#!/usr/bin/env python3

#

# SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.

# SPDX-License-Identifier: Apache-2.0

#

# 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.

#

import tensorrt as trt

import numpy as np

from polygraphy.logger import G_LOGGER

from polygraphy.backend.trt import (

CreateNetwork,

CreateConfig,

engine_bytes_from_network,

get_trt_logger

)

DEBUG_LOG = False # Turn on to print TRT verbose logs

if DEBUG_LOG:

verbose = G_LOGGER.verbosity(G_LOGGER.SUPER_VERBOSE)

verbose.__enter__()

else:

verbose = None

def build_network():

builder, network = CreateNetwork()()

# A simple network with internal tensors

input_tensor = network.add_input(name="input", dtype=trt.float32, shape=(1, 3, 224, 224))

conv1_w = np.random.randn(16, 3, 3, 3).astype(np.float32)

conv1_b = np.random.randn(16).astype(np.float32)

conv1 = network.add_convolution_nd(input=input_tensor, num_output_maps=16, kernel_shape=(3, 3), kernel=conv1_w, bias=conv1_b)

relu1 = network.add_activation(input=conv1.get_output(0), type=trt.ActivationType.RELU)

conv2_w = np.random.randn(32, 16, 3, 3).astype(np.float32)

conv2_b = np.random.randn(32).astype(np.float32)

conv2 = network.add_convolution_nd(input=relu1.get_output(0), num_output_maps=32, kernel_shape=(3, 3), kernel=conv2_w, bias=conv2_b)

relu2 = network.add_activation(input=conv2.get_output(0), type=trt.ActivationType.RELU)

network.mark_output(tensor=relu2.get_output(0))

return builder, network

class StreamWriter(trt.IStreamWriter):

def __init__(self):

trt.IStreamWriter.__init__(self)

self.bytes = bytes()

def write(self, data):

self.bytes += data

return len(data)

def build_engine():

print("Constructing network...")

builder, network = build_network()

config = CreateConfig()(builder, network)

stream_writer = StreamWriter()

print("Building engine and serializing to stream...")

engine_bytes = builder.build_serialized_network_to_stream(network, config, stream_writer)

print("The total bytes written to stream is: ", len(stream_writer.bytes))

runtime = trt.Runtime(get_trt_logger())

print("Deserializing engine from stream...")

engine = runtime.deserialize_cuda_engine(stream_writer.bytes)

assert engine is not None, "Engine deserialization failed"

print("Engine deserialized successfully")

if __name__ == "__main__":

build_engine()

if verbose is not None:

verbose.__exit__(None, None, None)