I got the following error when trying to load a ResNet50 model. Where should I download the resnet50.h5 file?
Traceback (most recent call last):
File "C:\Users\drlng\Desktop\image-captioning-keras-resnet-main\app.py", line 61, in <module>
resnet = load_model('resnet.h5')
File "C:\Users\drlng\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\saving\save.py", line 211, in load_model
loader_impl.parse_saved_model(filepath)
File "C:\Users\drlng\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\saved_model\loader_impl.py", line 111, in parse_saved_model
raise IOError("SavedModel file does not exist at: %s/{%s|%s}" %
OSError: SavedModel file does not exist at: resnet.h5/{saved_model.pbtxt|saved_model.pb}
I use resnet50.py for making my model
and read weight of resnet50 from below link:
weights best!
you can download pre train models
It works well
If you are looking for pre-trained weights of ResNet-50, you can find it here
Related
I'm trying to set up a pre-trained model from huggingface. This is the start of my code:
from transformers import pipeline, TFAutoModelForSequenceClassification
model_name = "microsoft/deberta-base-mnli"
text_classifier = pipeline("text-classification", model=model_name)
model = TFAutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2, ignore_mismatched_sizes=True, from_pt=True)
But already for this last line I get the following error:
Traceback (most recent call last):
File "/home/liv/PycharmProjects/ZSTC_for_MHC/models/baselines/single_task.py", line 29, in <module>
model = TFAutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2, ignore_mismatched_sizes=True,
File "/home/liv/PycharmProjects/ZSTC_for_MHC/.venv/lib/python3.9/site-packages/transformers/models/auto/auto_factory.py", line 446, in from_pretrained
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
File "/home/liv/PycharmProjects/ZSTC_for_MHC/.venv/lib/python3.9/site-packages/transformers/modeling_tf_utils.py", line 1972, in from_pretrained
return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file, allow_missing_keys=True)
File "/home/liv/PycharmProjects/ZSTC_for_MHC/.venv/lib/python3.9/site-packages/transformers/modeling_tf_pytorch_utils.py", line 122, in load_pytorch_checkpoint_in_tf2_model
logger.info(f"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()):,} parameters")
File "/home/liv/PycharmProjects/ZSTC_for_MHC/.venv/lib/python3.9/site-packages/transformers/modeling_tf_pytorch_utils.py", line 122, in <genexpr>
logger.info(f"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()):,} parameters")
AttributeError: 'dict' object has no attribute 'numel'
I'm not calling numel myself and I don't even know which dictionary. Furthermore, I want to use TensorFlow for this, so I don't know if it's alright that it shows a PyTorch error. Anyone know how to fix this? Google didn't provide any solutions.
I am saving and loading a model using torch.save() and torch.load() commands.
While loading a fine-tuned simple transformer model in Docker Container, I am facing this error which I am not able to resolve:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.7/dist-packages/torch/serialization.py", line 594, in load
return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
File "/usr/local/lib/python3.7/dist-packages/torch/serialization.py", line 853, in _load
result = unpickler.load()
File "/usr/local/lib/python3.7/dist-packages/transformers/models/xlm_roberta/tokenization_xlm_roberta.py", line 161, in __setstate__
self.sp_model.Load(self.vocab_file)
File "/usr/local/lib/python3.7/dist-packages/sentencepiece.py", line 367, in Load
return self.LoadFromFile(model_file)
File "/usr/local/lib/python3.7/dist-packages/sentencepiece.py", line 177, in LoadFromFile
return _sentencepiece.SentencePieceProcessor_LoadFromFile(self, arg)
OSError: Not found: "/home/jupyter/.cache/huggingface/transformers/9df9ae4442348b73950203b63d1b8ed2d18eba68921872aee0c3a9d05b9673c6.00628a9eeb8baf4080d44a0abe9fe8057893de20c7cb6e6423cddbf452f7d4d8": No such file or directory Error #2
If anyone has any idea about it, please let me know.
I am using:
torch ==1.7.1+cu101
sentence-transformers 0.3.9
simpletransformers 0.51.15
transformers 4.4.2
tensorflow 2.2.0
I suggest using state_dict objects - the Python dictionaries as they can be easily saved, updated and restored giving you a flexibility for restoring the model later. Here are the recommended Save/Load methods for saving models with state_dict:
Save
torch.save(model.state_dict(), PATH)
Load
model = TheModelClass(*args, **kwargs)
model.load_state_dict(torch.load(PATH))
model.eval()
I am trying to use Huggingface transformer api to load a locally downloaded M-BERT model but it is throwing an exception.
I clone this repo: https://huggingface.co/bert-base-multilingual-cased
bert = TFBertModel.from_pretrained("input/bert-base-multilingual-cased")
The directory structure is:
But I am getting this error:
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/transformers/modeling_tf_utils.py", line 1277, in from_pretrained
missing_keys, unexpected_keys = load_tf_weights(model, resolved_archive_file, load_weight_prefix)
File "/usr/local/lib/python3.7/dist-packages/transformers/modeling_tf_utils.py", line 467, in load_tf_weights
with h5py.File(resolved_archive_file, "r") as f:
File "/usr/local/lib/python3.7/dist-packages/h5py/_hl/files.py", line 408, in __init__
swmr=swmr)
File "/usr/local/lib/python3.7/dist-packages/h5py/_hl/files.py", line 173, in make_fid
fid = h5f.open(name, flags, fapl=fapl)
File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "h5py/h5f.pyx", line 88, in h5py.h5f.open
OSError: Unable to open file (file signature not found)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "train.py", line 81, in <module>
__main__()
File "train.py", line 59, in __main__
model = create_model(num_classes)
File "/content/drive/My Drive/msc-project/code/model.py", line 26, in create_model
bert = TFBertModel.from_pretrained("input/bert-base-multilingual-cased")
File "/usr/local/lib/python3.7/dist-packages/transformers/modeling_tf_utils.py", line 1280, in from_pretrained
"Unable to load weights from h5 file. "
OSError: Unable to load weights from h5 file. If you tried to load a TF 2.0 model from a PyTorch checkpoint, please set from_pt=True.
Where am I going wrong?
Need help!
Thanks in advance.
As it was already pointed in the comments - your from_pretrained param should be either id of a model hosted on huggingface.co or a local path:
A path to a directory containing model weights saved using
save_pretrained(), e.g., ./my_model_directory/.
See documentation
Looking at your stacktrace it seems like your code is run inside:
/content/drive/My Drive/msc-project/code/model.py so unless your model is in:
/content/drive/My Drive/msc-project/code/input/bert-base-multilingual-cased/ it won't load.
I would also set the path to be similar to documentation example ie:
bert = TFBertModel.from_pretrained("./input/bert-base-multilingual-cased/")
I have a checkpoint file saved after training a model in Pytorch. I have to inspect it in a different module so I tried to load the checkpoint using the following code.
map_location = lambda storage, loc: storage
checkpoint = torch.load("model.pt", map_location=map_location)
But it is raising ModuleNotFoundError issue, which I couldn't find a way to resolve.
The error traceback :
Traceback (most recent call last):
File "main.py", line 11, in <module>
model = loadmodel(hook_feature)
File "/home/../model_loader.py", line 21, in loadmodel
checkpoint = torch.load(settings.MODEL_FILE, map_location=map_location)
File "/home/../.conda/envs/envreporting/lib/python3.6/site-packages/torch/serialization.py", line 584, in load
return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
File "/home/../.conda/envs/envreporting/lib/python3.6/site-packages/torch/serialization.py", line 842, in _load
result = unpickler.load()
ModuleNotFoundError: No module named 'parse_config'
I couldn't find an already existing issue relevant to this one.
Is it possible that you have used https://github.com/victoresque/pytorch-template for training the model ? In that case, the project also saves its config in the checkpoint and you also need to import parse_config.py file in order to load it.
I have trained a Keras model with Tensorflow backend. It was saved with model.save. I now want to reload the model using model_load, however, I get the following error:
Traceback (most recent call last):
File "<ipython-input-235-387752c910a4>", line 1, in <module>
load_model('MyModel.h5')
File "C:\Anaconda\envs\tensorflow\lib\site-packages\keras\models.py", line 243, in load_model
model = model_from_config(model_config, custom_objects=custom_objects)
File "C:\Anaconda\envs\tensorflow\lib\site-packages\keras\models.py", line 317, in model_from_config
return layer_module.deserialize(config, custom_objects=custom_objects)
File "C:\Anaconda\envs\tensorflow\lib\site-packages\keras\layers\__init__.py", line 55, in deserialize
printable_module_name='layer')
File "C:\Anaconda\envs\tensorflow\lib\site-packages\keras\utils\generic_utils.py", line 144, in deserialize_keras_object
list(custom_objects.items())))
File "C:\Anaconda\envs\tensorflow\lib\site-packages\keras\engine\topology.py", line 2514, in from_config
process_layer(layer_data)
File "C:\Anaconda\envs\tensorflow\lib\site-packages\keras\engine\topology.py", line 2500, in process_layer
custom_objects=custom_objects)
File "C:\Anaconda\envs\tensorflow\lib\site-packages\keras\layers\__init__.py", line 55, in deserialize
printable_module_name='layer')
File "C:\Anaconda\envs\tensorflow\lib\site-packages\keras\utils\generic_utils.py", line 144, in deserialize_keras_object
list(custom_objects.items())))
File "C:\Anaconda\envs\tensorflow\lib\site-packages\keras\models.py", line 1367, in from_config
if 'class_name' not in config[0] or config[0]['class_name'] == 'Merge':
KeyError: 0
From what I read, there seems to be a bug in Keras when a model that was trained with an older version of Keras is loaded with a recent version. So there might be a version mismatch. However, I couldn't find a report that corresponds to my situation. Downgrading Keras or retraining is not an option.
Did anyone have this issue and maybe even found a solution? I would appreciate it a lot!
Thanks!
For future reference: It is an issue in the config files. Keras 2.2.4 has a fix for this:
Keras 2.2.4
#fchollet fchollet released this on Oct 3 ยท 79 commits to master since this release
Assets 2
This is a bugfix release, addressing two issues:
Ability to save a model when a file with the same name already exists.
Issue with loading legacy config files for the Sequential model.
So I ended up creating a new virtual environment with the most recent TF and Keras versions.