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 _init_from_converter(self,
options: QuantizationDebugOptions,
converter: TFLiteConverter,
calibrated_model: Optional[bytes] = None,
float_model: Optional[bytes] = None) -> None:
"""Convert the model and apply options.
Converts the quantized model and initializes a quantized model interpreter
with the quantized model. Returns a float model interpreter if float model
is provided.
Args:
options: a QuantizationDebugOptions object.
converter: an initialized tf.lite.TFLiteConverter.
calibrated_model: Calibrated model bytes.
float_model: Float model bytes.
"""
self.quant_model = convert.mlir_quantize(
calibrated_model,
disable_per_channel=converter._experimental_disable_per_channel, # pylint: disable=protected-access
fully_quantize=options.fully_quantize,
enable_numeric_verify=True,
denylisted_ops=options.denylisted_ops,
denylisted_nodes=options.denylisted_nodes)
self._quant_interpreter = _interpreter.Interpreter(
model_content=self.quant_model)
self._float_interpreter = None
if float_model is not None:
self._float_interpreter = _interpreter.Interpreter(
model_content=float_model)
def load_eval_model(model_path):
# support of tflite model
if model_path.endswith('.tflite'):
from tensorflow.lite.python import interpreter as interpreter_wrapper
model = interpreter_wrapper.Interpreter(model_path=model_path)
model.allocate_tensors()
model_format = 'TFLITE'
# support of MNN model
elif model_path.endswith('.mnn'):
model = MNN.Interpreter(model_path)
model_format = 'MNN'
# support of TF 1.x frozen pb model
elif model_path.endswith('.pb'):
model = load_graph(model_path)
model_format = 'PB'
# normal keras h5 model
elif model_path.endswith('.h5'):
model = load_model(model_path, compile=False)
model_format = 'H5'
K.set_learning_phase(0)
else:
raise ValueError('invalid model file')
return model, model_format
def _test_convert_result(inputs,
fpath,
mge_result,
max_err,
nhwc=True,
nhwc2=True,
disable_nhwc=False):
if nhwc and inputs.ndim == 4:
inputs = inputs.transpose((0, 2, 3, 1))
mge_to_tflite(
fpath + ".mge",
output=tmp_file + ".tflite",
mtk=False,
disable_nhwc=disable_nhwc,
)
tfl_model = interpreter.Interpreter(model_path=tmp_file + ".tflite")
tfl_model.allocate_tensors()
input_details = tfl_model.get_input_details()
tfl_model.set_tensor(input_details[0]["index"], inputs)
tfl_model.invoke()
pred_tfl = tfl_model.tensor(tfl_model.get_output_details()[0]["index"])()
if nhwc2 and pred_tfl.ndim == 4:
pred_tfl = pred_tfl.transpose((0, 3, 1, 2))
assert pred_tfl.shape == mge_result.shape
assert pred_tfl.dtype == mge_result.dtype
np.testing.assert_allclose(pred_tfl, mge_result, atol=max_err)
print("success!")
def load_eval_model(model_path):
# support of tflite model
if model_path.endswith('.tflite'):
from tensorflow.lite.python import interpreter as interpreter_wrapper
model = interpreter_wrapper.Interpreter(model_path=model_path)
model.allocate_tensors()
model_format = 'TFLITE'
# support of MNN model
elif model_path.endswith('.mnn'):
model = MNN.Interpreter(model_path)
model_format = 'MNN'
# support of TF 1.x frozen pb model
elif model_path.endswith('.pb'):
model = load_graph(model_path)
model_format = 'PB'
# support of ONNX model
elif model_path.endswith('.onnx'):
model = onnxruntime.InferenceSession(model_path)
model_format = 'ONNX'
# normal keras h5 model
elif model_path.endswith('.h5'):
custom_object_dict = get_custom_objects()
model = load_model(model_path, compile=False, custom_objects=custom_object_dict)
model_format = 'H5'
K.set_learning_phase(0)
else:
raise ValueError('invalid model file')
return model, model_format
def evaluate_tflite_model(tflite_model, input_ndarrays):
"""Evaluates the provided tf.lite model with the given input ndarrays.
Args:
tflite_model: bytes. The serialized tf.lite model.
input_ndarrays: A list of NumPy arrays to feed as input to the model.
Returns:
A list ndarrays produced by the model.
Raises:
ValueError: If the number of input arrays does not match the number of
inputs the model expects.
"""
the_interpreter = interpreter.Interpreter(model_content=tflite_model)
the_interpreter.allocate_tensors()
input_details = the_interpreter.get_input_details()
output_details = the_interpreter.get_output_details()
if len(input_details) != len(input_ndarrays):
raise ValueError('Wrong number of inputs: provided=%s, '
'input_details=%s output_details=%s' %
(input_ndarrays, input_details, output_details))
for input_tensor, data in zip(input_details, input_ndarrays):
the_interpreter.set_tensor(input_tensor['index'], data)
the_interpreter.invoke()
return [
the_interpreter.get_tensor(details['index'])
for details in output_details
]
def testInvokeBeforeReady(self):
interpreter = interpreter_wrapper.Interpreter(
model_path=resource_loader.get_path_to_datafile(
'testdata/permute_float.tflite'))
with self.assertRaisesRegexp(
RuntimeError, 'Invoke called on model that is not ready'):
interpreter.invoke()
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 extractor_predict(saved_model, image_file):
interpreter = interpreter_wrapper.Interpreter(model_path=saved_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# NxHxWxC, H:1, W:2
height = input_details[0]['shape'][1]
width = input_details[0]['shape'][2]
img = image.load_img(image_file, target_size=(height, width))
img = image.img_to_array(img)
# check the type of the input tensor
if input_details[0]['dtype'] == np.float32:
#img = preprocess_input(img)
#img = img / 255.
img = img / 127.5 - 1
elif input_details[0]['dtype'] == np.uint8:
img = img.astype(np.uint8)
input_data = np.expand_dims(img, axis=0)
# Predict!
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
return output_data[0]
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--model', help='File path of .tflite file.', required=True)
parser.add_argument(
'--labels', help='File path of labels file.', required=True)
args = parser.parse_args()
cv2.setUseOptimized(True)
client = mqtt.Client()
client.connect("127.0.0.1", 1883, 600)
labels = load_labels(args.labels)
interpreter = ip.Interpreter(args.model)
interpreter.allocate_tensors()
_, height, width, _ = interpreter.get_input_details()[0]['shape']
cap = cv2.VideoCapture("rtsp://192.168.1.164:554/user=admin&password=&channel=1&stream=1.sdp?")
while True:
ret, image_np = cap.read()
if ret == False:
break;
image = Image.fromarray(image_np.astype('uint8')).convert('RGB').resize((width, height), Image.ANTIALIAS)
results = classify_image(interpreter, image)
label_id, prob = results[0]
output = {}
prob = "scores %.f%%" % (prob * 100)
output[labels[label_id]] = prob
obj_ret = json.dumps(output)
print("output ", output)
client.publish('object_detection', obj_ret, qos=0)
cap.release()
def testFloat(self):
interpreter = interpreter_wrapper.Interpreter(
model_path=resource_loader.get_path_to_datafile(
'testdata/permute_float.tflite'))
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
self.assertEqual(1, len(input_details))
self.assertEqual('input', input_details[0]['name'])
self.assertEqual(np.float32, input_details[0]['dtype'])
self.assertTrue(([1, 4] == input_details[0]['shape']).all())
self.assertEqual((0.0, 0), input_details[0]['quantization'])
output_details = interpreter.get_output_details()
self.assertEqual(1, len(output_details))
self.assertEqual('output', output_details[0]['name'])
self.assertEqual(np.float32, output_details[0]['dtype'])
self.assertTrue(([1, 4] == output_details[0]['shape']).all())
self.assertEqual((0.0, 0), output_details[0]['quantization'])
test_input = np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32)
expected_output = np.array([[4.0, 3.0, 2.0, 1.0]], dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], test_input)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
self.assertTrue((expected_output == output_data).all())
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 testUint8(self):
model_path = resource_loader.get_path_to_datafile(
'testdata/permute_uint8.tflite')
with io.open(model_path, 'rb') as model_file:
data = model_file.read()
interpreter = interpreter_wrapper.Interpreter(model_content=data)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
self.assertEqual(1, len(input_details))
self.assertEqual('input', input_details[0]['name'])
self.assertEqual(np.uint8, input_details[0]['dtype'])
self.assertTrue(([1, 4] == input_details[0]['shape']).all())
self.assertEqual((1.0, 0), input_details[0]['quantization'])
output_details = interpreter.get_output_details()
self.assertEqual(1, len(output_details))
self.assertEqual('output', output_details[0]['name'])
self.assertEqual(np.uint8, output_details[0]['dtype'])
self.assertTrue(([1, 4] == output_details[0]['shape']).all())
self.assertEqual((1.0, 0), output_details[0]['quantization'])
test_input = np.array([[1, 2, 3, 4]], dtype=np.uint8)
expected_output = np.array([[4, 3, 2, 1]], dtype=np.uint8)
interpreter.resize_tensor_input(input_details[0]['index'],
test_input.shape)
interpreter.allocate_tensors()
interpreter.set_tensor(input_details[0]['index'], test_input)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
self.assertTrue((expected_output == output_data).all())
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 setUp(self):
self.interpreter = interpreter_wrapper.Interpreter(
model_path=resource_loader.get_path_to_datafile(
'testdata/permute_float.tflite'))
self.interpreter.allocate_tensors()
self.input0 = self.interpreter.get_input_details()[0]['index']
self.initial_data = np.array([[-1., -2., -3., -4.]], np.float32)
def testDenseTensorAccess(self):
interpreter = interpreter_wrapper.Interpreter(
model_path=resource_loader.get_path_to_datafile('testdata/pc_conv.bin'))
interpreter.allocate_tensors()
weight_details = interpreter.get_tensor_details()[1]
s_params = weight_details['sparsity_parameters']
self.assertEqual(s_params, {})
def testString(self):
interpreter = interpreter_wrapper.Interpreter(
model_path=resource_loader.get_path_to_datafile(
'testdata/gather_string.tflite'))
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
self.assertEqual(2, len(input_details))
self.assertEqual('input', input_details[0]['name'])
self.assertEqual(np.string_, input_details[0]['dtype'])
self.assertTrue(([10] == input_details[0]['shape']).all())
self.assertEqual((0.0, 0), input_details[0]['quantization'])
self.assertEqual('indices', input_details[1]['name'])
self.assertEqual(np.int64, input_details[1]['dtype'])
self.assertTrue(([3] == input_details[1]['shape']).all())
self.assertEqual((0.0, 0), input_details[1]['quantization'])
output_details = interpreter.get_output_details()
self.assertEqual(1, len(output_details))
self.assertEqual('output', output_details[0]['name'])
self.assertEqual(np.string_, output_details[0]['dtype'])
self.assertTrue(([3] == output_details[0]['shape']).all())
self.assertEqual((0.0, 0), output_details[0]['quantization'])
test_input = np.array([1, 2, 3], dtype=np.int64)
interpreter.set_tensor(input_details[1]['index'], test_input)
test_input = np.array(
['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'])
expected_output = np.array([b'b', b'c', b'd'])
interpreter.set_tensor(input_details[0]['index'], test_input)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
self.assertTrue((expected_output == output_data).all())
def testThreads_WrongType(self):
with self.assertRaisesRegex(ValueError,
'type of num_threads should be int'):
interpreter_wrapper.Interpreter(
model_path=resource_loader.get_path_to_datafile(
'testdata/permute_float.tflite'),
num_threads=4.2)
def testNotSupportedOpResolverTypes(self):
with self.assertRaisesRegex(
ValueError, 'Unrecognized passed in op resolver type: test'):
interpreter_wrapper.Interpreter(
model_path=resource_loader.get_path_to_datafile(
'testdata/permute_float.tflite'),
experimental_op_resolver_type='test')
def testInvalidIndex(self):
interpreter = interpreter_wrapper.Interpreter(
model_path=resource_loader.get_path_to_datafile(
'testdata/permute_float.tflite'))
interpreter.allocate_tensors()
# Invalid tensor index passed.
with self.assertRaisesRegexp(ValueError, 'Tensor with no shape found.'):
interpreter._get_tensor_details(4)
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 validate_yolo_model_tflite(model_path, image_file, anchors, class_names, loop_count):
interpreter = interpreter_wrapper.Interpreter(model_path=model_path)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
#print(input_details)
#print(output_details)
# check the type of the input tensor
if input_details[0]['dtype'] == np.float32:
floating_model = True
img = Image.open(image_file)
image = np.array(img, dtype='uint8')
height = input_details[0]['shape'][1]
width = input_details[0]['shape'][2]
image_data = preprocess_image(img, (height, width))
image_shape = img.size
# predict once first to bypass the model building time
interpreter.set_tensor(input_details[0]['index'], image_data)
interpreter.invoke()
start = time.time()
for i in range(loop_count):
interpreter.set_tensor(input_details[0]['index'], image_data)
interpreter.invoke()
end = time.time()
print("Average Inference time: {:.8f}ms".format((end - start) * 1000 /loop_count))
out_list = []
for output_detail in output_details:
output_data = interpreter.get_tensor(output_detail['index'])
out_list.append(output_data)
start = time.time()
predictions = yolo_head(out_list, anchors, num_classes=len(class_names), input_dims=(height, width))
boxes, classes, scores = handle_predictions(predictions, confidence=0.1, iou_threshold=0.4)
boxes = adjust_boxes(boxes, image_shape, (height, width))
end = time.time()
print("PostProcess time: {:.8f}ms".format((end - start) * 1000))
print('Found {} boxes for {}'.format(len(boxes), image_file))
for box, cls, score in zip(boxes, classes, scores):
print("Class: {}, Score: {}".format(class_names[cls], score))
colors = get_colors(class_names)
image = draw_boxes(image, boxes, classes, scores, class_names, colors)
Image.fromarray(image).show()
def __init__(self, model_path):
self.interpreter = interpreter_wrapper.Interpreter(
model_path=model_path)
self.interpreter.allocate_tensors()
self.input_details = self.interpreter.get_input_details()
self.output_details = self.interpreter.get_output_details()
print(self.input_details)
print(self.output_details)
def testPerChannelParams(self):
interpreter = interpreter_wrapper.Interpreter(
model_path=resource_loader.get_path_to_datafile('testdata/pc_conv.bin'))
interpreter.allocate_tensors()
# Tensor index 1 is the weight.
weight_details = interpreter.get_tensor_details()[1]
qparams = weight_details['quantization_parameters']
# Ensure that we retrieve per channel quantization params correctly.
self.assertEqual(len(qparams['scales']), 128)
def test_latency(self):
latency_op = 0.0
for test_case in TEST_CASES:
input_tensor = tf.ragged.constant(test_case)
rank = input_tensor.shape.rank
model = self._make_model(rank, 3, ragged_tensor=True, flex=False)
interpreter = interpreter_wrapper.InterpreterWithCustomOps(
model_content=model,
custom_op_registerers=['AddNgramsCustomOp'])
interpreter.resize_tensor_input(0, input_tensor.flat_values.shape)
for r in range(rank - 1):
interpreter.resize_tensor_input(
r + 1, input_tensor.nested_row_splits[r].shape)
interpreter.allocate_tensors()
interpreter.set_tensor(interpreter.get_input_details()[0]['index'],
input_tensor.flat_values.numpy())
for r in range(rank - 1):
interpreter.set_tensor(
interpreter.get_input_details()[r + 1]['index'],
input_tensor.nested_row_splits[r].numpy())
start_time = timeit.default_timer()
for _ in range(INVOKES_FOR_SINGLE_OP_BENCHMARK):
interpreter.invoke()
latency_op = latency_op + timeit.default_timer() - start_time
latency_op = latency_op / (INVOKES_FOR_SINGLE_OP_BENCHMARK *
len(TEST_CASES))
latency_flex = 0.0
for test_case in TEST_CASES:
input_tensor = tf.ragged.constant(test_case)
rank = input_tensor.shape.rank
model = self._make_model(rank, 3, ragged_tensor=True, flex=True)
interpreter = interpreter_wrapper.Interpreter(model_content=model)
interpreter.resize_tensor_input(0, input_tensor.flat_values.shape)
for r in range(rank - 1):
interpreter.resize_tensor_input(
r + 1, input_tensor.nested_row_splits[r].shape)
interpreter.allocate_tensors()
interpreter.set_tensor(interpreter.get_input_details()[0]['index'],
input_tensor.flat_values.numpy())
for r in range(rank - 1):
interpreter.set_tensor(
interpreter.get_input_details()[r + 1]['index'],
input_tensor.nested_row_splits[r].numpy())
start_time = timeit.default_timer()
for _ in range(INVOKES_FOR_FLEX_DELEGATE_BENCHMARK):
interpreter.invoke()
latency_flex = latency_flex + timeit.default_timer() - start_time
latency_flex = latency_flex / (INVOKES_FOR_FLEX_DELEGATE_BENCHMARK *
len(TEST_CASES))
logging.info('Latency (single op): %fms', latency_op * 1000.0)
logging.info('Latency (flex delegate): %fms', latency_flex * 1000.0)
def _test_convert_result(
inputs,
tm,
tm_result,
max_err=max_error,
nhwc=True,
nhwc2=True,
scale=1,
zero_point=0,
require_quantize=False,
):
if not isinstance(inputs, Sequence):
inputs = [
inputs,
]
if not isinstance(scale, Sequence):
scale = (scale, )
if not isinstance(zero_point, Sequence):
zero_point = (zero_point, )
for i, inp in enumerate(inputs):
if nhwc and inp.ndim == 4:
inputs[i] = inp.transpose((0, 2, 3, 1))
tracedmodule_to_tflite(tm,
output=tmp_file + ".tflite",
require_quantize=require_quantize)
tfl_model = interpreter.Interpreter(model_path=tmp_file + ".tflite")
tfl_model.allocate_tensors()
input_details = tfl_model.get_input_details()
for i, inp in enumerate(inputs):
tfl_model.set_tensor(input_details[i]["index"], inp)
tfl_model.invoke()
pred_tfl = []
if not isinstance(scale, Sequence):
scale = (scale, )
zero_point = (zero_point, )
for index, i in enumerate(tfl_model.get_output_details()):
out = tfl_model.tensor(i["index"])()
if nhwc2 and out.ndim == 4:
out = out.transpose((0, 3, 1, 2))
index = len(scale) - 1 if index >= len(scale) else index
out = ((out - float(zero_point[index])) *
scale[index]).astype("float32")
pred_tfl.append(out)
if not isinstance(tm_result, Sequence):
tm_result = (tm_result, )
for i, j, s in zip(tm_result, pred_tfl, scale):
assert i.shape == j.shape
assert i.dtype == j.dtype
atol = max_err if s == 1 else s
np.testing.assert_allclose(i, j, atol=atol)
def generate_results(tflite_model,
val_dir,
scale_value,
normal_val,
results_dir=''):
'''
:param tflite_model: (必填)tflite模型地址
:param val_dir:（必填）验证集路径
:param results_dir: （选填）目标生成文件的路径，若不赋值默认为读取模型位置
:param scale_value: （必填）图片下采样率
'''
interpreter = interpreter_wrapper.Interpreter(model_path=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
if not results_dir:
results_dir = os.path.join(os.path.dirname(tflite_model), 'result')
if not os.path.exists(results_dir):
os.mkdir(results_dir)
img_list = os.listdir(val_dir)
assert len(img_list) == 500, f'验证集图片数量为{len(img_list)}，原始验证集数量为500'
for img in tqdm(img_list):
#读取图片
img_path = os.path.join(val_dir, img)
image = tf.io.read_file(img_path)
image = tf.image.decode_png(image, channels=3, dtype=tf.dtypes.uint8)
image = np.asarray(image).astype(np.float32)
#图片预处理
image_shape = np.shape(image)
image /= 255.0
image_new = cv2.resize(image, (int(
image_shape[1] / scale_value), int(image_shape[0] / scale_value)),
interpolation=cv2.INTER_LINEAR)
image_new = np.reshape(image_new,
(1, int(image_shape[0] / scale_value),
int(image_shape[1] / scale_value), 3))
#推理
interpreter.set_tensor(input_details[0]['index'], image_new)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
results = np.squeeze(output_data)
results *= normal_val
results = results.astype(np.uint16)
result_path = os.path.join(results_dir, img)
cv2.imwrite(result_path, results)
print(f'结果生成从存放在{results_dir}')
def testStringZeroDim(self):
data = b'abcd' + bytes(16)
interpreter = interpreter_wrapper.Interpreter(
model_path=resource_loader.get_path_to_datafile(
'testdata/gather_string_0d.tflite'))
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
interpreter.set_tensor(input_details[0]['index'], np.array(data))
test_input_tensor = interpreter.get_tensor(input_details[0]['index'])
self.assertEqual(len(data), len(test_input_tensor.item(0)))
def validate_deeplab_model_tflite(model_path, image_file, class_names, do_crf,
label_file, loop_count):
interpreter = interpreter_wrapper.Interpreter(model_path=model_path)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
#print(input_details)
#print(output_details)
# check the type of the input tensor
if input_details[0]['dtype'] == np.float32:
floating_model = True
height = input_details[0]['shape'][1]
width = input_details[0]['shape'][2]
model_image_size = (height, width)
num_classes = output_details[0]['shape'][-1]
if class_names:
# check if classes number match with model prediction
assert num_classes == len(
class_names), 'classes number mismatch with model.'
# prepare input image
img = Image.open(image_file)
image_data = preprocess_image(img, model_image_size)
image = image_data[0].astype('uint8')
#origin image shape, in (width, height) format
origin_image_size = img.size
# predict once first to bypass the model building time
interpreter.set_tensor(input_details[0]['index'], image_data)
interpreter.invoke()
start = time.time()
for i in range(loop_count):
interpreter.set_tensor(input_details[0]['index'], image_data)
interpreter.invoke()
end = time.time()
print("Average Inference time: {:.8f}ms".format(
(end - start) * 1000 / loop_count))
prediction = []
for output_detail in output_details:
output_data = interpreter.get_tensor(output_detail['index'])
prediction.append(output_data)
handle_prediction(prediction, image, np.array(img), num_classes,
class_names, model_image_size, origin_image_size, do_crf,
label_file)
return
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)