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

import pickle
import sys

sys.path.append('./model')
import torch
import argparse
import functools


def pkl2onnx(args):
    # Set up options
    bs = args.batch_size
    pkl_file = args.pkl_file
    onnx_file = args.onnx_file

    # Load pkl model
    with open(pkl_file, 'rb') as f:
        G = pickle.load(f)['G_ema']
    G.forward = functools.partial(G.forward, force_fp32=True)

    # Prepare input
    input_names = ["z"]
    output_names = ["image"]
    z = torch.randn([bs, G.z_dim])
    c = torch.empty([bs, 0], device='cpu')
    dummy_input = (z, c)

    # Prepare output name
    if onnx_file is None:
        onnx = "G_ema_onnx_bs{}.onnx".format(z.shape[0])
    else:
        onnx = onnx_file
    # export onnx
    torch.onnx.export(G, dummy_input, onnx,
                      input_names=input_names,
                      output_names=output_names,
                      do_constant_folding=False,
                      opset_version=11, 
                      verbose=True)


#
# ----------------------------------------------------------------------------
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--pkl_file', type=str, default='./G_ema_bs8_8p_kimg1000.pkl')
    parser.add_argument('--onnx_file', type=str)
    parser.add_argument('--batch_size', type=int, default=8)
    args = parser.parse_args()
    pkl2onnx(args)
# ----------------------------------------------------------------------------