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 where image 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 additional image 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

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’}
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’ and ‘Classified_Tiles’
resize_to	Optional integer. Resize the image to given size.

Returns: data object

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)¶

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 <dataset-path>/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.

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 <dataset-path>/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.

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)¶

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.

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)¶

Deprecated since version 1.7.1: Please use arcgis.learn.classify_objects() instead

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 <dataset-path>/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 <dataset-path>/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].

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)¶: Not implemented for this 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 <dataset-path>/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.

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.

MaskRCNN¶

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: ‘resnet50’
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 <dataset-path>/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: MaskRCNN 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.

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 <dataset-path>/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.

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.