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Clean a1/a2/3 rsb _0 checkpoints properly, fix v2 loading.

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Ross Wightman 2021-10-04 16:46:00 -07:00
parent d123042605
commit ae1ff5792f
3 changed files with 5 additions and 13 deletions
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14
clean_checkpoint.py
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@ -13,6 +13,7 @@ import os
import hashlib import hashlib
import shutil import shutil
from collections import OrderedDict from collections import OrderedDict
from timm.models.helpers import load_state_dict
parser = argparse.ArgumentParser(description='PyTorch Checkpoint Cleaner') parser = argparse.ArgumentParser(description='PyTorch Checkpoint Cleaner')
parser.add_argument('--checkpoint', default='', type=str, metavar='PATH', parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
@ -37,17 +38,8 @@ def main():
# Load an existing checkpoint to CPU, strip everything but the state_dict and re-save # Load an existing checkpoint to CPU, strip everything but the state_dict and re-save
if args.checkpoint and os.path.isfile(args.checkpoint): if args.checkpoint and os.path.isfile(args.checkpoint):
print("=> Loading checkpoint '{}'".format(args.checkpoint)) print("=> Loading checkpoint '{}'".format(args.checkpoint))
checkpoint = torch.load(args.checkpoint, map_location='cpu') state_dict = load_state_dict(args.checkpoint, use_ema=args.use_ema)
new_state_dict = {}
new_state_dict = OrderedDict()
if isinstance(checkpoint, dict):
state_dict_key = 'state_dict_ema' if args.use_ema else 'state_dict'
if state_dict_key in checkpoint:
state_dict = checkpoint[state_dict_key]
else:
state_dict = checkpoint
else:
assert False
for k, v in state_dict.items(): for k, v in state_dict.items():
if args.clean_aux_bn and 'aux_bn' in k: if args.clean_aux_bn and 'aux_bn' in k:
# If all aux_bn keys are removed, the SplitBN layers will end up as normal and # If all aux_bn keys are removed, the SplitBN layers will end up as normal and

2
timm/models/resnet.py
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@ -53,7 +53,7 @@ default_cfgs = {
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet26t_256_ra2-6f6fa748.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet26t_256_ra2-6f6fa748.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8)), interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8)),
'resnet50': _cfg( 'resnet50': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1_0-00ca2c6a.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1_0-14fe96d1.pth',
interpolation='bicubic'), interpolation='bicubic'),
'resnet50d': _cfg( 'resnet50d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50d_ra2-464e36ba.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50d_ra2-464e36ba.pth',

2
timm/models/resnetv2.py
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@ -471,7 +471,7 @@ def _create_resnetv2(variant, pretrained=False, **kwargs):
ResNetV2, variant, pretrained, ResNetV2, variant, pretrained,
default_cfg=default_cfgs[variant], default_cfg=default_cfgs[variant],
feature_cfg=feature_cfg, feature_cfg=feature_cfg,
pretrained_custom_load=True, pretrained_custom_load='_bit' in variant,
**kwargs) **kwargs)