CODEOWNERS

 *          @tensorflow/micro
 
-/.github/  @advaitjain @rockyrhodes @vamsimanchala
-/ci/       @advaitjain @rockyrhodes @vamsimanchala
+/.github/  @advaitjain @rockyrhodes @rascani
+/ci/       @advaitjain @rockyrhodes @rascani

tensorflow/lite/kernels/internal/reference/resize_bilinear.h

@@ -212,9 +212,14 @@ inline void ResizeBilinearInteger(
               (input_y - (1 << 10) * y0) * (input_x - (1 << 10) * x0);
           const int64_t output_20 =
               output_20_ll + output_20_lu + output_20_rl + output_20_ru;
+#if TFLITE_SINGLE_ROUNDING
+          const int64_t round = 1 << 19;
+          const T interpolation = static_cast<T>((output_20 + round) >> 20);
+#else
           const int64_t round = (output_20 > 0) ? (1 << 19) : -(1 << 19);
           const T interpolation =
               static_cast<T>((output_20 + round) / (1 << 20));
+#endif  // TFLITE_SINGLE_ROUNDING
           output_data[Offset(output_shape, b, y, x, c)] = interpolation;
         }
       }

tensorflow/lite/micro/examples/hello_world/BUILD

@@ -54,7 +54,7 @@ py_binary(
         "@absl_py//absl/logging",
         requirement("numpy"),
         requirement("tensorflow-cpu"),
-        "//tensorflow/lite/micro/python/interpreter/src:tflm_runtime",
+        "//tensorflow/lite/micro/python/interpreter/src:runtime",
     ],
 )
 

tensorflow/lite/micro/examples/hello_world/evaluate.py

@@ -19,7 +19,7 @@ from absl import flags
 import numpy as np
 import matplotlib.pyplot as plt
 from tensorflow.python.platform import resource_loader
-from tflite_micro.tensorflow.lite.micro.python.interpreter.src import tflm_runtime
+from tflite_micro.tensorflow.lite.micro.python.interpreter.src import runtime
 
 _USE_TFLITE_INTERPRETER = flags.DEFINE_bool(
     'use_tflite',
@@ -73,7 +73,7 @@ def generate_random_float_input(sample_count=1000):
 # returns the prediction of the interpreter.
 def get_tflm_prediction(model_path, x_values):
   # Create the tflm interpreter
-  tflm_interpreter = tflm_runtime.Interpreter.from_file(model_path)
+  tflm_interpreter = runtime.Interpreter.from_file(model_path)
 
   input_shape = np.array(tflm_interpreter.get_input_details(0).get('shape'))
 

tensorflow/lite/micro/examples/hello_world/evaluate_test.py

@@ -18,8 +18,7 @@ import numpy as np
 from tensorflow.python.framework import test_util
 from tensorflow.python.platform import resource_loader
 from tensorflow.python.platform import test
-from tflite_micro.tensorflow.lite.micro.python.interpreter.src import \
-  tflm_runtime
+from tflite_micro.tensorflow.lite.micro.python.interpreter.src import runtime
 from tflite_micro.tensorflow.lite.micro.examples.hello_world import evaluate
 
 PREFIX_PATH = resource_loader.get_path_to_datafile('')
@@ -29,7 +28,7 @@ class HelloWorldFloatModelTest(test_util.TensorFlowTestCase):
   model_path = os.path.join(PREFIX_PATH, 'models/hello_world_float.tflite')
   input_shape = (1, 1)
   output_shape = (1, 1)
-  tflm_interpreter = tflm_runtime.Interpreter.from_file(model_path)
+  tflm_interpreter = runtime.Interpreter.from_file(model_path)
 
   def test_compare_with_tflite(self):
     x_values = evaluate.generate_random_float_input()
@@ -47,7 +46,7 @@ class HelloWorldQuantModelTest(test_util.TensorFlowTestCase):
   model_path = os.path.join(PREFIX_PATH, 'models/hello_world_int8.tflite')
   input_shape = (1, 1)
   output_shape = (1, 1)
-  tflm_interpreter = tflm_runtime.Interpreter.from_file(model_path)
+  tflm_interpreter = runtime.Interpreter.from_file(model_path)
 
   def test_compare_with_tflite(self):
     x_values = evaluate.generate_random_int8_input()

tensorflow/lite/micro/examples/hello_world/quantization/BUILD

@@ -12,6 +12,6 @@ py_binary(
         "@absl_py//absl/logging",
         requirement("numpy"),
         requirement("tensorflow-cpu"),
-        "//tensorflow/lite/micro/python/interpreter/src:tflm_runtime",
+        "//tensorflow/lite/micro/python/interpreter/src:runtime",
     ],
 )

tensorflow/lite/micro/examples/mnist_lstm/BUILD

@@ -15,7 +15,7 @@ py_binary(
     srcs = ["evaluate.py"],
     srcs_version = "PY3",
     deps = [
-        "//tensorflow/lite/micro/python/interpreter/src:tflm_runtime",
+        "//tensorflow/lite/micro/python/interpreter/src:runtime",
         "@absl_py//absl:app",
     ],
 )

tensorflow/lite/micro/examples/mnist_lstm/evaluate.py

@@ -28,7 +28,7 @@ from absl import logging
 import numpy as np
 from PIL import Image
 
-from tflite_micro.tensorflow.lite.micro.python.interpreter.src import tflm_runtime
+from tflite_micro.tensorflow.lite.micro.python.interpreter.src import runtime
 
 FLAGS = flags.FLAGS
 
@@ -113,7 +113,7 @@ def predict(interpreter, data):
   """Use TFLM interpreter to predict a MNIST image
 
   Args:
-      interpreter (tflm_runtime.Interpreter): the TFLM python interpreter
+      interpreter (runtime.Interpreter): the TFLM python interpreter
       data (np.array): data to be predicted
 
   Returns:
@@ -141,7 +141,7 @@ def predict_image(interpreter, image_path):
   """Use TFLM interpreter to predict a MNIST image
 
   Args:
-      interpreter (tflm_runtime.Interpreter): the TFLM python interpreter
+      interpreter (runtime.Interpreter): the TFLM python interpreter
       image_path (str): path for the image that need to be tested
 
   Returns:
@@ -158,7 +158,7 @@ def main(_):
   if not os.path.exists(FLAGS.img_path):
     raise ValueError("Image file does not exist. Please check the image path.")
 
-  tflm_interpreter = tflm_runtime.Interpreter.from_file(FLAGS.model_path)
+  tflm_interpreter = runtime.Interpreter.from_file(FLAGS.model_path)
   category_probabilities = predict_image(tflm_interpreter, FLAGS.img_path)
   predicted_category = np.argmax(category_probabilities)
   logging.info("Model predicts the image as %i with probability %.2f",

tensorflow/lite/micro/examples/mnist_lstm/evaluate_test.py

@@ -20,7 +20,7 @@ import tensorflow as tf
 from tensorflow.python.framework import test_util
 from tensorflow.python.platform import resource_loader
 from tensorflow.python.platform import test
-from tflite_micro.tensorflow.lite.micro.python.interpreter.src import tflm_runtime
+from tflite_micro.tensorflow.lite.micro.python.interpreter.src import runtime
 from tflite_micro.tensorflow.lite.micro.examples.mnist_lstm import evaluate
 from tflite_micro.tensorflow.lite.micro.tools import requantize_flatbuffer
 
@@ -33,7 +33,7 @@ class LSTMFloatModelTest(test_util.TensorFlowTestCase):
     self.model_path = os.path.join(PREFIX_PATH, "trained_lstm.tflite")
     self.input_shape = (1, 28, 28)
     self.output_shape = (1, 10)
-    self.tflm_interpreter = tflm_runtime.Interpreter.from_file(self.model_path)
+    self.tflm_interpreter = runtime.Interpreter.from_file(self.model_path)
     np.random.seed(42)  #Seed the random number generator
 
   def testInputErrHandling(self):
@@ -95,7 +95,7 @@ class LSTMInt8ModelTest(test_util.TensorFlowTestCase):
                                         "trained_lstm_int8.tflite")
     self.input_shape = (1, 28, 28)
     self.output_shape = (1, 10)
-    self.tflm_interpreter_quant = tflm_runtime.Interpreter.from_file(
+    self.tflm_interpreter_quant = runtime.Interpreter.from_file(
         self.int8_model_path)
     np.random.seed(42)  #Seed the random number generator
 
@@ -106,8 +106,7 @@ class LSTMInt8ModelTest(test_util.TensorFlowTestCase):
 
     # Create a float model for results comparison
     float_model_path = os.path.join(PREFIX_PATH, "trained_lstm.tflite")
-    tflm_interpreter_float = tflm_runtime.Interpreter.from_file(
-        float_model_path)
+    tflm_interpreter_float = runtime.Interpreter.from_file(float_model_path)
 
     num_test = 10
     for _ in range(num_test):
@@ -163,7 +162,7 @@ class LSTMInt16ModelTest(test_util.TensorFlowTestCase):
     self.int16_model = self.requantizer.model_bytearray()
     self.input_shape = (1, 28, 28)
     self.output_shape = (1, 10)
-    self.tflm_interpreter_quant = tflm_runtime.Interpreter.from_bytes(
+    self.tflm_interpreter_quant = runtime.Interpreter.from_bytes(
         self.int16_model)
     np.random.seed(42)  #Seed the random number generator
 
@@ -174,8 +173,7 @@ class LSTMInt16ModelTest(test_util.TensorFlowTestCase):
 
     # Create a float model for results comparison
     float_model_path = os.path.join(PREFIX_PATH, "trained_lstm.tflite")
-    tflm_interpreter_float = tflm_runtime.Interpreter.from_file(
-        float_model_path)
+    tflm_interpreter_float = runtime.Interpreter.from_file(float_model_path)
 
     num_test = 10
     for _ in range(num_test):

tensorflow/lite/micro/examples/recipes/BUILD

@@ -26,6 +26,8 @@ py_test(
     ],
     deps = [
         ":resource_variables_lib",
+        # TODO(b/286456378): update tflm_runtime to runtime when we are ready to
+        # remove the alias.
         "//tensorflow/lite/micro/python/interpreter/src:tflm_runtime",
     ],
 )

tensorflow/lite/micro/examples/recipes/resource_variables_test.py

@@ -17,6 +17,9 @@ import numpy as np
 from tensorflow.python.framework import test_util
 from tensorflow.python.platform import test
 from tflite_micro.tensorflow.lite.micro.examples.recipes import resource_variables_lib
+
+# TODO(b/286456378): change tflm_runtime to runtime when we all other usage has
+# been updated.
 from tflite_micro.tensorflow.lite.micro.python.interpreter.src import tflm_runtime
 
 

tensorflow/lite/micro/python/interpreter/README.md

@@ -13,7 +13,7 @@ in near future by installing a PyPi package.
 #### Build
 
 The only package that needs to be included in the `BUILD` file is
-`//tensorflow/lite/micro/python/interpreter/src:tflm_runtime`. It contains all
+`//tensorflow/lite/micro/python/interpreter/src:runtime`. It contains all
 the correct dependencies to build the Python interpreter.
 
 ### PyPi
@@ -34,13 +34,13 @@ bytearray format or file format.
 
 ```
 # For the Bazel workflow
-from tflite_micro.tensorflow.lite.micro.python.interpreter.src import tflm_runtime
+from tflite_micro.tensorflow.lite.micro.python.interpreter.src import runtime
 
 
 # If model is a bytearray
-tflm_interpreter = tflm_runtime.Interpreter.from_bytes(model_data)
+tflm_interpreter = runtime.Interpreter.from_bytes(model_data)
 # If model is a file
-tflm_interpreter = tflm_runtime.Interpreter.from_file(model_filepath)
+tflm_interpreter = runtime.Interpreter.from_file(model_filepath)
 
 # Run inference on TFLM using an ndarray `data_x`
 tflm_interpreter.set_input(data_x, 0)
@@ -62,7 +62,7 @@ expose an evolving set of C++ APIs. The Bazel build leverages the
 [pybind11_bazel extension](https://github.com/pybind/pybind11_bazel).
 
 The most updated Python APIs can be found in
-`tensorflow/lite/micro/python/interpreter/src/tflm_runtime.py`.
+`tensorflow/lite/micro/python/interpreter/src/runtime.py`.
 
 ## Custom Ops
 
@@ -116,7 +116,7 @@ in the target that calls the Python interpreter with custom ops.
 For example,
 
 ```
-interpreter = tflm_runtime.Interpreter.from_file(
+interpreter = runtime.Interpreter.from_file(
     model_path=model_path,
     custom_op_registerers=['SomeCustomRegisterer'])
 ```
@@ -152,7 +152,7 @@ will print
 
 10016 bytes is the actual memory arena size.
 
-During instantiation via the class methods `tflm_runtime.Interpreter.from_file`
-or `tflm_runtime.Interpreter.from_bytes`, if `arena_size` is not explicitly
+During instantiation via the class methods `runtime.Interpreter.from_file`
+or `runtime.Interpreter.from_bytes`, if `arena_size` is not explicitly
 specified, the interpreter will default to a heuristic which is 10x the model
 size. This can be adjusted manually if desired.

tensorflow/lite/micro/python/interpreter/src/BUILD

-load("@pybind11_bazel//:build_defs.bzl", "pybind_extension", "pybind_library")
+load("@pybind11_bazel//:build_defs.bzl", "pybind_extension")
 load("@tflm_pip_deps//:requirements.bzl", "requirement")
 load(
     "//tensorflow/lite/micro:build_def.bzl",
@@ -63,10 +63,23 @@ pybind_extension(
     ],
 )
 
+# tflm_runtime is deprecated, please use runtime instead.
+# TODO(b/286456378): remove once all usage is changed to the runtime target.
 py_library(
     name = "tflm_runtime",
     srcs = ["tflm_runtime.py"],
-    data = [":interpreter_wrapper_pybind.so"],
+    visibility = ["//visibility:public"],
+    deps = [":runtime"],
+)
+
+py_library(
+    name = "runtime",
+    srcs = [
+        "runtime.py",
+    ],
+    data = [
+        ":interpreter_wrapper_pybind.so",
+    ],
     srcs_version = "PY3",
     visibility = ["//visibility:public"],
     deps = [

tensorflow/lite/micro/python/interpreter/src/runtime.py

+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Python package for TFLM Python Interpreter"""
+
+import os
+
+from tflite_micro.tensorflow.lite.micro.python.interpreter.src import interpreter_wrapper_pybind
+from tflite_micro.tensorflow.lite.tools import flatbuffer_utils
+
+
+class Interpreter(object):
+
+  def __init__(self, model_data, custom_op_registerers, arena_size):
+    if model_data is None:
+      raise ValueError("Model must not be None")
+
+    if not isinstance(custom_op_registerers, list) or not all(
+        isinstance(s, str) for s in custom_op_registerers):
+      raise ValueError("Custom ops registerers must be a list of strings")
+
+    # This is a heuristic to ensure that the arena is sufficiently sized.
+    if arena_size is None:
+      arena_size = len(model_data) * 10
+
+    # Some models make use of resource variables ops, get the count here
+    num_resource_variables = flatbuffer_utils.count_resource_variables(
+        model_data)
+    print("Number of resource variables the model uses = ",
+          num_resource_variables)
+
+    self._interpreter = interpreter_wrapper_pybind.InterpreterWrapper(
+        model_data, custom_op_registerers, arena_size, num_resource_variables)
+
+  @classmethod
+  def from_file(self, model_path, custom_op_registerers=[], arena_size=None):
+    """Instantiates a TFLM interpreter from a model .tflite filepath.
+
+    Args:
+      model_path: Filepath to the .tflite model
+      custom_op_registerers: List of strings, each of which is the name of a
+        custom OP registerer
+      arena_size: Tensor arena size in bytes. If unused, tensor arena size will
+        default to 10 times the model size.
+
+    Returns:
+      An Interpreter instance
+    """
+    if model_path is None or not os.path.isfile(model_path):
+      raise ValueError("Invalid model file path")
+
+    with open(model_path, "rb") as f:
+      model_data = f.read()
+
+    return Interpreter(model_data, custom_op_registerers, arena_size)
+
+  @classmethod
+  def from_bytes(self, model_data, custom_op_registerers=[], arena_size=None):
+    """Instantiates a TFLM interpreter from a model in byte array.
+
+    Args:
+      model_data: Model in byte array format
+      custom_op_registerers: List of strings, each of which is the name of a
+        custom OP registerer
+      arena_size: Tensor arena size in bytes. If unused, tensor arena size will
+        default to 10 times the model size.
+
+    Returns:
+      An Interpreter instance
+    """
+
+    return Interpreter(model_data, custom_op_registerers, arena_size)
+
+  def print_allocations(self):
+    """Invoke the RecordingMicroAllocator to print the arena usage.
+
+    This should be called after `invoke()`.
+
+    Returns:
+      This method does not return anything, but It dumps the arena
+      usage to stderr.
+    """
+    self._interpreter.PrintAllocations()
+
+  def invoke(self):
+    """Invoke the TFLM interpreter to run an inference.
+
+    This should be called after `set_input()`.
+
+    Returns:
+      Status code of the C++ invoke function. A RuntimeError will be raised as
+      well upon any error.
+    """
+    return self._interpreter.Invoke()
+
+  def reset(self):
+    """Reset the model state to be what you would expect when the interpreter is first
+
+    created. i.e. after Init and Prepare is called for the very first time.
+
+    This should be called after invoke stateful model like LSTM.
+
+    Returns:
+      Status code of the C++ invoke function. A RuntimeError will be raised as
+      well upon any error.
+    """
+    return self._interpreter.Reset()
+
+  def set_input(self, input_data, index):
+    """Set input data into input tensor.
+
+    This should be called before `invoke()`.
+
+    Args:
+      input_data: Input data in numpy array format. The numpy array format is
+        chosen to be consistent with TFLite interpreter.
+      index: An integer between 0 and the number of input tensors (exclusive)
+        consistent with the order defined in the list of inputs in the .tflite
+        model
+    """
+    if input_data is None:
+      raise ValueError("Input data must not be None")
+    if index is None or index < 0:
+      raise ValueError("Index must be a non-negative integer")
+
+    self._interpreter.SetInputTensor(input_data, index)
+
+  def get_output(self, index):
+    """Get data from output tensor.
+
+    The output data correspond to the most recent `invoke()`.
+
+    Args:
+      index: An integer between 0 and the number of output tensors (exclusive)
+        consistent with the order defined in the list of outputs in the .tflite
+        model
+
+    Returns:
+      Output data in numpy array format. The numpy array format is chosen to
+      be consistent with TFLite interpreter.
+    """
+    if index is None or index < 0:
+      raise ValueError("Index must be a non-negative integer")
+
+    return self._interpreter.GetOutputTensor(index)
+
+  def get_input_details(self, index):
+    """Get input tensor information
+
+    Args:
+        index (int): An integer between 0 and the number of output tensors
+          (exclusive) consistent with the order defined in the list of outputs
+          in the .tflite model
+
+    Returns:
+        A dictionary from input index to tensor details where each item is a
+        dictionary with details about an input tensor. Each dictionary contains
+        the following fields that describe the tensor:
+        + `shape`: The shape of the tensor.
+        + `dtype`: The numpy data type (such as `np.int32` or `np.uint8`).
+        + `quantization_parameters`: A dictionary of parameters used to quantize
+          the tensor:
+          ~ `scales`: List of scales (one if per-tensor quantization).
+          ~ `zero_points`: List of zero_points (one if per-tensor quantization).
+          ~ `quantized_dimension`: Specifies the dimension of per-axis
+          quantization, in the case of multiple scales/zero_points.
+
+    """
+    if index is None or index < 0:
+      raise ValueError("Index must be a non-negative integer")
+
+    return self._interpreter.GetInputTensorDetails(index)
+
+  def get_output_details(self, index):
+    """Get output tensor information
+
+    Args:
+        index (int): An integer between 0 and the number of output tensors
+          (exclusive) consistent with the order defined in the list of outputs
+          in the .tflite model
+
+    Returns:
+        A dictionary from input index to tensor details where each item is a
+        dictionary with details about an input tensor. Each dictionary contains
+        the following fields that describe the tensor:
+        + `shape`: The shape of the tensor.
+        + `dtype`: The numpy data type (such as `np.int32` or `np.uint8`).
+        + `quantization_parameters`: A dictionary of parameters used to quantize
+          the tensor:
+          ~ `scales`: List of scales (one if per-tensor quantization).
+          ~ `zero_points`: List of zero_points (one if per-tensor quantization).
+          ~ `quantized_dimension`: Specifies the dimension of per-axis
+          quantization, in the case of multiple scales/zero_points.
+
+    """
+    if index is None or index < 0:
+      raise ValueError("Index must be a non-negative integer")
+
+    return self._interpreter.GetOutputTensorDetails(index)

tensorflow/lite/micro/python/interpreter/src/tflm_runtime.py

-# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,199 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Python package for TFLM Python Interpreter"""
 
-import os
-
-from tflite_micro.tensorflow.lite.micro.python.interpreter.src import interpreter_wrapper_pybind
-from tflite_micro.tensorflow.lite.tools import flatbuffer_utils
-
-
-class Interpreter(object):
-
-  def __init__(self, model_data, custom_op_registerers, arena_size):
-    if model_data is None:
-      raise ValueError("Model must not be None")
-
-    if not isinstance(custom_op_registerers, list) or not all(
-        isinstance(s, str) for s in custom_op_registerers):
-      raise ValueError("Custom ops registerers must be a list of strings")
-
-    # This is a heuristic to ensure that the arena is sufficiently sized.
-    if arena_size is None:
-      arena_size = len(model_data) * 10
-
-    # Some models make use of resource variables ops, get the count here
-    num_resource_variables = flatbuffer_utils.count_resource_variables(
-        model_data)
-    print("Number of resource variables the model uses = ",
-          num_resource_variables)
-
-    self._interpreter = interpreter_wrapper_pybind.InterpreterWrapper(
-        model_data, custom_op_registerers, arena_size, num_resource_variables)
-
-  @classmethod
-  def from_file(self, model_path, custom_op_registerers=[], arena_size=None):
-    """Instantiates a TFLM interpreter from a model .tflite filepath.
-
-    Args:
-      model_path: Filepath to the .tflite model
-      custom_op_registerers: List of strings, each of which is the name of a
-        custom OP registerer
-      arena_size: Tensor arena size in bytes. If unused, tensor arena size will
-        default to 10 times the model size.
-
-    Returns:
-      An Interpreter instance
-    """
-    if model_path is None or not os.path.isfile(model_path):
-      raise ValueError("Invalid model file path")
-
-    with open(model_path, "rb") as f:
-      model_data = f.read()
-
-    return Interpreter(model_data, custom_op_registerers, arena_size)
-
-  @classmethod
-  def from_bytes(self, model_data, custom_op_registerers=[], arena_size=None):
-    """Instantiates a TFLM interpreter from a model in byte array.
-
-    Args:
-      model_data: Model in byte array format
-      custom_op_registerers: List of strings, each of which is the name of a
-        custom OP registerer
-      arena_size: Tensor arena size in bytes. If unused, tensor arena size will
-        default to 10 times the model size.
-
-    Returns:
-      An Interpreter instance
-    """
-
-    return Interpreter(model_data, custom_op_registerers, arena_size)
-
-  def print_allocations(self):
-    """Invoke the RecordingMicroAllocator to print the arena usage.
-
-    This should be called after `invoke()`.
-
-    Returns:
-      This method does not return anything, but It dumps the arena
-      usage to stderr.
-    """
-    self._interpreter.PrintAllocations()
-
-  def invoke(self):
-    """Invoke the TFLM interpreter to run an inference.
-
-    This should be called after `set_input()`.
-
-    Returns:
-      Status code of the C++ invoke function. A RuntimeError will be raised as
-      well upon any error.
-    """
-    return self._interpreter.Invoke()
-
-  def reset(self):
-    """Reset the model state to be what you would expect when the interpreter is first
-
-    created. i.e. after Init and Prepare is called for the very first time.
-
-    This should be called after invoke stateful model like LSTM.
-
-    Returns:
-      Status code of the C++ invoke function. A RuntimeError will be raised as
-      well upon any error.
-    """
-    return self._interpreter.Reset()
-
-  def set_input(self, input_data, index):
-    """Set input data into input tensor.
-
-    This should be called before `invoke()`.
-
-    Args:
-      input_data: Input data in numpy array format. The numpy array format is
-        chosen to be consistent with TFLite interpreter.
-      index: An integer between 0 and the number of input tensors (exclusive)
-        consistent with the order defined in the list of inputs in the .tflite
-        model
-    """
-    if input_data is None:
-      raise ValueError("Input data must not be None")
-    if index is None or index < 0:
-      raise ValueError("Index must be a non-negative integer")
-
-    self._interpreter.SetInputTensor(input_data, index)
-
-  def get_output(self, index):
-    """Get data from output tensor.
-
-    The output data correspond to the most recent `invoke()`.
-
-    Args:
-      index: An integer between 0 and the number of output tensors (exclusive)
-        consistent with the order defined in the list of outputs in the .tflite
-        model
-
-    Returns:
-      Output data in numpy array format. The numpy array format is chosen to
-      be consistent with TFLite interpreter.
-    """
-    if index is None or index < 0:
-      raise ValueError("Index must be a non-negative integer")
-
-    return self._interpreter.GetOutputTensor(index)
-
-  def get_input_details(self, index):
-    """Get input tensor information
-
-    Args:
-        index (int): An integer between 0 and the number of output tensors
-          (exclusive) consistent with the order defined in the list of outputs
-          in the .tflite model
-
-    Returns:
-        A dictionary from input index to tensor details where each item is a
-        dictionary with details about an input tensor. Each dictionary contains
-        the following fields that describe the tensor:
-        + `shape`: The shape of the tensor.
-        + `dtype`: The numpy data type (such as `np.int32` or `np.uint8`).
-        + `quantization_parameters`: A dictionary of parameters used to quantize
-          the tensor:
-          ~ `scales`: List of scales (one if per-tensor quantization).
-          ~ `zero_points`: List of zero_points (one if per-tensor quantization).
-          ~ `quantized_dimension`: Specifies the dimension of per-axis
-          quantization, in the case of multiple scales/zero_points.
-
-    """
-    if index is None or index < 0:
-      raise ValueError("Index must be a non-negative integer")
-
-    return self._interpreter.GetInputTensorDetails(index)
-
-  def get_output_details(self, index):
-    """Get output tensor information
-
-    Args:
-        index (int): An integer between 0 and the number of output tensors
-          (exclusive) consistent with the order defined in the list of outputs
-          in the .tflite model
-
-    Returns:
-        A dictionary from input index to tensor details where each item is a
-        dictionary with details about an input tensor. Each dictionary contains
-        the following fields that describe the tensor:
-        + `shape`: The shape of the tensor.
-        + `dtype`: The numpy data type (such as `np.int32` or `np.uint8`).
-        + `quantization_parameters`: A dictionary of parameters used to quantize
-          the tensor:
-          ~ `scales`: List of scales (one if per-tensor quantization).
-          ~ `zero_points`: List of zero_points (one if per-tensor quantization).
-          ~ `quantized_dimension`: Specifies the dimension of per-axis
-          quantization, in the case of multiple scales/zero_points.
-
-    """
-    if index is None or index < 0:
-      raise ValueError("Index must be a non-negative integer")
-
-    return self._interpreter.GetOutputTensorDetails(index)
+# TODO(b/286456378): remove once all usage is switched to runtime
+from tflite_micro.tensorflow.lite.micro.python.interpreter.src.runtime import *

tensorflow/lite/micro/python/interpreter/tests/BUILD

@@ -15,7 +15,7 @@ py_test(
     deps = [
         requirement("numpy"),
         requirement("tensorflow-cpu"),
-        "//tensorflow/lite/micro/python/interpreter/src:tflm_runtime",
+        "//tensorflow/lite/micro/python/interpreter/src:runtime",
         "//tensorflow/lite/micro/testing:generate_test_models_lib",
     ],
 )

tensorflow/lite/micro/python/interpreter/tests/interpreter_test.py

@@ -30,7 +30,7 @@ import tensorflow as tf
 from tensorflow.python.framework import test_util
 from tensorflow.python.platform import test
 from tflite_micro.tensorflow.lite.micro.testing import generate_test_models
-from tflite_micro.tensorflow.lite.micro.python.interpreter.src import tflm_runtime
+from tflite_micro.tensorflow.lite.micro.python.interpreter.src import runtime
 
 
 class ConvModelTests(test_util.TensorFlowTestCase):
@@ -41,11 +41,11 @@ class ConvModelTests(test_util.TensorFlowTestCase):
   def testInitErrorHandling(self):
     with self.assertRaisesWithPredicateMatch(ValueError,
                                              "Invalid model file path"):
-      tflm_runtime.Interpreter.from_file("wrong.tflite")
+      runtime.Interpreter.from_file("wrong.tflite")
 
   def testInput(self):
     model_data = generate_test_models.generate_conv_model(False)
-    tflm_interpreter = tflm_runtime.Interpreter.from_bytes(model_data)
+    tflm_interpreter = runtime.Interpreter.from_bytes(model_data)
 
     data_x = np.random.randint(-127, 127, self.input_shape, dtype=np.int8)
     tflm_interpreter.set_input(data_x, 0)
@@ -68,7 +68,7 @@ class ConvModelTests(test_util.TensorFlowTestCase):
 
   def testInputErrorHandling(self):
     model_data = generate_test_models.generate_conv_model(True, self.filename)
-    tflm_interpreter = tflm_runtime.Interpreter.from_bytes(model_data)
+    tflm_interpreter = runtime.Interpreter.from_bytes(model_data)
 
     data_x = np.random.randint(-127, 127, self.input_shape, dtype=np.int8)
     # Try to access out of bound data
@@ -96,7 +96,7 @@ class ConvModelTests(test_util.TensorFlowTestCase):
 
   def testOutput(self):
     model_data = generate_test_models.generate_conv_model(True, self.filename)
-    tflm_interpreter = tflm_runtime.Interpreter.from_bytes(model_data)
+    tflm_interpreter = runtime.Interpreter.from_bytes(model_data)
 
     # Initial output values are all 0
     output = tflm_interpreter.get_output(0)
@@ -121,7 +121,7 @@ class ConvModelTests(test_util.TensorFlowTestCase):
 
   def testOutputErrorHandling(self):
     model_data = generate_test_models.generate_conv_model(True, self.filename)
-    tflm_interpreter = tflm_runtime.Interpreter.from_bytes(model_data)
+    tflm_interpreter = runtime.Interpreter.from_bytes(model_data)
     # Try to access out of bound data
     with self.assertRaisesWithPredicateMatch(IndexError,
                                              "Tensor is out of bound"):
@@ -134,7 +134,7 @@ class ConvModelTests(test_util.TensorFlowTestCase):
     model_data = generate_test_models.generate_conv_model(True, self.filename)
 
     # TFLM interpreter
-    tflm_interpreter = tflm_runtime.Interpreter.from_bytes(model_data)
+    tflm_interpreter = runtime.Interpreter.from_bytes(model_data)
 
     # TFLite interpreter
     tflite_interpreter = tf.lite.Interpreter(
@@ -169,8 +169,8 @@ class ConvModelTests(test_util.TensorFlowTestCase):
   def _helperModelFromFileAndBufferEqual(self):
     model_data = generate_test_models.generate_conv_model(True, self.filename)
 
-    file_interpreter = tflm_runtime.Interpreter.from_file(self.filename)
-    bytes_interpreter = tflm_runtime.Interpreter.from_bytes(model_data)
+    file_interpreter = runtime.Interpreter.from_file(self.filename)
+    bytes_interpreter = runtime.Interpreter.from_bytes(model_data)
 
     num_steps = 100
     for i in range(0, num_steps):
@@ -198,7 +198,7 @@ class ConvModelTests(test_util.TensorFlowTestCase):
     model_data = generate_test_models.generate_conv_model(False)
 
     interpreters = [
-        tflm_runtime.Interpreter.from_bytes(model_data) for i in range(10)
+        runtime.Interpreter.from_bytes(model_data) for i in range(10)
     ]
 
     num_steps = 100
@@ -221,7 +221,7 @@ class ConvModelTests(test_util.TensorFlowTestCase):
     pass
 
   def _helperOutputTensorMemoryLeak(self):
-    interpreter = tflm_runtime.Interpreter.from_file(self.filename)
+    interpreter = runtime.Interpreter.from_file(self.filename)
     int_ref = weakref.finalize(interpreter, self._helperNoop)
     some_output = interpreter.get_output(0)
     output_ref = weakref.finalize(some_output, self._helperNoop)
@@ -250,22 +250,22 @@ class ConvModelTests(test_util.TensorFlowTestCase):
     custom_op_registerers = [("wrong", "format")]
     with self.assertRaisesWithPredicateMatch(ValueError,
                                              "must be a list of strings"):
-      interpreter = tflm_runtime.Interpreter.from_bytes(
-          model_data, custom_op_registerers)
+      interpreter = runtime.Interpreter.from_bytes(model_data,
+                                                   custom_op_registerers)
 
     custom_op_registerers = "WrongFormat"
     with self.assertRaisesWithPredicateMatch(ValueError,
                                              "must be a list of strings"):
-      interpreter = tflm_runtime.Interpreter.from_bytes(
-          model_data, custom_op_registerers)
+      interpreter = runtime.Interpreter.from_bytes(model_data,
+                                                   custom_op_registerers)
 
   def testNonExistentCustomOps(self):
     model_data = generate_test_models.generate_conv_model(False)
     custom_op_registerers = ["SomeRandomOp"]
     with self.assertRaisesWithPredicateMatch(
         RuntimeError, "TFLM could not register custom op via SomeRandomOp"):
-      interpreter = tflm_runtime.Interpreter.from_bytes(
-          model_data, custom_op_registerers)
+      interpreter = runtime.Interpreter.from_bytes(model_data,
+                                                   custom_op_registerers)
 
 
 if __name__ == "__main__":

tensorflow/lite/micro/tools/BUILD

@@ -59,7 +59,7 @@ py_test(
     ],
     deps = [
         ":requantize_flatbuffer",
-        "//tensorflow/lite/micro/python/interpreter/src:tflm_runtime",
+        "//tensorflow/lite/micro/python/interpreter/src:runtime",
         requirement("numpy"),
         requirement("tensorflow-cpu"),
     ],

tensorflow/lite/micro/tools/requantize_flatbuffer_test.py

@@ -20,7 +20,7 @@ import tensorflow as tf
 from tensorflow.python.framework import test_util
 from tensorflow.python.platform import test
 from tflite_micro.tensorflow.lite.micro.tools import requantize_flatbuffer
-from tflite_micro.tensorflow.lite.micro.python.interpreter.src import tflm_runtime
+from tflite_micro.tensorflow.lite.micro.python.interpreter.src import runtime
 from tflite_micro.tensorflow.lite.tools import flatbuffer_utils
 
 
@@ -92,9 +92,9 @@ class SimpleFCModelTest(test_util.TensorFlowTestCase):
     int8_converted_int16_model = convert_8to16_requantizer(
         keras_model, representative_dataset_gen)
 
-    interpreter_tfl_converted = tflm_runtime.Interpreter.from_bytes(
+    interpreter_tfl_converted = runtime.Interpreter.from_bytes(
         tfl_converted_int16_model)
-    interpreter_tool_converted = tflm_runtime.Interpreter.from_bytes(
+    interpreter_tool_converted = runtime.Interpreter.from_bytes(
         int8_converted_int16_model)
 
     num_steps = 10