Rajeev Rao
114 lines
4.2 KiB
Python
114 lines
4.2 KiB
Python
#
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# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
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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 ctypes
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import numpy as np
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import os
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import pycuda.autoinit
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import sys
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import tensorrt as trt
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from lenet5 import MODEL_DIR, ModelData
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from random import randint
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# ../common.py
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sys.path.insert(1,
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os.path.join(
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os.path.dirname(os.path.realpath(__file__)),
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os.pardir
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)
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)
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import common
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WORKING_DIR = os.environ.get("TRT_WORKING_DIR") or os.path.dirname(os.path.realpath(__file__))
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# Path where clip plugin library will be built (check README.md)
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CLIP_PLUGIN_LIBRARY = os.path.join(
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WORKING_DIR,
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'build/libclipplugin.so'
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)
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# Path to which trained model will be saved (check README.md)
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# Define global logger object (it should be a singleton,
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# available for TensorRT from anywhere in code).
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# You can set the logger severity higher to suppress messages
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# (or lower to display more messages)
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TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
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# Builds TensorRT Engine
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def build_engine(model_path):
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with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network, builder.create_builder_config() as config, trt.UffParser() as parser:
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config.max_workspace_size = common.GiB(1)
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parser.register_input(ModelData.INPUT_NAME, ModelData.INPUT_SHAPE)
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parser.register_output(ModelData.OUTPUT_NAME)
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parser.parse(model_path, network)
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return builder.build_engine(network, config)
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def load_test_data():
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with open(os.path.join(MODEL_DIR, "x_test.npy"), 'rb') as f:
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x_test = np.load(f)
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with open(os.path.join(MODEL_DIR, "y_test.npy"), 'rb') as f:
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y_test = np.load(f)
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return x_test, y_test
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# Loads a test case into the provided pagelocked_buffer. Returns loaded test case label.
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def load_normalized_test_case(pagelocked_buffer):
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x_test, y_test = load_test_data()
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num_test = len(x_test)
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case_num = randint(0, num_test-1)
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img = x_test[case_num].ravel()
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np.copyto(pagelocked_buffer, img)
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return y_test[case_num]
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def main():
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# Load the shared object file containing the Clip plugin implementation.
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# By doing this, you will also register the Clip plugin with the TensorRT
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# PluginRegistry through use of the macro REGISTER_TENSORRT_PLUGIN present
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# in the plugin implementation. Refer to plugin/clipPlugin.cpp for more details.
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if not os.path.isfile(CLIP_PLUGIN_LIBRARY):
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raise IOError("\n{}\n{}\n{}\n".format(
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"Failed to load library ({}).".format(CLIP_PLUGIN_LIBRARY),
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"Please build the Clip sample plugin.",
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"For more information, see the included README.md"
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))
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ctypes.CDLL(CLIP_PLUGIN_LIBRARY)
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# Load pretrained model
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model_path = os.path.join(MODEL_DIR, "trained_lenet5.uff")
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if not os.path.isfile(model_path):
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raise IOError("\n{}\n{}\n{}\n".format(
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"Failed to load model file ({}).".format(model_path),
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"Please use 'python3 model.py' to train and save the UFF model.",
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"For more information, see README.md"
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))
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# Build an engine and retrieve the image mean from the model.
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with build_engine(model_path) as engine:
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inputs, outputs, bindings, stream = common.allocate_buffers(engine)
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with engine.create_execution_context() as context:
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print("\n=== Testing ===")
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test_case = load_normalized_test_case(inputs[0].host)
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print("Loading Test Case: " + str(test_case))
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# The common do_inference function will return a list of outputs - we only have one in this case.
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[pred] = common.do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
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print("Prediction: " + str(np.argmax(pred)))
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if __name__ == "__main__":
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main()
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