bigml-sensenet 0.6.2

Network builder for bigml deepnet topologies

BigML Sense/Net

Sense/Net is a BigML interface to Tensorflow, which takes a network specification as a dictionary (read from BigML's JSON model format) and instantiates a TensorFlow compute graph based on that specification.

Entry Points

The library is meant, in general, to take a BigML model specification as a JSON document, and an optional map of settings and return a lightweight wrapper around a tf.keras.Model based on these arguments. The wrapper creation function can be found in sensenet.models.wrappers.create_model

Pretrained Networks

Often, BigML trained deepnets will use networks pretrained on ImageNet either as a starting point for fine tuning, or as the base layers under a custom set of readout layers. The weights for these networks are stored in a public s3 bucket and downloaded as needed for training or inference (see the sensenet.pretrained module). If the pretrained weights are never needed, no downloading occurs.

By default, these are downloaded to and read from the directory ~/.bigml_sensenet (which is created if it is not present). To change the location of this directory, clients can set the environment variable BIGML_SENSENET_CACHE_PATH.

Model Instantiation

To instantiate a model, pass the model specification and the dict of additional, optional settings to create_model. For example:

model = create_model(a_dict, settings={'image_path_prefix': 'images/path/'})

Again, a_dict is typically a downloaded BigML model, read into a python dictionary via json.load or similar.

You may also pass the path to a file containing such a model:

model = create_model('model.json', settings=None)

For image models, settings is either None (the default) or a dict of optional settings which may contain:

Settings Arguments

• bounding_box_threshold: For object detection models only, the minimal score that an object can have and still be surfaced to the user as part of the output. The default is 0.5, and lower the score will have the effect of more (possibly spurious) boxes identified in each input image.

• color_space: A string which is one of ['rgb', 'rgba', 'bgr', 'bgra']. The first three letters give the order of the color channels (red, blue, and green) in the input tensors that will be passed to the model. The final presence or absence of an 'a' indicates that an alpha channel will be present (which will be ignored). This can be useful to match the color space of the output model to that provided by another library, such as open CV. Note that TensorFlow uses RGB ordering by default, and all files read by TensorFlow are automatically read as RGB files. This argument is generally only necessary if input_image_format is 'pixel_values', and will possibly break predictions if specified when the input is a file.

• image_path_prefix: A string directory indicating the path where images are to be found for image predictions. When an image path is passed at prediction time, this string is prepended to the given path.

• iou_threshold: A threshold indicating the amount of overlap boxes predicting the same class should have before they are considered to be bounding the same object. The default is 0.5, and lower values have the effect of eliminating boxes which would otherwise have been surfaced to the user.

• max_objects: The maximum number of bounding boxes to return for each image. The default is 32.

• rescale_type: A string which is one of ['warp', 'pad', 'crop']. If 'warp', input images are scaled to the input dimensions specified in the network, and their aspect ratios are not preserved. If 'pad', the image is resized to the smallest dimensions such that the image fits into the input dimensions of the network, then padded with constant pixels either below or to the right to create an appropriately sized image. For example, if the input dimensions of the network are 100 x 100, and we attempt to classify a 300 x 600 image, the image is first rescaled to 50 x 100 (preserving its aspect ratio) then padded on the right to create a 100 x 100 image. If 'crop', the image is resized to the smallest dimension such that the input dimensions fit in the image, then the image is centrally cropped to make the specified sizes. Using the sizes in previous example, the image would be rescaled to 100 x 200 (preserving its aspect ratio) then cropped by 50 pixels on the top and bottom to create a 100 x 100 image.

Model Formats and Conversion

The canonical format for sensenet models is the JSON format downloadable from BigML. However, as the JSON is fairly heavyweight, time-consuming to parse, and not consumable from certain locations, SenseNet offers a conversion utility, sensenet.models.wrappers.convert, which takes the JSON format as input and can output the following formats:

• tflite will export the model in the Tensorflow lite format, which allows lightweight prediction on mobile devices.

• tfjs exports the model to the format read by Tensorflow JS to do predictions in the browser and server-side in node.js.

• smbundle exports the model to a (proprietary) lightweight wrapper around the TensorFlow SavedModel format. The generated file is a concatenation of the files in the SavedModel directory, with some additional information written to the assets sub-directory. If this file is passed to create_model, the bundle is extracted to a temporary directory, the model instantiated, and the temporary files deleted. To extract the bundle without instantiating the model, see the functions in sensenet.models.bundle.

• h5 exports the model weights only to the keras h5 model format (i.e., via use of the TensorFlow function tf.keras.Model.save_weights) To use these, you'd instantiate the model from JSON and load the weights separately using the corresponding TensorFlow load_weights function.

Usage

Once instantiated, you can use the model to make predictions by using the returned model as a function, like so:

prediction = model([1.0, 2.0, 3.0])

The input point or points must be a list (or nested list) containing the input data for each point, in the order implied by model._preprocessors. Categorical and image variables should be passed as strings, where the image is either a path to the image on disk, or the raw compressed image bytes.

For classification or regression models, the function returns a numpy array where each row is the model's prediction for each input point. For classification models, there will be a probability for each class in each row. For regression models, each row will contain only a single entry.

For object detection models, the input should always be a single image (again, either as a file path, compressed byte string, or an array of pixel values, depending on the settings map, and the result will be list of detected boxes, each one represented as a dictionary. For example:

In [5]: model('pizza_people.jpg')
Out[5]:
[{'box': [16, 317, 283, 414], 'label': 'pizza', 'score': 0.9726969599723816},
 {'box': [323, 274, 414, 332], 'label': 'pizza', 'score': 0.7364346981048584},
 {'box': [158, 29, 400, 327], 'label': 'person', 'score': 0.6204285025596619},
 {'box': [15, 34, 283, 336], 'label': 'person', 'score': 0.5346986055374146},
 {'box': [311, 23, 416, 255], 'label': 'person', 'score': 0.41961848735809326}]

The box array contains the coordinates of the detected box, as x1, y1, x2, y2, where those coordinates represent the upper-left and lower-right corners of each bounding box, in a coordinate system with (0, 0) at the upper-left of the input image. The score is the rough probability that the object has been correctly identified, and the label is the detected class of the object.