_delegates_invoked(), 0)
self.assertEqual(lib.get_options_counter(), 2)
def testOptions(self):
# TODO(b/137299813): Enable when we fix for mac
if sys.platform == 'darwin': return
delegate_a = interpreter_wrapper.load_delegate(self._delegate_file)
lib = delegate_a._library
self.assertEqual(lib.get_num_delegates_created(), 1)
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
self.assertEqual(lib.get_num_delegates_invoked(), 0)
self.assertEqual(lib.get_options_counter(), 0)
delegate_b = interpreter_wrapper.load_delegate(self._delegate_file,
options={
'unused': False,
'options_counter': 2
})
lib = delegate_b._library
self.assertEqual(lib.get_num_delegates_created(), 2)
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
self.assertEqual(lib.get_num_delegates_invoked(), 0)
self.assertEqual(lib.get_options_counter(), 2)
del delegate_a
del delegate_b
self.assertEqual(lib.get_num_delegates_created(), 2)
self.assertEqual(lib.get_num_delegates_destroyed(), 2)
self.assertEqual(lib.get_num_delegates_invoked(), 0)
self.assertEqual(lib.get_options_counter(), 2)
def testFail(self):
# TODO(b/137299813): Enable when we fix for mac
if sys.platform == 'darwin': return
with self.assertRaisesRegexp(
ValueError,
'Failed to load delegate from .*\nFail argument sent.'):
interpreter_wrapper.load_delegate(self._delegate_file,
options={'fail': 'fail'})
def testFail(self):
with self.assertRaisesRegex(
# Due to exception chaining in PY3, we can't be more specific here and
# check that the phrase 'Fail argument sent' is present.
ValueError,
'Failed to load delegate from'):
interpreter_wrapper.load_delegate(self._delegate_file,
options={'fail': 'fail'})
def testDestructionOrder(self):
"""Make sure internal _interpreter object is destroyed before delegate."""
self.skipTest('TODO(b/142136355): fix flakiness and re-enable')
# Track which order destructions were doned in
# TODO(b/137299813): Enable when we fix for mac
if sys.platform == 'darwin': return
destructions = []
def register_destruction(x):
destructions.append(x if isinstance(x, str) else x.decode('utf-8'))
return 0
# Make a wrapper for the callback so we can send this to ctypes
delegate = interpreter_wrapper.load_delegate(self._delegate_file)
# Make an interpreter with the delegate
interpreter = interpreter_wrapper.Interpreter(
model_path=resource_loader.get_path_to_datafile(
'testdata/permute_float.tflite'),
experimental_delegates=[delegate])
class InterpreterDestroyCallback(object):
def __del__(self):
register_destruction('interpreter')
interpreter._interpreter.stuff = InterpreterDestroyCallback()
# Destroy both delegate and interpreter
library = delegate._library
prototype = ctypes.CFUNCTYPE(ctypes.c_int, (ctypes.c_char_p))
library.set_destroy_callback(prototype(register_destruction))
del delegate
del interpreter
library.set_destroy_callback(None)
# check the interpreter was destroyed before the delegate
self.assertEqual(destructions, ['interpreter', 'test_delegate'])
def load(self, model_path, inputs=None, outputs=None):
self.sess = interpreter_wrapper.Interpreter(model_path=model_path)
#self.sess = interpreter_wrapper.Interpreter(model_path=model_path, experimental_delegates=[delegate])
if self.do_batches:
input_details = self.sess.get_input_details()
self.sess.resize_tensor_input(input_details[0]['index'],
(self.batch_size, 224, 224, 3))
# We have to load the delegate after resizing the input tensor for batches
if self.do_delegate:
print('Loading delegate... ' + os.getenv("NCORE_DELEGATE"))
delegate = interpreter_wrapper.load_delegate(
os.getenv("NCORE_DELEGATE"))
self.sess.add_delegates(experimental_delegates=[delegate])
self.sess.allocate_tensors()
# keep input/output name to index mapping
self.input2index = {
i["name"]: i["index"]
for i in self.sess.get_input_details()
}
self.output2index = {
i["name"]: i["index"]
for i in self.sess.get_output_details()
}
# keep input/output names
self.inputs = list(self.input2index.keys())
self.outputs = list(self.output2index.keys())
return self
def testMultipleInterpreters(self):
delegate = interpreter_wrapper.load_delegate(self._delegate_file)
lib = delegate._library
self.assertEqual(lib.get_num_delegates_created(), 1)
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
self.assertEqual(lib.get_num_delegates_invoked(), 0)
interpreter_a = interpreter_wrapper.Interpreter(
model_path=self._model_file, experimental_delegates=[delegate])
self.assertEqual(lib.get_num_delegates_created(), 1)
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
self.assertEqual(lib.get_num_delegates_invoked(), 1)
interpreter_b = interpreter_wrapper.Interpreter(
model_path=self._model_file, experimental_delegates=[delegate])
self.assertEqual(lib.get_num_delegates_created(), 1)
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
self.assertEqual(lib.get_num_delegates_invoked(), 2)
del delegate
del interpreter_a
self.assertEqual(lib.get_num_delegates_created(), 1)
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
self.assertEqual(lib.get_num_delegates_invoked(), 2)
del interpreter_b
self.assertEqual(lib.get_num_delegates_created(), 1)
self.assertEqual(lib.get_num_delegates_destroyed(), 1)
self.assertEqual(lib.get_num_delegates_invoked(), 2)
def testDestructionOrder(self):
"""Make sure internal _interpreter object is destroyed before delegate."""
# Track which order destructions were doned in
destructions = []
def register_destruction(x):
destructions.append(x)
return 0
# Make a wrapper for the callback so we can send this to ctypes
delegate = interpreter_wrapper.load_delegate(self._delegate_file)
prototype = ctypes.CFUNCTYPE(ctypes.c_int, (ctypes.c_char_p))
destroy_callback = prototype(register_destruction)
delegate._library.set_destroy_callback(destroy_callback)
# Make an interpreter with the delegate
interpreter = interpreter_wrapper.Interpreter(
model_path=resource_loader.get_path_to_datafile(
'testdata/permute_float.tflite'), experimental_delegates=[delegate])
class InterpreterDestroyCallback(object):
def __del__(self):
register_destruction('interpreter')
interpreter._interpreter.stuff = InterpreterDestroyCallback()
# Destroy both delegate and interpreter
del delegate
del interpreter
# check the interpreter was destroyed before the delegate
self.assertEqual(destructions, ['interpreter', 'test_delegate'])
def _TestInterpreter(self, model_path, options=None):
"""Test wrapper function that creates an interpreter with the delegate."""
# TODO(b/137299813): Enable when we fix for mac
if sys.platform == 'darwin': return
delegate = interpreter_wrapper.load_delegate(self._delegate_file,
options)
return interpreter_wrapper.Interpreter(
model_path=model_path, experimental_delegates=[delegate])
def model_eval(model_name, test_set):
interpreter_quant = tf.lite.Interpreter(
model_name,
experimental_delegates=[load_delegate('libedgetpu.so.1.0')])
interpreter_quant.allocate_tensors()
input_index = interpreter_quant.get_input_details()[0]["index"]
output_index = interpreter_quant.get_output_details()[0]["index"]
total_seen = 0
print(model_name)
model_prediction = []
for img, label in test_set:
print("image", total_seen)
total_seen += 1
interpreter_quant.set_tensor(input_index, img)
interpreter_quant.invoke()
predictions = interpreter_quant.get_tensor(output_index)
model_prediction.append(predictions)
global model_pool_prediction
model_pool_prediction[model_name] = model_prediction
def load(self, model_path, inputs=None, outputs=None):
print('Loading delegate... ' + os.getenv("NCORE_DELEGATE"))
# /n/scr_ncore/parvizp/Git/mlperf_inference/v0.5/classification_and_detection/ncore_py_delegate.so
delegate = interpreter_wrapper.load_delegate(
os.getenv("NCORE_DELEGATE"))
self.sess = interpreter_wrapper.Interpreter(
model_path=model_path, experimental_delegates=[delegate])
self.sess.allocate_tensors()
# keep input/output name to index mapping
self.input2index = {
i["name"]: i["index"]
for i in self.sess.get_input_details()
}
self.output2index = {
i["name"]: i["index"]
for i in self.sess.get_output_details()
}
# keep input/output names
self.inputs = list(self.input2index.keys())
self.outputs = list(self.output2index.keys())
return self
def testMultipleInterpreters(self):
# TODO(b/137299813): Enable when we fix for mac
if sys.platform == 'darwin': return
delegate = interpreter_wrapper.load_delegate(self._delegate_file)
lib = delegate._library
self.assertEqual(lib.get_num_delegates_created(), 1)
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
self.assertEqual(lib.get_num_delegates_invoked(), 0)
interpreter_a = interpreter_wrapper.Interpreter(
model_path=self._model_file, experimental_delegates=[delegate])
self.assertEqual(lib.get_num_delegates_created(), 1)
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
self.assertEqual(lib.get_num_delegates_invoked(), 1)
interpreter_b = interpreter_wrapper.Interpreter(
model_path=self._model_file, experimental_delegates=[delegate])
self.assertEqual(lib.get_num_delegates_created(), 1)
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
self.assertEqual(lib.get_num_delegates_invoked(), 2)
del delegate
del interpreter_a
self.assertEqual(lib.get_num_delegates_created(), 1)
self.assertEqual(lib.get_num_delegates_destroyed(), 0)
self.assertEqual(lib.get_num_delegates_invoked(), 2)
del interpreter_b
self.assertEqual(lib.get_num_delegates_created(), 1)
self.assertEqual(lib.get_num_delegates_destroyed(), 1)
self.assertEqual(lib.get_num_delegates_invoked(), 2)
def _TestInterpreter(self, model_path, options=None):
"""Test wrapper function that creates an interpreter with the delegate."""
delegate = interpreter_wrapper.load_delegate(self._delegate_file,
options)
return interpreter_wrapper.Interpreter(
model_path=model_path, experimental_delegates=[delegate])
def testFail(self):
with self.assertRaisesRegexp(ValueError, 'Failed to load delegate from .*'):
interpreter_wrapper.load_delegate(
self._delegate_file, options={'fail': 'fail'})
from tensorflow.lite.python.interpreter import Interpreter, load_delegate
# curl https://dl.google.com/coral/canned_models/mobilenet_v1_1.0_224_quant_edgetpu.tflite --output mobilenet.tflite
# wget https://github.com/chengyehwang/termux_bootstrap/raw/main/libtensorflowlite_gpu_delegate.so
model = 'mobilenet.tflite'
interpreter = Interpreter(model)
interpreter = Interpreter(
model,
experimental_delegates=[
load_delegate('./libtensorflowlite_gpu_delegate.so')
])
# there is a runtime tf-lite interpreter and a python3 tf-lite interpreter, we wil include both in case
# the runtime-tflite is (to my knowledge) faster
# depending on if we are using the TPU or not, we will need to issue different commands to start
# the interpreter
tf_pkg = importlib.util.find_spec('tflite_runtime')
if use_TPU:
if tf_pkg is None:
from tensorflow.lite.python.interpreter import Interpreter
from tensorflow.lite.python.interpreter import load_delegate
else:
from tflite_runtime.interpreter import Interpreter
from tflite_runtime.interpreter import load_delegate
interpreter = Interpreter(
model_path=tf_model.graph,
experimental_delegates=[load_delegate('libedgetpu.so.1.0')])
else:
if tf_pkg is None:
from tensorflow.lite.python.interpreter import Interpreter
else:
from tflite_runtime.interpreter import Interpreter
interpreter = Interpreter(model_path=tf_model.graph)
interpreter.allocate_tensors()
# Get model details
input_tensor_details = interpreter.get_input_details()
output_tensor_details = interpreter.get_output_details()
height = input_tensor_details[0]['shape'][1]
width = input_tensor_details[0]['shape'][2]
