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import torch
import os
import argparse
import copy
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--source', type=str)
parser.add_argument('--target', type=str, default=None)
args = parser.parse_args()
return args
def main():
args = parse_args()
if args.target is None:
args.target = '/'.join(args.source.split('/')[:-1])
ckpt = torch.load(args.source, map_location='cpu')
experts = dict()
new_ckpt = copy.deepcopy(ckpt)
state_dict = new_ckpt['state_dict']
for key, value in state_dict.items():
if 'mlp.experts' in key:
experts[key] = value
keys = ckpt['state_dict'].keys()
target_expert = 0
new_ckpt = copy.deepcopy(ckpt)
for key in keys:
if 'mlp.fc2' in key:
value = new_ckpt['state_dict'][key]
value = torch.cat([value, experts[key.replace('fc2.', f'experts.{target_expert}.')]], dim=0)
new_ckpt['state_dict'][key] = value
torch.save(new_ckpt, os.path.join(args.target, 'coco.pth'))
names = ['aic', 'mpii', 'ap10k', 'apt36k','wholebody']
num_keypoints = [14, 16, 17, 17, 133]
weight_names = ['keypoint_head.deconv_layers.0.weight',
'keypoint_head.deconv_layers.1.weight',
'keypoint_head.deconv_layers.1.bias',
'keypoint_head.deconv_layers.1.running_mean',
'keypoint_head.deconv_layers.1.running_var',
'keypoint_head.deconv_layers.1.num_batches_tracked',
'keypoint_head.deconv_layers.3.weight',
'keypoint_head.deconv_layers.4.weight',
'keypoint_head.deconv_layers.4.bias',
'keypoint_head.deconv_layers.4.running_mean',
'keypoint_head.deconv_layers.4.running_var',
'keypoint_head.deconv_layers.4.num_batches_tracked',
'keypoint_head.final_layer.weight',
'keypoint_head.final_layer.bias']
exist_range = True
for i in range(5):
new_ckpt = copy.deepcopy(ckpt)
target_expert = i + 1
for key in keys:
if 'mlp.fc2' in key:
expert_key = key.replace('fc2.', f'experts.{target_expert}.')
if expert_key in experts:
value = new_ckpt['state_dict'][key]
value = torch.cat([value, experts[expert_key]], dim=0)
else:
exist_range = False
new_ckpt['state_dict'][key] = value
if not exist_range:
break
for tensor_name in weight_names:
new_ckpt['state_dict'][tensor_name] = new_ckpt['state_dict'][tensor_name.replace('keypoint_head', f'associate_keypoint_heads.{i}')]
for tensor_name in ['keypoint_head.final_layer.weight', 'keypoint_head.final_layer.bias']:
new_ckpt['state_dict'][tensor_name] = new_ckpt['state_dict'][tensor_name][:num_keypoints[i]]
# remove unnecessary part in the state dict
for j in range(5):
# remove associate part
for tensor_name in weight_names:
new_ckpt['state_dict'].pop(tensor_name.replace('keypoint_head', f'associate_keypoint_heads.{j}'))
# remove expert part
keys = new_ckpt['state_dict'].keys()
for key in list(keys):
if 'expert' in keys:
new_ckpt['state_dict'].pop(key)
torch.save(new_ckpt, os.path.join(args.target, f'{names[i]}.pth'))
if __name__ == '__main__':
main()
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