wehub-resource-sync
79 lines
3.0 KiB
Python
79 lines
3.0 KiB
Python
#!/usr/bin/env python3
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#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import tensorrt as trt
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import numpy as np
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from polygraphy.logger import G_LOGGER
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from polygraphy.backend.trt import (
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CreateNetwork,
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CreateConfig,
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engine_bytes_from_network,
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get_trt_logger
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)
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DEBUG_LOG = False # Turn on to print TRT verbose logs
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if DEBUG_LOG:
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verbose = G_LOGGER.verbosity(G_LOGGER.SUPER_VERBOSE)
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verbose.__enter__()
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else:
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verbose = None
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def build_network():
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builder, network = CreateNetwork()()
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# A simple network with internal tensors
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input_tensor = network.add_input(name="input", dtype=trt.float32, shape=(1, 3, 224, 224))
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conv1_w = np.random.randn(16, 3, 3, 3).astype(np.float32)
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conv1_b = np.random.randn(16).astype(np.float32)
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conv1 = network.add_convolution_nd(input=input_tensor, num_output_maps=16, kernel_shape=(3, 3), kernel=conv1_w, bias=conv1_b)
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relu1 = network.add_activation(input=conv1.get_output(0), type=trt.ActivationType.RELU)
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conv2_w = np.random.randn(32, 16, 3, 3).astype(np.float32)
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conv2_b = np.random.randn(32).astype(np.float32)
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conv2 = network.add_convolution_nd(input=relu1.get_output(0), num_output_maps=32, kernel_shape=(3, 3), kernel=conv2_w, bias=conv2_b)
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relu2 = network.add_activation(input=conv2.get_output(0), type=trt.ActivationType.RELU)
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network.mark_output(tensor=relu2.get_output(0))
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return builder, network
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class StreamWriter(trt.IStreamWriter):
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def __init__(self):
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trt.IStreamWriter.__init__(self)
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self.bytes = bytes()
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def write(self, data):
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self.bytes += data
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return len(data)
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def build_engine():
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print("Constructing network...")
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builder, network = build_network()
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config = CreateConfig()(builder, network)
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stream_writer = StreamWriter()
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print("Building engine and serializing to stream...")
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engine_bytes = builder.build_serialized_network_to_stream(network, config, stream_writer)
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print("The total bytes written to stream is: ", len(stream_writer.bytes))
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runtime = trt.Runtime(get_trt_logger())
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print("Deserializing engine from stream...")
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engine = runtime.deserialize_cuda_engine(stream_writer.bytes)
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assert engine is not None, "Engine deserialization failed"
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print("Engine deserialized successfully")
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if __name__ == "__main__":
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build_engine()
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if verbose is not None:
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verbose.__exit__(None, None, None)
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