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