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] # yolov5 strongsort root directory WEIGHTS = ROOT / 'weights' print(ROOT) 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 yolov5.utils.torch_utils import select_device from yolov5.models.common import DetectMultiBackend from yolov5.utils.general import LOGGER, colorstr, check_requirements, check_version from trackers.strong_sort.deep.models import build_model from trackers.strong_sort.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")