After successfully installing Layout Parser in Windows, getting the below OS Error.
Code Used:
model = lp.Detectron2LayoutModel(config_path="lp://PubLayNet/mask_rcnn_X_101_32x8d_FPN_3x/config",
extra_config=["MODEL.ROI_HEADS.SCORE_THRESH_TEST", 0.8],
label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"})
Using layout parser, trying to extract the content from image. But when I try to load models in Layout parser, it fails with the below error
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_178664\3254664512.py in <module>
1 model = lp.Detectron2LayoutModel(config_path="lp://PubLayNet/mask_rcnn_X_101_32x8d_FPN_3x/config",
2 extra_config=["MODEL.ROI_HEADS.SCORE_THRESH_TEST", 0.8],
----> 3 label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"})
4 # Load the deep layout model from the layoutparser API
5 # For all the supported model, please check the Model
~\Anaconda3\envs\layout\lib\site-packages\layoutparser\models\detectron2\layoutmodel.py in __init__(self, config_path, model_path, label_map, extra_config, enforce_cpu, device)
89 config_path, model_path, allow_empty_path=True
90 )
---> 91 config_path = PathManager.get_local_path(config_path)
92
93 if label_map is None:
~\Anaconda3\envs\layout\lib\site-packages\iopath\common\file_io.py in get_local_path(self, path, force, **kwargs)
1195 handler = self.__get_path_handler(path) # type: ignore
1196 try:
-> 1197 bret = handler._get_local_path(path, force=force, **kwargs)
1198 except TypeError:
1199 bret = handler._get_local_path(path, **kwargs)
~\Anaconda3\envs\layout\lib\site-packages\layoutparser\models\detectron2\catalog.py in _get_local_path(self, path, **kwargs)
134 else:
135 raise ValueError(f"Unknown data_type {data_type}")
--> 136 return PathManager.get_local_path(model_url, **kwargs)
137
138 def _open(self, path, mode="r", **kwargs):
~\Anaconda3\envs\layout\lib\site-packages\iopath\common\file_io.py in get_local_path(self, path, force, **kwargs)
1195 handler = self.__get_path_handler(path) # type: ignore
1196 try:
-> 1197 bret = handler._get_local_path(path, force=force, **kwargs)
1198 except TypeError:
1199 bret = handler._get_local_path(path, **kwargs)
~\Anaconda3\envs\layout\lib\site-packages\iopath\common\file_io.py in _get_local_path(self, path, force, cache_dir, **kwargs)
792
793 cached = os.path.join(dirname, filename)
--> 794 with file_lock(cached):
795 if not os.path.isfile(cached):
796 logger.info("Downloading {} ...".format(path))
~\Anaconda3\envs\layout\lib\site-packages\portalocker\utils.py in __enter__(self)
155
156 def __enter__(self):
--> 157 return self.acquire()
158
159 def __exit__(self,
~\Anaconda3\envs\layout\lib\site-packages\portalocker\utils.py in acquire(self, timeout, check_interval, fail_when_locked)
237
238 # Get a new filehandler
--> 239 fh = self._get_fh()
240
241 def try_close(): # pragma: no cover
~\Anaconda3\envs\layout\lib\site-packages\portalocker\utils.py in _get_fh(self)
287 def _get_fh(self) -> typing.IO:
288 '''Get a new filehandle'''
--> 289 return open(self.filename, self.mode, **self.file_open_kwargs)
290
291 def _get_lock(self, fh: typing.IO) -> typing.IO:
OSError: [Errno 22] Invalid argument: 'C:\\Users\\vchinna/.torch/iopath_cache\\s/nau5ut6zgthunil\\config.yaml?dl=1.lock'
Not sure whether it is a kind of lock or something.
Please help
Even I got a similar error. I tried out manually some work around in Windows.
I am using your case as example: OSError: [Errno 22] Invalid argument: 'C:\Users\vchinna/.torch/iopath_cache\s/nau5ut6zgthunil\config.yaml?dl=1.lock'
Please follow the following process.
Navigate to C:\Users\vchinna/.torch/iopath_cache\s/nau5ut6zgthunil\config.yaml
Open that config.yaml file
Scroll down to WEIGHTS: https://www.dropbox.com/s/h7th27jfv19rxiy/model_final.pth?dl=1 should be around 265 line.
Copy that link and paste it in your browser, a 'model_final.pth' will be downloaded. Copy this file to your desired folder.
Now replace the path to WEIGHTS: your_desired_folder/model_final.pth
Save it and run the code it works!
But there is a small work around I think before you do this (if you have not done)
[iopath work around][1]
https://github.com/Layout-Parser/layout-parser/issues/15 (Github link to the issue)
Related
I'm trying to download the MNIST dataset from openml, using the openml library.
I tried using Jupyter notebooks because I don't want to download the same dataset every time.
Problem is, after running the following code, I get an error:
from openml.datasets import get_dataset
mnist = get_dataset(554)
x, y, p, q = mnist.get_data(
dataset_format="dataframe", target=mnist.default_target_attribute
)
I'm pasting the whole error message I get, the problem occurs when I try assigning the .get_data to x, y, p and q.
The environment I'm running this on is called Oceanic.
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
File ~\anaconda3\envs\Oceanic\lib\site-packages\openml\datasets\dataset.py:491, in OpenMLDataset._cache_compressed_file_from_file(self, data_file)
490 try:
--> 491 data = pd.read_parquet(data_file)
492 except Exception as e:
File ~\anaconda3\envs\Oceanic\lib\site-packages\pandas\io\parquet.py:493, in read_parquet(path, engine, columns, storage_options, use_nullable_dtypes, **kwargs)
491 impl = get_engine(engine)
--> 493 return impl.read(
494 path,
495 columns=columns,
496 storage_options=storage_options,
497 use_nullable_dtypes=use_nullable_dtypes,
498 **kwargs,
499 )
File ~\anaconda3\envs\Oceanic\lib\site-packages\pandas\io\parquet.py:240, in PyArrowImpl.read(self, path, columns, use_nullable_dtypes, storage_options, **kwargs)
239 try:
--> 240 result = self.api.parquet.read_table(
241 path_or_handle, columns=columns, **kwargs
242 ).to_pandas(**to_pandas_kwargs)
243 if manager == "array":
File ~\anaconda3\envs\Oceanic\lib\site-packages\pyarrow\parquet.py:1731, in read_table(source, columns, use_threads, metadata, use_pandas_metadata, memory_map, read_dictionary, filesystem, filters, buffer_size, partitioning, use_legacy_dataset, ignore_prefixes)
1727 dataset = ParquetFile(
1728 source, metadata=metadata, read_dictionary=read_dictionary,
1729 memory_map=memory_map, buffer_size=buffer_size)
-> 1731 return dataset.read(columns=columns, use_threads=use_threads,
1732 use_pandas_metadata=use_pandas_metadata)
1734 if ignore_prefixes is not None:
File ~\anaconda3\envs\Oceanic\lib\site-packages\pyarrow\parquet.py:1608, in _ParquetDatasetV2.read(self, columns, use_threads, use_pandas_metadata)
1606 use_threads = False
-> 1608 table = self._dataset.to_table(
1609 columns=columns, filter=self._filter_expression,
1610 use_threads=use_threads
1611 )
1613 # if use_pandas_metadata, restore the pandas metadata (which gets
1614 # lost if doing a specific `columns` selection in to_table)
File ~\anaconda3\envs\Oceanic\lib\site-packages\pyarrow\_dataset.pyx:458, in pyarrow._dataset.Dataset.to_table()
File ~\anaconda3\envs\Oceanic\lib\site-packages\pyarrow\_dataset.pyx:2889, in pyarrow._dataset.Scanner.to_table()
File ~\anaconda3\envs\Oceanic\lib\site-packages\pyarrow\error.pxi:141, in pyarrow.lib.pyarrow_internal_check_status()
File ~\anaconda3\envs\Oceanic\lib\site-packages\pyarrow\error.pxi:112, in pyarrow.lib.check_status()
OSError: NotImplemented: Support for codec 'snappy' not built
The above exception was the direct cause of the following exception:
Exception Traceback (most recent call last)
Input In [10], in <cell line: 1>()
----> 1 x, y, p, q = mnist.get_data(
2 dataset_format="dataframe", target=mnist.default_target_attribute
3 )
File ~\anaconda3\envs\Oceanic\lib\site-packages\openml\datasets\dataset.py:698, in OpenMLDataset.get_data(self, target, include_row_id, include_ignore_attribute, dataset_format)
658 def get_data(
659 self,
660 target: Optional[Union[List[str], str]] = None,
(...)
668 List[str],
669 ]:
670 """ Returns dataset content as dataframes or sparse matrices.
671
672 Parameters
(...)
696 List of attribute names.
697 """
--> 698 data, categorical, attribute_names = self._load_data()
700 to_exclude = []
701 if not include_row_id and self.row_id_attribute is not None:
File ~\anaconda3\envs\Oceanic\lib\site-packages\openml\datasets\dataset.py:531, in OpenMLDataset._load_data(self)
528 self._download_data()
530 file_to_load = self.data_file if self.parquet_file is None else self.parquet_file
--> 531 return self._cache_compressed_file_from_file(file_to_load)
533 # helper variable to help identify where errors occur
534 fpath = self.data_feather_file if self.cache_format == "feather" else self.data_pickle_file
File ~\anaconda3\envs\Oceanic\lib\site-packages\openml\datasets\dataset.py:493, in OpenMLDataset._cache_compressed_file_from_file(self, data_file)
491 data = pd.read_parquet(data_file)
492 except Exception as e:
--> 493 raise Exception(f"File: {data_file}") from e
495 categorical = [data[c].dtype.name == "category" for c in data.columns]
496 attribute_names = list(data.columns)
Exception: File: C:\Users\Irving\.openml\org\openml\www\datasets\554\dataset.pq
Now, I've written the same code on Pycharm and it works just fine, I managed to correctly assign the dataframes and show them to me. I've got no idea why this isn't working and I would like to know why because I would prefer to work with Jupyter notebooks.
Any help is appreciated, thanks in advance.
I can save a random file to my drive colab as:
with open ("gdrive/My Drive/chapter_classification/output/hello.txt",'w')as f:
f.write('hello')
works fine but when I use the Official documentation approach of Optuna using the code:
direction = 'minimize'
name = 'opt1'
study = optuna.create_study(sampler=optuna.samplers.TPESampler(),direction=direction,study_name=name, storage=f"gdrive/My Drive/chapter_classification/output/sqlite:///{name}.db",load_if_exists=True)
study.optimize(tune, n_trials=1000)
throws an error as:
ArgumentError Traceback (most recent call last)
<ipython-input-177-f32da2c0f69a> in <module>()
2 direction = 'minimize'
3 name = 'opt1'
----> 4 study = optuna.create_study(sampler=optuna.samplers.TPESampler(),direction=direction,study_name=name, storage="gdrive/My Drive/chapter_classification/output/sqlite:///opt1.db",load_if_exists=True)
5 study.optimize(tune, n_trials=1000)
6 frames
/usr/local/lib/python3.7/dist-packages/optuna/study/study.py in create_study(storage, sampler, pruner, study_name, direction, load_if_exists, directions)
1134 ]
1135
-> 1136 storage = storages.get_storage(storage)
1137 try:
1138 study_id = storage.create_new_study(study_name)
/usr/local/lib/python3.7/dist-packages/optuna/storages/__init__.py in get_storage(storage)
29 return RedisStorage(storage)
30 else:
---> 31 return _CachedStorage(RDBStorage(storage))
32 elif isinstance(storage, RDBStorage):
33 return _CachedStorage(storage)
/usr/local/lib/python3.7/dist-packages/optuna/storages/_rdb/storage.py in __init__(self, url, engine_kwargs, skip_compatibility_check, heartbeat_interval, grace_period, failed_trial_callback)
173
174 try:
--> 175 self.engine = create_engine(self.url, **self.engine_kwargs)
176 except ImportError as e:
177 raise ImportError(
<string> in create_engine(url, **kwargs)
/usr/local/lib/python3.7/dist-packages/sqlalchemy/util/deprecations.py in warned(fn, *args, **kwargs)
307 stacklevel=3,
308 )
--> 309 return fn(*args, **kwargs)
310
311 doc = fn.__doc__ is not None and fn.__doc__ or ""
/usr/local/lib/python3.7/dist-packages/sqlalchemy/engine/create.py in create_engine(url, **kwargs)
528
529 # create url.URL object
--> 530 u = _url.make_url(url)
531
532 u, plugins, kwargs = u._instantiate_plugins(kwargs)
/usr/local/lib/python3.7/dist-packages/sqlalchemy/engine/url.py in make_url(name_or_url)
713
714 if isinstance(name_or_url, util.string_types):
--> 715 return _parse_rfc1738_args(name_or_url)
716 else:
717 return name_or_url
/usr/local/lib/python3.7/dist-packages/sqlalchemy/engine/url.py in _parse_rfc1738_args(name)
775 else:
776 raise exc.ArgumentError(
--> 777 "Could not parse rfc1738 URL from string '%s'" % name
778 )
779
ArgumentError: Could not parse rfc1738 URL from string 'gdrive/My Drive/chapter_classification/output/sqlite:///opt1.db'
So according to the official Documentation of create_study
When a database URL is passed, Optuna internally uses SQLAlchemy to handle the database. Please refer to SQLAlchemy’s document for further details. If you want to specify non-default options to SQLAlchemy Engine, you can instantiate RDBStorage with your desired options and pass it to the storage argument instead of a URL.
And when you visit the documentation of SQLAlchemy, you find that it uses absolute path.
So all you have to do is to change
storage=f"gdrive/My Drive/chapter_classification/output/sqlite:///{name}.db"
to the absolute path as:
storage = f"sqlite:///gdrive/My Drive/chapter_classification/output{name}.db"
I need to load some meteorological data to analyze several months but such data is stored in files that cover only one day so I need to acces many files at once.
I am following some pre-given instruction that told me to create a memory partition in my computer.
from datetime import datetime, timedelta
import dask.array as da
from dask.distributed import Client, LocalCluster
import xarray
try:
client
except NameError:
client = Client(n_workers=1, threads_per_worker=4, memory_limit='2GB')
else:
print("Client already exists")
After this, I create an array dates that goes from 1st June to 1st October and that is need in "files" to get the link to the meteorological data.
dates=[datetime(2019,6,1) + timedelta(days=i) for i in range(3*30)]
files= [date.strftime('http://mandeo.meteogalicia.es/thredds/dodsC/modelos/WRF_HIST/d03/%Y/%m/wrf_arw_det_history_d03_%Y%m%d_0000.nc4') for date in dates]
My issue starts when I try to unzip all that data as
multi = xarray.open_mfdataset(files, preprocess= lambda a : a.isel(time=slice(0,24)))
It raises the error:
KeyError Traceback (most recent call last)
~\Nueva carpeta\lib\site-packages\xarray\backends\file_manager.py in _acquire_with_cache_info(self, needs_lock)
197 try:
--> 198 file = self._cache[self._key]
199 except KeyError:
~\Nueva carpeta\lib\site-packages\xarray\backends\lru_cache.py in __getitem__(self, key)
52 with self._lock:
---> 53 value = self._cache[key]
54 self._cache.move_to_end(key)
KeyError: [<class 'netCDF4._netCDF4.Dataset'>, ('http://mandeo.meteogalicia.es/thredds/dodsC/modelos/WRF_HIST/d03/2019/06/wrf_arw_det_history_d03_20190626_0000.nc4',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False))]
During handling of the above exception, another exception occurred:
OSError Traceback (most recent call last)
<ipython-input-19-c3d0f4a8cc26> in <module>
----> 1 multi = xarray.open_mfdataset(files, preprocess= lambda a : a.isel(time=slice(0,24)))
~\Nueva carpeta\lib\site-packages\xarray\backends\api.py in open_mfdataset(paths, chunks, concat_dim, compat, preprocess, engine, lock, data_vars, coords, combine, autoclose, parallel, join, attrs_file, **kwargs)
916 getattr_ = getattr
917
--> 918 datasets = [open_(p, **open_kwargs) for p in paths]
919 file_objs = [getattr_(ds, "_file_obj") for ds in datasets]
920 if preprocess is not None:
~\Nueva carpeta\lib\site-packages\xarray\backends\api.py in <listcomp>(.0)
916 getattr_ = getattr
917
--> 918 datasets = [open_(p, **open_kwargs) for p in paths]
919 file_objs = [getattr_(ds, "_file_obj") for ds in datasets]
920 if preprocess is not None:
~\Nueva carpeta\lib\site-packages\xarray\backends\api.py in open_dataset(filename_or_obj, group, decode_cf, mask_and_scale, decode_times, autoclose, concat_characters, decode_coords, engine, chunks, lock, cache, drop_variables, backend_kwargs, use_cftime, decode_timedelta)
507 if engine == "netcdf4":
508 store = backends.NetCDF4DataStore.open(
--> 509 filename_or_obj, group=group, lock=lock, **backend_kwargs
510 )
511 elif engine == "scipy":
~\Nueva carpeta\lib\site-packages\xarray\backends\netCDF4_.py in open(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)
356 netCDF4.Dataset, filename, mode=mode, kwargs=kwargs
357 )
--> 358 return cls(manager, group=group, mode=mode, lock=lock, autoclose=autoclose)
359
360 def _acquire(self, needs_lock=True):
~\Nueva carpeta\lib\site-packages\xarray\backends\netCDF4_.py in __init__(self, manager, group, mode, lock, autoclose)
312 self._group = group
313 self._mode = mode
--> 314 self.format = self.ds.data_model
315 self._filename = self.ds.filepath()
316 self.is_remote = is_remote_uri(self._filename)
~\Nueva carpeta\lib\site-packages\xarray\backends\netCDF4_.py in ds(self)
365 #property
366 def ds(self):
--> 367 return self._acquire()
368
369 def open_store_variable(self, name, var):
~\Nueva carpeta\lib\site-packages\xarray\backends\netCDF4_.py in _acquire(self, needs_lock)
359
360 def _acquire(self, needs_lock=True):
--> 361 with self._manager.acquire_context(needs_lock) as root:
362 ds = _nc4_require_group(root, self._group, self._mode)
363 return ds
~\Nueva carpeta\lib\contextlib.py in __enter__(self)
110 del self.args, self.kwds, self.func
111 try:
--> 112 return next(self.gen)
113 except StopIteration:
114 raise RuntimeError("generator didn't yield") from None
~\Nueva carpeta\lib\site-packages\xarray\backends\file_manager.py in acquire_context(self, needs_lock)
184 def acquire_context(self, needs_lock=True):
185 """Context manager for acquiring a file."""
--> 186 file, cached = self._acquire_with_cache_info(needs_lock)
187 try:
188 yield file
~\Nueva carpeta\lib\site-packages\xarray\backends\file_manager.py in _acquire_with_cache_info(self, needs_lock)
202 kwargs = kwargs.copy()
203 kwargs["mode"] = self._mode
--> 204 file = self._opener(*self._args, **kwargs)
205 if self._mode == "w":
206 # ensure file doesn't get overriden when opened again
netCDF4\_netCDF4.pyx in netCDF4._netCDF4.Dataset.__init__()
netCDF4\_netCDF4.pyx in netCDF4._netCDF4._ensure_nc_success()
OSError: [Errno -37] NetCDF: Write to read only: b'http://mandeo.meteogalicia.es/thredds/dodsC/modelos/WRF_HIST/d03/2019/06/wrf_arw_det_history_d03_20190626_0000.nc4'
Does anyone know why this error occurs?
I've a notebook on google colab that fails with following error
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in fit(epochs, model, loss_func, opt, data, callbacks, metrics)
93 exception = e
---> 94 raise e
95 finally: cb_handler.on_train_end(exception)
/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in fit(epochs, model, loss_func, opt, data, callbacks, metrics)
83 xb, yb = cb_handler.on_batch_begin(xb, yb)
---> 84 loss = loss_batch(model, xb, yb, loss_func, opt, cb_handler)
85 if cb_handler.on_batch_end(loss): break
/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in loss_batch(model, xb, yb, loss_func, opt, cb_handler)
24 if opt is not None:
---> 25 loss = cb_handler.on_backward_begin(loss)
26 loss.backward()
/usr/local/lib/python3.6/dist-packages/fastai/callback.py in on_backward_begin(self, loss)
223 for cb in self.callbacks:
--> 224 a = cb.on_backward_begin(**self.state_dict)
225 if a is not None: self.state_dict['last_loss'] = a
/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in on_backward_begin(self, smooth_loss, **kwargs)
266 if self.pbar is not None and hasattr(self.pbar,'child'):
--> 267 self.pbar.child.comment = f'{smooth_loss:.4f}'
268
/usr/local/lib/python3.6/dist-packages/torch/tensor.py in __format__(self, format_spec)
377 if self.dim() == 0:
--> 378 return self.item().__format__(format_spec)
379 return object.__format__(self, format_spec)
RuntimeError: CUDA error: device-side assert triggered
During handling of the above exception, another exception occurred:
RuntimeError Traceback (most recent call last)
<ipython-input-33-dd390b1c8108> in <module>()
----> 1 lr_find(learn)
2 learn.recorder.plot()
/usr/local/lib/python3.6/dist-packages/fastai/train.py in lr_find(learn, start_lr, end_lr, num_it, stop_div, **kwargs)
26 cb = LRFinder(learn, start_lr, end_lr, num_it, stop_div)
27 a = int(np.ceil(num_it/len(learn.data.train_dl)))
---> 28 learn.fit(a, start_lr, callbacks=[cb], **kwargs)
29
30 def to_fp16(learn:Learner, loss_scale:float=512., flat_master:bool=False)->Learner:
/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in fit(self, epochs, lr, wd, callbacks)
160 callbacks = [cb(self) for cb in self.callback_fns] + listify(callbacks)
161 fit(epochs, self.model, self.loss_func, opt=self.opt, data=self.data, metrics=self.metrics,
--> 162 callbacks=self.callbacks+callbacks)
163
164 def create_opt(self, lr:Floats, wd:Floats=0.)->None:
/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in fit(epochs, model, loss_func, opt, data, callbacks, metrics)
93 exception = e
94 raise e
---> 95 finally: cb_handler.on_train_end(exception)
96
97 loss_func_name2activ = {'cross_entropy_loss': partial(F.softmax, dim=1), 'nll_loss': torch.exp, 'poisson_nll_loss': torch.exp,
/usr/local/lib/python3.6/dist-packages/fastai/callback.py in on_train_end(self, exception)
254 def on_train_end(self, exception:Union[bool,Exception])->None:
255 "Handle end of training, `exception` is an `Exception` or False if no exceptions during training."
--> 256 self('train_end', exception=exception)
257
258 class AverageMetric(Callback):
/usr/local/lib/python3.6/dist-packages/fastai/callback.py in __call__(self, cb_name, call_mets, **kwargs)
185 "Call through to all of the `CallbakHandler` functions."
186 if call_mets: [getattr(met, f'on_{cb_name}')(**self.state_dict, **kwargs) for met in self.metrics]
--> 187 return [getattr(cb, f'on_{cb_name}')(**self.state_dict, **kwargs) for cb in self.callbacks]
188
189 def on_train_begin(self, epochs:int, pbar:PBar, metrics:MetricFuncList)->None:
/usr/local/lib/python3.6/dist-packages/fastai/callback.py in <listcomp>(.0)
185 "Call through to all of the `CallbakHandler` functions."
186 if call_mets: [getattr(met, f'on_{cb_name}')(**self.state_dict, **kwargs) for met in self.metrics]
--> 187 return [getattr(cb, f'on_{cb_name}')(**self.state_dict, **kwargs) for cb in self.callbacks]
188
189 def on_train_begin(self, epochs:int, pbar:PBar, metrics:MetricFuncList)->None:
/usr/local/lib/python3.6/dist-packages/fastai/callbacks/lr_finder.py in on_train_end(self, **kwargs)
45 # restore the valid_dl we turned of on `__init__`
46 self.data.valid_dl = self.valid_dl
---> 47 self.learn.load('tmp')
48 if hasattr(self.learn.model, 'reset'): self.learn.model.reset()
49 print('LR Finder complete, type {learner_name}.recorder.plot() to see the graph.')
/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in load(self, name, device)
202 "Load model `name` from `self.model_dir` using `device`, defaulting to `self.data.device`."
203 if device is None: device = self.data.device
--> 204 self.model.load_state_dict(torch.load(self.path/self.model_dir/f'{name}.pth', map_location=device))
205 return self
206
/usr/local/lib/python3.6/dist-packages/torch/serialization.py in load(f, map_location, pickle_module)
356 f = open(f, 'rb')
357 try:
--> 358 return _load(f, map_location, pickle_module)
359 finally:
360 if new_fd:
/usr/local/lib/python3.6/dist-packages/torch/serialization.py in _load(f, map_location, pickle_module)
527 unpickler = pickle_module.Unpickler(f)
528 unpickler.persistent_load = persistent_load
--> 529 result = unpickler.load()
530
531 deserialized_storage_keys = pickle_module.load(f)
/usr/local/lib/python3.6/dist-packages/torch/serialization.py in persistent_load(saved_id)
493 if root_key not in deserialized_objects:
494 deserialized_objects[root_key] = restore_location(
--> 495 data_type(size), location)
496 storage = deserialized_objects[root_key]
497 if view_metadata is not None:
/usr/local/lib/python3.6/dist-packages/torch/serialization.py in restore_location(storage, location)
376 elif isinstance(map_location, torch.device):
377 def restore_location(storage, location):
--> 378 return default_restore_location(storage, str(map_location))
379 else:
380 def restore_location(storage, location):
/usr/local/lib/python3.6/dist-packages/torch/serialization.py in default_restore_location(storage, location)
102 def default_restore_location(storage, location):
103 for _, _, fn in _package_registry:
--> 104 result = fn(storage, location)
105 if result is not None:
106 return result
/usr/local/lib/python3.6/dist-packages/torch/serialization.py in _cuda_deserialize(obj, location)
84 'to an existing device.'.format(
85 device, torch.cuda.device_count()))
---> 86 return obj.cuda(device)
87
88
/usr/local/lib/python3.6/dist-packages/torch/_utils.py in _cuda(self, device, non_blocking, **kwargs)
74 else:
75 new_type = getattr(torch.cuda, self.__class__.__name__)
---> 76 return new_type(self.size()).copy_(self, non_blocking)
77
78
RuntimeError: cuda runtime error (59) : device-side assert triggered at /pytorch/aten/src/THC/generic/THCTensorCopy.cpp:20
There is no information about the real cause, I tried to get the stack trace by forcing cuda to run on one gpu (as suggested here) using a cell like this
!export CUDA_LAUNCH_BLOCKING=1
But this does not seem to work, still having the same error with.
Is there another way that works with Google Colab?
Be sure that your targets values starts from zero to number of classes - 1. Ex: you have 100 classification class so your target should be from 0 to 99
!export FOO=blah is usually not useful to run in a notebook because ! means run the following command in a sub-shell, so the effect of the statement is gone by the time the ! returns.
You might have more success by storing your python code in a file and then executing that file in a subshell:
In one cell:
%%writefile foo.py
[...your code...]
In the next cell:
!export CUDA_LAUNCH_BLOCKING=1; python3 foo.py
(or s/python3/python2/ if you're writing py2)
Switch Hardware Accelerator Type to "None" under Runtime->Change Runtime Type . This should give you a more meaningful error message.
The proper way to set environmental variables in Google Colab is to use os:
import os
os.environ['CUDA_LAUNCH_BLOCKING'] = "1"
Using the os library will allow you to set whatever environmental variables you need. Setting CUDA_LAUNCH_BLOCKING this way enables proper CUDA tracebacks in Google Colab.
I'm trying to convert a local .xml file to .ttl and I used this code:
import rdflib
g=rdflib.Graph()
g.parse("C:\\Users\\Username\\Desktop\\public-12T23.xml",format='xml')
print (g.serialize(format='turtle'))
and I got this error
TypeError Traceback (most recent call last)
<ipython-input-3-d52cc014f61e> in <module>()
2 g=rdflib.Graph()
3
----> 4 g.parse("C:\\Users\\Saeid\\Desktop\\public-12T23.xml", format='xml')
5 print (g.serialize(format='turtle'))
C:\Program Files\Anaconda3\lib\site-packages\rdflib\graph.py in parse(self, >source, publicID, format, location, file, data, **args)
1041 parser = plugin.get(format, Parser)()
1042 try:
-> 1043 parser.parse(source, self, **args)
1044 finally:
1045 if source.auto_close:
C:\Program Files\Anaconda3\lib\site-packages\rdflib\plugins\parsers\rdfxml.py >in parse(self, source, sink, **args)
576 # content_handler.reset()
577 # self._parser.reset()
--> 578 self._parser.parse(source)
C:\Program Files\Anaconda3\lib\xml\sax\expatreader.py in parse(self, source)
108 self.reset()
109 self._cont_handler.setDocumentLocator(ExpatLocator(self))
--> 110 xmlreader.IncrementalParser.parse(self, source)
111
112 def prepareParser(self, source):
C:\Program Files\Anaconda3\lib\xml\sax\xmlreader.py in parse(self, source)
123 buffer = file.read(self._bufsize)
124 while buffer:
--> 125 self.feed(buffer)
126 buffer = file.read(self._bufsize)
127 self.close()
C:\Program Files\Anaconda3\lib\xml\sax\expatreader.py in feed(self, data, >isFinal)
208 # document. When feeding chunks, they are not normally >final -
209 # except when invoked from close.
--> 210 self._parser.Parse(data, isFinal)
211 except expat.error as e:
212 exc = SAXParseException(expat.ErrorString(e.code), e, self)
..\Modules\pyexpat.c in EndElement()
C:\Program Files\Anaconda3\lib\xml\sax\expatreader.py in end_element_ns(self, >name)
368 pair = tuple(pair)
369
--> 370 self._cont_handler.endElementNS(pair, None)
371
372 # this is not used (call directly to ContentHandler)
C:\Program Files\Anaconda3\lib\site-packages\rdflib\plugins\parsers\rdfxml.py >in endElementNS(self, name, qname)
158
159 def endElementNS(self, name, qname):
--> 160 self.current.end(name, qname)
161 self.stack.pop()
162
C:\Program Files\Anaconda3\lib\site-packages\rdflib\plugins\parsers\rdfxml.py >in node_element_end(self, name, qname)
330 if self.parent.object and self.current != self.stack[2]:
331
--> 332 self.error("Repeat node-elements inside property elements: >%s"%"".join(name))
333
334 self.parent.object = self.current.subject
TypeError: sequence item 0: expected str instance, NoneType found
I know that it was not as easy as I was thought, but I got the same error when I used these script here.