Error(s) in loading state_dict for EfficientNet
#1
by
animikhaich
- opened
While running the provided code snippet, I got the following error:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[1], line 8
5 from PIL import Image
6 from urllib.request import urlopen
----> 8 model = timm.create_model("hf-hub:BVRA/MegaDescriptor-EfficientNetB3", pretrained=True)
File ~/Projects/MM.Research.BuckReID/.venv/lib/python3.10/site-packages/timm/models/_factory.py:117, in create_model(model_name, pretrained, pretrained_cfg, pretrained_cfg_overlay, checkpoint_path, scriptable, exportable, no_jit, **kwargs)
115 create_fn = model_entrypoint(model_name)
116 with set_layer_config(scriptable=scriptable, exportable=exportable, no_jit=no_jit):
--> 117 model = create_fn(
118 pretrained=pretrained,
119 pretrained_cfg=pretrained_cfg,
120 pretrained_cfg_overlay=pretrained_cfg_overlay,
121 **kwargs,
122 )
124 if checkpoint_path:
125 load_checkpoint(model, checkpoint_path)
File ~/Projects/MM.Research.BuckReID/.venv/lib/python3.10/site-packages/timm/models/efficientnet.py:1904, in efficientnet_b3(pretrained, **kwargs)
1902 """ EfficientNet-B3 """
1903 # NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
-> 1904 model = _gen_efficientnet(
1905 'efficientnet_b3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
1906 return model
File ~/Projects/MM.Research.BuckReID/.venv/lib/python3.10/site-packages/timm/models/efficientnet.py:651, in _gen_efficientnet(variant, channel_multiplier, depth_multiplier, channel_divisor, group_size, pretrained, **kwargs)
641 round_chs_fn = partial(round_channels, multiplier=channel_multiplier, divisor=channel_divisor)
642 model_kwargs = dict(
643 block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size),
644 num_features=round_chs_fn(1280),
(...)
649 **kwargs,
650 )
--> 651 model = _create_effnet(variant, pretrained, **model_kwargs)
652 return model
File ~/Projects/MM.Research.BuckReID/.venv/lib/python3.10/site-packages/timm/models/efficientnet.py:368, in _create_effnet(variant, pretrained, **kwargs)
365 model_cls = EfficientNetFeatures
366 features_mode = 'cls'
--> 368 model = build_model_with_cfg(
369 model_cls,
370 variant,
371 pretrained,
372 features_only=features_mode == 'cfg',
373 pretrained_strict=features_mode != 'cls',
374 kwargs_filter=kwargs_filter,
375 **kwargs,
376 )
377 if features_mode == 'cls':
378 model.pretrained_cfg = model.default_cfg = pretrained_cfg_for_features(model.pretrained_cfg)
File ~/Projects/MM.Research.BuckReID/.venv/lib/python3.10/site-packages/timm/models/_builder.py:418, in build_model_with_cfg(model_cls, variant, pretrained, pretrained_cfg, pretrained_cfg_overlay, model_cfg, feature_cfg, pretrained_strict, pretrained_filter_fn, kwargs_filter, **kwargs)
416 num_classes_pretrained = 0 if features else getattr(model, 'num_classes', kwargs.get('num_classes', 1000))
417 if pretrained:
--> 418 load_pretrained(
419 model,
420 pretrained_cfg=pretrained_cfg,
421 num_classes=num_classes_pretrained,
422 in_chans=kwargs.get('in_chans', 3),
423 filter_fn=pretrained_filter_fn,
424 strict=pretrained_strict,
425 )
427 # Wrap the model in a feature extraction module if enabled
428 if features:
File ~/Projects/MM.Research.BuckReID/.venv/lib/python3.10/site-packages/timm/models/_builder.py:243, in load_pretrained(model, pretrained_cfg, num_classes, in_chans, filter_fn, strict)
240 classifier_bias = state_dict[classifier_name + '.bias']
241 state_dict[classifier_name + '.bias'] = classifier_bias[label_offset:]
--> 243 load_result = model.load_state_dict(state_dict, strict=strict)
244 if load_result.missing_keys:
245 _logger.info(
246 f'Missing keys ({", ".join(load_result.missing_keys)}) discovered while loading pretrained weights.'
247 f' This is expected if model is being adapted.')
File ~/Projects/MM.Research.BuckReID/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:2593, in Module.load_state_dict(self, state_dict, strict, assign)
2585 error_msgs.insert(
2586 0,
2587 "Missing key(s) in state_dict: {}. ".format(
2588 ", ".join(f'"{k}"' for k in missing_keys)
2589 ),
2590 )
2592 if len(error_msgs) > 0:
-> 2593 raise RuntimeError(
2594 "Error(s) in loading state_dict for {}:\n\t{}".format(
2595 self.__class__.__name__, "\n\t".join(error_msgs)
2596 )
2597 )
2598 return _IncompatibleKeys(missing_keys, unexpected_keys)
RuntimeError: Error(s) in loading state_dict for EfficientNet:
Missing key(s) in state_dict: "conv_stem.weight", "bn1.weight", "bn1.bias", "bn1.running_mean", "bn1.running_var", "blocks.0.0.conv_dw.weight", "blocks.0.0.bn1.weight", "blocks.0.0.bn1.bias", "blocks.0.0.bn1.running_mean", "blocks.0.0.bn1.running_var", "blocks.0.0.se.conv_reduce.weight", "blocks.0.0.se.conv_reduce.bias", "blocks.0.0.se.conv_expand.weight", "blocks.0.0.se.conv_expand.bias", "blocks.0.0.conv_pw.weight", "blocks.0.0.bn2.weight", "blocks.0.0.bn2.bias", "blocks.0.0.bn2.running_mean", "blocks.0.0.bn2.running_var", "blocks.0.1.conv_dw.weight", "blocks.0.1.bn1.weight", "blocks.0.1.bn1.bias", "blocks.0.1.bn1.running_mean", "blocks.0.1.bn1.running_var", "blocks.0.1.se.conv_reduce.weight", "blocks.0.1.se.conv_reduce.bias", "blocks.0.1.se.conv_expand.weight", "blocks.0.1.se.conv_expand.bias", "blocks.0.1.conv_pw.weight", "blocks.0.1.bn2.weight", "blocks.0.1.bn2.bias", "blocks.0.1.bn2.running_mean", "blocks.0.1.bn2.running_var", "blocks.1.0.conv_pw.weight", "blocks.1.0.bn1.weight", "blocks.1.0.bn1.bias", "blocks.1.0.bn1.running_mean", "blocks.1.0.bn1.running_var", "blocks.1.0.conv_dw.weight", "blocks.1.0.bn2.weight", "blocks.1.0.bn2.bias", "blocks.1.0.bn2.running_mean", "blocks.1.0.bn2.running_var", "blocks.1.0.se.conv_reduce.weight", "blocks.1.0.se.conv_reduce.bias", "blocks.1.0.se.conv_expand.weight", "blocks.1.0.se.conv_expand.bias", "blocks.1.0.conv_pwl.weight", "blocks.1.0.bn3.weight", "blocks.1.0.bn3.bias", "blocks.1.0.bn3.running_mean", "blocks.1.0.bn3.running_var", "blocks.1.1.conv_pw.weight", "blocks.1.1.bn1.weight", "blocks.1.1.bn1.bias", "blocks.1.1.bn1.running_mean", "blocks.1.1.bn1.running_var", "blocks.1.1.conv_dw.weight", "blocks.1.1.bn2.weight", "blocks.1.1.bn2.bias", "blocks.1.1.bn2.running_mean", "blocks.1.1.bn2.running_var", "blocks.1.1.se.conv_reduce.weight", "blocks.1.1.se.conv_reduce.bias", "blocks.1.1.se.conv_expand.weight", "blocks.1.1.se.conv_expand.bias", "blocks.1.1.conv_pwl.weight", "blocks.1.1.bn3.weight", "blocks.1.1.bn3.bias", "blocks.1.1.bn3.running_mean", "blocks.1.1.bn3.running_var", "blocks.1.2.conv_pw.weight", "blocks.1.2.bn1.weight", "blocks.1.2.bn1.bias", "blocks.1.2.bn1.running_mean", "blocks.1.2.bn1.running_var", "blocks.1.2.conv_dw.weight", "blocks.1.2.bn2.weight", "blocks.1.2.bn2.bias", "blocks.1.2.bn2.running_mean", "blocks.1.2.bn2.running_var", "blocks.1.2.se.conv_reduce.weight", "blocks.1.2.se.conv_reduce.bias", "blocks.1.2.se.conv_expand.weight", "blocks.1.2.se.conv_expand.bias", "blocks.1.2.conv_pwl.weight", "blocks.1.2.bn3.weight", "blocks.1.2.bn3.bias", "blocks.1.2.bn3.running_mean", "blocks.1.2.bn3.running_var", "blocks.2.0.conv_pw.weight", "blocks.2.0.bn1.weight", "blocks.2.0.bn1.bias", "blocks.2.0.bn1.running_mean", "blocks.2.0.bn1.running_var", "blocks.2.0.conv_dw.weight", "blocks.2.0.bn2.weight", "blocks.2.0.bn2.bias", "blocks.2.0.bn2.running_mean", "blocks.2.0.bn2.running_var", "blocks.2.0.se.conv_reduce.weight", "blocks.2.0.se.conv_reduce.bias", "blocks.2.0.se.conv_expand.weight", "blocks.2.0.se.conv_expand.bias", "blocks.2.0.conv_pwl.weight", "blocks.2.0.bn3.weight", "blocks.2.0.bn3.bias", "blocks.2.0.bn3.running_mean", "blocks.2.0.bn3.running_var", "blocks.2.1.conv_pw.weight", "blocks.2.1.bn1.weight", "blocks.2.1.bn1.bias", "blocks.2.1.bn1.running_mean", "blocks.2.1.bn1.running_var", "blocks.2.1.conv_dw.weight", "blocks.2.1.bn2.weight", "blocks.2.1.bn2.bias", "blocks.2.1.bn2.running_mean", "blocks.2.1.bn2.running_var", "blocks.2.1.se.conv_reduce.weight", "blocks.2.1.se.conv_reduce.bias", "blocks.2.1.se.conv_expand.weight", "blocks.2.1.se.conv_expand.bias", "blocks.2.1.conv_pwl.weight", "blocks.2.1.bn3.weight", "blocks.2.1.bn3.bias", "blocks.2.1.bn3.running_mean", "blocks.2.1.bn3.running_var", "blocks.2.2.conv_pw.weight", "blocks.2.2.bn1.weight", "blocks.2.2.bn1.bias", "blocks.2.2.bn1.running_mean", "blocks.2.2.bn1.running_var", "blocks.2.2.conv_dw.weight", "blocks.2.2.bn2.weight", "blocks.2.2.bn2.bias", "blocks.2.2.bn2.running_mean", "blocks.2.2.bn2.running_var", "blocks.2.2.se.conv_reduce.weight", "blocks.2.2.se.conv_reduce.bias", "blocks.2.2.se.conv_expand.weight", "blocks.2.2.se.conv_expand.bias", "blocks.2.2.conv_pwl.weight", "blocks.2.2.bn3.weight", "blocks.2.2.bn3.bias", "blocks.2.2.bn3.running_mean", "blocks.2.2.bn3.running_var", "blocks.3.0.conv_pw.weight", "blocks.3.0.bn1.weight", "blocks.3.0.bn1.bias", "blocks.3.0.bn1.running_mean", "blocks.3.0.bn1.running_var", "blocks.3.0.conv_dw.weight", "blocks.3.0.bn2.weight", "blocks.3.0.bn2.bias", "blocks.3.0.bn2.running_mean", "blocks.3.0.bn2.running_var", "blocks.3.0.se.conv_reduce.weight", "blocks.3.0.se.conv_reduce.bias", "blocks.3.0.se.conv_expand.weight", "blocks.3.0.se.conv_expand.bias", "blocks.3.0.conv_pwl.weight", "blocks.3.0.bn3.weight", "blocks.3.0.bn3.bias", "blocks.3.0.bn3.running_mean", "blocks.3.0.bn3.running_var", "blocks.3.1.conv_pw.weight", "blocks.3.1.bn1.weight", "blocks.3.1.bn1.bias", "blocks.3.1.bn1.running_mean", "blocks.3.1.bn1.running_var", "blocks.3.1.conv_dw.weight", "blocks.3.1.bn2.weight", "blocks.3.1.bn2.bias", "blocks.3.1.bn2.running_mean", "blocks.3.1.bn2.running_var", "blocks.3.1.se.conv_reduce.weight", "blocks.3.1.se.conv_reduce.bias", "blocks.3.1.se.conv_expand.weight", "blocks.3.1.se.conv_expand.bias", "blocks.3.1.conv_pwl.weight", "blocks.3.1.bn3.weight", "blocks.3.1.bn3.bias", "blocks.3.1.bn3.running_mean", "blocks.3.1.bn3.running_var", "blocks.3.2.conv_pw.weight", "blocks.3.2.bn1.weight", "blocks.3.2.bn1.bias", "blocks.3.2.bn1.running_mean", "blocks.3.2.bn1.running_var", "blocks.3.2.conv_dw.weight", "blocks.3.2.bn2.weight", "blocks.3.2.bn2.bias", "blocks.3.2.bn2.running_mean", "blocks.3.2.bn2.running_var", "blocks.3.2.se.conv_reduce.weight", "blocks.3.2.se.conv_reduce.bias", "blocks.3.2.se.conv_expand.weight", "blocks.3.2.se.conv_expand.bias", "blocks.3.2.conv_pwl.weight", "blocks.3.2.bn3.weight", "blocks.3.2.bn3.bias", "blocks.3.2.bn3.running_mean", "blocks.3.2.bn3.running_var", "blocks.3.3.conv_pw.weight", "blocks.3.3.bn1.weight", "blocks.3.3.bn1.bias", "blocks.3.3.bn1.running_mean", "blocks.3.3.bn1.running_var", "blocks.3.3.conv_dw.weight", "blocks.3.3.bn2.weight", "blocks.3.3.bn2.bias", "blocks.3.3.bn2.running_mean", "blocks.3.3.bn2.running_var", "blocks.3.3.se.conv_reduce.weight", "blocks.3.3.se.conv_reduce.bias", "blocks.3.3.se.conv_expand.weight", "blocks.3.3.se.conv_expand.bias", "blocks.3.3.conv_pwl.weight", "blocks.3.3.bn3.weight", "blocks.3.3.bn3.bias", "blocks.3.3.bn3.running_mean", "blocks.3.3.bn3.running_var", "blocks.3.4.conv_pw.weight", "blocks.3.4.bn1.weight", "blocks.3.4.bn1.bias", "blocks.3.4.bn1.running_mean", "blocks.3.4.bn1.running_var", "blocks.3.4.conv_dw.weight", "blocks.3.4.bn2.weight", "blocks.3.4.bn2.bias", "blocks.3.4.bn2.running_mean", "blocks.3.4.bn2.running_var", "blocks.3.4.se.conv_reduce.weight", "blocks.3.4.se.conv_reduce.bias", "blocks.3.4.se.conv_expand.weight", "blocks.3.4.se.conv_expand.bias", "blocks.3.4.conv_pwl.weight", "blocks.3.4.bn3.weight", "blocks.3.4.bn3.bias", "blocks.3.4.bn3.running_mean", "blocks.3.4.bn3.running_var", "blocks.4.0.conv_pw.weight", "blocks.4.0.bn1.weight", "blocks.4.0.bn1.bias", "blocks.4.0.bn1.running_mean", "blocks.4.0.bn1.running_var", "blocks.4.0.conv_dw.weight", "blocks.4.0.bn2.weight", "blocks.4.0.bn2.bias", "blocks.4.0.bn2.running_mean", "blocks.4.0.bn2.running_var", "blocks.4.0.se.conv_reduce.weight", "blocks.4.0.se.conv_reduce.bias", "blocks.4.0.se.conv_expand.weight", "blocks.4.0.se.conv_expand.bias", "blocks.4.0.conv_pwl.weight", "blocks.4.0.bn3.weight", "blocks.4.0.bn3.bias", "blocks.4.0.bn3.running_mean", "blocks.4.0.bn3.running_var", "blocks.4.1.conv_pw.weight", "blocks.4.1.bn1.weight", "blocks.4.1.bn1.bias", "blocks.4.1.bn1.running_mean", "blocks.4.1.bn1.running_var", "blocks.4.1.conv_dw.weight", "blocks.4.1.bn2.weight", "blocks.4.1.bn2.bias", "blocks.4.1.bn2.running_mean", "blocks.4.1.bn2.running_var", "blocks.4.1.se.conv_reduce.weight", "blocks.4.1.se.conv_reduce.bias", "blocks.4.1.se.conv_expand.weight", "blocks.4.1.se.conv_expand.bias", "blocks.4.1.conv_pwl.weight", "blocks.4.1.bn3.weight", "blocks.4.1.bn3.bias", "blocks.4.1.bn3.running_mean", "blocks.4.1.bn3.running_var", "blocks.4.2.conv_pw.weight", "blocks.4.2.bn1.weight", "blocks.4.2.bn1.bias", "blocks.4.2.bn1.running_mean", "blocks.4.2.bn1.running_var", "blocks.4.2.conv_dw.weight", "blocks.4.2.bn2.weight", "blocks.4.2.bn2.bias", "blocks.4.2.bn2.running_mean", "blocks.4.2.bn2.running_var", "blocks.4.2.se.conv_reduce.weight", "blocks.4.2.se.conv_reduce.bias", "blocks.4.2.se.conv_expand.weight", "blocks.4.2.se.conv_expand.bias", "blocks.4.2.conv_pwl.weight", "blocks.4.2.bn3.weight", "blocks.4.2.bn3.bias", "blocks.4.2.bn3.running_mean", "blocks.4.2.bn3.running_var", "blocks.4.3.conv_pw.weight", "blocks.4.3.bn1.weight", "blocks.4.3.bn1.bias", "blocks.4.3.bn1.running_mean", "blocks.4.3.bn1.running_var", "blocks.4.3.conv_dw.weight", "blocks.4.3.bn2.weight", "blocks.4.3.bn2.bias", "blocks.4.3.bn2.running_mean", "blocks.4.3.bn2.running_var", "blocks.4.3.se.conv_reduce.weight", "blocks.4.3.se.conv_reduce.bias", "blocks.4.3.se.conv_expand.weight", "blocks.4.3.se.conv_expand.bias", "blocks.4.3.conv_pwl.weight", "blocks.4.3.bn3.weight", "blocks.4.3.bn3.bias", "blocks.4.3.bn3.running_mean", "blocks.4.3.bn3.running_var", "blocks.4.4.conv_pw.weight", "blocks.4.4.bn1.weight", "blocks.4.4.bn1.bias", "blocks.4.4.bn1.running_mean", "blocks.4.4.bn1.running_var", "blocks.4.4.conv_dw.weight", "blocks.4.4.bn2.weight", "blocks.4.4.bn2.bias", "blocks.4.4.bn2.running_mean", "blocks.4.4.bn2.running_var", "blocks.4.4.se.conv_reduce.weight", "blocks.4.4.se.conv_reduce.bias", "blocks.4.4.se.conv_expand.weight", "blocks.4.4.se.conv_expand.bias", "blocks.4.4.conv_pwl.weight", "blocks.4.4.bn3.weight", "blocks.4.4.bn3.bias", "blocks.4.4.bn3.running_mean", "blocks.4.4.bn3.running_var", "blocks.5.0.conv_pw.weight", "blocks.5.0.bn1.weight", "blocks.5.0.bn1.bias", "blocks.5.0.bn1.running_mean", "blocks.5.0.bn1.running_var", "blocks.5.0.conv_dw.weight", "blocks.5.0.bn2.weight", "blocks.5.0.bn2.bias", "blocks.5.0.bn2.running_mean", "blocks.5.0.bn2.running_var", "blocks.5.0.se.conv_reduce.weight", "blocks.5.0.se.conv_reduce.bias", "blocks.5.0.se.conv_expand.weight", "blocks.5.0.se.conv_expand.bias", "blocks.5.0.conv_pwl.weight", "blocks.5.0.bn3.weight", "blocks.5.0.bn3.bias", "blocks.5.0.bn3.running_mean", "blocks.5.0.bn3.running_var", "blocks.5.1.conv_pw.weight", "blocks.5.1.bn1.weight", "blocks.5.1.bn1.bias", "blocks.5.1.bn1.running_mean", "blocks.5.1.bn1.running_var", "blocks.5.1.conv_dw.weight", "blocks.5.1.bn2.weight", "blocks.5.1.bn2.bias", "blocks.5.1.bn2.running_mean", "blocks.5.1.bn2.running_var", "blocks.5.1.se.conv_reduce.weight", "blocks.5.1.se.conv_reduce.bias", "blocks.5.1.se.conv_expand.weight", "blocks.5.1.se.conv_expand.bias", "blocks.5.1.conv_pwl.weight", "blocks.5.1.bn3.weight", "blocks.5.1.bn3.bias", "blocks.5.1.bn3.running_mean", "blocks.5.1.bn3.running_var", "blocks.5.2.conv_pw.weight", "blocks.5.2.bn1.weight", "blocks.5.2.bn1.bias", "blocks.5.2.bn1.running_mean", "blocks.5.2.bn1.running_var", "blocks.5.2.conv_dw.weight", "blocks.5.2.bn2.weight", "blocks.5.2.bn2.bias", "blocks.5.2.bn2.running_mean", "blocks.5.2.bn2.running_var", "blocks.5.2.se.conv_reduce.weight", "blocks.5.2.se.conv_reduce.bias", "blocks.5.2.se.conv_expand.weight", "blocks.5.2.se.conv_expand.bias", "blocks.5.2.conv_pwl.weight", "blocks.5.2.bn3.weight", "blocks.5.2.bn3.bias", "blocks.5.2.bn3.running_mean", "blocks.5.2.bn3.running_var", "blocks.5.3.conv_pw.weight", "blocks.5.3.bn1.weight", "blocks.5.3.bn1.bias", "blocks.5.3.bn1.running_mean", "blocks.5.3.bn1.running_var", "blocks.5.3.conv_dw.weight", "blocks.5.3.bn2.weight", "blocks.5.3.bn2.bias", "blocks.5.3.bn2.running_mean", "blocks.5.3.bn2.running_var", "blocks.5.3.se.conv_reduce.weight", "blocks.5.3.se.conv_reduce.bias", "blocks.5.3.se.conv_expand.weight", "blocks.5.3.se.conv_expand.bias", "blocks.5.3.conv_pwl.weight", "blocks.5.3.bn3.weight", "blocks.5.3.bn3.bias", "blocks.5.3.bn3.running_mean", "blocks.5.3.bn3.running_var", "blocks.5.4.conv_pw.weight", "blocks.5.4.bn1.weight", "blocks.5.4.bn1.bias", "blocks.5.4.bn1.running_mean", "blocks.5.4.bn1.running_var", "blocks.5.4.conv_dw.weight", "blocks.5.4.bn2.weight", "blocks.5.4.bn2.bias", "blocks.5.4.bn2.running_mean", "blocks.5.4.bn2.running_var", "blocks.5.4.se.conv_reduce.weight", "blocks.5.4.se.conv_reduce.bias", "blocks.5.4.se.conv_expand.weight", "blocks.5.4.se.conv_expand.bias", "blocks.5.4.conv_pwl.weight", "blocks.5.4.bn3.weight", "blocks.5.4.bn3.bias", "blocks.5.4.bn3.running_mean", "blocks.5.4.bn3.running_var", "blocks.5.5.conv_pw.weight", "blocks.5.5.bn1.weight", "blocks.5.5.bn1.bias", "blocks.5.5.bn1.running_mean", "blocks.5.5.bn1.running_var", "blocks.5.5.conv_dw.weight", "blocks.5.5.bn2.weight", "blocks.5.5.bn2.bias", "blocks.5.5.bn2.running_mean", "blocks.5.5.bn2.running_var", "blocks.5.5.se.conv_reduce.weight", "blocks.5.5.se.conv_reduce.bias", "blocks.5.5.se.conv_expand.weight", "blocks.5.5.se.conv_expand.bias", "blocks.5.5.conv_pwl.weight", "blocks.5.5.bn3.weight", "blocks.5.5.bn3.bias", "blocks.5.5.bn3.running_mean", "blocks.5.5.bn3.running_var", "blocks.6.0.conv_pw.weight", "blocks.6.0.bn1.weight", "blocks.6.0.bn1.bias", "blocks.6.0.bn1.running_mean", "blocks.6.0.bn1.running_var", "blocks.6.0.conv_dw.weight", "blocks.6.0.bn2.weight", "blocks.6.0.bn2.bias", "blocks.6.0.bn2.running_mean", "blocks.6.0.bn2.running_var", "blocks.6.0.se.conv_reduce.weight", "blocks.6.0.se.conv_reduce.bias", "blocks.6.0.se.conv_expand.weight", "blocks.6.0.se.conv_expand.bias", "blocks.6.0.conv_pwl.weight", "blocks.6.0.bn3.weight", "blocks.6.0.bn3.bias", "blocks.6.0.bn3.running_mean", "blocks.6.0.bn3.running_var", "blocks.6.1.conv_pw.weight", "blocks.6.1.bn1.weight", "blocks.6.1.bn1.bias", "blocks.6.1.bn1.running_mean", "blocks.6.1.bn1.running_var", "blocks.6.1.conv_dw.weight", "blocks.6.1.bn2.weight", "blocks.6.1.bn2.bias", "blocks.6.1.bn2.running_mean", "blocks.6.1.bn2.running_var", "blocks.6.1.se.conv_reduce.weight", "blocks.6.1.se.conv_reduce.bias", "blocks.6.1.se.conv_expand.weight", "blocks.6.1.se.conv_expand.bias", "blocks.6.1.conv_pwl.weight", "blocks.6.1.bn3.weight", "blocks.6.1.bn3.bias", "blocks.6.1.bn3.running_mean", "blocks.6.1.bn3.running_var", "conv_head.weight", "bn2.weight", "bn2.bias", "bn2.running_mean", "bn2.running_var", "classifier.weight", "classifier.bias".
Unexpected key(s) in state_dict: "model", "objective", "optimizer", "epoch", "rng_states", "scheduler". ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[1], line 8
5 from PIL import Image
6 from urllib.request import urlopen
----> 8 model = timm.create_model("hf-hub:BVRA/MegaDescriptor-EfficientNetB3", pretrained=True)
File ~/Projects/MM.Research.BuckReID/.venv/lib/python3.10/site-packages/timm/models/_factory.py:117, in create_model(model_name, pretrained, pretrained_cfg, pretrained_cfg_overlay, checkpoint_path, scriptable, exportable, no_jit, **kwargs)
115 create_fn = model_entrypoint(model_name)
116 with set_layer_config(scriptable=scriptable, exportable=exportable, no_jit=no_jit):
--> 117 model = create_fn(
118 pretrained=pretrained,
119 pretrained_cfg=pretrained_cfg,
120 pretrained_cfg_overlay=pretrained_cfg_overlay,
121 **kwargs,
122 )
124 if checkpoint_path:
125 load_checkpoint(model, checkpoint_path)
File ~/Projects/MM.Research.BuckReID/.venv/lib/python3.10/site-packages/timm/models/efficientnet.py:1904, in efficientnet_b3(pretrained, **kwargs)
1902 """ EfficientNet-B3 """
1903 # NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
-> 1904 model = _gen_efficientnet(
1905 'efficientnet_b3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
1906 return model
File ~/Projects/MM.Research.BuckReID/.venv/lib/python3.10/site-packages/timm/models/efficientnet.py:651, in _gen_efficientnet(variant, channel_multiplier, depth_multiplier, channel_divisor, group_size, pretrained, **kwargs)
641 round_chs_fn = partial(round_channels, multiplier=channel_multiplier, divisor=channel_divisor)
642 model_kwargs = dict(
643 block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size),
644 num_features=round_chs_fn(1280),
(...)
649 **kwargs,
650 )
--> 651 model = _create_effnet(variant, pretrained, **model_kwargs)
652 return model
File ~/Projects/MM.Research.BuckReID/.venv/lib/python3.10/site-packages/timm/models/efficientnet.py:368, in _create_effnet(variant, pretrained, **kwargs)
365 model_cls = EfficientNetFeatures
366 features_mode = 'cls'
--> 368 model = build_model_with_cfg(
369 model_cls,
370 variant,
371 pretrained,
372 features_only=features_mode == 'cfg',
373 pretrained_strict=features_mode != 'cls',
374 kwargs_filter=kwargs_filter,
375 **kwargs,
376 )
377 if features_mode == 'cls':
378 model.pretrained_cfg = model.default_cfg = pretrained_cfg_for_features(model.pretrained_cfg)
File ~/Projects/MM.Research.BuckReID/.venv/lib/python3.10/site-packages/timm/models/_builder.py:418, in build_model_with_cfg(model_cls, variant, pretrained, pretrained_cfg, pretrained_cfg_overlay, model_cfg, feature_cfg, pretrained_strict, pretrained_filter_fn, kwargs_filter, **kwargs)
416 num_classes_pretrained = 0 if features else getattr(model, 'num_classes', kwargs.get('num_classes', 1000))
417 if pretrained:
--> 418 load_pretrained(
419 model,
420 pretrained_cfg=pretrained_cfg,
421 num_classes=num_classes_pretrained,
422 in_chans=kwargs.get('in_chans', 3),
423 filter_fn=pretrained_filter_fn,
424 strict=pretrained_strict,
425 )
427 # Wrap the model in a feature extraction module if enabled
428 if features:
File ~/Projects/MM.Research.BuckReID/.venv/lib/python3.10/site-packages/timm/models/_builder.py:243, in load_pretrained(model, pretrained_cfg, num_classes, in_chans, filter_fn, strict)
240 classifier_bias = state_dict[classifier_name + '.bias']
241 state_dict[classifier_name + '.bias'] = classifier_bias[label_offset:]
--> 243 load_result = model.load_state_dict(state_dict, strict=strict)
244 if load_result.missing_keys:
245 _logger.info(
246 f'Missing keys ({", ".join(load_result.missing_keys)}) discovered while loading pretrained weights.'
247 f' This is expected if model is being adapted.')
File ~/Projects/MM.Research.BuckReID/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:2593, in Module.load_state_dict(self, state_dict, strict, assign)
2585 error_msgs.insert(
2586 0,
2587 "Missing key(s) in state_dict: {}. ".format(
2588 ", ".join(f'"{k}"' for k in missing_keys)
2589 ),
2590 )
2592 if len(error_msgs) > 0:
-> 2593 raise RuntimeError(
2594 "Error(s) in loading state_dict for {}:\n\t{}".format(
2595 self.__class__.__name__, "\n\t".join(error_msgs)
2596 )
2597 )
2598 return _IncompatibleKeys(missing_keys, unexpected_keys)
RuntimeError: Error(s) in loading state_dict for EfficientNet:
Missing key(s) in state_dict: "conv_stem.weight", "bn1.weight", "bn1.bias", "bn1.running_mean", "bn1.running_var", "blocks.0.0.conv_dw.weight", "blocks.0.0.bn1.weight", "blocks.0.0.bn1.bias", "blocks.0.0.bn1.running_mean", "blocks.0.0.bn1.running_var", "blocks.0.0.se.conv_reduce.weight", "blocks.0.0.se.conv_reduce.bias", "blocks.0.0.se.conv_expand.weight", "blocks.0.0.se.conv_expand.bias", "blocks.0.0.conv_pw.weight", "blocks.0.0.bn2.weight", "blocks.0.0.bn2.bias", "blocks.0.0.bn2.running_mean", "blocks.0.0.bn2.running_var", "blocks.0.1.conv_dw.weight", "blocks.0.1.bn1.weight", "blocks.0.1.bn1.bias", "blocks.0.1.bn1.running_mean", "blocks.0.1.bn1.running_var", "blocks.0.1.se.conv_reduce.weight", "blocks.0.1.se.conv_reduce.bias", "blocks.0.1.se.conv_expand.weight", "blocks.0.1.se.conv_expand.bias", "blocks.0.1.conv_pw.weight", "blocks.0.1.bn2.weight", "blocks.0.1.bn2.bias", "blocks.0.1.bn2.running_mean", "blocks.0.1.bn2.running_var", "blocks.1.0.conv_pw.weight", "blocks.1.0.bn1.weight", "blocks.1.0.bn1.bias", "blocks.1.0.bn1.running_mean", "blocks.1.0.bn1.running_var", "blocks.1.0.conv_dw.weight", "blocks.1.0.bn2.weight", "blocks.1.0.bn2.bias", "blocks.1.0.bn2.running_mean", "blocks.1.0.bn2.running_var", "blocks.1.0.se.conv_reduce.weight", "blocks.1.0.se.conv_reduce.bias", "blocks.1.0.se.conv_expand.weight", "blocks.1.0.se.conv_expand.bias", "blocks.1.0.conv_pwl.weight", "blocks.1.0.bn3.weight", "blocks.1.0.bn3.bias", "blocks.1.0.bn3.running_mean", "blocks.1.0.bn3.running_var", "blocks.1.1.conv_pw.weight", "blocks.1.1.bn1.weight", "blocks.1.1.bn1.bias", "blocks.1.1.bn1.running_mean", "blocks.1.1.bn1.running_var", "blocks.1.1.conv_dw.weight", "blocks.1.1.bn2.weight", "blocks.1.1.bn2.bias", "blocks.1.1.bn2.running_mean", "blocks.1.1.bn2.running_var", "blocks.1.1.se.conv_reduce.weight", "blocks.1.1.se.conv_reduce.bias", "blocks.1.1.se.conv_expand.weight", "blocks.1.1.se.conv_expand.bias", "blocks.1.1.conv_pwl.weight", "blocks.1.1.bn3.weight", "blocks.1.1.bn3.bias", "blocks.1.1.bn3.running_mean", "blocks.1.1.bn3.running_var", "blocks.1.2.conv_pw.weight", "blocks.1.2.bn1.weight", "blocks.1.2.bn1.bias", "blocks.1.2.bn1.running_mean", "blocks.1.2.bn1.running_var", "blocks.1.2.conv_dw.weight", "blocks.1.2.bn2.weight", "blocks.1.2.bn2.bias", "blocks.1.2.bn2.running_mean", "blocks.1.2.bn2.running_var", "blocks.1.2.se.conv_reduce.weight", "blocks.1.2.se.conv_reduce.bias", "blocks.1.2.se.conv_expand.weight", "blocks.1.2.se.conv_expand.bias", "blocks.1.2.conv_pwl.weight", "blocks.1.2.bn3.weight", "blocks.1.2.bn3.bias", "blocks.1.2.bn3.running_mean", "blocks.1.2.bn3.running_var", "blocks.2.0.conv_pw.weight", "blocks.2.0.bn1.weight", "blocks.2.0.bn1.bias", "blocks.2.0.bn1.running_mean", "blocks.2.0.bn1.running_var", "blocks.2.0.conv_dw.weight", "blocks.2.0.bn2.weight", "blocks.2.0.bn2.bias", "blocks.2.0.bn2.running_mean", "blocks.2.0.bn2.running_var", "blocks.2.0.se.conv_reduce.weight", "blocks.2.0.se.conv_reduce.bias", "blocks.2.0.se.conv_expand.weight", "blocks.2.0.se.conv_expand.bias", "blocks.2.0.conv_pwl.weight", "blocks.2.0.bn3.weight", "blocks.2.0.bn3.bias", "blocks.2.0.bn3.running_mean", "blocks.2.0.bn3.running_var", "blocks.2.1.conv_pw.weight", "blocks.2.1.bn1.weight", "blocks.2.1.bn1.bias", "blocks.2.1.bn1.running_mean", "blocks.2.1.bn1.running_var", "blocks.2.1.conv_dw.weight", "blocks.2.1.bn2.weight", "blocks.2.1.bn2.bias", "blocks.2.1.bn2.running_mean", "blocks.2.1.bn2.running_var", "blocks.2.1.se.conv_reduce.weight", "blocks.2.1.se.conv_reduce.bias", "blocks.2.1.se.conv_expand.weight", "blocks.2.1.se.conv_expand.bias", "blocks.2.1.conv_pwl.weight", "blocks.2.1.bn3.weight", "blocks.2.1.bn3.bias", "blocks.2.1.bn3.running_mean", "blocks.2.1.bn3.running_var", "blocks.2.2.conv_pw.weight", "blocks.2.2.bn1.weight", "blocks.2.2.bn1.bias", "blocks.2.2.bn1.running_mean", "blocks.2.2.bn1.running_var", "blocks.2.2.conv_dw.weight", "blocks.2.2.bn2.weight", "blocks.2.2.bn2.bias", "blocks.2.2.bn2.running_mean", "blocks.2.2.bn2.running_var", "blocks.2.2.se.conv_reduce.weight", "blocks.2.2.se.conv_reduce.bias", "blocks.2.2.se.conv_expand.weight", "blocks.2.2.se.conv_expand.bias", "blocks.2.2.conv_pwl.weight", "blocks.2.2.bn3.weight", "blocks.2.2.bn3.bias", "blocks.2.2.bn3.running_mean", "blocks.2.2.bn3.running_var", "blocks.3.0.conv_pw.weight", "blocks.3.0.bn1.weight", "blocks.3.0.bn1.bias", "blocks.3.0.bn1.running_mean", "blocks.3.0.bn1.running_var", "blocks.3.0.conv_dw.weight", "blocks.3.0.bn2.weight", "blocks.3.0.bn2.bias", "blocks.3.0.bn2.running_mean", "blocks.3.0.bn2.running_var", "blocks.3.0.se.conv_reduce.weight", "blocks.3.0.se.conv_reduce.bias", "blocks.3.0.se.conv_expand.weight", "blocks.3.0.se.conv_expand.bias", "blocks.3.0.conv_pwl.weight", "blocks.3.0.bn3.weight", "blocks.3.0.bn3.bias", "blocks.3.0.bn3.running_mean", "blocks.3.0.bn3.running_var", "blocks.3.1.conv_pw.weight", "blocks.3.1.bn1.weight", "blocks.3.1.bn1.bias", "blocks.3.1.bn1.running_mean", "blocks.3.1.bn1.running_var", "blocks.3.1.conv_dw.weight", "blocks.3.1.bn2.weight", "blocks.3.1.bn2.bias", "blocks.3.1.bn2.running_mean", "blocks.3.1.bn2.running_var", "blocks.3.1.se.conv_reduce.weight", "blocks.3.1.se.conv_reduce.bias", "blocks.3.1.se.conv_expand.weight", "blocks.3.1.se.conv_expand.bias", "blocks.3.1.conv_pwl.weight", "blocks.3.1.bn3.weight", "blocks.3.1.bn3.bias", "blocks.3.1.bn3.running_mean", "blocks.3.1.bn3.running_var", "blocks.3.2.conv_pw.weight", "blocks.3.2.bn1.weight", "blocks.3.2.bn1.bias", "blocks.3.2.bn1.running_mean", "blocks.3.2.bn1.running_var", "blocks.3.2.conv_dw.weight", "blocks.3.2.bn2.weight", "blocks.3.2.bn2.bias", "blocks.3.2.bn2.running_mean", "blocks.3.2.bn2.running_var", "blocks.3.2.se.conv_reduce.weight", "blocks.3.2.se.conv_reduce.bias", "blocks.3.2.se.conv_expand.weight", "blocks.3.2.se.conv_expand.bias", "blocks.3.2.conv_pwl.weight", "blocks.3.2.bn3.weight", "blocks.3.2.bn3.bias", "blocks.3.2.bn3.running_mean", "blocks.3.2.bn3.running_var", "blocks.3.3.conv_pw.weight", "blocks.3.3.bn1.weight", "blocks.3.3.bn1.bias", "blocks.3.3.bn1.running_mean", "blocks.3.3.bn1.running_var", "blocks.3.3.conv_dw.weight", "blocks.3.3.bn2.weight", "blocks.3.3.bn2.bias", "blocks.3.3.bn2.running_mean", "blocks.3.3.bn2.running_var", "blocks.3.3.se.conv_reduce.weight", "blocks.3.3.se.conv_reduce.bias", "blocks.3.3.se.conv_expand.weight", "blocks.3.3.se.conv_expand.bias", "blocks.3.3.conv_pwl.weight", "blocks.3.3.bn3.weight", "blocks.3.3.bn3.bias", "blocks.3.3.bn3.running_mean", "blocks.3.3.bn3.running_var", "blocks.3.4.conv_pw.weight", "blocks.3.4.bn1.weight", "blocks.3.4.bn1.bias", "blocks.3.4.bn1.running_mean", "blocks.3.4.bn1.running_var", "blocks.3.4.conv_dw.weight", "blocks.3.4.bn2.weight", "blocks.3.4.bn2.bias", "blocks.3.4.bn2.running_mean", "blocks.3.4.bn2.running_var", "blocks.3.4.se.conv_reduce.weight", "blocks.3.4.se.conv_reduce.bias", "blocks.3.4.se.conv_expand.weight", "blocks.3.4.se.conv_expand.bias", "blocks.3.4.conv_pwl.weight", "blocks.3.4.bn3.weight", "blocks.3.4.bn3.bias", "blocks.3.4.bn3.running_mean", "blocks.3.4.bn3.running_var", "blocks.4.0.conv_pw.weight", "blocks.4.0.bn1.weight", "blocks.4.0.bn1.bias", "blocks.4.0.bn1.running_mean", "blocks.4.0.bn1.running_var", "blocks.4.0.conv_dw.weight", "blocks.4.0.bn2.weight", "blocks.4.0.bn2.bias", "blocks.4.0.bn2.running_mean", "blocks.4.0.bn2.running_var", "blocks.4.0.se.conv_reduce.weight", "blocks.4.0.se.conv_reduce.bias", "blocks.4.0.se.conv_expand.weight", "blocks.4.0.se.conv_expand.bias", "blocks.4.0.conv_pwl.weight", "blocks.4.0.bn3.weight", "blocks.4.0.bn3.bias", "blocks.4.0.bn3.running_mean", "blocks.4.0.bn3.running_var", "blocks.4.1.conv_pw.weight", "blocks.4.1.bn1.weight", "blocks.4.1.bn1.bias", "blocks.4.1.bn1.running_mean", "blocks.4.1.bn1.running_var", "blocks.4.1.conv_dw.weight", "blocks.4.1.bn2.weight", "blocks.4.1.bn2.bias", "blocks.4.1.bn2.running_mean", "blocks.4.1.bn2.running_var", "blocks.4.1.se.conv_reduce.weight", "blocks.4.1.se.conv_reduce.bias", "blocks.4.1.se.conv_expand.weight", "blocks.4.1.se.conv_expand.bias", "blocks.4.1.conv_pwl.weight", "blocks.4.1.bn3.weight", "blocks.4.1.bn3.bias", "blocks.4.1.bn3.running_mean", "blocks.4.1.bn3.running_var", "blocks.4.2.conv_pw.weight", "blocks.4.2.bn1.weight", "blocks.4.2.bn1.bias", "blocks.4.2.bn1.running_mean", "blocks.4.2.bn1.running_var", "blocks.4.2.conv_dw.weight", "blocks.4.2.bn2.weight", "blocks.4.2.bn2.bias", "blocks.4.2.bn2.running_mean", "blocks.4.2.bn2.running_var", "blocks.4.2.se.conv_reduce.weight", "blocks.4.2.se.conv_reduce.bias", "blocks.4.2.se.conv_expand.weight", "blocks.4.2.se.conv_expand.bias", "blocks.4.2.conv_pwl.weight", "blocks.4.2.bn3.weight", "blocks.4.2.bn3.bias", "blocks.4.2.bn3.running_mean", "blocks.4.2.bn3.running_var", "blocks.4.3.conv_pw.weight", "blocks.4.3.bn1.weight", "blocks.4.3.bn1.bias", "blocks.4.3.bn1.running_mean", "blocks.4.3.bn1.running_var", "blocks.4.3.conv_dw.weight", "blocks.4.3.bn2.weight", "blocks.4.3.bn2.bias", "blocks.4.3.bn2.running_mean", "blocks.4.3.bn2.running_var", "blocks.4.3.se.conv_reduce.weight", "blocks.4.3.se.conv_reduce.bias", "blocks.4.3.se.conv_expand.weight", "blocks.4.3.se.conv_expand.bias", "blocks.4.3.conv_pwl.weight", "blocks.4.3.bn3.weight", "blocks.4.3.bn3.bias", "blocks.4.3.bn3.running_mean", "blocks.4.3.bn3.running_var", "blocks.4.4.conv_pw.weight", "blocks.4.4.bn1.weight", "blocks.4.4.bn1.bias", "blocks.4.4.bn1.running_mean", "blocks.4.4.bn1.running_var", "blocks.4.4.conv_dw.weight", "blocks.4.4.bn2.weight", "blocks.4.4.bn2.bias", "blocks.4.4.bn2.running_mean", "blocks.4.4.bn2.running_var", "blocks.4.4.se.conv_reduce.weight", "blocks.4.4.se.conv_reduce.bias", "blocks.4.4.se.conv_expand.weight", "blocks.4.4.se.conv_expand.bias", "blocks.4.4.conv_pwl.weight", "blocks.4.4.bn3.weight", "blocks.4.4.bn3.bias", "blocks.4.4.bn3.running_mean", "blocks.4.4.bn3.running_var", "blocks.5.0.conv_pw.weight", "blocks.5.0.bn1.weight", "blocks.5.0.bn1.bias", "blocks.5.0.bn1.running_mean", "blocks.5.0.bn1.running_var", "blocks.5.0.conv_dw.weight", "blocks.5.0.bn2.weight", "blocks.5.0.bn2.bias", "blocks.5.0.bn2.running_mean", "blocks.5.0.bn2.running_var", "blocks.5.0.se.conv_reduce.weight", "blocks.5.0.se.conv_reduce.bias", "blocks.5.0.se.conv_expand.weight", "blocks.5.0.se.conv_expand.bias", "blocks.5.0.conv_pwl.weight", "blocks.5.0.bn3.weight", "blocks.5.0.bn3.bias", "blocks.5.0.bn3.running_mean", "blocks.5.0.bn3.running_var", "blocks.5.1.conv_pw.weight", "blocks.5.1.bn1.weight", "blocks.5.1.bn1.bias", "blocks.5.1.bn1.running_mean", "blocks.5.1.bn1.running_var", "blocks.5.1.conv_dw.weight", "blocks.5.1.bn2.weight", "blocks.5.1.bn2.bias", "blocks.5.1.bn2.running_mean", "blocks.5.1.bn2.running_var", "blocks.5.1.se.conv_reduce.weight", "blocks.5.1.se.conv_reduce.bias", "blocks.5.1.se.conv_expand.weight", "blocks.5.1.se.conv_expand.bias", "blocks.5.1.conv_pwl.weight", "blocks.5.1.bn3.weight", "blocks.5.1.bn3.bias", "blocks.5.1.bn3.running_mean", "blocks.5.1.bn3.running_var", "blocks.5.2.conv_pw.weight", "blocks.5.2.bn1.weight", "blocks.5.2.bn1.bias", "blocks.5.2.bn1.running_mean", "blocks.5.2.bn1.running_var", "blocks.5.2.conv_dw.weight", "blocks.5.2.bn2.weight", "blocks.5.2.bn2.bias", "blocks.5.2.bn2.running_mean", "blocks.5.2.bn2.running_var", "blocks.5.2.se.conv_reduce.weight", "blocks.5.2.se.conv_reduce.bias", "blocks.5.2.se.conv_expand.weight", "blocks.5.2.se.conv_expand.bias", "blocks.5.2.conv_pwl.weight", "blocks.5.2.bn3.weight", "blocks.5.2.bn3.bias", "blocks.5.2.bn3.running_mean", "blocks.5.2.bn3.running_var", "blocks.5.3.conv_pw.weight", "blocks.5.3.bn1.weight", "blocks.5.3.bn1.bias", "blocks.5.3.bn1.running_mean", "blocks.5.3.bn1.running_var", "blocks.5.3.conv_dw.weight", "blocks.5.3.bn2.weight", "blocks.5.3.bn2.bias", "blocks.5.3.bn2.running_mean", "blocks.5.3.bn2.running_var", "blocks.5.3.se.conv_reduce.weight", "blocks.5.3.se.conv_reduce.bias", "blocks.5.3.se.conv_expand.weight", "blocks.5.3.se.conv_expand.bias", "blocks.5.3.conv_pwl.weight", "blocks.5.3.bn3.weight", "blocks.5.3.bn3.bias", "blocks.5.3.bn3.running_mean", "blocks.5.3.bn3.running_var", "blocks.5.4.conv_pw.weight", "blocks.5.4.bn1.weight", "blocks.5.4.bn1.bias", "blocks.5.4.bn1.running_mean", "blocks.5.4.bn1.running_var", "blocks.5.4.conv_dw.weight", "blocks.5.4.bn2.weight", "blocks.5.4.bn2.bias", "blocks.5.4.bn2.running_mean", "blocks.5.4.bn2.running_var", "blocks.5.4.se.conv_reduce.weight", "blocks.5.4.se.conv_reduce.bias", "blocks.5.4.se.conv_expand.weight", "blocks.5.4.se.conv_expand.bias", "blocks.5.4.conv_pwl.weight", "blocks.5.4.bn3.weight", "blocks.5.4.bn3.bias", "blocks.5.4.bn3.running_mean", "blocks.5.4.bn3.running_var", "blocks.5.5.conv_pw.weight", "blocks.5.5.bn1.weight", "blocks.5.5.bn1.bias", "blocks.5.5.bn1.running_mean", "blocks.5.5.bn1.running_var", "blocks.5.5.conv_dw.weight", "blocks.5.5.bn2.weight", "blocks.5.5.bn2.bias", "blocks.5.5.bn2.running_mean", "blocks.5.5.bn2.running_var", "blocks.5.5.se.conv_reduce.weight", "blocks.5.5.se.conv_reduce.bias", "blocks.5.5.se.conv_expand.weight", "blocks.5.5.se.conv_expand.bias", "blocks.5.5.conv_pwl.weight", "blocks.5.5.bn3.weight", "blocks.5.5.bn3.bias", "blocks.5.5.bn3.running_mean", "blocks.5.5.bn3.running_var", "blocks.6.0.conv_pw.weight", "blocks.6.0.bn1.weight", "blocks.6.0.bn1.bias", "blocks.6.0.bn1.running_mean", "blocks.6.0.bn1.running_var", "blocks.6.0.conv_dw.weight", "blocks.6.0.bn2.weight", "blocks.6.0.bn2.bias", "blocks.6.0.bn2.running_mean", "blocks.6.0.bn2.running_var", "blocks.6.0.se.conv_reduce.weight", "blocks.6.0.se.conv_reduce.bias", "blocks.6.0.se.conv_expand.weight", "blocks.6.0.se.conv_expand.bias", "blocks.6.0.conv_pwl.weight", "blocks.6.0.bn3.weight", "blocks.6.0.bn3.bias", "blocks.6.0.bn3.running_mean", "blocks.6.0.bn3.running_var", "blocks.6.1.conv_pw.weight", "blocks.6.1.bn1.weight", "blocks.6.1.bn1.bias", "blocks.6.1.bn1.running_mean", "blocks.6.1.bn1.running_var", "blocks.6.1.conv_dw.weight", "blocks.6.1.bn2.weight", "blocks.6.1.bn2.bias", "blocks.6.1.bn2.running_mean", "blocks.6.1.bn2.running_var", "blocks.6.1.se.conv_reduce.weight", "blocks.6.1.se.conv_reduce.bias", "blocks.6.1.se.conv_expand.weight", "blocks.6.1.se.conv_expand.bias", "blocks.6.1.conv_pwl.weight", "blocks.6.1.bn3.weight", "blocks.6.1.bn3.bias", "blocks.6.1.bn3.running_mean", "blocks.6.1.bn3.running_var", "conv_head.weight", "bn2.weight", "bn2.bias", "bn2.running_mean", "bn2.running_var", "classifier.weight", "classifier.bias".
Unexpected key(s) in state_dict: "model", "objective", "optimizer", "epoch", "rng_states", "scheduler".