# Copyright (c) OpenMMLab. All rights reserved.

import argparse

from functools import partial

import torch

from mmpose.apis.inference import init_pose_model

try:

    from mmcv.cnn import get_model_complexity_info

except ImportError:

    raise ImportError('Please upgrade mmcv to >0.6.2')

def parse_args():

    parser = argparse.ArgumentParser(description='Train a recognizer')

    parser.add_argument('config', help='train config file path')

    parser.add_argument(

        '--shape',

        type=int,

        nargs='+',

        default=[256, 192],

        help='input image size')

    parser.add_argument(

        '--input-constructor',

        '-c',

        type=str,

        choices=['none', 'batch'],

        default='none',

        help='If specified, it takes a callable method that generates '

        'input. Otherwise, it will generate a random tensor with '

        'input shape to calculate FLOPs.')

    parser.add_argument(

        '--batch-size', '-b', type=int, default=1, help='input batch size')

    parser.add_argument(

        '--not-print-per-layer-stat',

        '-n',

        action='store_true',

        help='Whether to print complexity information'

        'for each layer in a model')

    args = parser.parse_args()

    return args

def batch_constructor(flops_model, batch_size, input_shape):

    """Generate a batch of tensors to the model."""

    batch = {}

    img = torch.ones(()).new_empty(

        (batch_size, *input_shape),

        dtype=next(flops_model.parameters()).dtype,

        device=next(flops_model.parameters()).device)

    batch['img'] = img

    return batch

def main():

    args = parse_args()

    if len(args.shape) == 1:

        input_shape = (3, args.shape[0], args.shape[0])

    elif len(args.shape) == 2:

        input_shape = (3, ) + tuple(args.shape)

    else:

        raise ValueError('invalid input shape')

    model = init_pose_model(args.config)

    if args.input_constructor == 'batch':

        input_constructor = partial(batch_constructor, model, args.batch_size)

    else:

        input_constructor = None

    if args.input_constructor == 'batch':

        input_constructor = partial(batch_constructor, model, args.batch_size)

    else:

        input_constructor = None

    if hasattr(model, 'forward_dummy'):

        model.forward = model.forward_dummy

    else:

        raise NotImplementedError(

            'FLOPs counter is currently not currently supported with {}'.

            format(model.__class__.__name__))

    flops, params = get_model_complexity_info(

        model,

        input_shape,

        input_constructor=input_constructor,

        print_per_layer_stat=(not args.not_print_per_layer_stat))

    split_line = '=' * 30

    input_shape = (args.batch_size, ) + input_shape

    print(f'{split_line}\nInput shape: {input_shape}\n'

          f'Flops: {flops}\nParams: {params}\n{split_line}')

    print('!!!Please be cautious if you use the results in papers. '

          'You may need to check if all ops are supported and verify that the '

          'flops computation is correct.')

if __name__ == '__main__':

    main()