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 bla

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

export_point_dataset

arcgis.learn.export_point_dataset(data_path, output_path, block_size=50.0, max_points=8192, extra_features=[], **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

Optional float. Size of the h5 block file. The unit of this parameter is same as, that of the dataset’s coordinate system. Default: 50.0 Units

max_points

Optional integer. Maximum number of points to be included in each h5 block file. Default: 8192 points.

extra_features

Optional list of tuple. Extra features to read from las files. The length of tuple is 3, which contain feature name, max, and min values respectively. For example: If you want extra features like intensity or number of returns to be considered while training, set this parameter like: extra_features=[(‘intensity’, 5000, 0), (‘num_returns’, 5, 0)]. The default behavior has changed from v1.8.0. Default: [].

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, default 224. Size of the image to train the model. Images are cropped to the specified chip_size. If image size is less than chip_size, the image size is used as chip_size.

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. If the dataset_type is ‘PointCloud’, use Transform3d class from arcgis.learn.

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’, ‘Imagenet’ and ‘PointCloud’.

resize_to

Optional integer. Resize the image to given size.

Keyword Arguments

Argument

Description

imagery_type

Optional string. Type of imagery used to export the training data, valid values are:

  • ‘naip’

  • ‘sentinel2’

  • ‘landsat8’

  • ‘ms’ - any other type of imagery

bands

Optional list. Bands of the imagery used to export training data. For example [‘r’, ‘g’, ‘b’, ‘nir’, ‘u’] where ‘nir’ is near infrared band and ‘u’ is a miscellaneous band.

rgb_bands

Optional list. Indices of red, green and blue bands in the imagery used to export the training data. for example: [2, 1, 0]

extract_bands

Optional list. Indices of bands to be used for training the model, same as in the imagery used to export the training data. for example: [3, 1, 0] where we will not be using the band at index 2 to train our model.

norm_pct

Optional float. Percentage of training data to be used for calculating imagery statistics for normalizing the data. Default is 0.3 (30%) of data.

downsample_factor

Optional integer. Factor to downsample the images for image SuperResolution. for example: if value is 2 and image size 256x256, it will create label images of size 128x128. Default is 4

Returns

data object

prepare_tabulardata

arcgis.learn.prepare_tabulardata(input_features, variable_predict, explanatory_variables=None, explanatory_rasters=None, date_field=None, distance_features=None, preprocessors=None, val_split_pct=0.1, seed=42, batch_size=64)

Prepares a databunch object from dataframe and fields_mapping dictionary. The first two inputs can be prepared using process_dataframe function.

Argument

Description

input_features

Required input feature layer or spatially enabled dataframe. This contains features denoting the value of the dependent variable.

variable_predict

Required String, optionally 2-sized tuple denoting field_name, Categorical/Continuous. For example:

(“Field_Name”, True)

By default: Automatically deduces the type.

explanatory_variables

Optional list containing field names from input_features By default the field type is continuous. To override field type to categorical, pass a 2-sized tuple containing:

  1. field to be taken as input from the input_features.

  2. True/False denoting Categorical/Continuous variable.

explanatory_rasters

Optional list containing Raster objects. By default the rasters are continuous. To mark a raster categorical, pass a 2-sized tuple containing:

  1. Raster object.

  2. True/False denoting Categorical/Continuous variable.

date_field

Optional field_name. This field contains the date in the input_layer. If specified, the field will be split into Year, month, week, day, dayofweek, dayofyear, is_month_end, is_month_start, is_quarter_end, is_quarter_start, is_year_end, is_year_start, hour, minute, second, elapsed. If specified here, no need to specify in the feature_variables list.

distance_features

Optional list of feature_layers. These layers are used for calculation of field “NEAR_DIST_1”, “NEAR_DIST_2” etc, in the output dataframe. These field contains the nearest feature distance from the input_layer feature.

preprocessors

For Fastai: Optional transforms list. For Scikit-learn: supply a column transformer object. Categorical data is by default encoded. If nothing is specified, default transforms are applied to fill missing values and normalize categorical data.

val_split_pct

Optional float. Percentage of training data to keep as validation. By default 10% data is kept for validation.

seed

Optional integer. Random seed for reproducible train-validation split. Default value is 42.

batch_size

Optional integer. Batch size for mini batch gradient descent (Reduce it if getting CUDA Out of Memory Errors). Default value is 64.

Returns

TabularData 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, backend='pytorch', *args, **kwargs)

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

backend

Optional string. Controls the backend framework to be used for this model, which is ‘pytorch’ by default.

valid options are ‘pytorch’, ‘tensorflow’

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.

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.

plot_losses()

Plot validation and training losses after fitting 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, backend='pytorch', *args, **kwargs)

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.

backend

Optional string. Controls the backend framework to be used for this model, which is ‘pytorch’ by default.

valid options are ‘pytorch’, ‘tensorflow’

kwargs

Argument

Description

class_balancing

Optional boolean. If True, it will balance the cross-entropy loss inverse to the frequency of pixels per class. Default: False.

mixup

Optional boolean. If True, it will use mixup augmentation and mixup loss. Default: False

focal_loss

Optional boolean. If True, it will use focal loss Default: False

ignore_classes

Optional list. It will contain the list of class values on which model will not incur loss. Default: []

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.

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

per_class_metrics()

Computer per class precision, recall and f1-score on validation set.

plot_losses()

Plot validation and training losses after fitting 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, oversample=False, backend='pytorch', *args, **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.

backend

Optional string. Controls the backend framework to be used for this model, which is ‘pytorch’ by default.

valid options are ‘pytorch’, ‘tensorflow’

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.

plot_losses()

Plot validation and training losses after fitting the model.

predict(img_path)

Runs prediction on an Image.

Argument

Description

image_path

Required. Path to the image file to make the predictions on.

Returns

prediciton label and confidence

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, *args, **kwargs)

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.

plot_losses()

Plot validation and training losses after fitting 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', *args, **kwargs)

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

f1_score()
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.

metrics_per_label()
precision_score()
recall_score()
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, *args, **kwargs)

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.

kwargs

Argument

Description

class_balancing

Optional boolean. If True, it will balance the cross-entropy loss inverse to the frequency of pixels per class. Default: False.

mixup

Optional boolean. If True, it will use mixup augmentation and mixup loss. Default: False

focal_loss

Optional boolean. If True, it will use focal loss. Default: False

ignore_classes

Optional list. It will contain the list of class values on which model will not incur loss. Default: []

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.

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

per_class_metrics()

Computer per class precision, recall and f1-score on validation set.

plot_losses()

Plot validation and training losses after fitting 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, *args, **kwargs)

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 <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.

plot_losses()

Plot validation and training losses after fitting 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, *args, **kwargs)

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.

kwargs

Argument

Description

class_balancing

Optional boolean. If True, it will balance the cross-entropy loss inverse to the frequency of pixels per class. Default: False.

mixup

Optional boolean. If True, it will use mixup augmentation and mixup loss. Default: False

focal_loss

Optional boolean. If True, it will use focal loss. Default: False

ignore_classes

Optional list. It will contain the list of class values on which model will not incur loss. Default: []

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.

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

per_class_metrics()

Computer per class precision, recall and f1-score on validation set.

plot_losses()

Plot validation and training losses after fitting 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.

PointCNN

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

Optional 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 Explanation
  • ‘out_channels’: Number of channels in each layer multiplied by m,

  • ‘P’: Number of points in each layer,

  • ‘K’: Number of K-nearest neighbor 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

Optional 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 <dataset-path>/training_log which can be visualized in tensorboard. Required tensorboardx version=1.7 (Experimental support). The default value is ‘False’.

kwargs

Argument

Description

iters_per_epoch

Optional integer. The number of iterations to run during the training phase.

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.

plot_losses()

Plot validation and training losses after fitting the model.

predict_h5(path, output_path=None, **kwargs)

Predicts and writes the resulting las file on the disk. The block size which was used for training will be used for prediction.

Argument

Description

path

Required string. The path to folder where the h5 files which needs to be predicted are present.

output_path

Optional string. The path to folder where to dump the resulting h5 block files. Defaults to results folder in input path.

Returns

Path where files are dumped.

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

Predicts and writes the resulting las file on the disk. The block size which was used for training will be used for prediction.

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.

kwargs

Argument

Description

remap_classes

Optional dictionary {int:int}. Mapping from class values to user defined values. Please query pointcnn._data.classes to get the class values on which the model is trained on. Default is {}

selective_classify

Optional list of integers. If passed, predict_las will selectively classify only those points belonging to the specified class-codes. Other points in the input point clouds will retain their class-codes. Please query pointcnn._data.classes to get the class values on which the model is trained on. If remap_classes is specified, the new mapped values will be used for classification. Default value is [].

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 from your model on the validation set with ground truth on the left and predictions on the right.

Argument

Description

rows

Optional rows. Number of rows to show. Default value is 2 and maximum value is the batch_size passed in prepare_data.

kwargs

Argument

Description

color_mapping

Optional dictionary. Mapping from class value to RGB values. Default value Example: {0:[220,220,220],

1:[255,0,0], 2:[0,255,0], 3:[0,0,255]}

mask_class

Optional list of integers. Array containing class values to mask. Use this parameter to display the classes of interest. Default value is []. Example: All the classes are in [0, 1, 2] to display only class 0 set the mask class parameter to be [1, 2]. List of all classes can be accessed from data.classes attribute where data is the Databunch object returned by prepare_data function.

width

Optional integer. Width of the plot. Default value is 750.

height

Optional integer. Height of the plot. Default value is 512.

max_display_point

Optional integer. Maximum number of points to display. Default is 20000. A warning will be raised if the total points to display exceeds this parameter. Setting this parameter will randomly sample the specified number of points and once set, it will be used for future uses.

unfreeze()

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

FullyConnectedNetwork

class arcgis.learn.FullyConnectedNetwork(data, layers=None, emb_szs=None, **kwargs)

Creates a FullyConnectedNetwork Object with specified layers and emb_szs. Based on the Fast.ai’s Tabular Learner

Argument

Description

data

Required TabularDataObject. Returned data object from prepare_tabulardata function.

layers

Optional list of 2 values, specifying the number of layers.

emb_szs

Optional dict

Returns

FullyConnectedNetwork Object

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 FullyConnectedNetwork 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_tabulardata function or None for inferencing.

Returns

FullyConnectedNetwork 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_losses()

Plot validation and training losses after fitting the model.

predict(input_features=None, explanatory_rasters=None, datefield=None, distance_features=None, output_layer_name='Prediction Layer', gis=None, prediction_type='features', output_raster_path=None, match_field_names=None)

Predict on data from feature layer and or raster data.

Argument

Description

input_features

Optional Feature Layer or spatial dataframe. Required is prediction_type=’features’. Contains features with location and some or all fields required to infer the dependent variable value.

explanatory_rasters

Optional list. Required if prediction_type=’raster’. Contains a list of raster objects containing some or all fields required to infer the dependent variable value.

datefield

Optional string. Field name from feature layer that contains the date, time for the input features. Same as prepare_tabulardata().

distance_features

Optional List of Feature Layers. These layers are used for calculation of field “NEAR_DIST_1”, “NEAR_DIST_2” etc in the output dataframe. These fields contain the nearest feature distance from the input_features. Same as prepare_tabulardata().

output_layer_name

Optional string. Used for publishing the output layer.

gis

Optional GIS Object. Used for publishing the item. If not specified then active gis user is taken.

prediction_type

Optional String. Set ‘features’ to make output feature layer predictions. With this feature_layer argument is required.

Set ‘raster’, to make prediction raster. With this rasters must be specified.

output_raster_path

Optional path. Required when prediction_type=’raster’, saves the output raster to this path.

match_field_names

Optional dictionary. Specify mapping of field names from prediction set to training set. For example:

{

“Field_Name_1”: “Field_1”, “Field_Name_2”: “Field_2”

}

:returns Feature Layer predict_features=True or creates an output raster.

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. Folder path to save the model.

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.

score()

:returns MSE for regression model and Accuracy for classification model.

show_results(rows=5)

Prints the rows of the dataframe with target and prediction columns.

Argument

Description

rows

Optional Integer. Number of rows to print.

Returns

dataframe

unfreeze()

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

MLModel

class arcgis.learn.MLModel(data, model_type, **kwargs)

Creates a machine learning model based on it’s implementation from scikit-learn. Refer https://scikit-learn.org/stable/supervised_learning.html#supervised-learning

Argument

Description

data

Required TabularDataObject. Returned data object from prepare_tabulardata function.

model_type

Required string path to the module. For example for SVM:

sklearn.svm.SVR or sklearn.svm.SVC

For tree:

sklearn.tree.DecisionTreeRegressor or sklearn.tree.DecisionTreeClassifier

**kwargs

model_type specific arguments. Refer Parameters section https://scikit-learn.org/stable/supervised_learning.html#supervised-learning

Returns

MLModel Object

fit()
classmethod from_model(emd_path, data=None)

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

Argument

Description

emd_path

Required string. Path to Esri Model Definition file.

data

Required TabularDataObject or None. Returned data object from prepare_tabulardata function or None for inferencing.

Returns

MLModel Object

predict(input_features=None, explanatory_rasters=None, datefield=None, distance_features=None, output_layer_name='Prediction Layer', gis=None, prediction_type='features', output_raster_path=None, match_field_names=None)

Predict on data from feature layer and or raster data.

Argument

Description

input_features

Optional Feature Layer or spatial dataframe. Required if prediction_type=’features’. Contains features with location and some or all fields required to infer the dependent variable value.

explanatory_rasters

Optional list. Required if prediction_type=’raster’. Contains a list of raster objects containing some or all fields required to infer the dependent variable value.

datefield

Optional string. Field name from feature layer that contains the date, time for the input features. Same as prepare_tabulardata().

distance_features

Optional List of Feature Layers. These layers are used for calculation of field “NEAR_DIST_1”, “NEAR_DIST_2” etc in the output dataframe. These fields contain the nearest feature distance from the input_features. Same as prepare_tabulardata().

output_layer_name

Optional string. Used for publishing the output layer.

gis

Optional GIS Object. Used for publishing the item. If not specified then active gis user is taken.

prediction_type

Optional String. Set ‘features’ to make output feature layer predictions. With this feature_layer argument is required.

Set ‘raster’, to make prediction raster. With this rasters must be specified.

output_raster_path

Optional path. Required when prediction_type=’raster’, saves the output raster to this path.

match_field_names

Optional dictionary. Specify mapping of field names from prediction set to training set. For example:

{

“Field_Name_1”: “Field_1”, “Field_Name_2”: “Field_2”

}

:returns Feature Layer prediction_type=’features’ or creates an output raster.

save(name_or_path)

Saves the model, creates an Esri Model Definition. Uses pickle to save the model. Using protocol level 2.Protocol level is backward compatible.

:returns dataframe

score()

:returns output from scikit-learn’s model.score()

show_results(rows=5)

Shows sample results for the model.

:returns dataframe

Transform3d

class arcgis.learn.Transform3d(rotation_range=[0.04363323129985824, 3.141592653589793, 0.04363323129985824, 'u'], scaling_range=[0.05, 0.05, 0.05, 'g'], jitter=0.0)

Creates a Transform3d object which, when passed in prepare data will apply data augmentation to the PointCloud data.

Argument

Description

rotation_range

Optional tuple of length 4. It contains a list of angles(in radians) for X, Z and Y coordinates respectively. These angles will rotate the point cloud block according to the randomly selected angle. The fourth value in the tuple is the sampling method where ‘u’ means uniform and ‘g’ means gaussian. Deafult: [math.pi / 72, math.pi, math.pi / 72, ‘u’]

scaling_range

Optional tuple of length 4. It contains a list of scaling ranges[0-1] which will scale the points. Please keep it a very small number otherwise, point cloud block may get distorted. The fourth value in the tuple is the sampling method where ‘u’ means uniform and ‘g’ means gaussian. Default: [0.05, 0.05, 0.05, ‘g’]

jitter

Optional float. The scale to which randomly jitter the points in the point cloud block. Default: 0.0

Returns

Transform3d object

ModelExtension

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

Creates a ModelExtension object, object detection model to train a model from your own source.

Argument

Description

data

Required fastai Databunch. Returned data object from prepare_data function.

model_conf

A class definition contains the following methods:

  • get_model(self, data, backbone=None): for model definition,

  • on_batch_begin(self, learn, model_input_batch, model_target_batch): for feeding input to the model during training,

  • transform_input(self, xb): for feeding input to the model during inferencing/validation,

  • transform_input_multispectral(self, xb): for feeding input to the model during inferencing/validation in case of multispectral data,

  • loss(self, model_output, *model_target): to return loss value of the model, and

  • post_process(self, pred, nms_overlap, thres, chip_size, device): to post-process the output of the model.

backbone

Optional function. If custom model requires any backbone.

pretrained_path

Optional string. Path where pre-trained model is saved.

Returns

ModelExtension 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_model(emd_path, data=None)

Creates a ModelExtension 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

ModelExtension 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_losses()

Plot validation and training losses after fitting 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, thresh=0.5, nms_overlap=0.1)

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

class train_callback(learn, on_batch_begin_fn)
property cb_name
get_state(minimal: bool = True)

Return the inner state of the Callback, minimal or not.

jump_to_epoch(epoch) → None

To resume training at epoch directly.

property learn
on_backward_begin(**kwargs: Any) → None

Called after the forward pass and the loss has been computed, but before backprop.

on_backward_end(**kwargs: Any) → None

Called after backprop but before optimizer step. Useful for true weight decay in AdamW.

on_batch_begin(last_input, last_target, train, **kwargs)

Set HP before the output and loss are computed.

on_batch_end(**kwargs: Any) → None

Called at the end of the batch.

on_epoch_begin(**kwargs: Any) → None

At the beginning of each epoch.

on_epoch_end(**kwargs: Any) → None

Called at the end of an epoch.

on_loss_begin(**kwargs: Any) → None

Called after forward pass but before loss has been computed.

on_step_end(**kwargs: Any) → None

Called after the step of the optimizer but before the gradients are zeroed.

on_train_begin(**kwargs: Any) → None

To initialize constants in the callback.

on_train_end(**kwargs: Any) → None

Useful for cleaning up things and saving files/models.

unfreeze()

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

FasterRCNN

class arcgis.learn.FasterRCNN(data, backbone='resnet50', pretrained_path=None)

Creates a FasterRCNN 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 FasterRCNN, which is resnet50 by default. Compatible backbones: ‘resnet18’, ‘resnet34’, ‘resnet50’, ‘resnet101’, ‘resnet152’

pretrained_path

Optional string. Path where pre-trained model is saved.

Returns

FasterRCNN 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_model(emd_path, data=None)

Creates a FasterRCNN 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

FasterRCNN 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_losses()

Plot validation and training losses after fitting 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, thresh=0.5, nms_overlap=0.1)

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

class train_callback(learn, on_batch_begin_fn)
property cb_name
get_state(minimal: bool = True)

Return the inner state of the Callback, minimal or not.

jump_to_epoch(epoch) → None

To resume training at epoch directly.

property learn
on_backward_begin(**kwargs: Any) → None

Called after the forward pass and the loss has been computed, but before backprop.

on_backward_end(**kwargs: Any) → None

Called after backprop but before optimizer step. Useful for true weight decay in AdamW.

on_batch_begin(last_input, last_target, train, **kwargs)

Set HP before the output and loss are computed.

on_batch_end(**kwargs: Any) → None

Called at the end of the batch.

on_epoch_begin(**kwargs: Any) → None

At the beginning of each epoch.

on_epoch_end(**kwargs: Any) → None

Called at the end of an epoch.

on_loss_begin(**kwargs: Any) → None

Called after forward pass but before loss has been computed.

on_step_end(**kwargs: Any) → None

Called after the step of the optimizer but before the gradients are zeroed.

on_train_begin(**kwargs: Any) → None

To initialize constants in the callback.

on_train_end(**kwargs: Any) → None

Useful for cleaning up things and saving files/models.

unfreeze()

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

SuperResolution

class arcgis.learn.SuperResolution(data, backbone=None, pretrained_path=None, *args, **kwargs)

Creates a model object which increases the resolution and improves the quality of images. Based on Fast.ai MOOC Lesson 7.

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

SuperResolution Object

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 SuperResolution object 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

SuperResolution Object

classmethod from_model(emd_path, data=None)

Creates a SuperResolution 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

SuperResolution 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_losses()

Plot validation and training losses after fitting the model.

predict(img_path, width=None, height=None)

Predicts and display the image.

Argument

Description

img_path

Required path of an image.

width

Optional int. Width of the predicted output image.

height

Optional int. Height of the predicted output image.

psnr_metric(accuracy=True, show_progress=True)

Computes peak signal-to-noise ratio (PSNR) on validation set.

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)

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.

property supported_backbones

Supported torchvision backbones for this model.

unfreeze()

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

YOLOv3

class arcgis.learn.YOLOv3(data=None, pretrained_path=None, **kwargs)

Creates a YOLOv3 object detector.

Argument

Description

data

Required fastai Databunch. Returned data object from prepare_data function.

pretrained_path

Optional string. Path where pre-trained model is saved.

Returns

YOLOv3 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 YOLOv3 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

YOLOv3 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_losses()

Plot validation and training losses after fitting the model.

predict(image_path, threshold=0.1, 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=0.1, 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 backbones for this model.

unfreeze()

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