# arcgis.learn module¶

Functions for calling the Deep Learning Tools.

## detect_objects¶

arcgis.learn.detect_objects(input_raster, model, model_arguments=None, output_name=None, run_nms=False, confidence_score_field=None, class_value_field=None, max_overlap_ratio=0, context=None, process_all_raster_items=False, *, gis=None, future=False, **kwargs)

Function can be used to generate feature service that contains polygons on detected objects found in the imagery data using the designated deep learning model. Note that the deep learning library needs to be installed separately, in addition to the server’s built in Python 3.x library.

 Argument Description input_raster Required. raster layer that contains objects that needs to be detected. model Required model object. model_arguments Optional dictionary. Name-value pairs of arguments and their values that can be customized by the clients. eg: {“name1”:”value1”, “name2”: “value2”} output_name Optional. If not provided, a Feature layer is created by the method and used as the output . You can pass in an existing Feature Service Item from your GIS to use that instead. Alternatively, you can pass in the name of the output Feature Service that should be created by this method to be used as the output for the tool. A RuntimeError is raised if a service by that name already exists run_nms Optional bool. Default value is False. If set to True, runs the Non Maximum Suppression tool. confidence_score_field Optional string. The field in the feature class that contains the confidence scores as output by the object detection method. This parameter is required when you set the run_nms to True class_value_field Optional string. The class value field in the input feature class. If not specified, the function will use the standard class value fields Classvalue and Value. If these fields do not exist, all features will be treated as the same object class. Set only if run_nms is set to True max_overlap_ratio Optional integer. The maximum overlap ratio for two overlapping features. Defined as the ratio of intersection area over union area. Set only if run_nms is set to True context Optional dictionary. Context contains additional settings that affect task execution. Dictionary can contain value for following keys: cellSize - Set the output raster cell size, or resolution extent - Sets the processing extent used by the function parallelProcessingFactor - Sets the parallel processing factor. Default is “80%” processorType - Sets the processor type. “CPU” or “GPU” Eg: {“processorType” : “CPU”} Setting context parameter will override the values set using arcgis.env variable for this particular function. process_all_raster_items Optional bool. Specifies how all raster items in an image service will be processed. False : all raster items in the image service will be mosaicked together and processed. This is the default. True : all raster items in the image service will be processed as separate images. gis Optional GIS. The GIS on which this tool runs. If not specified, the active GIS is used. future Keyword only parameter. Optional boolean. If True, the result will be a GPJob object and results will be returned asynchronously.
Returns

The output feature layer item containing the detected objects

## classify_objects¶

arcgis.learn.classify_objects(input_raster, model, model_arguments=None, input_features=None, class_label_field=None, process_all_raster_items=False, output_name=None, context=None, *, gis=None, future=False, **kwargs)

Function can be used to output feature service with assigned class label for each feature based on information from overlapped imagery data using the designated deep learning model.

 Argument Description input_raster Required. raster layer that contains objects that needs to be classified. model Required model object. model_arguments Optional dictionary. Name-value pairs of arguments and their values that can be customized by the clients. eg: {“name1”:”value1”, “name2”: “value2”} input_features Optional feature layer. The point, line, or polygon input feature layer that identifies the location of each object to be classified and labelled. Each row in the input feature layer represents a single object. If no input feature layer is specified, the function assumes that each input image contains a single object to be classified. If the input image or images use a spatial reference, the output from the function is a feature layer, where the extent of each image is used as the bounding geometry for each labelled feature layer. If the input image or images are not spatially referenced, the output from the function is a table containing the image ID values and the class labels for each image. class_label_field Optional str. The name of the field that will contain the classification label in the output feature layer. If no field name is specified, a new field called ClassLabel will be generated in the output feature layer. Example:“ClassLabel” process_all_raster_items Optional bool. If set to False, all raster items in the image service will be mosaicked together and processed. This is the default. If set to True, all raster items in the image service will be processed as separate images. output_name Optional. If not provided, a Feature layer is created by the method and used as the output . You can pass in an existing Feature Service Item from your GIS to use that instead. Alternatively, you can pass in the name of the output Feature Service that should be created by this method to be used as the output for the tool. A RuntimeError is raised if a service by that name already exists context Optional dictionary. Context contains additional settings that affect task execution. Dictionary can contain value for following keys: cellSize - Set the output raster cell size, or resolution extent - Sets the processing extent used by the function parallelProcessingFactor - Sets the parallel processing factor. Default is “80%” processorType - Sets the processor type. “CPU” or “GPU” Eg: {“processorType” : “CPU”} Setting context parameter will override the values set using arcgis.env variable for this particular function. gis Optional GIS. The GIS on which this tool runs. If not specified, the active GIS is used.
Returns

The output feature layer item containing the classified objects

## classify_pixels¶

arcgis.learn.classify_pixels(input_raster, model, model_arguments=None, output_name=None, context=None, process_all_raster_items=False, *, gis=None, future=False, **kwargs)

Function to classify input imagery data using a deep learning model. Note that the deep learning library needs to be installed separately, in addition to the server’s built in Python 3.x library.

 Argument Description input_raster Required. raster layer that needs to be classified model Required model object. model_arguments Optional dictionary. Name-value pairs of arguments and their values that can be customized by the clients. eg: {“name1”:”value1”, “name2”: “value2”} output_name Optional. If not provided, an imagery layer is created by the method and used as the output . You can pass in an existing Image Service Item from your GIS to use that instead. Alternatively, you can pass in the name of the output Image Service that should be created by this method to be used as the output for the tool. A RuntimeError is raised if a service by that name already exists context Optional dictionary. Context contains additional settings that affect task execution.Dictionary can contain value for following keys: outSR - (Output Spatial Reference) Saves the result in the specified spatial reference snapRaster - Function will adjust the extent of output rasters so that they match the cell alignment of the specified snap raster. cellSize - Set the output raster cell size, or resolution extent - Sets the processing extent used by the function parallelProcessingFactor - Sets the parallel processing factor. Default is “80%” processorType - Sets the processor type. “CPU” or “GPU” Eg: {“outSR” : {spatial reference}} Setting context parameter will override the values set using arcgis.env variable for this particular function. process_all_raster_items Optional bool. Specifies how all raster items in an image service will be processed. False : all raster items in the image service will be mosaicked together and processed. This is the default. True : all raster items in the image service will be processed as separate images. gis Optional GIS. The GIS on which this tool runs. If not specified, the active GIS is used. future Keyword only parameter. Optional boolean. If True, the result will be a GPJob object and results will be returned asynchronously.
Returns

The classified imagery layer item

## export_training_data¶

arcgis.learn.export_training_data(input_raster, input_class_data=None, chip_format=None, tile_size=None, stride_size=None, metadata_format=None, classvalue_field=None, buffer_radius=None, output_location=None, context=None, input_mask_polygons=None, rotation_angle=0, reference_system='MAP_SPACE', process_all_raster_items=False, blacken_around_feature=False, fix_chip_size=True, *, gis=None, future=False, **kwargs)

Function is designed to generate training sample image chips from the input imagery data with labeled vector data or classified images. The output of this service tool is the data store string where the output image chips, labels and metadata files are going to be stored.

 Argument Description input_raster Required. Raster layer that needs to be exported for training input_class_data Labeled data, either a feature layer or image layer. Vector inputs should follow a training sample format as generated by the ArcGIS Pro Training Sample Manager. Raster inputs should follow a classified raster format as generated by the Classify Raster tool. chip_format Optional string. The raster format for the image chip outputs. TIFF: TIFF format PNG: PNG format JPEG: JPEG format MRF: MRF (Meta Raster Format) tile_size Optional dictionary. The size of the image chips. Example: {“x”: 256, “y”: 256} stride_size Optional dictionary. The distance to move in the X and Y when creating the next image chip. When stride is equal to the tile size, there will be no overlap. When stride is equal to half of the tile size, there will be 50% overlap. Example: {“x”: 128, “y”: 128} metadata_format Optional string. The format of the output metadata labels. There are 4 options for output metadata labels for the training data,KITTI Rectangles, PASCAL VOCrectangles, Classified Tiles (a class map) and RCNN_Masks. If your input training sample data is a feature class layer such as building layer or standard classification training sample file, use the KITTI or PASCAL VOC rectangle option. The output metadata is a .txt file or .xml file containing the training sample data contained in the minimum bounding rectangle. The name of the metadata file matches the input source image name. If your input training sample data is a class map, use the Classified Tiles as your output metadata format option. KITTI_rectangles: The metadata follows the same format as the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) Object Detection Evaluation dataset. The KITTI dataset is a vision benchmark suite. This is the default.The label files are plain text files. All values, both numerical or strings, are separated by spaces, and each row corresponds to one object. PASCAL_VOC_rectangles: The metadata follows the same format as the Pattern Analysis, Statistical Modeling and Computational Learning, Visual Object Classes (PASCAL_VOC) dataset. The PASCAL VOC dataset is a standardized image data set for object class recognition.The label files are XML files and contain information about image name, class value, and bounding box(es). Classified_Tiles: This option will output one classified image chip per input image chip. No other meta data for each image chip. Only the statistics output has more information on the classes such as class names, class values, and output statistics. RCNN_Masks: This option will output image chips that have a mask on the areas where the sample exists. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It’s based on Feature Pyramid Network (FPN) and a ResNet101 backbone. Labeled_Tiles : This option will label each output tile with a specific class. classvalue_field Optional string. Specifies the field which contains the class values. If no field is specified, the system will look for a ‘value’ or ‘classvalue’ field. If this feature does not contain a class field, the system will presume all records belong the 1 class. buffer_radius Optional integer. Specifies a radius for point feature classes to specify training sample area. output_location This is the output location for training sample data.It can be the server data store path or a shared file system path. Example: Server datastore path -/fileShares/deeplearning/rooftoptrainingsamples /rasterStores/rasterstorename/rooftoptrainingsamples /cloudStores/cloudstorename/rooftoptrainingsamples File share path -\\servername\deeplearning\rooftoptrainingsamples context Optional dictionary. Context contains additional settings that affect task execution.Dictionary can contain value for following keys: exportAllTiles - Choose if the image chips with overlapped labeled data will be exported.True - Export all the image chips, including those that do not overlap labeled data. False - Export only the image chips that overlap the labelled data. This is the default. startIndex - Allows you to set the start index for the sequence of image chips.This lets you append more image chips to an existing sequence. The default value is 0. cellSize - cell size can be set using this key in context parameter extent - Sets the processing extent used by the function Setting context parameter will override the values set using arcgis.env variable for this particular function.(cellSize, extent) eg: {“exportAllTiles” : False, “startIndex”: 0 } input_mask_polygons Optional feature layer. The feature layer that delineates the area whereimage chips will be created. Only image chips that fall completely within the polygons will be created. rotation_angle Optional float. The rotation angle that will be used to generate additionalimage chips. An image chip will be generated with a rotation angle of 0, which means no rotation. It will then be rotated at the specified angle to create an additional image chip. The same training samples will be captured at multiple angles in multiple image chips for data augmentation. The default rotation angle is 0. reference_system Optional string. Specifies the type of reference system to be used to interpret the input image. The reference system specified should match the reference system used to train the deep learning model. MAP_SPACE : The input image is in a map-based coordinate system. This is the default. IMAGE_SPACE : The input image is in image space, viewed from the direction of the sensor that captured the image, and rotated such that the tops of buildings and trees point upward in the image. PIXEL_SPACE : The input image is in image space, with no rotation and no distortion. process_all_raster_items Optional bool. Specifies how all raster items in an image service will be processed. False : all raster items in the image service will be mosaicked together and processed. This is the default. True : all raster items in the image service will be processed as separate images. blacken_around_feature Optional bool. Specifies whether to blacken the pixels around each object or feature in each image tile. This parameter only applies when the metadata format is set to Labeled_Tiles and an input feature class or classified raster has been specified. False : Pixels surrounding objects or features will not be blackened. This is the default. True : Pixels surrounding objects or features will be blackened. fix_chip_size Optional bool. Specifies whether to crop the exported tiles such that they are all the same size. This parameter only applies when the metadata format is set to Labeled_Tiles and an input feature class or classified raster has been specified. True : Exported tiles will be the same size and will center on the feature. This is the default. False : Exported tiles will be cropped such that the bounding geometry surrounds only the feature in the tile. gis Optional GIS. The GIS on which this tool runs. If not specified, the active GIS is used. future Keyword only parameter. Optional boolean. If True, the result will be a GPJob object and results will be returned asynchronously.
Returns

Output string containing the location of the exported training data

## export_point_dataset¶

arcgis.learn.export_point_dataset(data_path, output_path, block_size=50.0, max_points=8192, extra_features=[('intensity', 5000, 0), ('num_returns', 5, 0)], **kwargs)

Exports the las files into h5 blocks.

 Argument Description data_path Required string. Folder containing two folders with las files. Folder structure: train/*.las val/*.las output_path Required string. Path where exported files will be dumped. This directory either should be empty or be a totally new directory. block_size Optinal float. Size of the block to contain in one exported file. Default 50.0 max_points Required integer. Maximum number of points to contain in each block. Default 8192 extra_features Optional list of tuple. Extra features to read from las files. The first value of tuple is the key name of the features. The second value of the tuple is max value of the feature. The third value is the minimum value of that feature. Deafult: [(‘intensity’, 5000, 0), (‘num_returns’, 5, 0)]

## list_models¶

arcgis.learn.list_models(*, gis=None, future=False, **kwargs)

Function is used to list all the installed deep learning models.

 Argument Description gis Optional GIS. The GIS on which this tool runs. If not specified, the active GIS is used. future Keyword only parameter. Optional boolean. If True, the result will be a GPJob object and results will be returned asynchronously.
Returns

list of deep learning models installed

## Model¶

class arcgis.learn.Model(model=None)
from_json(model)

Function is used to initialise Model object from model definition JSON

eg usage:

model = Model()

model.from_json({“Framework” :”TensorFlow”,

“ModelConfiguration”:”DeepLab”, “InferenceFunction”:”[functions]System\DeepLearning\ImageClassifier.py”, “ModelFile”:”\\folder_path_of_pb_file\frozen_inference_graph.pb”, “ExtractBands”:[0,1,2], “ImageWidth”:513, “ImageHeight”:513, “Classes”: [ { “Value”:0, “Name”:”Evergreen Forest”, “Color”:[0, 51, 0] },

{ “Value”:1, “Name”:”Grassland/Herbaceous”, “Color”:[241, 185, 137] }, { “Value”:2, “Name”:”Bare Land”, “Color”:[236, 236, 0] }, { “Value”:3, “Name”:”Open Water”, “Color”:[0, 0, 117] }, { “Value”:4, “Name”:”Scrub/Shrub”, “Color”:[102, 102, 0] }, { “Value”:5, “Name”:”Impervious Surface”, “Color”:[236, 236, 236] } ] })

from_model_path(model)

Function is used to initialise Model object from url of model package or path of model definition file eg usage:

model = Model()

model.from_model_path(“https://xxxportal.esri.com/sharing/rest/content/items/<itemId>”)

or model = Model()

model.from_model_path("\\sharedstorage\sharefolder\findtrees.emd")

install(*, gis=None, future=False, **kwargs)

Function is used to install the uploaded model package (*.dlpk). Optionally after inferencing the necessary information using the model, the model can be uninstalled by uninstall_model()

 Argument Description gis Optional GIS. The GIS on which this tool runs. If not specified, the active GIS is used. future Keyword only parameter. Optional boolean. If True, the result will be a GPJob object and results will be returned asynchronously.
Returns

Path where model is installed

query_info(*, gis=None, future=False, **kwargs)

Function is used to extract the deep learning model specific settings from the model package item or model definition file.

 Argument Description gis Optional GIS. The GIS on which this tool runs. If not specified, the active GIS is used. future Keyword only parameter. Optional boolean. If True, the result will be a GPJob object and results will be returned asynchronously.
Returns

The key model information in dictionary format that describes what the settings are essential for this type of deep learning model.

uninstall(*, gis=None, future=False, **kwargs)

Function is used to uninstall the uploaded model package that was installed using the install_model() This function will delete the named deep learning model from the server but not the portal item.

 Argument Description gis Optional GIS. The GIS on which this tool runs. If not specified, the active GIS is used. future Keyword only parameter. Optional boolean. If True, the result will be a GPJob object and results will be returned asynchronously.
Returns

itemId of the uninstalled model package item

## prepare_data¶

arcgis.learn.prepare_data(path, class_mapping=None, chip_size=224, val_split_pct=0.1, batch_size=64, transforms=None, collate_fn=<function _bb_pad_collate>, seed=42, dataset_type=None, resize_to=None, **kwargs)

Prepares a data object from training sample exported by the Export Training Data tool in ArcGIS Pro or Image Server, or training samples in the supported dataset formats. This data object consists of training and validation data sets with the specified transformations, chip size, batch size, split percentage, etc. -For object detection, use Pascal_VOC_rectangles format. -For feature categorization use Labelled Tiles or ImageNet format. -For pixel classification, use Classified Tiles format. -For entity extraction from text, use IOB, BILUO or ner_json formats.

 Argument Description path Required string. Path to data directory. class_mapping Optional dictionary. Mapping from id to its string label. For dataset_type=IOB, BILUO or ner_json: Provide address field as class mapping in below format: class_mapping={‘address_tag’:’address_field’}. Field defined as ‘address_tag’ will be treated as a location. In cases where trained model extracts multiple locations from a single document, that document will be replicated for each location. chip_size Optional integer. Size of the image to train the model. val_split_pct Optional float. Percentage of training data to keep as validation. batch_size Optional integer. Batch size for mini batch gradient descent (Reduce it if getting CUDA Out of Memory Errors). transforms Optional tuple. Fast.ai transforms for data augmentation of training and validation datasets respectively (We have set good defaults which work for satellite imagery well). If transforms is set to False no transformation will take place and chip_size parameter will also not take effect. collate_fn Optional function. Passed to PyTorch to collate data into batches(usually default works). seed Optional integer. Random seed for reproducible train-validation split. dataset_type Optional string. prepare_data function will infer the dataset_type on its own if it contains a map.txt file. If the path does not contain the map.txt file pass either of ‘PASCAL_VOC_rectangles’, ‘RCNN_Masks’, ‘Classified_Tiles’, ‘Labeled_Tiles’ and ‘Imagenet’. resize_to Optional integer. Resize the image to given size.
Returns

data object

kwargs documentation * imagery_type=’RGB’ # Change to known imagery_type or anything else to trigger multispectral * bands=None # specify bands type for unknown imagery [‘r’, ‘g’, ‘b’, ‘nir’] * rgb_bands=[0, 1, 2] # specify rgb bands indices for unknown imagery * norm_pct=0.3 # sample of images to calculate normalization stats on * do_normalize=True # Normalize data

## SingleShotDetector¶

class arcgis.learn.SingleShotDetector(data, grids=None, zooms=[1.0], ratios=[[1.0, 1.0]], backbone=None, drop=0.3, bias=-4.0, focal_loss=False, pretrained_path=None, location_loss_factor=None, ssd_version=2, backend='pytorch')

Creates a Single Shot Detector with the specified grid sizes, zoom scales and aspect ratios. Based on Fast.ai MOOC Version2 Lesson 9.

 Argument Description data Required fastai Databunch. Returned data object from prepare_data function. grids Required list. Grid sizes used for creating anchor boxes. zooms Optional list. Zooms of anchor boxes. ratios Optional list of tuples. Aspect ratios of anchor boxes. backbone Optional function. Backbone CNN model to be used for creating the base of the SingleShotDetector, which is resnet34 by default. dropout Optional float. Dropout propbability. Increase it to reduce overfitting. bias Optional float. Bias for SSD head. focal_loss Optional boolean. Uses Focal Loss if True. pretrained_path Optional string. Path where pre-trained model is saved. location_loss_factor Optional float. Sets the weight of the bounding box loss. This should be strictly between 0 and 1. This is default None which gives equal weight to both location and classification loss. This factor adjusts the focus of model on the location of bounding box. ssd_version Optional int within [1,2]. Use version=1 for arcgis v1.6.2 or earlier
Returns

SingleShotDetector Object

average_precision_score(detect_thresh=0.2, iou_thresh=0.1, mean=False, show_progress=True)

Computes average precision on the validation set for each class.

 Argument Description detect_thresh Optional float. The probabilty above which a detection will be considered for computing average precision. iou_thresh Optional float. The intersection over union threshold with the ground truth labels, above which a predicted bounding box will be considered a true positive. mean Optional bool. If False returns class-wise average precision otherwise returns mean average precision.
Returns

dict if mean is False otherwise float

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

 Argument Description epochs Required integer. Number of cycles of training on the data. Increase it if underfitting. lr Optional float or slice of floats. Learning rate to be used for training the model. If lr=None, an optimal learning rate is automatically deduced for training the model. one_cycle Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used. early_stopping Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if validation loss stops improving for 5 epochs. checkpoint Optional boolean. Parameter to save the best model during training. If set to True the best model based on validation loss will be saved during training. tensorboard Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at /training_log which can be visualized in tensorboard. Required tensorboardx version=1.7 (Experimental support). The default value is ‘False’.
classmethod from_emd(data, emd_path)

Creates a Single Shot Detector from an Esri Model Definition (EMD) file.

 Argument Description data Required fastai Databunch or None. Returned data object from prepare_data function or None for inferencing. emd_path Required string. Path to Esri Model Definition file.
Returns

SingleShotDetector Object

classmethod from_model(emd_path, data=None)

Creates a Single Shot Detector from an Esri Model Definition (EMD) file.

Note: Only supported for Pytorch models.

 Argument Description emd_path Required string. Path to Esri Model Definition file. data Required fastai Databunch or None. Returned data object from prepare_data function or None for inferencing.
Returns

SingleShotDetector Object

load(name_or_path)

Loads a saved model for inferencing or fine tuning from the specified path or model name.

 Argument Description name_or_path Required string. Name of the model to load from the pre-defined location. If path is passed then it loads from the specified path with model name as directory name. Path to “.pth” file can also be passed
lr_find(allow_plot=True)

Runs the Learning Rate Finder, and displays the graph of it’s output. Helps in choosing the optimum learning rate for training the model.

predict(image_path, threshold=0.5, nms_overlap=0.1, return_scores=False, visualize=False, resize=False)

Runs prediction on an Image.

 Argument Description image_path Required. Path to the image file to make the predictions on. threshold Optional float. The probability above which a detection will be considered valid. nms_overlap Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive. return_scores Optional boolean. Will return the probability scores of the bounding box predictions if True. visualize Optional boolean. Displays the image with predicted bounding boxes if True. resize Optional boolean. Resizes the image to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the image is resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the image (of the same size as the model was trained on).
Returns

‘List’ of xmin, ymin, width, height of predicted bounding boxes on the given image

predict_video(input_video_path, metadata_file, threshold=0.5, nms_overlap=0.1, track=False, visualize=False, output_file_path=None, multiplex=False, multiplex_file_path=None, tracker_options={'assignment_iou_thrd': 0.3, 'detect_frames': 10, 'vanish_frames': 40}, visual_options={'color': (255, 255, 255), 'fontface': 0, 'show_labels': True, 'show_scores': True, 'thickness': 2}, resize=False)

Runs prediction on a video and appends the output VMTI predictions in the metadata file.

 Argument Description input_video_path Required. Path to the video file to make the predictions on. metadata_file Required. Path to the metadata csv file where the predictions will be saved in VMTI format. threshold Optional float. The probability above which a detection will be considered. nms_overlap Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive. track Optional bool. Set this parameter as True to enable object tracking. visualize Optional boolean. If True a video is saved with prediction results. output_file_path Optional path. Path of the final video to be saved. If not supplied, video will be saved at path input_video_path appended with _prediction. multiplex Optional boolean. Runs Multiplex using the VMTI detections. multiplex_file_path Optional path. Path of the multiplexed video to be saved. By default a new file with _multiplex.MOV extension is saved in the same folder. tracking_options Optional dictionary. Set different parameters for object tracking. assignment_iou_thrd parameter is used to assign threshold for assignment of trackers, vanish_frames is the number of frames the object should be absent to consider it as vanished, detect_frames is the number of frames an object should be detected to track it. visual_options Optional dictionary. Set different parameters for visualization. show_scores boolean, to view scores on predictions, show_labels boolean, to view labels on predictions, thickness integer, to set the thickness level of box, fontface integer, fontface value from opencv values, color tuple (B, G, R), tuple containing values between 0-255. resize Optional boolean. Resizes the video frames to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the video frames are resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the frame (of the same size as the model was trained on).
save(name_or_path, framework='PyTorch', publish=False, gis=None, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

 Argument Description name_or_path Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories. framework Optional string. Defines the framework of the model. (Only supported by SingleShotDetector, currently.) If framework used is TF-ONNX, batch_size can be passed as an optional keyword argument. Framework choice: ‘PyTorch’ and ‘TF-ONNX’ publish Optional boolean. Publishes the DLPK as an item. gis Optional GIS Object. Used for publishing the item. If not specified then active gis user is taken. kwargs Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.
show_results(rows=5, thresh=0.5, nms_overlap=0.1)

Displays the results of a trained model on a part of the validation set.

property supported_backbones

Supported torchvision backbones for this model.

unfreeze()

Unfreezes the earlier layers of the model for fine-tuning.

## UnetClassifier¶

class arcgis.learn.UnetClassifier(data, backbone=None, pretrained_path=None)

Creates a Unet like classifier based on given pretrained encoder.

 Argument Description data Required fastai Databunch. Returned data object from prepare_data function. backbone Optional function. Backbone CNN model to be used for creating the base of the UnetClassifier, which is resnet34 by default. pretrained_path Optional string. Path where pre-trained model is saved.
Returns

UnetClassifier Object

accuracy()
fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

 Argument Description epochs Required integer. Number of cycles of training on the data. Increase it if underfitting. lr Optional float or slice of floats. Learning rate to be used for training the model. If lr=None, an optimal learning rate is automatically deduced for training the model. one_cycle Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used. early_stopping Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if validation loss stops improving for 5 epochs. checkpoint Optional boolean. Parameter to save the best model during training. If set to True the best model based on validation loss will be saved during training. tensorboard Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at /training_log which can be visualized in tensorboard. Required tensorboardx version=1.7 (Experimental support). The default value is ‘False’.
classmethod from_emd(data, emd_path)

Creates a Unet like classifier from an Esri Model Definition (EMD) file.

 Argument Description data Required fastai Databunch or None. Returned data object from prepare_data function or None for inferencing. emd_path Required string. Path to Esri Model Definition file.
Returns

UnetClassifier Object

classmethod from_model(emd_path, data=None)

Creates a Unet like classifier from an Esri Model Definition (EMD) file.

 Argument Description emd_path Required string. Path to Esri Model Definition file. data Required fastai Databunch or None. Returned data object from prepare_data function or None for inferencing.
Returns

UnetClassifier Object

load(name_or_path)

Loads a saved model for inferencing or fine tuning from the specified path or model name.

 Argument Description name_or_path Required string. Name of the model to load from the pre-defined location. If path is passed then it loads from the specified path with model name as directory name. Path to “.pth” file can also be passed
lr_find(allow_plot=True)

Runs the Learning Rate Finder, and displays the graph of it’s output. Helps in choosing the optimum learning rate for training the model.

mIOU(mean=False, show_progress=True)

Computes mean IOU on the validation set for each class.

 Argument Description mean Optional bool. If False returns class-wise mean IOU, otherwise returns mean iou of all classes combined. show_progress Optional bool. Displays the prgress bar if True.
Returns

dict if mean is False otherwise float

save(name_or_path, framework='PyTorch', publish=False, gis=None, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

 Argument Description name_or_path Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories. framework Optional string. Defines the framework of the model. (Only supported by SingleShotDetector, currently.) If framework used is TF-ONNX, batch_size can be passed as an optional keyword argument. Framework choice: ‘PyTorch’ and ‘TF-ONNX’ publish Optional boolean. Publishes the DLPK as an item. gis Optional GIS Object. Used for publishing the item. If not specified then active gis user is taken. kwargs Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.
show_results(rows=5, **kwargs)

Displays the results of a trained model on a part of the validation set.

property supported_backbones

Supported torchvision backbones for this model.

unfreeze()

Unfreezes the earlier layers of the model for fine-tuning.

## FeatureClassifier¶

class arcgis.learn.FeatureClassifier(data, backbone=None, pretrained_path=None, mixup=False, oversample=False, backend='pytorch', **kwargs)

Creates an image classifier to classify the area occupied by a geographical feature based on the imagery it overlaps with.

 Argument Description data Required fastai Databunch. Returned data object from prepare_data function. backbone Optional torchvision model. Backbone CNN model to be used for creating the base of the FeatureClassifier, which is resnet34 by default. pretrained_path Optional string. Path where pre-trained model is saved. mixup Optional boolean. If set to True, it creates new training images by randomly mixing training set images. The default is set to False. oversample Optional boolean. If set to True, it oversamples unbalanced classes of the dataset during training.
Returns

FeatureClassifier Object

categorize_features(feature_layer, raster=None, class_value_field='class_val', class_name_field='prediction', confidence_field='confidence', cell_size=1, coordinate_system=None, predict_function=None, batch_size=64, overwrite=False)

Categorizes each feature by classifying its attachments or an image of its geographical area (using the provided Imagery Layer) and updates the feature layer with the prediction results in the output_label_field. Deprecated, Please use arcgis.learn.classify_objects() instead.

 Argument Description feature_layer Required. Public Feature Layer or path of local feature class for classification with read, write, edit permissions. raster Optional. Imagery layer or path of local raster to be used for exporting image chips. (Requires arcpy) class_value_field Required string. Output field to be added in the layer, containing class value of predictions. class_name_field Required string. Output field to be added in the layer, containing class name of predictions. confidence_field Optional string. Output column name to be added in the layer which contains the confidence score. cell_size Optional float. Cell size to be used for exporting the image chips. coordinate_system Optional. Cartographic Coordinate System to be used for exporting the image chips. predict_function Optional list of tuples. Used for calculation of final prediction result when each feature has more than one attachment. The predict_function takes as input a list of tuples. Each tuple has first element as the class predicted and second element is the confidence score. The function should return the final tuple classifying the feature and its confidence. batch_size Optional integer. The no of images or tiles to process in a single go. The default value is 64. overwrite Optional boolean. If set to True the output fields will be overwritten by new values. The default value is False.
Returns

Boolean : True if operation is successful, False otherwise

classify_features(feature_layer, labeled_tiles_directory, input_label_field, output_label_field, confidence_field=None, predict_function=None)

Classifies the exported images and updates the feature layer with the prediction results in the output_label_field.

 Argument Description feature_layer Required. Feature Layer for classification. labeled_tiles_directory Required. Folder structure containing images and labels folder. The chips should have been generated using the export training data tool in the Labeled Tiles format, and the labels should contain the OBJECTIDs of the features to be classified. input_label_field Required. Value field name which created the labeled tiles. This field should contain the OBJECTIDs of the features to be classified. In case of attachments this field is not used. output_label_field Required. Output column name to be added in the layer which contains predictions. confidence_field Optional. Output column name to be added in the layer which contains the confidence score. predict_function Optional. Used for calculation of final prediction result when each feature has more than one attachment. The predict_function takes as input a list of tuples. Each tuple has first element as the class predicted and second element is the confidence score. The function should return the final tuple classifying the feature and its confidence
Returns

Boolean : True/False if operation is sucessful

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

 Argument Description epochs Required integer. Number of cycles of training on the data. Increase it if underfitting. lr Optional float or slice of floats. Learning rate to be used for training the model. If lr=None, an optimal learning rate is automatically deduced for training the model. one_cycle Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used. early_stopping Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if validation loss stops improving for 5 epochs. checkpoint Optional boolean. Parameter to save the best model during training. If set to True the best model based on validation loss will be saved during training. tensorboard Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at /training_log which can be visualized in tensorboard. Required tensorboardx version=1.7 (Experimental support). The default value is ‘False’.
classmethod from_model(emd_path, data=None)

Creates a Feature classifier from an Esri Model Definition (EMD) file.

 Argument Description emd_path Required string. Path to Esri Model Definition file. data Required fastai Databunch or None. Returned data object from prepare_data function or None for inferencing.
Returns

FeatureClassifier Object

load(name_or_path)

Loads a saved model for inferencing or fine tuning from the specified path or model name.

 Argument Description name_or_path Required string. Name of the model to load from the pre-defined location. If path is passed then it loads from the specified path with model name as directory name. Path to “.pth” file can also be passed
lr_find(allow_plot=True)

Runs the Learning Rate Finder, and displays the graph of it’s output. Helps in choosing the optimum learning rate for training the model.

plot_confusion_matrix()

Plots a confusion matrix of the model predictions to evaluate accuracy

plot_hard_examples(num_examples)

Plots the hard examples with their heatmaps.

 Argument Description num_examples Number of hard examples to plot prepare_data function.
predict(img_path)

Runs prediction on an Image. ===================== =========================================== Argument Description ——————— ——————————————- image_path Required. Path to the image file to make the

predictions on.

predict_folder_and_create_layer(folder, feature_layer_name, gis=None, prediction_field='predict', confidence_field='confidence')

Predicts on images present in the given folder and creates a feature layer.

 Argument Description folder Required String. Folder to inference on. feature_layer_name Required String. The name of the feature layer used to publish. gis Optional GIS Object, the GIS on which this tool runs. If not specified, the active GIS is used. prediction_field Optional String. The field name to use to add predictions. confidence_field Optional String. The field name to use to add confidence.
Returns

FeatureCollection Object

save(name_or_path, framework='PyTorch', publish=False, gis=None, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

 Argument Description name_or_path Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories. framework Optional string. Defines the framework of the model. (Only supported by SingleShotDetector, currently.) If framework used is TF-ONNX, batch_size can be passed as an optional keyword argument. Framework choice: ‘PyTorch’ and ‘TF-ONNX’ publish Optional boolean. Publishes the DLPK as an item. gis Optional GIS Object. Used for publishing the item. If not specified then active gis user is taken. kwargs Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.
show_results(rows=5, **kwargs)

Displays the results of a trained model on a part of the validation set.

property supported_backbones

Supported torchvision backbones for this model.

unfreeze()

Unfreezes the earlier layers of the model for fine-tuning.

## RetinaNet¶

class arcgis.learn.RetinaNet(data, scales=None, ratios=None, backbone=None, pretrained_path=None)

Creates a RetinaNet Object Detector with the specified zoom scales and aspect ratios. Based on the Fast.ai notebook at https://github.com/fastai/fastai_dev/blob/master/dev_nb/102a_coco.ipynb

 Argument Description data Required fastai Databunch. Returned data object from prepare_data function. scales Optional list of float values. Zoom scales of anchor boxes. ratios Optional list of float values. Aspect ratios of anchor boxes. backbone Optional function. Backbone CNN model to be used for creating the base of the RetinaNet, which is resnet50 by default. Compatible backbones: ‘resnet18’, ‘resnet34’, ‘resnet50’, ‘resnet101’, ‘resnet152’ pretrained_path Optional string. Path where pre-trained model is saved.
Returns

RetinaNet Object

average_precision_score(detect_thresh=0.5, iou_thresh=0.1, mean=False, show_progress=True)

Computes average precision on the validation set for each class.

 Argument Description detect_thresh Optional float. The probabilty above which a detection will be considered for computing average precision. iou_thresh Optional float. The intersection over union threshold with the ground truth labels, above which a predicted bounding box will be considered a true positive. mean Optional bool. If False returns class-wise average precision otherwise returns mean average precision.
Returns

dict if mean is False otherwise float

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

 Argument Description epochs Required integer. Number of cycles of training on the data. Increase it if underfitting. lr Optional float or slice of floats. Learning rate to be used for training the model. If lr=None, an optimal learning rate is automatically deduced for training the model. one_cycle Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used. early_stopping Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if validation loss stops improving for 5 epochs. checkpoint Optional boolean. Parameter to save the best model during training. If set to True the best model based on validation loss will be saved during training. tensorboard Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at /training_log which can be visualized in tensorboard. Required tensorboardx version=1.7 (Experimental support). The default value is ‘False’.
classmethod from_model(emd_path, data=None)

Creates a RetinaNet Object Detector from an Esri Model Definition (EMD) file.

 Argument Description emd_path Required string. Path to Esri Model Definition file. data Required fastai Databunch or None. Returned data object from prepare_data function or None for inferencing.
Returns

RetinaNet Object

load(name_or_path)

Loads a saved model for inferencing or fine tuning from the specified path or model name.

 Argument Description name_or_path Required string. Name of the model to load from the pre-defined location. If path is passed then it loads from the specified path with model name as directory name. Path to “.pth” file can also be passed
lr_find(allow_plot=True)

Runs the Learning Rate Finder, and displays the graph of it’s output. Helps in choosing the optimum learning rate for training the model.

predict(image_path, threshold=0.5, nms_overlap=0.1, return_scores=True, visualize=False, resize=False)

Predicts and displays the results of a trained model on a single image.

 Argument Description image_path Required. Path to the image file to make the predictions on. thresh Optional float. The probabilty above which a detection will be considered valid. nms_overlap Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive. return_scores Optional boolean. Will return the probability scores of the bounding box predictions if True. visualize Optional boolean. Displays the image with predicted bounding boxes if True. resize Optional boolean. Resizes the image to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the image is resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the image (of the same size as the model was trained on).
Returns

‘List’ of xmin, ymin, width, height of predicted bounding boxes on the given image

predict_video(input_video_path, metadata_file, threshold=0.5, nms_overlap=0.1, track=False, visualize=False, output_file_path=None, multiplex=False, multiplex_file_path=None, tracker_options={'assignment_iou_thrd': 0.3, 'detect_frames': 10, 'vanish_frames': 40}, visual_options={'color': (255, 255, 255), 'fontface': 0, 'show_labels': True, 'show_scores': True, 'thickness': 2}, resize=False)

Runs prediction on a video and appends the output VMTI predictions in the metadata file.

 Argument Description input_video_path Required. Path to the video file to make the predictions on. metadata_file Required. Path to the metadata csv file where the predictions will be saved in VMTI format. threshold Optional float. The probability above which a detection will be considered. nms_overlap Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive. track Optional bool. Set this parameter as True to enable object tracking. visualize Optional boolean. If True a video is saved with prediction results. output_file_path Optional path. Path of the final video to be saved. If not supplied, video will be saved at path input_video_path appended with _prediction. multiplex Optional boolean. Runs Multiplex using the VMTI detections. multiplex_file_path Optional path. Path of the multiplexed video to be saved. By default a new file with _multiplex.MOV extension is saved in the same folder. tracking_options Optional dictionary. Set different parameters for object tracking. assignment_iou_thrd parameter is used to assign threshold for assignment of trackers, vanish_frames is the number of frames the object should be absent to consider it as vanished, detect_frames is the number of frames an object should be detected to track it. visual_options Optional dictionary. Set different parameters for visualization. show_scores boolean, to view scores on predictions, show_labels boolean, to view labels on predictions, thickness integer, to set the thickness level of box, fontface integer, fontface value from opencv values, color tuple (B, G, R), tuple containing values between 0-255. resize Optional boolean. Resizes the video frames to the same size (chip_size parameter in prepare_data) that the model was trained on, before detecting objects. Note that if resize_to parameter was used in prepare_data, the video frames are resized to that size instead. By default, this parameter is false and the detections are run in a sliding window fashion by applying the model on cropped sections of the frame (of the same size as the model was trained on).
save(name_or_path, framework='PyTorch', publish=False, gis=None, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

 Argument Description name_or_path Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories. framework Optional string. Defines the framework of the model. (Only supported by SingleShotDetector, currently.) If framework used is TF-ONNX, batch_size can be passed as an optional keyword argument. Framework choice: ‘PyTorch’ and ‘TF-ONNX’ publish Optional boolean. Publishes the DLPK as an item. gis Optional GIS Object. Used for publishing the item. If not specified then active gis user is taken. kwargs Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.
show_results(rows=5, thresh=0.5, nms_overlap=0.1)

Displays the results of a trained model on a part of the validation set.

 Argument Description rows Optional int. Number of rows of results to be displayed. thresh Optional float. The probabilty above which a detection will be considered valid. nms_overlap Optional float. The intersection over union threshold with other predicted bounding boxes, above which the box with the highest score will be considered a true positive.
property supported_backbones

Supported torchvision backbones for this model.

unfreeze()

Unfreezes the earlier layers of the model for fine-tuning.

## EntityRecognizer¶

class arcgis.learn.EntityRecognizer(data=None, lang='en')

Creates an entity recognition model to extract text entities from unstructured text documents. Based on Spacy’s EntityRecognizer

 Argument Description data Requires data object returned from prepare_data function. lang Optional string. Language-specific code, named according to the language’s ISO code The default value is ‘en’ for English.
Returns

EntityRecognizer Object

extract_entities(text_list, drop=True)

Extracts the entities from [documents in the mentioned path or text_list].

Field defined as ‘address_tag’ in prepare_data() function’s class mapping attribute will be treated as a location. In cases where trained model extracts multiple locations from a single document, that document will be replicated for each location in the resulting dataframe.

 Argument Description text_list Required string(path) or list(documents). List of documents for entity extraction OR path to the documents. drop Optional bool. If documents without address needs to be dropped from the results.
Returns

Pandas DataFrame

fit(epochs=20, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, **kwargs)

Trains an EntityRecognition model for ‘n’ number of epochs..

 Argument Description epoch Optional integer. Number of times the model will train on the complete dataset. lr Optional float. Learning rate to be used for training the model. one_cycle Not implemented for this model. early_stopping Not implemented for this model. early_stopping Not implemented for this model.
classmethod from_model(emd_path, data=None)

Creates an EntityRecognizer from an Esri Model Definition (EMD) file.

 Argument Description emd_path Required string. Path to Esri Model Definition file. data Required DatabunchNER object or None. Returned data object from prepare_data function or None for inferencing.
Returns

EntityRecognizer Object

load(name_or_path)

Loads a saved EntityRecognition model from disk.

 Argument Description name_or_path Required string. Path of the emd file.
lr_find(allow_plot=True)

Runs the Learning Rate Finder, and displays the graph of it’s output. Helps in choosing the optimum learning rate for training the model.

save(name_or_path, **kwargs)

Saves the model weights, creates an Esri Model Definition. Train the model for the specified number of epochs and using the specified learning rates.

 Argument Description name_or_path Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name. and creates all the intermediate directories.
show_results(ds_type='valid')

Runs entity extraction on a random batch from the mentioned ds_type.

 Argument Description ds_type Optional string, defaults to valid.
Returns

Pandas DataFrame

unfreeze()

Not implemented for this model.

## PSPNetClassifier¶

class arcgis.learn.PSPNetClassifier(data, backbone=None, use_unet=True, pyramid_sizes=[1, 2, 3, 6], pretrained_path=None, unet_aux_loss=False)

Model architecture from https://arxiv.org/abs/1612.01105. Creates a PSPNet Image Segmentation/ Pixel Classification model.

 Argument Description data Required fastai Databunch. Returned data object from prepare_data function. backbone Optional function. Backbone CNN model to be used for creating the base of the PSPNetClassifier, which is resnet50 by default. It supports the ResNet, DenseNet, and VGG families. use_unet Optional Bool. Specify whether to use Unet-Decoder or not, Default True. pyramid_sizes Optional List. The sizes at which the feature map is pooled at. Currently set to the best set reported in the paper, i.e, (1, 2, 3, 6) pretrained Optional Bool. If True, use the pretrained backbone pretrained_path Optional string. Path where pre-trained PSPNet model is saved. unet_aux_loss Optional. Bool If True will use auxillary loss for PSUnet. Default set to False. This flag is applicable only when use_unet is True.
Returns

PSPNetClassifier Object

accuracy(input=None, target=None, void_code=0, class_mapping=None)
fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

 Argument Description epochs Required integer. Number of cycles of training on the data. Increase it if underfitting. lr Optional float or slice of floats. Learning rate to be used for training the model. If lr=None, an optimal learning rate is automatically deduced for training the model. one_cycle Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used. early_stopping Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if validation loss stops improving for 5 epochs. checkpoint Optional boolean. Parameter to save the best model during training. If set to True the best model based on validation loss will be saved during training. tensorboard Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at /training_log which can be visualized in tensorboard. Required tensorboardx version=1.7 (Experimental support). The default value is ‘False’.
freeze()

Freezes the pretrained backbone.

classmethod from_model(emd_path, data=None)

Creates a PSPNet classifier from an Esri Model Definition (EMD) file.

 Argument Description emd_path Required string. Path to Esri Model Definition file. data Required fastai Databunch or None. Returned data object from prepare_data function or None for inferencing.
Returns

PSPNetClassifier Object

load(name_or_path)

Loads a saved model for inferencing or fine tuning from the specified path or model name.

 Argument Description name_or_path Required string. Name of the model to load from the pre-defined location. If path is passed then it loads from the specified path with model name as directory name. Path to “.pth” file can also be passed
lr_find(allow_plot=True)

Runs the Learning Rate Finder, and displays the graph of it’s output. Helps in choosing the optimum learning rate for training the model.

mIOU(mean=False, show_progress=True)

Computes mean IOU on the validation set for each class.

 Argument Description mean Optional bool. If False returns class-wise mean IOU, otherwise returns mean iou of all classes combined. show_progress Optional bool. Displays the prgress bar if True.
Returns

dict if mean is False otherwise float

save(name_or_path, framework='PyTorch', publish=False, gis=None, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

 Argument Description name_or_path Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories. framework Optional string. Defines the framework of the model. (Only supported by SingleShotDetector, currently.) If framework used is TF-ONNX, batch_size can be passed as an optional keyword argument. Framework choice: ‘PyTorch’ and ‘TF-ONNX’ publish Optional boolean. Publishes the DLPK as an item. gis Optional GIS Object. Used for publishing the item. If not specified then active gis user is taken. kwargs Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.
show_results(rows=5, **kwargs)

Displays the results of a trained model on a part of the validation set.

property supported_backbones

Supported torchvision backbones for this model.

unfreeze()

Unfreezes the earlier layers of the model for fine-tuning.

class arcgis.learn.MaskRCNN(data, backbone=None, pretrained_path=None)

Creates a MaskRCNN Instance segmentation object

 Argument Description data Required fastai Databunch. Returned data object from prepare_data function. backbone Optional function. Backbone CNN model to be used for creating the base of the MaskRCNN, which is resnet50 by default. Compatible backbones: ‘resnet18’, ‘resnet34’, ‘resnet50’, ‘resnet101’, ‘resnet152’ pretrained_path Optional string. Path where pre-trained model is saved.
Returns

MaskRCNN Object

average_precision_score(detect_thresh=0.5, iou_thresh=0.5, mean=False, show_progress=True)

Computes average precision on the validation set for each class.

Returns

dict if mean is False otherwise float

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

 Argument Description epochs Required integer. Number of cycles of training on the data. Increase it if underfitting. lr Optional float or slice of floats. Learning rate to be used for training the model. If lr=None, an optimal learning rate is automatically deduced for training the model. one_cycle Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used. early_stopping Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if validation loss stops improving for 5 epochs. checkpoint Optional boolean. Parameter to save the best model during training. If set to True the best model based on validation loss will be saved during training. tensorboard Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at /training_log which can be visualized in tensorboard. Required tensorboardx version=1.7 (Experimental support). The default value is ‘False’.
classmethod from_model(emd_path, data=None)

Creates a MaskRCNN Instance segmentation object from an Esri Model Definition (EMD) file.

 Argument Description emd_path Required string. Path to Esri Model Definition file. data Required fastai Databunch or None. Returned data object from prepare_data function or None for inferencing.
Returns

load(name_or_path)

Loads a saved model for inferencing or fine tuning from the specified path or model name.

 Argument Description name_or_path Required string. Name of the model to load from the pre-defined location. If path is passed then it loads from the specified path with model name as directory name. Path to “.pth” file can also be passed
lr_find(allow_plot=True)

Runs the Learning Rate Finder, and displays the graph of it’s output. Helps in choosing the optimum learning rate for training the model.

save(name_or_path, framework='PyTorch', publish=False, gis=None, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

 Argument Description name_or_path Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories. framework Optional string. Defines the framework of the model. (Only supported by SingleShotDetector, currently.) If framework used is TF-ONNX, batch_size can be passed as an optional keyword argument. Framework choice: ‘PyTorch’ and ‘TF-ONNX’ publish Optional boolean. Publishes the DLPK as an item. gis Optional GIS Object. Used for publishing the item. If not specified then active gis user is taken. kwargs Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.
show_results(rows=4, mode='mask', mask_threshold=0.5, box_threshold=0.7, imsize=5, index=0, alpha=0.5, cmap='tab20', **kwargs)

Displays the results of a trained model on a part of the validation set.

 Argument Description mode Required arguments within [‘bbox’, ‘mask’, ‘bbox_mask’]. bbox - For visualizing only boundig boxes. mask - For visualizing only mask bbox_mask - For visualizing both mask and bounding boxes. mask_threshold Optional float. The probabilty above which a pixel will be considered mask. box_threshold Optional float. The pobabilty above which a detection will be considered valid. nrows Optional int. Number of rows of results to be displayed.
property supported_backbones

Supported torchvision backbones for this model.

unfreeze()

Unfreezes the earlier layers of the model for fine-tuning.

## DeepLab¶

class arcgis.learn.DeepLab(data, backbone=None, pretrained_path=None)

Creates a DeepLab Semantic segmentation object

 Argument Description data Required fastai Databunch. Returned data object from prepare_data function. backbone Optional function. Backbone CNN model to be used for creating the base of the DeepLab, which is resnet101 by default since it is pretrained in torchvision. It supports the ResNet, DenseNet, and VGG families. pretrained_path Optional string. Path where pre-trained model is saved.
Returns

DeepLab Object

accuracy()
fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

 Argument Description epochs Required integer. Number of cycles of training on the data. Increase it if underfitting. lr Optional float or slice of floats. Learning rate to be used for training the model. If lr=None, an optimal learning rate is automatically deduced for training the model. one_cycle Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used. early_stopping Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if validation loss stops improving for 5 epochs. checkpoint Optional boolean. Parameter to save the best model during training. If set to True the best model based on validation loss will be saved during training. tensorboard Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at /training_log which can be visualized in tensorboard. Required tensorboardx version=1.7 (Experimental support). The default value is ‘False’.
classmethod from_model(emd_path, data=None)

Creates a DeepLab semantic segmentation object from an Esri Model Definition (EMD) file.

 Argument Description emd_path Required string. Path to Esri Model Definition file. data Required fastai Databunch or None. Returned data object from prepare_data function or None for inferencing.
Returns

DeepLab Object

load(name_or_path)

Loads a saved model for inferencing or fine tuning from the specified path or model name.

 Argument Description name_or_path Required string. Name of the model to load from the pre-defined location. If path is passed then it loads from the specified path with model name as directory name. Path to “.pth” file can also be passed
lr_find(allow_plot=True)

Runs the Learning Rate Finder, and displays the graph of it’s output. Helps in choosing the optimum learning rate for training the model.

mIOU(mean=False, show_progress=True)

Computes mean IOU on the validation set for each class.

 Argument Description mean Optional bool. If False returns class-wise mean IOU, otherwise returns mean iou of all classes combined. show_progress Optional bool. Displays the prgress bar if True.
Returns

dict if mean is False otherwise float

save(name_or_path, framework='PyTorch', publish=False, gis=None, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

 Argument Description name_or_path Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories. framework Optional string. Defines the framework of the model. (Only supported by SingleShotDetector, currently.) If framework used is TF-ONNX, batch_size can be passed as an optional keyword argument. Framework choice: ‘PyTorch’ and ‘TF-ONNX’ publish Optional boolean. Publishes the DLPK as an item. gis Optional GIS Object. Used for publishing the item. If not specified then active gis user is taken. kwargs Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.
show_results(rows=5, **kwargs)

Displays the results of a trained model on a part of the validation set.

property supported_backbones
unfreeze()

Unfreezes the earlier layers of the model for fine-tuning.

## PointCNN¶

class arcgis.learn.PointCNN(data, pretrained_path=None, **kwargs)

Model architecture from https://arxiv.org/abs/1801.07791. Creates a Point Cloud Segmentation/ Point Classification model.

 Argument Description data Required fastai Databunch. Returned data object from prepare_data function. pretrained_path Optional string. Path where pre-trained PointCNN model is saved.

kwargs

 Argument Description encoder_params Optinal dictionary. The keys of the dictionary are out_channels, P, K, D and m. Examples:{‘out_channels’:[16, 32, 64, 96], ‘P’:[-1, 768, 384, 128], ‘K’:[12, 16, 16, 16], ‘D’:[1, 1, 2, 2], ‘m’:8 } Length of out_channels, P, K, D should be same. The length denotes the number of layers in encoder. Parameter Explaination ‘out_channels’: Number of channels in each layer multiplied by m, ‘P’: Number of points in each layer, ‘K’: Number of K-nearest neighbour in each layer, ‘D’: Dilation in each layer, ‘m’: Multiplier which is multiplied by each out_channel. dropout Optional float. This parameter will control overfitting. The range of this parameter is [0,1). sample_point_num Optinal integer. The number of points that the models will actually process.
Returns

PointCNN Object

compute_precision_recall()

Computes precision, recall and f1-score on the validation sets.

fit(epochs=10, lr=None, one_cycle=True, early_stopping=False, checkpoint=True, tensorboard=False, **kwargs)

Train the model for the specified number of epochs and using the specified learning rates

 Argument Description epochs Required integer. Number of cycles of training on the data. Increase it if underfitting. lr Optional float or slice of floats. Learning rate to be used for training the model. If lr=None, an optimal learning rate is automatically deduced for training the model. one_cycle Optional boolean. Parameter to select 1cycle learning rate schedule. If set to False no learning rate schedule is used. early_stopping Optional boolean. Parameter to add early stopping. If set to ‘True’ training will stop if validation loss stops improving for 5 epochs. checkpoint Optional boolean. Parameter to save the best model during training. If set to True the best model based on validation loss will be saved during training. tensorboard Optional boolean. Parameter to write the training log. If set to ‘True’ the log will be saved at /training_log which can be visualized in tensorboard. Required tensorboardx version=1.7 (Experimental support). The default value is ‘False’.
classmethod from_model(emd_path, data=None)

Creates a PointCNN model from an Esri Model Definition (EMD) file.

 Argument Description emd_path Required string. Path to Esri Model Definition file. data Required fastai Databunch or None. Returned data object from prepare_data function or None for inferencing.
Returns

PointCNN Object

load(name_or_path)

Loads a saved model for inferencing or fine tuning from the specified path or model name.

 Argument Description name_or_path Required string. Name of the model to load from the pre-defined location. If path is passed then it loads from the specified path with model name as directory name. Path to “.pth” file can also be passed
lr_find(allow_plot=True)

Runs the Learning Rate Finder, and displays the graph of it’s output. Helps in choosing the optimum learning rate for training the model.

predict_las(path, output_path=None, print_metrics=False, **kwargs)

Predicts and writes the resulting las file on the disk.

 Argument Description path Required string. The path to folder where the las files which needs to be predicted are present. output_path Optional string. The path to folder where to dump the resulting las files. Defaults to results folder in input path. print_metrics Optional boolean. If True, print metrics such as precision, recall and f1_score. Defaults to False.
Returns

Path where files are dumped.

save(name_or_path, framework='PyTorch', publish=False, gis=None, **kwargs)

Saves the model weights, creates an Esri Model Definition and Deep Learning Package zip for deployment to Image Server or ArcGIS Pro.

 Argument Description name_or_path Required string. Name of the model to save. It stores it at the pre-defined location. If path is passed then it stores at the specified path with model name as directory name and creates all the intermediate directories. framework Optional string. Defines the framework of the model. (Only supported by SingleShotDetector, currently.) If framework used is TF-ONNX, batch_size can be passed as an optional keyword argument. Framework choice: ‘PyTorch’ and ‘TF-ONNX’ publish Optional boolean. Publishes the DLPK as an item. gis Optional GIS Object. Used for publishing the item. If not specified then active gis user is taken. kwargs Optional Parameters: Boolean overwrite if True, it will overwrite the item on ArcGIS Online/Enterprise, default False.
show_results(rows=2, **kwargs)

Displays the results of a trained model on a part of the validation set.

unfreeze()

Unfreezes the earlier layers of the model for fine-tuning.