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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import subprocess
from collections import OrderedDict
import torch
from mmengine.runner import CheckpointLoader
convert_dict_fpn = {
'backbone.fpn_lateral3': 'neck.lateral_convs.0.conv',
'backbone.fpn_lateral4': 'neck.lateral_convs.1.conv',
'backbone.fpn_lateral5': 'neck.lateral_convs.2.conv',
'backbone.fpn_output3': 'neck.fpn_convs.0.conv',
'backbone.fpn_output4': 'neck.fpn_convs.1.conv',
'backbone.fpn_output5': 'neck.fpn_convs.2.conv',
'backbone.top_block.p6': 'neck.fpn_convs.3.conv',
'backbone.top_block.p7': 'neck.fpn_convs.4.conv',
}
convert_dict_rpn = {
'proposal_generator.centernet_head.bbox_tower.0':
'rpn_head.reg_convs.0.conv',
'proposal_generator.centernet_head.bbox_tower.1':
'rpn_head.reg_convs.0.gn',
'proposal_generator.centernet_head.bbox_tower.3':
'rpn_head.reg_convs.1.conv',
'proposal_generator.centernet_head.bbox_tower.4':
'rpn_head.reg_convs.1.gn',
'proposal_generator.centernet_head.bbox_tower.6':
'rpn_head.reg_convs.2.conv',
'proposal_generator.centernet_head.bbox_tower.7':
'rpn_head.reg_convs.2.gn',
'proposal_generator.centernet_head.bbox_tower.9':
'rpn_head.reg_convs.3.conv',
'proposal_generator.centernet_head.bbox_tower.10':
'rpn_head.reg_convs.3.gn',
'proposal_generator.centernet_head.bbox_pred': 'rpn_head.conv_reg',
'proposal_generator.centernet_head.scales.0.scale':
'rpn_head.scales.0.scale',
'proposal_generator.centernet_head.scales.1.scale':
'rpn_head.scales.1.scale',
'proposal_generator.centernet_head.scales.2.scale':
'rpn_head.scales.2.scale',
'proposal_generator.centernet_head.scales.3.scale':
'rpn_head.scales.3.scale',
'proposal_generator.centernet_head.scales.4.scale':
'rpn_head.scales.4.scale',
'proposal_generator.centernet_head.agn_hm': 'rpn_head.conv_cls',
}
convert_dict_roi = {
'roi_heads.box_head.0.fc1': 'roi_head.bbox_head.0.shared_fcs.0',
'roi_heads.box_head.0.fc2': 'roi_head.bbox_head.0.shared_fcs.1',
'roi_heads.box_head.1.fc1': 'roi_head.bbox_head.1.shared_fcs.0',
'roi_heads.box_head.1.fc2': 'roi_head.bbox_head.1.shared_fcs.1',
'roi_heads.box_head.2.fc1': 'roi_head.bbox_head.2.shared_fcs.0',
'roi_heads.box_head.2.fc2': 'roi_head.bbox_head.2.shared_fcs.1',
'roi_heads.box_predictor.0.freq_weight':
'roi_head.bbox_head.0.freq_weight',
'roi_heads.box_predictor.0.cls_score.zs_weight':
'roi_head.bbox_head.0.fc_cls.zs_weight',
'roi_heads.box_predictor.0.cls_score.linear':
'roi_head.bbox_head.0.fc_cls.linear',
'roi_heads.box_predictor.0.bbox_pred.0': 'roi_head.bbox_head.0.fc_reg.0',
'roi_heads.box_predictor.0.bbox_pred.2': 'roi_head.bbox_head.0.fc_reg.2',
'roi_heads.box_predictor.1.freq_weight':
'roi_head.bbox_head.1.freq_weight',
'roi_heads.box_predictor.1.cls_score.zs_weight':
'roi_head.bbox_head.1.fc_cls.zs_weight',
'roi_heads.box_predictor.1.cls_score.linear':
'roi_head.bbox_head.1.fc_cls.linear',
'roi_heads.box_predictor.1.bbox_pred.0': 'roi_head.bbox_head.1.fc_reg.0',
'roi_heads.box_predictor.1.bbox_pred.2': 'roi_head.bbox_head.1.fc_reg.2',
'roi_heads.box_predictor.2.freq_weight':
'roi_head.bbox_head.2.freq_weight',
'roi_heads.box_predictor.2.cls_score.zs_weight':
'roi_head.bbox_head.2.fc_cls.zs_weight',
'roi_heads.box_predictor.2.cls_score.linear':
'roi_head.bbox_head.2.fc_cls.linear',
'roi_heads.box_predictor.2.bbox_pred.0': 'roi_head.bbox_head.2.fc_reg.0',
'roi_heads.box_predictor.2.bbox_pred.2': 'roi_head.bbox_head.2.fc_reg.2',
'roi_heads.mask_head.mask_fcn1': 'roi_head.mask_head.convs.0.conv',
'roi_heads.mask_head.mask_fcn2': 'roi_head.mask_head.convs.1.conv',
'roi_heads.mask_head.mask_fcn3': 'roi_head.mask_head.convs.2.conv',
'roi_heads.mask_head.mask_fcn4': 'roi_head.mask_head.convs.3.conv',
'roi_heads.mask_head.deconv': 'roi_head.mask_head.upsample',
'roi_heads.mask_head.predictor': 'roi_head.mask_head.conv_logits',
}
def correct_unfold_reduction_order(x):
out_channel, in_channel = x.shape
x = x.reshape(out_channel, 4, in_channel // 4)
x = x[:, [0, 2, 1, 3], :].transpose(1, 2).reshape(out_channel, in_channel)
return x
def correct_unfold_norm_order(x):
in_channel = x.shape[0]
x = x.reshape(4, in_channel // 4)
x = x[[0, 2, 1, 3], :].transpose(0, 1).reshape(in_channel)
return x
def convert(ckpt):
new_ckpt = OrderedDict()
for k, v in list(ckpt.items()):
new_v = v
if 'backbone.bottom_up' in k:
new_k = k.replace('backbone.bottom_up', 'backbone')
# for Transformer backbone
if 'patch_embed.proj' in new_k:
new_k = new_k.replace('patch_embed.proj',
'patch_embed.projection')
elif 'pos_drop' in new_k:
new_k = new_k.replace('pos_drop', 'drop_after_pos')
if 'layers' in new_k:
new_k = new_k.replace('layers', 'stages')
if 'mlp.fc1' in new_k:
new_k = new_k.replace('mlp.fc1', 'ffn.layers.0.0')
elif 'mlp.fc2' in new_k:
new_k = new_k.replace('mlp.fc2', 'ffn.layers.1')
elif 'attn' in new_k:
new_k = new_k.replace('attn', 'attn.w_msa')
if 'downsample' in k:
if 'reduction.' in k:
new_v = correct_unfold_reduction_order(v)
elif 'norm.' in k:
new_v = correct_unfold_norm_order(v)
# for resnet
if 'base.' in k:
new_k = new_k.replace('base.', '')
elif 'backbone.fpn' in k or 'backbone.top_block' in k:
old_k = k.replace('.weight', '')
old_k = old_k.replace('.bias', '')
new_k = k.replace(old_k, convert_dict_fpn[old_k])
elif 'proposal_generator' in k:
old_k = k.replace('.weight', '')
old_k = old_k.replace('.bias', '')
new_k = k.replace(old_k, convert_dict_rpn[old_k])
elif 'roi_heads' in k:
old_k = k.replace('.weight', '')
old_k = old_k.replace('.bias', '')
new_k = k.replace(old_k, convert_dict_roi[old_k])
else:
print('skip:', k)
continue
new_ckpt[new_k] = new_v
return new_ckpt
def main():
parser = argparse.ArgumentParser(
description='Convert keys in pretrained eva '
'models to mmpretrain style.')
parser.add_argument(
'--src',
default='Detic_LbaseI_CLIP_SwinB_896b32_4x_ft4x_max-size.pth',
help='src model path or url')
# The dst path must be a full path of the new checkpoint.
parser.add_argument(
'--dst',
default='detic_centernet2_swin-b_fpn_4x_lvis-base_in21k-lvis.pth',
help='save path')
args = parser.parse_args()
checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu')
if 'model' in checkpoint:
state_dict = checkpoint['model']
else:
state_dict = checkpoint
weight = {}
new_state_dict = convert(state_dict)
if 'backbone.fc.weight' in new_state_dict.keys():
del [new_state_dict['backbone.fc.weight']]
if 'backbone.fc.bias' in new_state_dict.keys():
del [new_state_dict['backbone.fc.bias']]
weight['state_dict'] = new_state_dict
torch.save(weight, args.dst)
sha = subprocess.check_output(['sha256sum', args.dst]).decode()
final_file = args.dst.replace('.pth', '') + '-{}.pth'.format(sha[:8])
subprocess.Popen(['mv', args.dst, final_file])
print(f'Done!!, save to {final_file}')
if __name__ == '__main__':
main()
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