d9e2cbc294
* add centernet dla34 ctdet task. * update readme. * update readme and fix a bug in sample. Co-authored-by: chandler <chandler@invix.com>
239 lines
9.4 KiB
Python
239 lines
9.4 KiB
Python
#
|
|
# Copyright 1993-2020 NVIDIA Corporation. All rights reserved.
|
|
#
|
|
# NOTICE TO LICENSEE:
|
|
#
|
|
# This source code and/or documentation ("Licensed Deliverables") are
|
|
# subject to NVIDIA intellectual property rights under U.S. and
|
|
# international Copyright laws.
|
|
#
|
|
# These Licensed Deliverables contained herein is PROPRIETARY and
|
|
# CONFIDENTIAL to NVIDIA and is being provided under the terms and
|
|
# conditions of a form of NVIDIA software license agreement by and
|
|
# between NVIDIA and Licensee ("License Agreement") or electronically
|
|
# accepted by Licensee. Notwithstanding any terms or conditions to
|
|
# the contrary in the License Agreement, reproduction or disclosure
|
|
# of the Licensed Deliverables to any third party without the express
|
|
# written consent of NVIDIA is prohibited.
|
|
#
|
|
# NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
|
|
# LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
|
|
# SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS
|
|
# PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
|
|
# NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
|
|
# DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
|
|
# NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
|
|
# NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
|
|
# LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
|
|
# SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
|
|
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
|
|
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
|
|
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
|
|
# OF THESE LICENSED DELIVERABLES.
|
|
#
|
|
# U.S. Government End Users. These Licensed Deliverables are a
|
|
# "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
|
|
# 1995), consisting of "commercial computer software" and "commercial
|
|
# computer software documentation" as such terms are used in 48
|
|
# C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
|
|
# only as a commercial end item. Consistent with 48 C.F.R.12.212 and
|
|
# 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
|
|
# U.S. Government End Users acquire the Licensed Deliverables with
|
|
# only those rights set forth herein.
|
|
#
|
|
# Any use of the Licensed Deliverables in individual and commercial
|
|
# software must include, in the user documentation and internal
|
|
# comments to the code, the above Disclaimer and U.S. Government End
|
|
# Users Notice.
|
|
#
|
|
|
|
from itertools import chain
|
|
import argparse
|
|
import os
|
|
|
|
import pycuda.driver as cuda
|
|
import pycuda.autoinit
|
|
import numpy as np
|
|
|
|
import tensorrt as trt
|
|
|
|
try:
|
|
# Sometimes python2 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]
|
|
|
|
|
|
# `retry_call` and `retry` are used to wrap the function we want to try multiple times
|
|
def retry_call(func, args=[], kwargs={}, n_retries=3):
|
|
"""Wrap a function to retry it several times.
|
|
|
|
Args:
|
|
func: function to call
|
|
args (List): args parsed to func
|
|
kwargs (Dict): kwargs parsed to func
|
|
n_retries (int): maximum times of tries
|
|
"""
|
|
for i_try in range(n_retries):
|
|
try:
|
|
func(*args, **kwargs)
|
|
break
|
|
except:
|
|
if i_try == n_retries - 1:
|
|
raise
|
|
print("retry...")
|
|
|
|
# Usage: @retry(n_retries)
|
|
def retry(n_retries=3):
|
|
"""Wrap a function to retry it several times. Decorator version of `retry_call`.
|
|
|
|
Args:
|
|
n_retries (int): maximum times of tries
|
|
|
|
Usage:
|
|
@retry(n_retries)
|
|
def func(...):
|
|
pass
|
|
"""
|
|
def wrapper(func):
|
|
def _wrapper(*args, **kwargs):
|
|
retry_call(func, args, kwargs, n_retries)
|
|
return _wrapper
|
|
return wrapper
|