export.py

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 End2End, attempt_load
from utils.activations import Hardswish, SiLU
from utils.add_nms import RegisterNMS
from utils.general import check_img_size, set_logging
from utils.torch_utils import select_device

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)
    )