After running 'Export training data for deep learning' tool in Arcgis Pro, named as 'training_folder', I did data augmentation on the 'training_folder' using arcgis.learn.prepare_data. Then,I would like to save the result of data augmentation into a folder. However, it has error Below are my code in Python Arcgis Pro:
import arcpy, arcgis from arcpy.ia import * arcpy.CheckOutExtension("ImageAnalyst") data=arcgis.learn.prepare_data(r'C:\training_folder') data.save(r'C:\Data\Deep_Learning\test')
When I run this script, it has error as below:
Traceback (most recent call last): File "<string>", line 1, in <module> File "C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\fastai\basic_data.py", line 155, in save try_save(self.label_list, self.path, file) File "C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\fastai\torch_core.py", line 411, in try_save target = open(path/file, 'wb') if is_pathlike(file) else file PermissionError: [Errno 13] Permission denied: 'C:\\Data\\Deep_Learning\\test'
Could you please help me how to save the result of data augmentation into a folder?
Thank you for your help.
Solved! Go to Solution.
@lienpham83 we do not support this method as of now, it is inherited from fastai which is the underlying technology driving this datatype. It does not save augmented files on chip but just a reference of data object. In order to train models with augmented dataset you need not do this step. You can directly use this data object and pass in to the target model.
For example
model = arcgis.learn.UnetClassifier(data)
Thanks,
Sandeep
@lienpham83 we do not support this method as of now, it is inherited from fastai which is the underlying technology driving this datatype. It does not save augmented files on chip but just a reference of data object. In order to train models with augmented dataset you need not do this step. You can directly use this data object and pass in to the target model.
For example
model = arcgis.learn.UnetClassifier(data)
Thanks,
Sandeep