<filename>model_compiler/tests/model_compiler/models/targets/test_tensorrt_model.py
# Copyright 2019 ZTE corporation. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0

import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import numpy
import pytest
import tensorflow as tf
import tensorrt
from tensorrt import Builder, DataType, ElementWiseOperation, Logger, Weights

import model_compiler.compilers.repository as compiler_repository
from model_compiler.models.irs.tf_model import Input as TfInput, TensorFlowModel
from model_compiler.models.targets.tensorrt_model import TensorRTModel
from model_compiler.protos.generated.model_config_pb2 import ModelInput, ModelOutput


def _make_tensorrt_model() -> TensorRTModel:
    with tf.Graph().as_default(), tf.compat.v1.Session().as_default() as session:
        input_x = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, 4], name='x')
        input_y = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, 4], name='y')
        weight = tf.Variable(initial_value=[2.0, 3.0, 4.0, 5.0], dtype=tf.float32)
        output_z = tf.add(input_x + input_y, weight, name='z')

        session.run(weight.initializer)

    compiler, config_type = compiler_repository.REPOSITORY.get(TensorFlowModel, TensorRTModel)

    return compiler(source=TensorFlowModel(inputs=[TfInput(tensor=input_x), TfInput(tensor=input_y)],
                                           outputs=[output_z],
                                           session=session),
                    config=config_type.from_json({'max_batch_size': 4}))


def _make_implicit_batch_size_tensorrt_model() -> TensorRTModel:
    with Logger() as logger, Builder(logger) as builder, builder.create_network() as network:
        input_x = network.add_input(name='x', dtype=DataType.FLOAT, shape=[4])
        input_y = network.add_input(name='y', dtype=DataType.FLOAT, shape=[4])

        weight = network.add_constant(
            shape=[4],
            weights=Weights(a=numpy.array([2.0, 3.0, 4.0, 5.0], dtype=numpy.float32))
        ).get_output(0)

        output_z = network.add_elementwise(input1=network.add_elementwise(input1=input_x,
                                                                          input2=input_y,
                                                                          op=ElementWiseOperation.SUM).get_output(0),
                                           input2=weight,
                                           op=ElementWiseOperation.SUM).get_output(0)

        output_z.name = 'z'

        network.mark_output(tensor=output_z)

        return TensorRTModel(cuda_engine=builder.build_cuda_engine(network), input_data_formats=[None, None])


@pytest.mark.gpu_test
class KerasModelFileTestCase(TestCase):
    def test_get_inputs(self):
        saved_model = _make_tensorrt_model()

        self.assertEqual(saved_model.get_inputs(),
                         [ModelInput(name='x:0', data_type=tf.float32.as_datatype_enum, format=None, dims=[4]),
                          ModelInput(name='y:0', data_type=tf.float32.as_datatype_enum, format=None, dims=[4])])

    def test_get_outputs(self):
        saved_model = _make_tensorrt_model()

        self.assertEqual(saved_model.get_outputs(),
                         [ModelOutput(name='z:0', data_type=tf.float32.as_datatype_enum, dims=[4])])

    def test_get_inputs_implicit_batch_size(self):
        saved_model = _make_implicit_batch_size_tensorrt_model()

        self.assertEqual(saved_model.get_inputs(),
                         [ModelInput(name='x', data_type=tf.float32.as_datatype_enum, format=None, dims=[4]),
                          ModelInput(name='y', data_type=tf.float32.as_datatype_enum, format=None, dims=[4])])

    def test_get_outputs_implicit_batch_size(self):
        saved_model = _make_implicit_batch_size_tensorrt_model()

        self.assertEqual(saved_model.get_outputs(),
                         [ModelOutput(name='z', data_type=tf.float32.as_datatype_enum, dims=[4])])

    def test_save(self):
        saved_model = _make_tensorrt_model()

        with TemporaryDirectory() as save_path:
            saved_model.save(save_path)

            self.assertEqual(os.listdir(save_path), ['model.plan'])

    def test_get_platform(self):
        self.assertEqual(TensorRTModel.get_platform(), ('tensorrt_plan', tensorrt.__version__))

<filename>model_compiler/tests/model_compiler/models/targets/test_tensorrt_model.py
# Copyright 2019 ZTE corporation. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0

import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import numpy
import pytest
import tensorflow as tf
import tensorrt
from tensorrt import Builder, DataType, ElementWiseOperation, Logger, Weights

import model_compiler.compilers.repository as compiler_repository
from model_compiler.models.irs.tf_model import Input as TfInput, TensorFlowModel
from model_compiler.models.targets.tensorrt_model import TensorRTModel
from model_compiler.protos.generated.model_config_pb2 import ModelInput, ModelOutput


def _make_tensorrt_model() -> TensorRTModel:
    with tf.Graph().as_default(), tf.compat.v1.Session().as_default() as session:
        input_x = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, 4], name='x')
        input_y = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, 4], name='y')
        weight = tf.Variable(initial_value=[2.0, 3.0, 4.0, 5.0], dtype=tf.float32)
        output_z = tf.add(input_x + input_y, weight, name='z')

        session.run(weight.initializer)

    compiler, config_type = compiler_repository.REPOSITORY.get(TensorFlowModel, TensorRTModel)

    return compiler(source=TensorFlowModel(inputs=[TfInput(tensor=input_x), TfInput(tensor=input_y)],
                                           outputs=[output_z],
                                           session=session),
                    config=config_type.from_json({'max_batch_size': 4}))


def _make_implicit_batch_size_tensorrt_model() -> TensorRTModel:
    with Logger() as logger, Builder(logger) as builder, builder.create_network() as network:
        input_x = network.add_input(name='x', dtype=DataType.FLOAT, shape=[4])
        input_y = network.add_input(name='y', dtype=DataType.FLOAT, shape=[4])

        weight = network.add_constant(
            shape=[4],
            weights=Weights(a=numpy.array([2.0, 3.0, 4.0, 5.0], dtype=numpy.float32))
        ).get_output(0)

        output_z = network.add_elementwise(input1=network.add_elementwise(input1=input_x,
                                                                          input2=input_y,
                                                                          op=ElementWiseOperation.SUM).get_output(0),
                                           input2=weight,
                                           op=ElementWiseOperation.SUM).get_output(0)

        output_z.name = 'z'

        network.mark_output(tensor=output_z)

        return TensorRTModel(cuda_engine=builder.build_cuda_engine(network), input_data_formats=[None, None])


@pytest.mark.gpu_test
class KerasModelFileTestCase(TestCase):
    def test_get_inputs(self):
        saved_model = _make_tensorrt_model()

        self.assertEqual(saved_model.get_inputs(),
                         [ModelInput(name='x:0', data_type=tf.float32.as_datatype_enum, format=None, dims=[4]),
                          ModelInput(name='y:0', data_type=tf.float32.as_datatype_enum, format=None, dims=[4])])

    def test_get_outputs(self):
        saved_model = _make_tensorrt_model()

        self.assertEqual(saved_model.get_outputs(),
                         [ModelOutput(name='z:0', data_type=tf.float32.as_datatype_enum, dims=[4])])

    def test_get_inputs_implicit_batch_size(self):
        saved_model = _make_implicit_batch_size_tensorrt_model()

        self.assertEqual(saved_model.get_inputs(),
                         [ModelInput(name='x', data_type=tf.float32.as_datatype_enum, format=None, dims=[4]),
                          ModelInput(name='y', data_type=tf.float32.as_datatype_enum, format=None, dims=[4])])

    def test_get_outputs_implicit_batch_size(self):
        saved_model = _make_implicit_batch_size_tensorrt_model()

        self.assertEqual(saved_model.get_outputs(),
                         [ModelOutput(name='z', data_type=tf.float32.as_datatype_enum, dims=[4])])

    def test_save(self):
        saved_model = _make_tensorrt_model()

        with TemporaryDirectory() as save_path:
            saved_model.save(save_path)

            self.assertEqual(os.listdir(save_path), ['model.plan'])

    def test_get_platform(self):
        self.assertEqual(TensorRTModel.get_platform(), ('tensorrt_plan', tensorrt.__version__))

# Copyright 2019 ZTE corporation. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0