AttributeError: 'ResNet' object has no attribute 'grad_checkpointing'

[edwardchalstrey1]

On trying to run the notebook I got the following error:

Full error:

========================================
Slicing mr_tmp_1672936332/orig_image.png
----------------------------------------
[INFO] added 'infer_test' dataloader with 8217 elements.
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[7], line 1
----> 1 model.predict(plant_flower[1], slice_size=25)

File /opt/anaconda3/envs/plant-pheno/lib/python3.8/site-packages/scivision/io/wrapper.py:19, in PretrainedModel.predict(self, X, *args, **kwargs)
     17 """Run the prediction."""
     18 # note, we may need to pass other kwargs to the prediction here
---> 19 return self._plumbing(X, **kwargs)

File /opt/anaconda3/envs/plant-pheno/lib/python3.8/site-packages/scivision/io/autoplumber.py:79, in AutoPlumber.__call__(self, X, **kwargs)
     77 # optionally, we can also pass on other keyword arguments to the model
     78 model_kwargs.update(kwargs)
---> 79 return self._fn(*model_args, **model_kwargs)

File /opt/anaconda3/envs/plant-pheno/lib/python3.8/site-packages/mapreader-plant-scivision/model.py:192, in MapReader_model.predict(self, path2images, slice_size, plot_output, **slice_kwds)
    183 def predict(self, 
    184             path2images: str,
    185             slice_size: int=100,
    186             plot_output: bool=True,
    187             **slice_kwds
    188             ):
    190     self.load_image(path2images, slice_size=slice_size, **slice_kwds)
--> 192     return self.inference(plot_output=plot_output)

File /opt/anaconda3/envs/plant-pheno/lib/python3.8/site-packages/mapreader-plant-scivision/model.py:145, in MapReader_model.inference(self, plot_output)
    138 self.pretrained_model.add2dataloader(patches2infer_dataset, 
    139                                      set_name=self.infer_name, 
    140                                      batch_size=self.batch_size, 
    141                                      shuffle=False, 
    142                                      num_workers=0)
    144 # ---- INFERENCE
--> 145 self.pretrained_model.inference(set_name=self.infer_name)
    147 patches2infer['pred'] = self.pretrained_model.pred_label
    148 patches2infer['conf'] = np.max(np.array(self.pretrained_model.pred_conf), axis=1)

File /opt/anaconda3/envs/plant-pheno/lib/python3.8/site-packages/mapreader/train/classifier.py:363, in classifier.inference(self, set_name, verbosity_level, print_info_batch_freq)
    361 def inference(self, set_name="infer", verbosity_level=0, print_info_batch_freq: int=5):
    362     """Model inference on dataset: set_name"""
--> 363     self.train(phases=[set_name], 
    364                num_epochs=1, 
    365                save_model_dir=None,
    366                verbosity_level=verbosity_level,
    367                tensorboard_path=None,
    368                tmp_file_save_freq=2,
    369                remove_after_load=False,
    370                print_info_batch_freq=print_info_batch_freq)

File /opt/anaconda3/envs/plant-pheno/lib/python3.8/site-packages/mapreader/train/classifier.py:400, in classifier.train(self, phases, num_epochs, save_model_dir, verbosity_level, tensorboard_path, tmp_file_save_freq, remove_after_load, print_info_batch_freq)
    393 """Wrapper function for train_core method to capture exceptions. Supported exceptions so far:
    394 - KeyboardInterrupt
    395 
    396 Refer to train_core for more information.
    397 """
    399 try:
--> 400     self.train_core(phases, 
    401                     num_epochs, 
    402                     save_model_dir, 
    403                     verbosity_level, 
    404                     tensorboard_path,
    405                     tmp_file_save_freq,
    406                     print_info_batch_freq=print_info_batch_freq)
    407 except KeyboardInterrupt:
    408     print("KeyboardInterrupted...Exiting...")

File /opt/anaconda3/envs/plant-pheno/lib/python3.8/site-packages/mapreader/train/classifier.py:551, in classifier.train_core(self, phases, num_epochs, save_model_dir, verbosity_level, tensorboard_path, tmp_file_save_freq, print_info_batch_freq)
    545     running_loss += loss.item() * inputs.size(0)
    547     # TQDM
    548     # batch_loop.set_postfix(loss=loss.data)
    549     # batch_loop.refresh()
    550 else:
--> 551     outputs = self.model(inputs)
    552     _, pred_label = torch.max(outputs, dim=1)
    554 running_pred_conf.extend(
    555     torch.nn.functional.softmax(outputs, dim=1).cpu().tolist()
    556 )

File /opt/anaconda3/envs/plant-pheno/lib/python3.8/site-packages/torch/nn/modules/module.py:1110, in Module._call_impl(self, *input, **kwargs)
   1106 # If we don't have any hooks, we want to skip the rest of the logic in
   1107 # this function, and just call forward.
   1108 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1109         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110     return forward_call(*input, **kwargs)
   1111 # Do not call functions when jit is used
   1112 full_backward_hooks, non_full_backward_hooks = [], []

File /opt/anaconda3/envs/plant-pheno/lib/python3.8/site-packages/timm/models/resnet.py:730, in ResNet.forward(self, x)
    729 def forward(self, x):
--> 730     x = self.forward_features(x)
    731     x = self.forward_head(x)
    732     return x

File /opt/anaconda3/envs/plant-pheno/lib/python3.8/site-packages/timm/models/resnet.py:714, in ResNet.forward_features(self, x)
    711 x = self.act1(x)
    712 x = self.maxpool(x)
--> 714 if self.grad_checkpointing and not torch.jit.is_scripting():
    715     x = checkpoint_seq([self.layer1, self.layer2, self.layer3, self.layer4], x, flatten=True)
    716 else:

File /opt/anaconda3/envs/plant-pheno/lib/python3.8/site-packages/torch/nn/modules/module.py:1185, in Module.__getattr__(self, name)
   1183     if name in modules:
   1184         return modules[name]
-> 1185 raise AttributeError("'{}' object has no attribute '{}'".format(
   1186     type(self).__name__, name))

AttributeError: 'ResNet' object has no attribute 'grad_checkpointing'

[IFenton]

This problem relates to the timm package - the latest version, 0.6.12 causes the error; it runs fine if v 0.5.4 is used. However, just pinning this version in requirement.txt of this notebook doesn't solve the problem, as during load_pretrained_model the mapreader-plant-scivision package updates timm to the latest version.

So to solve this, I think that either the mapreader-plant-scivision model needs to pin the earlier version of timm in some way (maybe the requirements file), or it needs to be updated to handle the problem. @evangeline-corcoran do you have a preference for which solution is better?

[IFenton]

Pinning the timm package to 0.5.4. in the mapreader-plant-scivision model seems to have fixed this issue for Mac / Linux. However, the binder is still crashing on model.predict, and some Windows machines are still not working properly.