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ppyoloe_to_mmyolo.py
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import argparse
import pickle
from collections import OrderedDict
import torch
def convert_bn(k: str):
name = k.replace('._mean',
'.running_mean').replace('._variance', '.running_var')
return name
def convert_repvgg(k: str):
if '.conv2.conv1.' in k:
name = k.replace('.conv2.conv1.', '.conv2.rbr_dense.')
return name
elif '.conv2.conv2.' in k:
name = k.replace('.conv2.conv2.', '.conv2.rbr_1x1.')
return name
else:
return k
def convert(src: str, dst: str, imagenet_pretrain: bool = False):
with open(src, 'rb') as f:
model = pickle.load(f)
new_state_dict = OrderedDict()
if imagenet_pretrain:
for k, v in model.items():
if '@@' in k:
continue
if 'stem.' in k:
# backbone.stem.conv1.conv.weight
# -> backbone.stem.0.conv.weight
org_ind = k.split('.')[1][-1]
new_ind = str(int(org_ind) - 1)
name = k.replace('stem.conv%s.' % org_ind,
'stem.%s.' % new_ind)
else:
# backbone.stages.1.conv2.bn._variance
# -> backbone.stage2.0.conv2.bn.running_var
org_stage_ind = k.split('.')[1]
new_stage_ind = str(int(org_stage_ind) + 1)
name = k.replace('stages.%s.' % org_stage_ind,
'stage%s.0.' % new_stage_ind)
name = convert_repvgg(name)
if '.attn.' in k:
name = name.replace('.attn.fc.', '.attn.fc.conv.')
name = convert_bn(name)
name = 'backbone.' + name
new_state_dict[name] = torch.from_numpy(v)
else:
for k, v in model.items():
name = k
if k.startswith('backbone.'):
if '.stem.' in k:
# backbone.stem.conv1.conv.weight
# -> backbone.stem.0.conv.weight
org_ind = k.split('.')[2][-1]
new_ind = str(int(org_ind) - 1)
name = k.replace('.stem.conv%s.' % org_ind,
'.stem.%s.' % new_ind)
else:
# backbone.stages.1.conv2.bn._variance
# -> backbone.stage2.0.conv2.bn.running_var
org_stage_ind = k.split('.')[2]
new_stage_ind = str(int(org_stage_ind) + 1)
name = k.replace('.stages.%s.' % org_stage_ind,
'.stage%s.0.' % new_stage_ind)
name = convert_repvgg(name)
if '.attn.' in k:
name = name.replace('.attn.fc.', '.attn.fc.conv.')
name = convert_bn(name)
elif k.startswith('neck.'):
# fpn_stages
if k.startswith('neck.fpn_stages.'):
# neck.fpn_stages.0.0.conv1.conv.weight
# -> neck.reduce_layers.2.0.conv1.conv.weight
if k.startswith('neck.fpn_stages.0.0.'):
name = k.replace('neck.fpn_stages.0.0.',
'neck.reduce_layers.2.0.')
if '.spp.' in name:
name = name.replace('.spp.conv.', '.spp.conv2.')
# neck.fpn_stages.1.0.conv1.conv.weight
# -> neck.top_down_layers.0.0.conv1.conv.weight
elif k.startswith('neck.fpn_stages.1.0.'):
name = k.replace('neck.fpn_stages.1.0.',
'neck.top_down_layers.0.0.')
elif k.startswith('neck.fpn_stages.2.0.'):
name = k.replace('neck.fpn_stages.2.0.',
'neck.top_down_layers.1.0.')
else:
raise NotImplementedError('Not implemented.')
name = name.replace('.0.convs.', '.0.blocks.')
elif k.startswith('neck.fpn_routes.'):
# neck.fpn_routes.0.conv.weight
# -> neck.upsample_layers.0.0.conv.weight
index = k.split('.')[2]
name = 'neck.upsample_layers.' + index + '.0.' + '.'.join(
k.split('.')[-2:])
name = name.replace('.0.convs.', '.0.blocks.')
elif k.startswith('neck.pan_stages.'):
# neck.pan_stages.0.0.conv1.conv.weight
# -> neck.bottom_up_layers.1.0.conv1.conv.weight
ind = k.split('.')[2]
name = k.replace(
'neck.pan_stages.' + ind, 'neck.bottom_up_layers.' +
('0' if ind == '1' else '1'))
name = name.replace('.0.convs.', '.0.blocks.')
elif k.startswith('neck.pan_routes.'):
# neck.pan_routes.0.conv.weight
# -> neck.downsample_layers.0.conv.weight
ind = k.split('.')[2]
name = k.replace(
'neck.pan_routes.' + ind, 'neck.downsample_layers.' +
('0' if ind == '1' else '1'))
name = name.replace('.0.convs.', '.0.blocks.')
else:
raise NotImplementedError('Not implement.')
name = convert_repvgg(name)
name = convert_bn(name)
elif k.startswith('yolo_head.'):
if ('anchor_points' in k) or ('stride_tensor' in k):
continue
if 'proj_conv' in k:
name = k.replace('yolo_head.proj_conv.',
'bbox_head.head_module.proj_conv.')
else:
for org_key, rep_key in [
[
'yolo_head.stem_cls.',
'bbox_head.head_module.cls_stems.'
],
[
'yolo_head.stem_reg.',
'bbox_head.head_module.reg_stems.'
],
[
'yolo_head.pred_cls.',
'bbox_head.head_module.cls_preds.'
],
[
'yolo_head.pred_reg.',
'bbox_head.head_module.reg_preds.'
]
]:
name = name.replace(org_key, rep_key)
name = name.split('.')
ind = name[3]
name[3] = str(2 - int(ind))
name = '.'.join(name)
name = convert_bn(name)
else:
continue
new_state_dict[name] = torch.from_numpy(v)
data = {'state_dict': new_state_dict}
torch.save(data, dst)
def main():
parser = argparse.ArgumentParser(description='Convert model keys')
parser.add_argument(
'--src',
default='ppyoloe_plus_crn_s_80e_coco.pdparams',
help='src ppyoloe model path')
parser.add_argument(
'--dst', default='mmppyoloe_plus_s.pt', help='save path')
parser.add_argument(
'--imagenet-pretrain',
action='store_true',
default=False,
help='Load model pretrained on imagenet dataset which only '
'have weight for backbone.')
args = parser.parse_args()
convert(args.src, args.dst, args.imagenet_pretrain)
if __name__ == '__main__':
main()