"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats Usage: $ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1 """ import argparse import sys import time sys.path.append('./') # to run '$ python *.py' files in subdirectories import torch import torch.nn as nn import models from models.experimental import attempt_load from utils.activations import Hardswish, SiLU from utils.general import set_logging, check_img_size import onnx if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path') # from yolov5/models/ 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', default=False, help='enable dynamic axis in onnx model') parser.add_argument('--onnx2pb', action='store_true', default=False, help='export onnx to pb') parser.add_argument('--onnx_infer', action='store_true', default=True, help='onnx infer test') #=======================TensorRT================================= parser.add_argument('--onnx2trt', action='store_true', default=False, help='export onnx to tensorrt') parser.add_argument('--fp16_trt', action='store_true', default=False, help='fp16 infer') #================================================================ opt = parser.parse_args() opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand print(opt) set_logging() t = time.time() # Load PyTorch model model = attempt_load(opt.weights, map_location=torch.device('cpu')) # load FP32 model delattr(model.model[-1], 'anchor_grid') model.model[-1].anchor_grid=[torch.zeros(1)] * 3 # nl=3 number of detection layers model.model[-1].export_cat = True model.eval() 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) # 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) if isinstance(m, models.common.ShuffleV2Block):#shufflenet block nn.SiLU for i in range(len(m.branch1)): if isinstance(m.branch1[i], nn.SiLU): m.branch1[i] = SiLU() for i in range(len(m.branch2)): if isinstance(m.branch2[i], nn.SiLU): m.branch2[i] = SiLU() if isinstance(m, models.common.BlazeBlock):#shufflenet block nn.SiLU if isinstance(m.relu, nn.SiLU): m.relu = SiLU() if isinstance(m, models.common.DoubleBlazeBlock):#shufflenet block nn.SiLU if isinstance(m.relu, nn.SiLU): m.relu = SiLU() for i in range(len(m.branch1)): if isinstance(m.branch1[i], nn.SiLU): m.branch1[i] = SiLU() # for i in range(len(m.branch2)): # if isinstance(m.branch2[i], nn.SiLU): # m.branch2[i] = SiLU() y = model(img) # dry run # ONNX export print('\nStarting ONNX export with onnx %s...' % onnx.__version__) f = opt.weights.replace('.pt', '.onnx') # filename model.fuse() # only for ONNX input_names=['input'] output_names=['output'] #tensorrt 7 # grid = model.model[-1].anchor_grid # model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid] #tensorrt 7 torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=input_names, output_names=output_names, dynamic_axes = {'input': {0: 'batch'}, 'output': {0: 'batch'} } if opt.dynamic else None) # model.model[-1].anchor_grid = grid # Checks onnx_model = onnx.load(f) # load onnx model onnx.checker.check_model(onnx_model) # check onnx model print('ONNX export success, saved as %s' % f) # Finish print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t)) # onnx infer if opt.onnx_infer: import onnxruntime import numpy as np providers = ['CPUExecutionProvider'] session = onnxruntime.InferenceSession(f, providers=providers) im = img.cpu().numpy().astype(np.float32) # torch to numpy y_onnx = session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: im})[0] print("pred's shape is ",y_onnx.shape) print("max(|torch_pred - onnx_pred|） =",abs(y.cpu().numpy()-y_onnx).max()) # TensorRT export if opt.onnx2trt: from torch2trt.trt_model import ONNX_to_TRT print('\nStarting TensorRT...') ONNX_to_TRT(onnx_model_path=f,trt_engine_path=f.replace('.onnx', '.trt'),fp16_mode=opt.fp16_trt) # PB export if opt.onnx2pb: print('download the newest onnx_tf by https://github.com/onnx/onnx-tensorflow/tree/master/onnx_tf') from onnx_tf.backend import prepare import tensorflow as tf outpb = f.replace('.onnx', '.pb') # filename # strict=True maybe leads to KeyError: 'pyfunc_0', check: https://github.com/onnx/onnx-tensorflow/issues/167 tf_rep = prepare(onnx_model, strict=False) # prepare tf representation tf_rep.export_graph(outpb) # export the model out_onnx = tf_rep.run(img) # onnx output # check pb with tf.Graph().as_default(): graph_def = tf.GraphDef() with open(outpb, "rb") as f: graph_def.ParseFromString(f.read()) tf.import_graph_def(graph_def, name="") with tf.Session() as sess: init = tf.global_variables_initializer() input_x = sess.graph.get_tensor_by_name(input_names[0]+':0') # input outputs = [] for i in output_names: outputs.append(sess.graph.get_tensor_by_name(i+':0')) out_pb = sess.run(outputs, feed_dict={input_x: img}) print(f'out_pytorch {y}') print(f'out_onnx {out_onnx}') print(f'out_pb {out_pb}')