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# ============================================================================

import torch
import argparse
import os

from unilm.beit.modeling_finetune import beit_base_patch16_224
#============================================================================
# Functions
#============================================================================


def pth2onnx(input_file, output_file, batch_size):
    if os.path.exists(input_file):
        checkpoint = torch.load(input_file, map_location=torch.device('cpu'))
    else:
        print("download checkpoint from readme ...")

    model = beit_base_patch16_224(pretrained=False, num_classes=1000, drop_rate=0.0,
                                  drop_path_rate=0.1, attn_drop_rate=0.0, use_mean_pooling=True,
                                  init_scale=0.001, use_rel_pos_bias=True, use_abs_pos_emb=False,
                                  init_values=0.1,)

    model.load_state_dict(checkpoint['model'])
    model.eval()

    input_names = ['image']
    output_names = ['class']
    dynamic_axes = {'image': {0: '-1'}, 'class':{0: '-1'}}

    dummy_img = torch.randn(batch_size, 3, 224, 224)
    torch.onnx.export(model, dummy_img, output_file, dynamic_axes=dynamic_axes,
                      verbose=True, input_names=input_names, output_names=output_names, opset_version=11)

#============================================================================
# Main
#============================================================================
if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--source', type=str, default="beit_base_patch16_224_pt22k_ft22kto1k.pth")
    parser.add_argument('--target', type=str, default="beit_base_patch16_224.onnx")
    parser.add_argument('--batch_size', type=int, default=8)
    args = parser.parse_args()

    pth2onnx(args.source, args.target, args.batch_size)