Latest Contributions by Shingo-Ikeda

Hi @Sage_Wall, Thank you for your reply. I am using python flask server to return GeoJson response as follows: if format == 'json': response = Response(df_forecast_site_status.to_json(), mimetype='application/json') else: response = Response(df_forecast_site_status.to_csv()) response.headers.add('Access-Control-Allow-Origin', '*') return response It works if environment is in dev or staging environment where x-api-key is not required. Soon as I add custom header, it fails with CORS error. Shingo   ... View more

Hi Rhys, I am also trying to pass api-key in header using esriConfig.request.interceptors as well as esriConfig.request.trustedServers, however, it doesn't return the geojson results as I am able to extract through postman.  Any crue what is causing CORS error through this request method?    esriConfig.request.trustedServers.push(this.base_url); esriConfig.request.interceptors.push({ urls: geojson_url, before: function (params: any) { params.requestOptions.headers = { 'x-api-key': this.x_api_key, 'Access-Control-Allow-Origin': this.base_url, }; }, after: function(response) { if (!response.ssl) { response.ssl = true; } const geojsonLayer = new GeoJSONLayer({ title: 'Forecast Kind', url: response.data, popupTemplate: template_forecast_kind, visible: false, outFields:["*"] }); }, headers: { 'x-api-key': this.x_api_key, 'Access-Control-Allow-Origin': '*', }, });     The error shows: Access to fetch at 'https://.....com/sites/current_forecast_status' from origin 'http://localhost:4200' has been blocked by CORS policy: Response to preflight request doesn't pass access control check: No 'Access-Control-Allow-Origin' header is present on the requested resource. If an opaque response serves your needs, set the request's mode to 'no-cors' to fetch the resource with CORS disabled. Thanks, Shingo     ... View more

I believe the resolution was included at 10.8.3 or a newer release, but not in the older version.  ... View more

Hi Xavier, To uninstall 2.8 DL Libraries, you can uninstall from standard windows add or remove program from the settings if you have installed from msi. If you are installing manually from conda deeplearning essential, you must have installed to your clone of arcgispro-py3 default python library, so you will just need to remove the environment and recreate a new environment once you have reinstalled 2.7. With the recent 2.8.1, my "classify object using deep learning" issue has been resolved. It looks like 2.8.2 is about to be released, so I would stick with 2.8.1 and update the new version when it becomes available. If you need to install 2.7, you would need to uninstall Pro and reinstall 2.7 and get the installer from here.  ... View more

Could you specify the details of the bug that was fixed for the Classify Objects. I am running into some issues, but I am not sure if they are related. ... View more

Thanks for the update. I will look for the release and apply it as soon as it becomes available. In a meantime, I have reverted back to 2.7.4. ... View more

Hi Tim. Thanks for the quick response. I think I will need to revert back to 2.7 as well.  It is unfortunate/headache to revert back to 2.7 with python downgrades after all the improvements and bug fixes were announced. ... View more

Hi All,  Any updates on this? I am also having the same issue with 2.8.            I see this particular error dialog when executing "Classify Objects Using Deep Learning" from both GUI and arcpy function. It creates an empty output feature class. Please advise if we need to reinstall 2.7 from 2.8 to use deep learning tools.  ... View more

Hi All,  That's what happened to me yesterday by upgrading to 2.8 without keeping a backup python environment with deep learning packages. I installed DL package from the *.msi installer previously, but there is no 2.8 version of the installer out yet.  Once I created and activated a new environment, I installed DL package by executing the following command:   conda install -c esri deep-learning-essentials   This installed all, but gave me some errors at the end:   Preparing transaction: done Verifying transaction: - SafetyError: The package for fastai located at C:\Users\G0003563\AppData\Local\ESRI\conda\pkgs\fastai-1.0.60-py37_0 appears to be corrupted. The path 'Lib/site-packages/fastai/basic_train.py' has an incorrect size. reported size: 31850 bytes actual size: 31851 bytes ClobberError: This transaction has incompatible packages due to a shared path. packages: esri/win-64::torch-cluster-1.5.4-py37_1, esri/win-64::torch-scatter-2.0.4-py37_2, esri/win-64::torch-spline-conv-1.2.0-py37_1, esri/win-64::torch-sparse-0.6.1-py37_1 path: 'lib/site-packages/test/__init__.py' ClobberError: This transaction has incompatible packages due to a shared path. packages: esri/win-64::torch-cluster-1.5.4-py37_1, esri/win-64::torch-scatter-2.0.4-py37_2, esri/win-64::torch-spline-conv-1.2.0-py37_1, esri/win-64::torch-sparse-0.6.1-py37_1 path: 'lib/site-packages/test/__pycache__/__init__.cpython-37.pyc' ClobberError: This transaction has incompatible packages due to a shared path. packages: esri/win-64::torch-cluster-1.5.4-py37_1, esri/win-64::torch-scatter-2.0.4-py37_2, esri/win-64::torch-spline-conv-1.2.0-py37_1, esri/win-64::torch-sparse-0.6.1-py37_1 path: 'lib/site-packages/test/__pycache__/utils.cpython-37.pyc' ClobberError: This transaction has incompatible packages due to a shared path. packages: esri/win-64::torch-cluster-1.5.4-py37_1, esri/win-64::torch-scatter-2.0.4-py37_2, esri/win-64::torch-spline-conv-1.2.0-py37_1, esri/win-64::torch-sparse-0.6.1-py37_1 path: 'lib/site-packages/test/utils.py' done Executing transaction: done   It looks like I can still use this environment and execute DL functions such as model.lr_find() and model.fit() This could be just a warning, but I am not sure this is OK to ignore. The link for the deep-learning-essentials is here: https://anaconda.org/esri/deep-learning-essentials ... View more

Thanks for the explanation and confirmation of the shortcoming of the classifier. Although, it seems like this is just an issue with the iterator that cannot handle another image to continue train images.  For example, when "Export Training Data For Deep Learning" exports chips and labels, the input (single) image is required even though the input feature class has an attribute field that has ImageURI. If the tool takes the path from the ImageURI and continues to export outputs, a merged training feature class can be used. Another implementation can be the use of time series Mosaic Dataset to be used for training and exporting. Currently, the use of a time series mosaic dataset does not respect attribute filter (such as time slider) and export training randomly takes a layer and the ImageURL only specifies the ImageURL path to the mosaic dataset root and does not specify which image in the mosaic dataset was used. I believe this feature is critical for continuous site monitoring and inferencing using deep learning.    ... View more

I think it is a good idea to have a recursive training method for image classification in order to enrich the deep learning package so that one operation can constantly add new training features; however, it also makes sense to centralize the training to a single datastore such as AWS S3 to append image chips. The only concern with this approach is that it is hard to retrain the specific image due to misclassification or to improve the classification.  We may need to go back to the collection on the previous image and label it differently. If we separate the training set by input image, it is far easier to replace the specific image chips and generate a *.dlpk that can be used to re-train deep learning. If there is a best practice that I might have missed, please let me know. ... View more