IDD-YOLO-Tracking / trackers /reid_export.py
Bhaskar Saranga
Added tracker
e215925
import argparse
import os
# limit the number of cpus used by high performance libraries
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["OPENBLAS_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["VECLIB_MAXIMUM_THREADS"] = "1"
os.environ["NUMEXPR_NUM_THREADS"] = "1"
import sys
import numpy as np
from pathlib import Path
import torch
import time
import platform
import pandas as pd
import subprocess
import torch.backends.cudnn as cudnn
from torch.utils.mobile_optimizer import optimize_for_mobile
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0].parents[0] # yolov5 strongsort root directory
WEIGHTS = ROOT / 'weights'
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
if str(ROOT / 'yolov5') not in sys.path:
sys.path.append(str(ROOT / 'yolov5')) # add yolov5 ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
import logging
from ultralytics.yolo.utils.torch_utils import select_device
from ultralytics.yolo.utils import LOGGER, colorstr, ops
from ultralytics.yolo.utils.checks import check_requirements, check_version
from trackers.strongsort.deep.models import build_model
from trackers.strongsort.deep.reid_model_factory import get_model_name, load_pretrained_weights
def file_size(path):
# Return file/dir size (MB)
path = Path(path)
if path.is_file():
return path.stat().st_size / 1E6
elif path.is_dir():
return sum(f.stat().st_size for f in path.glob('**/*') if f.is_file()) / 1E6
else:
return 0.0
def export_formats():
# YOLOv5 export formats
x = [
['PyTorch', '-', '.pt', True, True],
['TorchScript', 'torchscript', '.torchscript', True, True],
['ONNX', 'onnx', '.onnx', True, True],
['OpenVINO', 'openvino', '_openvino_model', True, False],
['TensorRT', 'engine', '.engine', False, True],
['TensorFlow Lite', 'tflite', '.tflite', True, False],
]
return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
# YOLOv5 TorchScript model export
try:
LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
f = file.with_suffix('.torchscript')
ts = torch.jit.trace(model, im, strict=False)
if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
optimize_for_mobile(ts)._save_for_lite_interpreter(str(f))
else:
ts.save(str(f))
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
return f
except Exception as e:
LOGGER.info(f'{prefix} export failure: {e}')
def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX:')):
# ONNX export
try:
check_requirements(('onnx',))
import onnx
f = file.with_suffix('.onnx')
LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
if dynamic:
dynamic = {'images': {0: 'batch'}} # shape(1,3,640,640)
dynamic['output'] = {0: 'batch'} # shape(1,25200,85)
torch.onnx.export(
model.cpu() if dynamic else model, # --dynamic only compatible with cpu
im.cpu() if dynamic else im,
f,
verbose=False,
opset_version=opset,
do_constant_folding=True,
input_names=['images'],
output_names=['output'],
dynamic_axes=dynamic or None
)
# Checks
model_onnx = onnx.load(f) # load onnx model
onnx.checker.check_model(model_onnx) # check onnx model
onnx.save(model_onnx, f)
# Simplify
if simplify:
try:
cuda = torch.cuda.is_available()
check_requirements(('onnxruntime-gpu' if cuda else 'onnxruntime', 'onnx-simplifier>=0.4.1'))
import onnxsim
LOGGER.info(f'simplifying with onnx-simplifier {onnxsim.__version__}...')
model_onnx, check = onnxsim.simplify(model_onnx)
assert check, 'assert check failed'
onnx.save(model_onnx, f)
except Exception as e:
LOGGER.info(f'simplifier failure: {e}')
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
return f
except Exception as e:
LOGGER.info(f'export failure: {e}')
def export_openvino(file, half, prefix=colorstr('OpenVINO:')):
# YOLOv5 OpenVINO export
check_requirements(('openvino-dev',)) # requires openvino-dev: https://pypi.org/project/openvino-dev/
import openvino.inference_engine as ie
try:
LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
f = str(file).replace('.pt', f'_openvino_model{os.sep}')
cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}"
subprocess.check_output(cmd.split()) # export
except Exception as e:
LOGGER.info(f'export failure: {e}')
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
return f
def export_tflite(file, half, prefix=colorstr('TFLite:')):
# YOLOv5 OpenVINO export
try:
check_requirements(('openvino2tensorflow', 'tensorflow', 'tensorflow_datasets')) # requires openvino-dev: https://pypi.org/project/openvino-dev/
import openvino.inference_engine as ie
LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
output = Path(str(file).replace(f'_openvino_model{os.sep}', f'_tflite_model{os.sep}'))
modelxml = list(Path(file).glob('*.xml'))[0]
cmd = f"openvino2tensorflow \
--model_path {modelxml} \
--model_output_path {output} \
--output_pb \
--output_saved_model \
--output_no_quant_float32_tflite \
--output_dynamic_range_quant_tflite"
subprocess.check_output(cmd.split()) # export
LOGGER.info(f'{prefix} export success, results saved in {output} ({file_size(f):.1f} MB)')
return f
except Exception as e:
LOGGER.info(f'\n{prefix} export failure: {e}')
def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
# YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt
try:
assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`'
try:
import tensorrt as trt
except Exception:
if platform.system() == 'Linux':
check_requirements(('nvidia-tensorrt',), cmds=('-U --index-url https://pypi.ngc.nvidia.com',))
import tensorrt as trt
if trt.__version__[0] == '7': # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012
grid = model.model[-1].anchor_grid
model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]
export_onnx(model, im, file, 12, dynamic, simplify) # opset 12
model.model[-1].anchor_grid = grid
else: # TensorRT >= 8
check_version(trt.__version__, '8.0.0', hard=True) # require tensorrt>=8.0.0
export_onnx(model, im, file, 12, dynamic, simplify) # opset 13
onnx = file.with_suffix('.onnx')
LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
assert onnx.exists(), f'failed to export ONNX file: {onnx}'
f = file.with_suffix('.engine') # TensorRT engine file
logger = trt.Logger(trt.Logger.INFO)
if verbose:
logger.min_severity = trt.Logger.Severity.VERBOSE
builder = trt.Builder(logger)
config = builder.create_builder_config()
config.max_workspace_size = workspace * 1 << 30
# config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) # fix TRT 8.4 deprecation notice
flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
network = builder.create_network(flag)
parser = trt.OnnxParser(network, logger)
if not parser.parse_from_file(str(onnx)):
raise RuntimeError(f'failed to load ONNX file: {onnx}')
inputs = [network.get_input(i) for i in range(network.num_inputs)]
outputs = [network.get_output(i) for i in range(network.num_outputs)]
LOGGER.info(f'{prefix} Network Description:')
for inp in inputs:
LOGGER.info(f'{prefix}\tinput "{inp.name}" with shape {inp.shape} and dtype {inp.dtype}')
for out in outputs:
LOGGER.info(f'{prefix}\toutput "{out.name}" with shape {out.shape} and dtype {out.dtype}')
if dynamic:
if im.shape[0] <= 1:
LOGGER.warning(f"{prefix}WARNING: --dynamic model requires maximum --batch-size argument")
profile = builder.create_optimization_profile()
for inp in inputs:
profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)
config.add_optimization_profile(profile)
LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine in {f}')
if builder.platform_has_fast_fp16 and half:
config.set_flag(trt.BuilderFlag.FP16)
with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
t.write(engine.serialize())
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
return f
except Exception as e:
LOGGER.info(f'\n{prefix} export failure: {e}')
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="ReID export")
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[256, 128], help='image (h, w)')
parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
parser.add_argument('--dynamic', action='store_true', help='ONNX/TF/TensorRT: dynamic axes')
parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
parser.add_argument('--opset', type=int, default=12, help='ONNX: opset version')
parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)')
parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log')
parser.add_argument('--weights', nargs='+', type=str, default=WEIGHTS / 'osnet_x0_25_msmt17.pt', help='model.pt path(s)')
parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
parser.add_argument('--include',
nargs='+',
default=['torchscript'],
help='torchscript, onnx, openvino, engine')
args = parser.parse_args()
t = time.time()
include = [x.lower() for x in args.include] # to lowercase
fmts = tuple(export_formats()['Argument'][1:]) # --include arguments
flags = [x in include for x in fmts]
assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {fmts}'
jit, onnx, openvino, engine, tflite = flags # export booleans
args.device = select_device(args.device)
if args.half:
assert args.device.type != 'cpu', '--half only compatible with GPU export, i.e. use --device 0'
assert not args.dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both'
if type(args.weights) is list:
args.weights = Path(args.weights[0])
model = build_model(
get_model_name(args.weights),
num_classes=1,
pretrained=not (args.weights and args.weights.is_file() and args.weights.suffix == '.pt'),
use_gpu=args.device
).to(args.device)
load_pretrained_weights(model, args.weights)
model.eval()
if args.optimize:
assert device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
im = torch.zeros(args.batch_size, 3, args.imgsz[0], args.imgsz[1]).to(args.device) # image size(1,3,640,480) BCHW iDetection
for _ in range(2):
y = model(im) # dry runs
if args.half:
im, model = im.half(), model.half() # to FP16
shape = tuple((y[0] if isinstance(y, tuple) else y).shape) # model output shape
LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {args.weights} with output shape {shape} ({file_size(args.weights):.1f} MB)")
# Exports
f = [''] * len(fmts) # exported filenames
if jit:
f[0] = export_torchscript(model, im, args.weights, args.optimize) # opset 12
if engine: # TensorRT required before ONNX
f[1] = export_engine(model, im, args.weights, args.half, args.dynamic, args.simplify, args.workspace, args.verbose)
if onnx: # OpenVINO requires ONNX
f[2] = export_onnx(model, im, args.weights, args.opset, args.dynamic, args.simplify) # opset 12
if openvino:
f[3] = export_openvino(args.weights, args.half)
if tflite:
export_tflite(f, False)
# Finish
f = [str(x) for x in f if x] # filter out '' and None
if any(f):
LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
f"\nResults saved to {colorstr('bold', args.weights.parent.resolve())}"
f"\nVisualize: https://netron.app")