fast-reid/tools/deploy/onnx_export.py

# encoding: utf-8
"""
@author:  xingyu liao
@contact: sherlockliao01@gmail.com
"""

import os
import argparse
import io
import sys

import onnx
import onnxoptimizer
import torch
from onnxsim import simplify
from torch.onnx import OperatorExportTypes

sys.path.append('../../')

from fastreid.config import get_cfg
from fastreid.modeling.meta_arch import build_model
from fastreid.utils.file_io import PathManager
from fastreid.utils.checkpoint import Checkpointer
from fastreid.utils.logger import setup_logger

# import some modules added in project like this below
# sys.path.append('../../projects/FastDistill')
# from fastdistill import *

logger = setup_logger(name='onnx_export')


def setup_cfg(args):
    cfg = get_cfg()
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()
    return cfg


def get_parser():
    parser = argparse.ArgumentParser(description="Convert Pytorch to ONNX model")

    parser.add_argument(
        "--config-file",
        metavar="FILE",
        help="path to config file",
    )
    parser.add_argument(
        "--name",
        default="baseline",
        help="name for converted model"
    )
    parser.add_argument(
        "--output",
        default='onnx_model',
        help='path to save converted onnx model'
    )
    parser.add_argument(
        '--batch-size',
        default=1,
        type=int,
        help="the maximum batch size of onnx runtime"
    )
    parser.add_argument(
        "--opts",
        help="Modify config options using the command-line 'KEY VALUE' pairs",
        default=[],
        nargs=argparse.REMAINDER,
    )
    return parser


def remove_initializer_from_input(model):
    if model.ir_version < 4:
        print(
            'Model with ir_version below 4 requires to include initilizer in graph input'
        )
        return

    inputs = model.graph.input
    name_to_input = {}
    for input in inputs:
        name_to_input[input.name] = input

    for initializer in model.graph.initializer:
        if initializer.name in name_to_input:
            inputs.remove(name_to_input[initializer.name])

    return model


def export_onnx_model(model, inputs):
    """
    Trace and export a model to onnx format.
    Args:
        model (nn.Module):
        inputs (torch.Tensor): the model will be called by `model(*inputs)`
    Returns:
        an onnx model
    """
    assert isinstance(model, torch.nn.Module)

    # make sure all modules are in eval mode, onnx may change the training state
    # of the module if the states are not consistent
    def _check_eval(module):
        assert not module.training

    model.apply(_check_eval)

    logger.info("Beginning ONNX file converting")
    # Export the model to ONNX
    with torch.no_grad():
        with io.BytesIO() as f:
            torch.onnx.export(
                model,
                inputs,
                f,
                operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK,
                # verbose=True,  # NOTE: uncomment this for debugging
                # export_params=True,
            )
            onnx_model = onnx.load_from_string(f.getvalue())

    logger.info("Completed convert of ONNX model")

    # Apply ONNX's Optimization
    logger.info("Beginning ONNX model path optimization")
    all_passes = onnxoptimizer.get_available_passes()
    passes = ["extract_constant_to_initializer", "eliminate_unused_initializer", "fuse_bn_into_conv"]
    assert all(p in all_passes for p in passes)
    onnx_model = onnxoptimizer.optimize(onnx_model, passes)
    logger.info("Completed ONNX model path optimization")
    return onnx_model


if __name__ == '__main__':
    args = get_parser().parse_args()
    cfg = setup_cfg(args)

    cfg.defrost()
    cfg.MODEL.BACKBONE.PRETRAIN = False
    if cfg.MODEL.HEADS.POOL_LAYER == 'fastavgpool':
        cfg.MODEL.HEADS.POOL_LAYER = 'avgpool'
    model = build_model(cfg)
    Checkpointer(model).load(cfg.MODEL.WEIGHTS)
    if hasattr(model.backbone, 'deploy'):
        model.backbone.deploy(True)
    model.eval()
    logger.info(model)

    inputs = torch.randn(args.batch_size, 3, cfg.INPUT.SIZE_TEST[0], cfg.INPUT.SIZE_TEST[1])
    onnx_model = export_onnx_model(model, inputs)

    model_simp, check = simplify(onnx_model)

    model_simp = remove_initializer_from_input(model_simp)

    assert check, "Simplified ONNX model could not be validated"

    PathManager.mkdirs(args.output)

    save_path = os.path.join(args.output, args.name+'.onnx')
    onnx.save_model(model_simp, save_path)
    logger.info("ONNX model file has already saved to {}!".format(save_path))