import argparse import sys import time import warnings sys.path.append('./') # to run '$ python *.py' files in subdirectories import torch import torch.nn as nn from torch.utils.mobile_optimizer import optimize_for_mobile import models from models.experimental import attempt_load, End2End from utils.activations import Hardswish, SiLU from utils.general import set_logging, check_img_size from utils.torch_utils import select_device from utils.add_nms import RegisterNMS if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--weights', type=str, default='./yolor-csp-c.pt', help='weights path') parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width parser.add_argument('--batch-size', type=int, default=1, help='batch size') parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes') parser.add_argument('--dynamic-batch', action='store_true', help='dynamic batch onnx for tensorrt and onnx-runtime') parser.add_argument('--grid', action='store_true', help='export Detect() layer grid') parser.add_argument('--end2end', action='store_true', help='export end2end onnx') parser.add_argument('--max-wh', type=int, default=None, help='None for tensorrt nms, int value for onnx-runtime nms') parser.add_argument('--topk-all', type=int, default=100, help='topk objects for every images') parser.add_argument('--iou-thres', type=float, default=0.45, help='iou threshold for NMS') parser.add_argument('--conf-thres', type=float, default=0.25, help='conf threshold for NMS') parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--simplify', action='store_true', help='simplify onnx model') parser.add_argument('--include-nms', action='store_true', help='export end2end onnx') parser.add_argument('--fp16', action='store_true', help='CoreML FP16 half-precision export') parser.add_argument('--int8', action='store_true', help='CoreML INT8 quantization') opt = parser.parse_args() opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand opt.dynamic = opt.dynamic and not opt.end2end opt.dynamic = False if opt.dynamic_batch else opt.dynamic print(opt) set_logging() t = time.time() # Load PyTorch model device = select_device(opt.device) model = attempt_load(opt.weights, map_location=device) # load FP32 model labels = model.names # Checks gs = int(max(model.stride)) # grid size (max stride) opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples # Input img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection # Update model for k, m in model.named_modules(): m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility if isinstance(m, models.common.Conv): # assign export-friendly activations if isinstance(m.act, nn.Hardswish): m.act = Hardswish() elif isinstance(m.act, nn.SiLU): m.act = SiLU() # elif isinstance(m, models.yolo.Detect): # m.forward = m.forward_export # assign forward (optional) model.model[-1].export = not opt.grid # set Detect() layer grid export y = model(img) # dry run if opt.include_nms: model.model[-1].include_nms = True y = None # TorchScript export try: print('\nStarting TorchScript export with torch %s...' % torch.__version__) f = opt.weights.replace('.pt', '.torchscript.pt') # filename ts = torch.jit.trace(model, img, strict=False) ts.save(f) print('TorchScript export success, saved as %s' % f) except Exception as e: print('TorchScript export failure: %s' % e) # CoreML export try: import coremltools as ct print('\nStarting CoreML export with coremltools %s...' % ct.__version__) # convert model from torchscript and apply pixel scaling as per detect.py ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])]) bits, mode = (8, 'kmeans_lut') if opt.int8 else (16, 'linear') if opt.fp16 else (32, None) if bits < 32: if sys.platform.lower() == 'darwin': # quantization only supported on macOS with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=DeprecationWarning) # suppress numpy==1.20 float warning ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode) else: print('quantization only supported on macOS, skipping...') f = opt.weights.replace('.pt', '.mlmodel') # filename ct_model.save(f) print('CoreML export success, saved as %s' % f) except Exception as e: print('CoreML export failure: %s' % e) # TorchScript-Lite export try: print('\nStarting TorchScript-Lite export with torch %s...' % torch.__version__) f = opt.weights.replace('.pt', '.torchscript.ptl') # filename tsl = torch.jit.trace(model, img, strict=False) tsl = optimize_for_mobile(tsl) tsl._save_for_lite_interpreter(f) print('TorchScript-Lite export success, saved as %s' % f) except Exception as e: print('TorchScript-Lite export failure: %s' % e) # ONNX export try: import onnx print('\nStarting ONNX export with onnx %s...' % onnx.__version__) f = opt.weights.replace('.pt', '.onnx') # filename model.eval() output_names = ['classes', 'boxes'] if y is None else ['output'] dynamic_axes = None if opt.dynamic: dynamic_axes = {'images': {0: 'batch', 2: 'height', 3: 'width'}, # size(1,3,640,640) 'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.dynamic_batch: opt.batch_size = 'batch' dynamic_axes = { 'images': { 0: 'batch', }, } if opt.end2end and opt.max_wh is None: output_axes = { 'num_dets': {0: 'batch'}, 'det_boxes': {0: 'batch'}, 'det_scores': {0: 'batch'}, 'det_classes': {0: 'batch'}, } else: output_axes = { 'output': {0: 'batch'}, } dynamic_axes.update(output_axes) if opt.grid: if opt.end2end: print('\nStarting export end2end onnx model for %s...' % 'TensorRT' if opt.max_wh is None else 'onnxruntime') model = End2End(model,opt.topk_all,opt.iou_thres,opt.conf_thres,opt.max_wh,device,len(labels)) if opt.end2end and opt.max_wh is None: output_names = ['num_dets', 'det_boxes', 'det_scores', 'det_classes'] shapes = [opt.batch_size, 1, opt.batch_size, opt.topk_all, 4, opt.batch_size, opt.topk_all, opt.batch_size, opt.topk_all] else: output_names = ['output'] else: model.model[-1].concat = True torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'], output_names=output_names, dynamic_axes=dynamic_axes) # Checks onnx_model = onnx.load(f) # load onnx model onnx.checker.check_model(onnx_model) # check onnx model if opt.end2end and opt.max_wh is None: for i in onnx_model.graph.output: for j in i.type.tensor_type.shape.dim: j.dim_param = str(shapes.pop(0)) # print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model # # Metadata # d = {'stride': int(max(model.stride))} # for k, v in d.items(): # meta = onnx_model.metadata_props.add() # meta.key, meta.value = k, str(v) # onnx.save(onnx_model, f) if opt.simplify: try: import onnxsim print('\nStarting to simplify ONNX...') onnx_model, check = onnxsim.simplify(onnx_model) assert check, 'assert check failed' except Exception as e: print(f'Simplifier failure: {e}') # print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model onnx.save(onnx_model,f) print('ONNX export success, saved as %s' % f) if opt.include_nms: print('Registering NMS plugin for ONNX...') mo = RegisterNMS(f) mo.register_nms() mo.save(f) except Exception as e: print('ONNX export failure: %s' % e) # Finish print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t))