YOLOV7算法(五)pth/pt转onnx学习记录

输入指令

python export.py --weights /kaxier01/projects/FAS/yolov7/weights/yolov7.pt --grid --end2end --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 --max-wh 640

export.py代码学习

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
import sys
import warnings
warnings.filterwarnings('ignore')


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', type=str, default='/kaxier01/projects/FAS/yolov7/weights/yolov7.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  # opt.img_size=[640, 640]
    opt.dynamic = opt.dynamic and not opt.end2end  # False
    opt.dynamic = False if opt.dynamic_batch else opt.dynamic  # False
    print(opt)
    set_logging()
    t = time.time()

    # Load PyTorch model
    device = select_device(opt.device)  # device='cpu'
    model = attempt_load(opt.weights, map_location=device)  # load FP32 model
    labels = model.names
    '''
    labels=
    ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 
     'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 
     'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 
     'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 
     'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 
     'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 
     'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 
     'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 
     'scissors', 'teddy bear', 'hair drier', 'toothbrush']
    '''

    # Checks
    gs = int(max(model.stride))  # grid size (max stride), gs=32
    opt.img_size = [check_img_size(x, gs) for x in opt.img_size]  # verify img_size are gs-multiples, opt.img_size=[640, 640]

    # Input
    img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device)  # image size(1,3,320,192) iDetection, img.shape=torch.Size([1, 3, 640, 640])

    # 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()
    
    model.model[-1].export = not opt.grid  # set Detect() layer grid export, model.model[-1].export=False
    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')  # f='/kaxier01/projects/FAS/yolov7/weights/yolov7.torchscript.pt'
        ts = torch.jit.trace(model, img, strict=False)
        ts.save(f)  # .torchscript.pt模型可以不依赖于python而直接在c++等环境中运行
        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')  # f='/kaxier01/projects/FAS/yolov7/weights/yolov7.mlmodel'
        ct_model.save(f)  # .mlmodel可部署到IOS端
        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')  # f='/kaxier01/projects/FAS/yolov7/weights/yolov7.torchscript.ptl'
        tsl = torch.jit.trace(model, img, strict=False)
        tsl = optimize_for_mobile(tsl)
        tsl._save_for_lite_interpreter(f)  # .torchscript.ptl模型可部署到Android端
        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')  # f='/kaxier01/projects/FAS/yolov7/weights/yolov7.onnx'
        model.eval()
        output_names = ['classes', 'boxes'] if y is None else ['output']  # output_names=['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

        if opt.simplify:
            try:
                import onnxsim

                print('\nStarting to simplify ONNX...')
                onnx_model, check = onnxsim.simplify(onnx_model)  # 简化模型
                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))

如果遇到

CoreML export failure: Core ML only supports tensors with rank <= 5. Layer "model.105.anchor_grid", with type "const", outputs a rank 6 tensor.

则把输入指令改成

python export.py --weights /kaxier01/projects/FAS/yolov7/weights/yolov7.pt --end2end --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 --max-wh 640

yolov7.onnx网络结构图文章来源地址https://www.toymoban.com/news/detail-508695.html