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108 lines
3.9 KiB
108 lines
3.9 KiB
import argparse
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import hashlib
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import os
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import mxnet as mx
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import gluoncv
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import torch
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from models.model_factory import create_model
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parser = argparse.ArgumentParser(description='Training')
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parser.add_argument('--model', default='all', type=str, metavar='MODEL',
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help='Name of model to train (default: "all"')
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def convert(mxnet_name, torch_name):
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# download and load the pre-trained model
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net = gluoncv.model_zoo.get_model(mxnet_name, pretrained=True)
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# create corresponding torch model
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torch_net = create_model(torch_name)
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mxp = [(k, v) for k, v in net.collect_params().items() if 'running' not in k]
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torchp = list(torch_net.named_parameters())
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torch_params = {}
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# convert parameters
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# NOTE: we are relying on the fact that the order of parameters
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# are usually exactly the same between these models, thus no key name mapping
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# is necessary. Asserts will trip if this is not the case.
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for (tn, tv), (mn, mv) in zip(torchp, mxp):
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m_split = mn.split('_')
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t_split = tn.split('.')
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print(t_split, m_split)
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print(tv.shape, mv.shape)
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# ensure ordering of BN params match since their sizes are not specific
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if m_split[-1] == 'gamma':
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assert t_split[-1] == 'weight'
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if m_split[-1] == 'beta':
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assert t_split[-1] == 'bias'
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# ensure shapes match
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assert all(t == m for t, m in zip(tv.shape, mv.shape))
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torch_tensor = torch.from_numpy(mv.data().asnumpy())
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torch_params[tn] = torch_tensor
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# convert buffers (batch norm running stats)
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mxb = [(k, v) for k, v in net.collect_params().items() if any(x in k for x in ['running_mean', 'running_var'])]
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torchb = [(k, v) for k, v in torch_net.named_buffers() if 'num_batches' not in k]
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for (tn, tv), (mn, mv) in zip(torchb, mxb):
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print(tn, mn)
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print(tv.shape, mv.shape)
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# ensure ordering of BN params match since their sizes are not specific
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if 'running_var' in tn:
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assert 'running_var' in mn
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if 'running_mean' in tn:
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assert 'running_mean' in mn
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torch_tensor = torch.from_numpy(mv.data().asnumpy())
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torch_params[tn] = torch_tensor
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torch_net.load_state_dict(torch_params)
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torch_filename = './%s.pth' % torch_name
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torch.save(torch_net.state_dict(), torch_filename)
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with open(torch_filename, 'rb') as f:
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sha_hash = hashlib.sha256(f.read()).hexdigest()
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final_filename = os.path.splitext(torch_filename)[0] + '-' + sha_hash[:8] + '.pth'
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os.rename(torch_filename, final_filename)
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print("=> Saved converted model to '{}, SHA256: {}'".format(final_filename, sha_hash))
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def map_mx_to_torch_model(mx_name):
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torch_name = mx_name.lower()
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if torch_name.startswith('se_'):
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torch_name = torch_name.replace('se_', 'se')
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elif torch_name.startswith('senet_'):
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torch_name = torch_name.replace('senet_', 'senet')
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elif torch_name.startswith('inceptionv3'):
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torch_name = torch_name.replace('inceptionv3', 'inception_v3')
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torch_name = 'gluon_' + torch_name
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return torch_name
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ALL = ['resnet18_v1b', 'resnet34_v1b', 'resnet50_v1b', 'resnet101_v1b', 'resnet152_v1b',
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'resnet50_v1c', 'resnet101_v1c', 'resnet152_v1c', 'resnet50_v1d', 'resnet101_v1d', 'resnet152_v1d',
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#'resnet50_v1e', 'resnet101_v1e', 'resnet152_v1e',
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'resnet50_v1s', 'resnet101_v1s', 'resnet152_v1s', 'resnext50_32x4d', 'resnext101_32x4d', 'resnext101_64x4d',
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'se_resnext50_32x4d', 'se_resnext101_32x4d', 'se_resnext101_64x4d', 'senet_154', 'inceptionv3']
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def main():
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args = parser.parse_args()
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if not args.model or args.model == 'all':
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for mx_model in ALL:
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torch_model = map_mx_to_torch_model(mx_model)
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convert(mx_model, torch_model)
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else:
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mx_model = args.model
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torch_model = map_mx_to_torch_model(mx_model)
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convert(mx_model, torch_model)
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if __name__ == '__main__':
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main()
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