import fastai.version
print(fastai.version.__version__)
#output: 1.0.50.post1
learn152 = cnn_learner(data, models.resnet152, metrics = error_rate)
learn152.save('untrained')
learn152.load('untrained') #this is ok
learn152.fit_one_cycle(4, max_lr = slice(1e-4, 1e-2), callbacks=[callbacks.SaveModelCallback(learn, every='epoch', monitor='error_rate', name='model152_lr1e_2')])
#some output of training here
learn152.load('model152_lr1e_2_0')
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-45-6838926e850c> in <module>
----> 1 learn152.load('model152_lr1e_2_0')
~/anaconda3/lib/python3.7/site-packages/fastai/basic_train.py in load(self, name, device, strict, with_opt, purge, remove_module)
266 model_state = state['model']
267 if remove_module: model_state = remove_module_load(model_state)
--> 268 get_model(self.model).load_state_dict(model_state, strict=strict)
269 if ifnone(with_opt,True):
270 if not hasattr(self, 'opt'): self.create_opt(defaults.lr, self.wd)
~/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in load_state_dict(self, state_dict, strict)
767 if len(error_msgs) > 0:
768 raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
--> 769 self.__class__.__name__, "\n\t".join(error_msgs)))
770
771 def _named_members(self, get_members_fn, prefix='', recurse=True):
RuntimeError: Error(s) in loading state_dict for Sequential:
Missing key(s) in state_dict: "0.5.4.conv1.weight", "0.5.4.bn1.weight", "0.5.4.bn1.bias", "0.5.4.bn1.running_mean", "0.5.4.bn1.running_var", "0.5.4.conv2.weight", "0.5.4.bn2.weight", "0.5.4.bn2.bias", "0.5.4.bn2.running_mean", "0.5.4.bn2.running_var", "0.5.4.conv3.weight", "0.5.4.bn3.weight", "0.5.4.bn3.bias", "0.5.4.bn3.running_mean", "0.5.4.bn3.running_var", "0.5.5.conv1.weight", "0.5.5.bn1.weight", "0.5.5.bn1.bias", "0.5.5.bn1.running_mean", "0.5.5.bn1.running_var", "0.5.5.conv2.weight", "0.5.5.bn2.weight", "0.5.5.bn2.bias", "0.5.5.bn2.running_mean", "0.5.5.bn2.running_var", "0.5.5.conv3.weight", "0.5.5.bn3.weight", "0.5.5.bn3.bias", "0.5.5.bn3.running_mean", "0.5.5.bn3.running_var", "0.5.6.conv1.weight", "0.5.6.bn1.weight", "0.5.6.bn1.bias", "0.5.6.bn1.running_mean", "0.5.6.bn1.running_var", "0.5.6.conv2.weight", "0.5.6.bn2.weight", "0.5.6.bn2.bias", "0.5.6.bn2.running_mean", "0.5.6.bn2.running_var", "0.5.6.conv3.weight", "0.5.6.bn3.weight", "0.5.6.bn3.bias", "0.5.6.bn3.running_mean", "0.5.6.bn3.running_var", "0.5.7.conv1.weight", "0.5.7.bn1.weight", "0.5.7.bn1.bias", "0.5.7.bn1.running_mean", "0.5.7.bn1.running_var", "0.5.7.conv2.weight", "0.5.7.bn2.weight", "0.5.7.bn2.bias", "0.5.7.bn2.running_mean", "0.5.7.bn2.running_var", "0.5.7.conv3.weight", "0.5.7.bn3.weight", "0.5.7.bn3.bias", "0.5.7.bn3.running_mean", "0.5.7.bn3.running_var", "0.6.6.conv1.weight", "0.6.6.bn1.weight", "0.6.6.bn1.bias", "0.6.6.bn1.running_mean", "0.6.6.bn1.running_var", "0.6.6.conv2.weight", "0.6.6.bn2.weight", "0.6.6.bn2.bias", "0.6.6.bn2.running_mean", "0.6.6.bn2.running_var", "0.6.6.conv3.weight", "0.6.6.bn3.weight", "0.6.6.bn3.bias", "0.6.6.bn3.running_mean", "0.6.6.bn3.running_var", "0.6.7.conv1.weight", "0.6.7.bn1.weight", "0.6.7.bn1.bias", "0.6.7.bn1.running_mean", "0.6.7.bn1.running_var", "0.6.7.conv2.weight", "0.6.7.bn2.weight", "0.6.7.bn2.bias", "0.6.7.bn2.running_mean", "0.6.7.bn2.running_var", "0.6.7.conv3.weight", "0.6.7.bn3.weight", "0.6.7.bn3.bias", "0.6.7.bn3.running_mean", "0.6.7.bn3.running_var", "0.6.8.conv1.weight", "0.6.8.bn1.weight", "0.6.8.bn1.bias", "0.6.8.bn1.running_mean", "0.6.8.bn1.running_var", "0.6.8.conv2.weight", "0.6.8.bn2.weight", "0.6.8.bn2.bias", "0.6.8.bn2.running_mean", "0.6.8.bn2.running_var", "0.6.8.conv3.weight", "0.6.8.bn3.weight", "0.6.8.bn3.bias", "0.6.8.bn3.running_mean", "0.6.8.bn3.running_var", "0.6.9.conv1.weight", "0.6.9.bn1.weight", "0.6.9.bn1.bias", "0.6.9.bn1.running_mean", "0.6.9.bn1.running_var", "0.6.9.conv2.weight", "0.6.9.bn2.weight", "0.6.9.bn2.bias", "0.6.9.bn2.running_mean", "0.6.9.bn2.running_var", "0.6.9.conv3.weight", "0.6.9.bn3.weight", "0.6.9.bn3.bias", "0.6.9.bn3.running_mean", "0.6.9.bn3.running_var", "0.6.10.conv1.weight", "0.6.10.bn1.weight", "0.6.10.bn1.bias", "0.6.10.bn1.running_mean", "0.6.10.bn1.running_var", "0.6.10.conv2.weight", "0.6.10.bn2.weight", "0.6.10.bn2.bias", "0.6.10.bn2.running_mean", "0.6.10.bn2.running_var", "0.6.10.conv3.weight", "0.6.10.bn3.weight", 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"0.6.33.bn2.running_mean", "0.6.33.bn2.running_var", "0.6.33.conv3.weight", "0.6.33.bn3.weight", "0.6.33.bn3.bias", "0.6.33.bn3.running_mean", "0.6.33.bn3.running_var", "0.6.34.conv1.weight", "0.6.34.bn1.weight", "0.6.34.bn1.bias", "0.6.34.bn1.running_mean", "0.6.34.bn1.running_var", "0.6.34.conv2.weight", "0.6.34.bn2.weight", "0.6.34.bn2.bias", "0.6.34.bn2.running_mean", "0.6.34.bn2.running_var", "0.6.34.conv3.weight", "0.6.34.bn3.weight", "0.6.34.bn3.bias", "0.6.34.bn3.running_mean", "0.6.34.bn3.running_var", "0.6.35.conv1.weight", "0.6.35.bn1.weight", "0.6.35.bn1.bias", "0.6.35.bn1.running_mean", "0.6.35.bn1.running_var", "0.6.35.conv2.weight", "0.6.35.bn2.weight", "0.6.35.bn2.bias", "0.6.35.bn2.running_mean", "0.6.35.bn2.running_var", "0.6.35.conv3.weight", "0.6.35.bn3.weight", "0.6.35.bn3.bias", "0.6.35.bn3.running_mean", "0.6.35.bn3.running_var".
But I can successfully load resnet50 model saved by callbacks.
Could anyone tell me what’s wrong with my process? Thanks
