feat: TensorRT model & inference

TensorRT_Model/convert_model.py

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+import multiprocessing
+
+keras_path = '../model_without_preprocess_finetuned.h5'
+onnx_path = 'model.onnx'
+
+# convert to onnx
+def keras2onnx():
+    import os
+
+    import onnx
+    import onnxmltools
+    import tensorflow as tf
+    from tensorflow import keras
+
+    print('[*] Converting Keras Model to onnx')
+
+    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+    tf.get_logger().setLevel('ERROR')
+
+    keras_model = keras.models.load_model(keras_path)
+    onnx_model = onnxmltools.convert_keras(keras_model)
+
+    # one data at a time
+    onnx_model.graph.input[0].type.tensor_type.shape.dim[0].dim_value = 1
+    onnx_model.graph.input[1].type.tensor_type.shape.dim[0].dim_value = 1
+    onnx_model.graph.output[0].type.tensor_type.shape.dim[0].dim_value = 1
+
+    onnx.checker.check_model(onnx_model)
+    onnx.save(onnx_model, onnx_path)
+
+    print(f'[*] onnx file saved as {onnx_path}')
+
+def onnx2rt():
+    # convert to tensorrt
+    import tensorrt as trt
+
+    TRT_LOGGER = trt.Logger(trt.Logger.ERROR)
+    EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
+
+    trt_file = '../model.trt'
+
+    print('[*] Converting onnx to tensorrt')
+
+    with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(network, TRT_LOGGER) as parser:
+        with open(onnx_path, 'rb') as f:
+            if not parser.parse(f.read()):
+                print('ERROR: Failed to parse the ONNX file.')
+                for error in range(parser.num_errors):
+                    print (parser.get_error(error))
+
+        config = builder.create_builder_config()
+        profile = builder.create_optimization_profile()
+
+        config.add_optimization_profile(profile)
+        # config.flags = 1 << (int)(trt.BuilderFlag.DEBUG)
+
+        with open(trt_file, "wb") as f:
+            f.write(builder.build_serialized_network(network, config))
+
+    print(f'[*] tensorrt file saved as {trt_file}')
+
+if __name__ == "__main__":
+    onnx = multiprocessing.Process(target=keras2onnx)
+    rt = multiprocessing.Process(target=onnx2rt)
+
+    onnx.start()
+    onnx.join()
+
+    rt.start()
+    rt.join()

common.py

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+#
+# SPDX-FileCopyrightText: Copyright (c) 1993-2022 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 argparse
+import os
+
+import numpy as np
+import pycuda.autoinit
+import pycuda.driver as cuda
+import tensorrt as trt
+
+try:
+    # Sometimes python does not understand FileNotFoundError
+    FileNotFoundError
+except NameError:
+    FileNotFoundError = IOError
+
+EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
+
+
+def GiB(val):
+    return val * 1 << 30
+
+
+def add_help(description):
+    parser = argparse.ArgumentParser(description=description, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    args, _ = parser.parse_known_args()
+
+
+def find_sample_data(description="Runs a TensorRT Python sample", subfolder="", find_files=[], err_msg=""):
+    """
+    Parses sample arguments.
+
+    Args:
+        description (str): Description of the sample.
+        subfolder (str): The subfolder containing data relevant to this sample
+        find_files (str): A list of filenames to find. Each filename will be replaced with an absolute path.
+
+    Returns:
+        str: Path of data directory.
+    """
+
+    # Standard command-line arguments for all samples.
+    kDEFAULT_DATA_ROOT = os.path.join(os.sep, "usr", "src", "tensorrt", "data")
+    parser = argparse.ArgumentParser(description=description, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument(
+        "-d",
+        "--datadir",
+        help="Location of the TensorRT sample data directory, and any additional data directories.",
+        action="append",
+        default=[kDEFAULT_DATA_ROOT],
+    )
+    args, _ = parser.parse_known_args()
+
+    def get_data_path(data_dir):
+        # If the subfolder exists, append it to the path, otherwise use the provided path as-is.
+        data_path = os.path.join(data_dir, subfolder)
+        if not os.path.exists(data_path):
+            if data_dir != kDEFAULT_DATA_ROOT:
+                print("WARNING: " + data_path + " does not exist. Trying " + data_dir + " instead.")
+            data_path = data_dir
+        # Make sure data directory exists.
+        if not (os.path.exists(data_path)) and data_dir != kDEFAULT_DATA_ROOT:
+            print(
+                "WARNING: {:} does not exist. Please provide the correct data path with the -d option.".format(
+                    data_path
+                )
+            )
+        return data_path
+
+    data_paths = [get_data_path(data_dir) for data_dir in args.datadir]
+    return data_paths, locate_files(data_paths, find_files, err_msg)
+
+
+def locate_files(data_paths, filenames, err_msg=""):
+    """
+    Locates the specified files in the specified data directories.
+    If a file exists in multiple data directories, the first directory is used.
+
+    Args:
+        data_paths (List[str]): The data directories.
+        filename (List[str]): The names of the files to find.
+
+    Returns:
+        List[str]: The absolute paths of the files.
+
+    Raises:
+        FileNotFoundError if a file could not be located.
+    """
+    found_files = [None] * len(filenames)
+    for data_path in data_paths:
+        # Find all requested files.
+        for index, (found, filename) in enumerate(zip(found_files, filenames)):
+            if not found:
+                file_path = os.path.abspath(os.path.join(data_path, filename))
+                if os.path.exists(file_path):
+                    found_files[index] = file_path
+
+    # Check that all files were found
+    for f, filename in zip(found_files, filenames):
+        if not f or not os.path.exists(f):
+            raise FileNotFoundError(
+                "Could not find {:}. Searched in data paths: {:}\n{:}".format(filename, data_paths, err_msg)
+            )
+    return found_files
+
+
+# Simple helper data class that's a little nicer to use than a 2-tuple.
+class HostDeviceMem(object):
+    def __init__(self, host_mem, device_mem):
+        self.host = host_mem
+        self.device = device_mem
+
+    def __str__(self):
+        return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device)
+
+    def __repr__(self):
+        return self.__str__()
+
+
+# Allocates all buffers required for an engine, i.e. host/device inputs/outputs.
+def allocate_buffers(engine):
+    inputs = []
+    outputs = []
+    bindings = []
+    stream = cuda.Stream()
+    for binding in engine:
+        size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
+        dtype = trt.nptype(engine.get_binding_dtype(binding))
+        # Allocate host and device buffers
+        host_mem = cuda.pagelocked_empty(size, dtype)
+        device_mem = cuda.mem_alloc(host_mem.nbytes)
+        # Append the device buffer to device bindings.
+        bindings.append(int(device_mem))
+        # Append to the appropriate list.
+        if engine.binding_is_input(binding):
+            inputs.append(HostDeviceMem(host_mem, device_mem))
+        else:
+            outputs.append(HostDeviceMem(host_mem, device_mem))
+    return inputs, outputs, bindings, stream
+
+
+# This function is generalized for multiple inputs/outputs.
+# inputs and outputs are expected to be lists of HostDeviceMem objects.
+def do_inference(context, bindings, inputs, outputs, stream, batch_size=1):
+    # Transfer input data to the GPU.
+    [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
+    # Run inference.
+    context.execute_async(batch_size=batch_size, bindings=bindings, stream_handle=stream.handle)
+    # Transfer predictions back from the GPU.
+    [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
+    # Synchronize the stream
+    stream.synchronize()
+    # Return only the host outputs.
+    return [out.host for out in outputs]
+
+
+# This function is generalized for multiple inputs/outputs for full dimension networks.
+# inputs and outputs are expected to be lists of HostDeviceMem objects.
+def do_inference_v2(context, bindings, inputs, outputs, stream):
+    # Transfer input data to the GPU.
+    [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
+    # Run inference.
+    context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)
+    # Transfer predictions back from the GPU.
+    [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
+    # Synchronize the stream
+    stream.synchronize()
+    # Return only the host outputs.
+    return [out.host for out in outputs]

inference_rt.py

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+labels = ['can', 'paper_cup', 'paper_box', 'paper_milkbox', 'plastic']
+
+print("[*] Importing packages...")
+import common
+import tensorrt as trt
+import os
+import cv2
+import pandas as pd
+import numpy as np
+
+TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
+
+print("[*] Loading model...")
+
+# load trt engine
+trt_path = 'model.trt'
+with open(trt_path, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime:
+    engine = runtime.deserialize_cuda_engine(f.read())
+    inputs, outputs, bindings, stream = common.allocate_buffers(engine)
+
+if __name__ == '__main__':
+    def pred(f, dirpath):
+        img = cv2.imread(os.path.join(dirpath, f))
+        weight = df.loc[f]['weight']
+
+        inputs[0].host = np.expand_dims(img, 0).astype('float32')
+        inputs[1].host = np.expand_dims(weight, 0).astype('float32')
+
+        # inference
+        with engine.create_execution_context() as context:
+            trt_outputs = common.do_inference_v2(context, bindings=bindings, inputs=inputs, outputs=outputs,stream=stream)
+
+        result = trt_outputs[0].argmax(-1)
+        return labels[result]
+
+
+    df = pd.read_csv('test_data/weights_test.csv')
+    df = df.set_index('name')
+
+    for dirpath, dirnames, filenames in os.walk('test_data'):
+        for f in filenames:
+            if f.endswith('.jpg'):
+                print(f'{f}: {pred(f, dirpath)}')
+
+