<reponame>huawei-noah/vega
# -*- coding: utf-8 -*-

# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
# This program is free software; you can redistribute it and/or modify
# it under the terms of the MIT License.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# MIT License for more details.

"""Inference of vega detection model."""

import vega
from vega.common import argment_parser
from vega.common import FileOps


def _load_data(args):
    """Load data from path."""
    if args.data_format == 'CULANE':
        return vega.dataset("AutoLaneDataset", dataset_format="CULane", data_path=args.data_path, mode="test",
                                batch_size=args.batch_size).loader
    elif args.data_format == 'COCO':
        return vega.dataset("CocoDataset", data_root=args.data_path, mode="test",
                                batch_size=args.batch_size).loader


def _get_model(args):
    """Get model."""
    from vega.model_zoo import ModelZoo
    model = ModelZoo.get_model(args.model_desc, args.model)
    if vega.is_torch_backend():
        if args.device == "GPU":
            model = model.cuda()
        model.eval()
    return model


def _infer(args, loader, model=None):
    """Choose backend."""
    if vega.is_torch_backend():
        return _infer_pytorch(args, model, loader)
    elif vega.is_tf_backend():
        return _infer_tf(args, model, loader)
    elif vega.is_ms_backend():
        return _infer_ms(args, model, loader)


def _infer_pytorch(args, model, loader):
    """Infer with pytorch."""
    infer_result = []
    import torch
    with torch.no_grad():
        for batch in loader:
            if args.data_format == 'CULANE':
                image = batch.pop('image').cuda(non_blocking=True).float()
                infer_result = model(input=image,
                                     forward_switch='valid',
                                     **batch)
            elif args.data_format == 'COCO':
                infer_result = model(**batch)
        return infer_result


def _infer_tf(args, model, loader):
    """Infer with tf."""
    raise ValueError('Not currently supported.')


def _infer_ms():
    """Infer with ms."""
    raise ValueError('Not currently supported.')


def _save_result(args, result):
    """Save results."""
    _output_file = args.output_file
    if not _output_file:
        _output_file = "./result.pkl"
    FileOps.dump_pickle(result, _output_file)
    print('Results of Inference is saved in {}.'.format(_output_file))


def parse_args_parser():
    """Parse parameters."""
    parser = argment_parser('Vega Inference.')
    parser.add_argument("-c", "--model_desc", default=None, type=str, required=True,
                        help="model description file, generally in json format, contains 'module' node.")
    parser.add_argument("-m", "--model", default=None, type=str, required=True,
                        help="model weight file, usually ends with pth, ckpl, etc.")
    parser.add_argument("-df", "--data_format", default="CULANE", type=str, required=True,
                        choices=["COCO",
                                 "CULANE"
                                 ],
                        help="data type, "
                        )
    parser.add_argument("-bs", "--batch_size", default=1, type=str,
                        help="Batch size of inference, default: 1.")
    parser.add_argument("-dp", "--data_path", default=None, type=str, required=True,
                        help="the folder where the file to be inferred is located.")
    parser.add_argument("-b", "--backend", default="pytorch", type=str,
                        choices=["pytorch", "tensorflow", "mindspore"],
                        help="set training platform")
    parser.add_argument("-d", "--device", default="GPU", type=str,
                        choices=["CPU", "GPU", "NPU"],
                        help="set training device")
    parser.add_argument("-o", "--output_file", default=None, type=str,
                        help="output file. "
                        "type: pkl"
                        )
    args = parser.parse_args()
    return args


def main():
    """Inference."""
    args = parse_args_parser()
    vega.set_backend(args.backend, args.device)
    print("Start building model.")
    model = _get_model(args)
    print("Start loading data.")
    loader = _load_data(args)
    print("Start inferencing.")
    result = _infer(args, loader, model)
    _save_result(args, result)
    print("Completed successfully.")


if __name__ == '__main__':
    main()

# -*- coding: utf-8 -*-

# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
# This program is free software; you can redistribute it and/or modify
# it under the terms of the MIT License.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# MIT License for more details.

"""Inference of vega detection model."""

import vega
from vega.common import argment_parser
from vega.common import FileOps


def _load_data(args):
    """Load data from path."""
    if args.data_format == 'CULANE':
        return vega.dataset("AutoLaneDataset", dataset_format="CULane", data_path=args.data_path, mode="test",
                                batch_size=args.batch_size).loader
    elif args.data_format == 'COCO':
        return vega.dataset("CocoDataset", data_root=args.data_path, mode="test",
                                batch_size=args.batch_size).loader


def _get_model(args):
    """Get model."""
    from vega.model_zoo import ModelZoo
    model = ModelZoo.get_model(args.model_desc, args.model)
    if vega.is_torch_backend():
        if args.device == "GPU":
            model = model.cuda()
        model.eval()
    return model


def _infer(args, loader, model=None):
    """Choose backend."""
    if vega.is_torch_backend():
        return _infer_pytorch(args, model, loader)
    elif vega.is_tf_backend():
        return _infer_tf(args, model, loader)
    elif vega.is_ms_backend():
        return _infer_ms(args, model, loader)


def _infer_pytorch(args, model, loader):
    """Infer with pytorch."""
    infer_result = []
    import torch
    with torch.no_grad():
        for batch in loader:
            if args.data_format == 'CULANE':
                image = batch.pop('image').cuda(non_blocking=True).float()
                infer_result = model(input=image,
                                     forward_switch='valid',
                                     **batch)
            elif args.data_format == 'COCO':
                infer_result = model(**batch)
        return infer_result


def _infer_tf(args, model, loader):
    """Infer with tf."""
    raise ValueError('Not currently supported.')


def _infer_ms():
    """Infer with ms."""
    raise ValueError('Not currently supported.')


def _save_result(args, result):
    """Save results."""
    _output_file = args.output_file
    if not _output_file:
        _output_file = "./result.pkl"
    FileOps.dump_pickle(result, _output_file)
    print('Results of Inference is saved in {}.'.format(_output_file))


def parse_args_parser():
    """Parse parameters."""
    parser = argment_parser('Vega Inference.')
    parser.add_argument("-c", "--model_desc", default=None, type=str, required=True,
                        help="model description file, generally in json format, contains 'module' node.")
    parser.add_argument("-m", "--model", default=None, type=str, required=True,
                        help="model weight file, usually ends with pth, ckpl, etc.")
    parser.add_argument("-df", "--data_format", default="CULANE", type=str, required=True,
                        choices=["COCO",
                                 "CULANE"
                                 ],
                        help="data type, "
                        )
    parser.add_argument("-bs", "--batch_size", default=1, type=str,
                        help="Batch size of inference, default: 1.")
    parser.add_argument("-dp", "--data_path", default=None, type=str, required=True,
                        help="the folder where the file to be inferred is located.")
    parser.add_argument("-b", "--backend", default="pytorch", type=str,
                        choices=["pytorch", "tensorflow", "mindspore"],
                        help="set training platform")
    parser.add_argument("-d", "--device", default="GPU", type=str,
                        choices=["CPU", "GPU", "NPU"],
                        help="set training device")
    parser.add_argument("-o", "--output_file", default=None, type=str,
                        help="output file. "
                        "type: pkl"
                        )
    args = parser.parse_args()
    return args


def main():
    """Inference."""
    args = parse_args_parser()
    vega.set_backend(args.backend, args.device)
    print("Start building model.")
    model = _get_model(args)
    print("Start loading data.")
    loader = _load_data(args)
    print("Start inferencing.")
    result = _infer(args, loader, model)
    _save_result(args, result)
    print("Completed successfully.")


if __name__ == '__main__':
    main()
# -*- coding: utf-8 -*- # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved. # This program is free software; you can redistribute it and/or modify # it under the terms of the MIT License. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # MIT License for more details. Inference of vega detection model. Load data from path. Get model. Choose backend. Infer with pytorch. Infer with tf. Infer with ms. Save results. Parse parameters. Inference.