Akida models API
Imports models.
Layer blocks
conv_block
- akida_models.layer_blocks.conv_block(inputs, filters, kernel_size, pooling=None, pool_size=(2, 2), add_batchnorm=False, add_activation=True, **kwargs)[source]
Adds a convolutional layer with optional layers in the following order: max pooling, batch normalization, activation.
- Parameters
inputs (tf.Tensor) – input tensor of shape (rows, cols, channels)
filters (int) – the dimensionality of the output space (i.e. the number of output filters in the convolution).
kernel_size (int or tuple of 2 integers) – specifying the height and width of the 2D convolution kernel. Can be a single integer to specify the same value for all spatial dimensions.
pooling (str) – add a pooling layer of type ‘pooling’ among the values ‘max’, ‘avg’, ‘global_max’ or ‘global_avg’, with pooling size set to pool_size. If ‘None’, no pooling will be added.
pool_size (int or tuple of 2 integers) – factors by which to downscale (vertical, horizontal). (2, 2) will halve the input in both spatial dimension. If only one integer is specified, the same window length will be used for both dimensions.
add_batchnorm (bool) – add a BatchNormalization layer
add_activation (bool) – add a ReLU layer
**kwargs – arguments passed to the keras.Conv2D layer, such as strides, padding, use_bias, weight_regularizer, etc.
- Returns
output tensor of conv2D block.
- Return type
tf.Tensor
separable_conv_block
- akida_models.layer_blocks.separable_conv_block(inputs, filters, kernel_size, pooling=None, pool_size=(2, 2), add_batchnorm=False, add_activation=True, **kwargs)[source]
Adds a separable convolutional layer with optional layers in the following order: global average pooling, max pooling, batch normalization, activation.
- Parameters
inputs (tf.Tensor) – input tensor of shape (height, width, channels)
filters (int) – the dimensionality of the output space (i.e. the number of output filters in the pointwise convolution).
kernel_size (int or tuple of 2 integers) – specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
pooling (str) – add a pooling layer of type ‘pooling’ among the values ‘max’, ‘avg’, ‘global_max’ or ‘global_avg’, with pooling size set to pool_size. If ‘None’, no pooling will be added.
pool_size (int or tuple of 2 integers) – factors by which to downscale (vertical, horizontal). (2, 2) will halve the input in both spatial dimension. If only one integer is specified, the same window length will be used for both dimensions.
add_batchnorm (bool) – add a BatchNormalization layer
add_activation (bool) – add a ReLU layer
**kwargs – arguments passed to the keras.SeparableConv2D layer, such as strides, padding, use_bias, etc.
- Returns
output tensor of separable conv block.
- Return type
tf.Tensor
dense_block
- akida_models.layer_blocks.dense_block(inputs, units, add_batchnorm=False, add_activation=True, **kwargs)[source]
Adds a dense layer with optional layers in the following order: batch normalization, activation.
- Parameters
inputs (tf.Tensor) – Input tensor of shape (rows, cols, channels)
units (int) – dimensionality of the output space
add_batchnorm (bool) – add a BatchNormalization layer
add_activation (bool) – add a ReLU layer
**kwargs – arguments passed to the Dense layer, such as use_bias, kernel_initializer, weight_regularizer, etc.
- Returns
output tensor of the dense block.
- Return type
tf.Tensor
Helpers
BatchNormalization gamma constraint
- akida_models.add_gamma_constraint(model)[source]
Method helper to add a MinValueConstraint to an existing model so that gamma values of its BatchNormalization layers are above a defined minimum.
This is typically used to help having a model that will be Akida compatible after conversion. In some cases, the mapping on hardware will fail because of huge values for threshold or act_step with a message indicating that a value cannot fit in a 20 bit signed or unsigned integer. In such a case, this helper can be called to apply a constraint that can fix the issue.
Note that in order for the constraint to be applied to the actual weights, some training must be done: for an already trained model, it can be on a few batches, one epoch or more depending on the impact the constraint has on accuracy. This helper can also be called to a new model that has not been trained yet.
- Parameters
model (keras.Model) – the model for which gamma constraints will be added.
- Returns
the same model with BatchNormalisation layers updated.
- Return type
keras.Model
Knowledge distillation
- class akida_models.distiller.Distiller(*args, **kwargs)[source]
The class that will be used to train the student model using the distillation knowledge method.
Reference Hinton et al. (2015).
- Parameters
student (keras.Model) – the student model
teacher (keras.Model) – the well trained teacher model
Methods:
compile(optimizer, metrics, student_loss_fn, ...)Configure the distiller.
test_step(data)The logic for one evaluation step.
train_step(data)The logic for one training step.
- compile(optimizer, metrics, student_loss_fn, distillation_loss_fn, alpha=0.1)[source]
Configure the distiller.
- Parameters
optimizer (keras.optimizers.Optimizer) – Keras optimizer for the student weights
metrics (keras.metrics.Metric) – Keras metrics for evaluation
student_loss_fn (keras.losses.Loss) – loss function of difference between student predictions and ground-truth
distillation_loss_fn (keras.losses.Loss) – loss function of difference between student predictions and teacher predictions
alpha (float) – weight to student_loss_fn and 1-alpha to distillation_loss_fn
- test_step(data)[source]
The logic for one evaluation step.
This method can be overridden to support custom evaluation logic. This method is called by Model.make_test_function.
This function should contain the mathematical logic for one step of evaluation. This typically includes the forward pass, loss calculation, and metrics updates.
Configuration details for how this logic is run (e.g. tf.function and tf.distribute.Strategy settings), should be left to Model.make_test_function, which can also be overridden.
- Parameters
data – A nested structure of `Tensor`s.
- Returns
A dict containing values that will be passed to tf.keras.callbacks.CallbackList.on_train_batch_end. Typically, the values of the Model’s metrics are returned.
- train_step(data)[source]
The logic for one training step.
This method can be overridden to support custom training logic. For concrete examples of how to override this method see [Customizing what happends in fit](https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit). This method is called by Model.make_train_function.
This method should contain the mathematical logic for one step of training. This typically includes the forward pass, loss calculation, backpropagation, and metric updates.
Configuration details for how this logic is run (e.g. tf.function and tf.distribute.Strategy settings), should be left to Model.make_train_function, which can also be overridden.
- Parameters
data – A nested structure of `Tensor`s.
- Returns
A dict containing values that will be passed to tf.keras.callbacks.CallbackList.on_train_batch_end. Typically, the values of the Model’s metrics are returned. Example: {‘loss’: 0.2, ‘accuracy’: 0.7}.
- akida_models.distiller.KLDistillationLoss(temperature=3)[source]
The KLDistillationLoss is a simple wrapper around the KLDivergence loss that accepts raw predictions instead of probability distributions.
Before invoking the KLDivergence loss, it converts the inputs predictions to probabilities by dividing them by a constant ‘temperature’ and applies a softmax.
- Parameters
temperature (float) – temperature for softening probability distributions. Larger temperature gives softer distributions.
Pruning
- akida_models.prune_model(model, acceptance_function, pruning_rates=None, prunable_layers_policy=<function neural_layers>, prunable_filters_policy=<function smallest_filters>)[source]
Prune model automatically based on an acceptance function.
The algorithm for filter pruning is as follows:
Select the first prunable layer (according to the
prunable_layers_policyfunction).As long as the
acceptance_functionreturns True, prune successively the layer with different pruning rates (according topruning_ratesandprunable_filters_policy).When the current pruned model is not acceptable, the last valid pruning rate is selected for the final pruned model.
Repeat steps 1, 2 and 3 for the next prunable layers.
Examples
acceptable_drop = 0.05 def acceptance_function(base_model, pruned_model): # This function returns True if the pruned_model is acceptable. # Here, the pruned model is acceptable if the accuracy drops # less than 5% from the base model. def evaluate(model): _, accuracy = model.evaluate(data, labels) return accuracy return evaluate(base_model) - evaluate(pruned_model) <= acceptable_drop # Prune model pruned_model, pruning_rates = prune_model(model, acceptance_function)
- Parameters
model (keras.Model) – a keras model to prune
acceptance_function (function) – a criterion function that returns True if the pruned model is acceptable. The signature must be function(base_model, pruned_model).
pruning_rates (list, optional) – a list of pruning rates to test. Default is [0.1, 0.2, …, 0.9].
prunable_layers_policy (function, optional) – a function returning the layers to prune in the model. The signature must be function(model), and must return a list of prunable layer names. By default, all neural layers (Conv2D/SeparableConv2D/Dense) are candidates for pruning.
prunable_filters_policy (function, optional) – a function that returns the filters to prune in a given layer for a specific pruning rate. The signature must be function(layer, pruning_rate) and returns a list of indices to prune. By default, filters with the lowest magnitude are pruned.
- Returns
the pruned model and the pruning rates.
- Return type
tuple
- akida_models.delete_filters(model, layer_to_prune, filters_to_prune)[source]
Deletes filters in the given layer and updates weights in it and its subsequent layers.
A pruned model is returned. Only linear models are supported.
- Parameters
model (keras.Model) – the model to prune.
layer_to_prune (str) – the name of the neural layer where filters will be deleted.
filters_to_prune (list) – indices of filters to delete in the given layer.
- Returns
the pruned model
- Return type
keras.Sequential
Model zoo
Mobilenet
ImageNet
- akida_models.mobilenet_imagenet(input_shape=None, alpha=1.0, dropout=0.001, include_top=True, pooling=None, classes=1000, use_stride2=False, weight_quantization=0, activ_quantization=0, input_weight_quantization=None, input_scaling=(128, - 1))[source]
Instantiates the MobileNet architecture.
- Parameters
input_shape (tuple) – optional shape tuple.
alpha (float) –
controls the width of the model.
If alpha < 1.0, proportionally decreases the number of filters in each layer.
If alpha > 1.0, proportionally increases the number of filters in each layer.
If alpha = 1, default number of filters from the paper are used at each layer.
dropout (float) – dropout rate
include_top (bool) – whether to include the fully-connected layer at the top of the model.
pooling (str) –
Optional pooling mode for feature extraction when include_top is False.
None means that the output of the model will be the 4D tensor output of the last convolutional block.
avg means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor.
classes (int) – optional number of classes to classify images into, only to be specified if include_top is True.
use_stride2 (bool) – optional, replace max pooling operations by stride 2 convolutions in layers separable 2, 4, 6 and 12.
weight_quantization (int) –
sets all weights in the model to have a particular quantization bitwidth except for the weights in the first layer.
’0’ implements floating point 32-bit weights.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
activ_quantization –
sets all activations in the model to have a particular activation quantization bitwidth.
’0’ implements floating point 32-bit activations.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
input_weight_quantization –
sets weight quantization in the first layer. Defaults to weight_quantization value.
’0’ implements floating point 32-bit weights.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
input_scaling (tuple, optional) – scale factor and offset to apply to inputs. Defaults to (128, -1). Note that following Akida convention, the scale factor is an integer used as a divider.
- Returns
a Keras model for MobileNet/ImageNet.
- Return type
keras.Model
- Raises
ValueError – in case of invalid input shape.
- akida_models.mobilenet_imagenet_pretrained(alpha=1.0)[source]
Helper method to retrieve a mobilenet_imagenet model that was trained on ImageNet dataset.
- Parameters
alpha (float) – width of the model.
- Returns
a Keras Model instance.
- Return type
keras.Model
- akida_models.mobilenet_imagenette_pretrained(alpha=1.0)[source]
Helper method to retrieve a mobilenet_imagenet model that was trained on Imagenette dataset.
- Parameters
alpha (float) – width of the model.
- Returns
a Keras Model instance.
- Return type
keras.Model
- akida_models.mobilenet_cats_vs_dogs_pretrained()[source]
Helper method to retrieve a mobilenet_imagenet model that was trained on Cats vs.Dogs dataset.
- Returns
a Keras Model instance.
- Return type
keras.Model
- akida_models.mobilenet_faceidentification_pretrained()[source]
Helper method to retrieve a mobilenet_imagenet model that was trained on CASIA Webface dataset and that performs face identification.
- Returns
a Keras Model instance.
- Return type
keras.Model
- akida_models.mobilenet_faceverification_pretrained()[source]
Helper method to retrieve a mobilenet_imagenet model that was trained on CASIA Webface dataset and optimized with ArcFace that can perform face verification on LFW.
- Returns
a Keras Model instance.
- Return type
keras.Model
- akida_models.mobilenet_edge_imagenet(base_model, classes)[source]
Instantiates a MobileNet-edge architecture.
- Parameters
base_model (str/keras.Model) – a mobilenet_imagenet quantized model.
classes (int) – the number of classes for the edge classifier.
- Returns
a Keras Model instance.
- Return type
keras.Model
- akida_models.mobilenet_edge_imagenet_pretrained()[source]
Helper method to retrieve a mobilenet_edge_imagenet model that was trained on ImageNet dataset.
- Returns
a Keras Model instance.
- Return type
keras.Model
Preprocessing
- akida_models.imagenet.preprocessing.process_record_dataset(dataset, is_training, batch_size, im_size, shuffle_buffer, parse_record_fn, dtype=tf.float32, datasets_num_private_threads=None, drop_remainder=False, tf_data_experimental_slack=False)[source]
Given a Dataset with raw records, return an iterator over the records.
- Parameters
dataset – A Dataset representing raw records
is_training – A boolean denoting whether the input is for training.
batch_size – The number of samples per batch.
shuffle_buffer – The buffer size to use when shuffling records. A larger value results in better randomness, but smaller values reduce startup time and use less memory.
parse_record_fn – A function that takes a raw record and returns the corresponding (image, label) pair.
num_epochs – The number of epochs to repeat the dataset.
dtype – Data type to use for images/features.
datasets_num_private_threads – Number of threads for a private threadpool created for all datasets computation.
drop_remainder – A boolean indicates whether to drop the remainder of the batches. If True, the batch dimension will be static.
tf_data_experimental_slack – Whether to enable tf.data’s experimental_slack option.
- Returns
Dataset of (image, label) pairs ready for iteration.
- akida_models.imagenet.preprocessing.get_filenames(is_training, data_dir)[source]
Return filenames for dataset.
- akida_models.imagenet.preprocessing.parse_record(raw_record, im_size, is_training, dtype)[source]
Parses a record containing a training example of an image.
The input record is parsed into a label and image, and the image is passed through preprocessing steps (cropping, flipping, and so on).
- Parameters
raw_record – scalar Tensor tf.string containing a serialized Example protocol buffer.
is_training – A boolean denoting whether the input is for training.
dtype – data type to use for images/features.
- Returns
Tuple with processed image tensor and one-hot-encoded label tensor.
- akida_models.imagenet.preprocessing.input_fn(is_training, data_dir, batch_size, im_size, dtype=tf.float32, datasets_num_private_threads=None, parse_record_fn=<function parse_record>, input_context=None, drop_remainder=False, tf_data_experimental_slack=False, training_dataset_cache=False)[source]
Input function which provides batches for train or eval.
- Parameters
is_training – A boolean denoting whether the input is for training.
data_dir – The directory containing the input data.
batch_size – The number of samples per batch.
num_epochs – The number of epochs to repeat the dataset.
dtype – Data type to use for images/features
datasets_num_private_threads – Number of private threads for tf.data.
parse_record_fn – Function to use for parsing the records.
input_context – A tf.distribute.InputContext object passed in by tf.distribute.Strategy.
drop_remainder – A boolean indicates whether to drop the remainder of the batches. If True, the batch dimension will be static.
tf_data_experimental_slack – Whether to enable tf.data’s experimental_slack option.
training_dataset_cache – Whether to cache the training dataset on workers. Typically used to improve training performance when training data is in remote storage and can fit into worker memory.
- Returns
A dataset that can be used for iteration.
- akida_models.imagenet.preprocessing.preprocess_image(image_buffer, bbox, output_height, output_width, num_channels, is_training=False, decode=True)[source]
Preprocesses the given image.
Preprocessing includes decoding, cropping, and resizing for both training and eval images. Training preprocessing, however, introduces some random distortion of the image to improve accuracy.
- Parameters
image_buffer – scalar string Tensor representing the raw JPEG image buffer.
bbox – 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords] where each coordinate is [0, 1) and the coordinates are arranged as [ymin, xmin, ymax, xmax].
output_height – The height of the image after preprocessing.
output_width – The width of the image after preprocessing.
num_channels – Integer depth of the image buffer for decoding.
is_training – True if we’re preprocessing the image for training and False otherwise.
decode – whether to decode the image or not
- Returns
A preprocessed image.
- akida_models.imagenet.preprocessing.index_to_label(index)[source]
Function to get an ImageNet label from an index.
- Parameters
index – between 0 and 999
- Returns
a string of coma separated labels
- akida_models.imagenet.preprocessing.resize_and_crop(image_buffer, output_height, output_width, num_channels)[source]
Resize and crop the given image.
- Parameters
image_buffer – scalar string Tensor representing the raw JPEG image buffer.
output_height – The height of the image after preprocessing.
output_width – The width of the image after preprocessing.
num_channels – Integer depth of the image buffer for decoding.
- Returns
A resized and cropped image as a numpy array in uint8.
DS-CNN
CIFAR-10
- akida_models.ds_cnn_cifar10(input_shape=(32, 32, 3), classes=10, weight_quantization=0, activ_quantization=0, input_weight_quantization=None, input_scaling=(255, 0))[source]
Instantiates a MobileNet-like model for the “CIFAR-10” example. This model is based on the MobileNet architecture, mainly with fewer layers. The weights and activations are quantized such that it can be converted into an Akida model.
This architecture is originated from https://arxiv.org/abs/1704.04861 and inspired from https://arxiv.org/pdf/1711.07128.pdf.
- Parameters
input_shape (tuple) – input shape tuple of the model
classes (int) – number of classes to classify images into
weight_quantization (int) –
sets all weights in the model to have a particular quantization bitwidth except for the weights in the first layer.
’0’ implements floating point 32-bit weights
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
activ_quantization (int) –
sets all activations in the model to have a. particular activation quantization bitwidth.
’0’ implements floating point 32-bit activations.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
input_weight_quantization (int) –
sets weight quantization in the first layer. Defaults to weight_quantization value.
’None’ implements the same bitwidth as the other weights.
’0’ implements floating point 32-bit weights.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
input_scaling (tuple, optional) – scale factor and offset to apply to inputs. Defaults to (255, 0). Note that following Akida convention, the scale factor is an integer used as a divider.
- Returns
a Keras model for DS-CNN/CIFAR-10
- Return type
keras.Model
KWS
- akida_models.ds_cnn_kws(input_shape=(49, 10, 1), classes=33, include_top=True, weight_quantization=0, activ_quantization=0, input_weight_quantization=None, input_scaling=(255, 0))[source]
Instantiates a MobileNet-like model for the “Keyword Spotting” example.
This model is based on the MobileNet architecture, mainly with fewer layers. The weights and activations are quantized such that it can be converted into an Akida model.
This architecture is originated from https://arxiv.org/pdf/1711.07128.pdf and was created for the “Keyword Spotting” (KWS) or “Speech Commands” dataset.
- Parameters
input_shape (tuple) – input shape tuple of the model
classes (int) – optional number of classes to classify words into, only be specified if include_top is True.
include_top (bool) – whether to include the fully-connected layer at the top of the model.
weight_quantization (int) –
sets all weights in the model to have a particular quantization bitwidth except for the weights in the first layer.
’0’ implements floating point 32-bit weights.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
activ_quantization (int) –
sets all activations in the model to have a particular activation quantization bitwidth.
’0’ implements floating point 32-bit activations.
’1’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
input_weight_quantization (int) –
sets weight quantization in the first layer. Defaults to weight_quantization value.
’None’ implements the same bitwidth as the other weights.
’0’ implements floating point 32-bit weights.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
input_scaling (tuple, optional) – scale factor and offset to apply to inputs. Defaults to (255, 0). Note that following Akida convention, the scale factor is an integer used as a divider.
- Returns
a Keras model for MobileNet/KWS
- Return type
keras.Model
- akida_models.ds_cnn_kws_pretrained()[source]
Helper method to retrieve a ds_cnn_kws model that was trained on KWS dataset.
- Returns
a Keras Model instance.
- Return type
keras.Model
Preprocessing
- akida_models.kws.preprocessing.prepare_model_settings(sample_rate, clip_duration_ms, window_size_ms, window_stride_ms, feature_bin_count)[source]
Calculates common settings needed for all models.
- Parameters
sample_rate – Number of audio samples per second.
clip_duration_ms – Length of each audio clip to be analyzed.
window_size_ms – Duration of frequency analysis window.
window_stride_ms – How far to move in time between frequency windows.
feature_bin_count – Number of frequency bins to use for analysis.
- Returns
Dictionary containing common settings.
- Raises
ValueError – If the preprocessing mode isn’t recognized.
- akida_models.kws.preprocessing.prepare_words_list(wanted_words)[source]
Prepends common tokens to the custom word list.
- Parameters
wanted_words – List of strings containing the custom words.
- Returns
List with the standard silence and unknown tokens added.
- akida_models.kws.preprocessing.which_set(filename, validation_percentage, testing_percentage)[source]
Determines which data partition the file should belong to.
We want to keep files in the same training, validation, or testing sets even if new ones are added over time. This makes it less likely that testing samples will accidentally be reused in training when long runs are restarted for example. To keep this stability, a hash of the filename is taken and used to determine which set it should belong to. This determination only depends on the name and the set proportions, so it won’t change as other files are added.
It’s also useful to associate particular files as related (for example words spoken by the same person), so anything after ‘_nohash_’ in a filename is ignored for set determination. This ensures that ‘bobby_nohash_0.wav’ and ‘bobby_nohash_1.wav’ are always in the same set, for example.
- Parameters
filename – File path of the data sample.
validation_percentage – How much of the data set to use for validation.
testing_percentage – How much of the data set to use for testing.
- Returns
String, one of ‘training’, ‘validation’, or ‘testing’.
- class akida_models.kws.preprocessing.AudioProcessor(sample_rate, clip_duration_ms, window_size_ms, window_stride_ms, feature_bin_count, data_url=None, data_dir=None, silence_percentage=0, unknown_percentage=0, wanted_words=None, validation_percentage=0, testing_percentage=0)[source]
Handles loading, partitioning, and preparing audio training data.
Methods:
get_augmented_data_for_wav(wav_filename, ...)Applies the feature transformation process to a wav audio file,
get_data(how_many, offset, ...)Gather samples from the data set, applying transformations as needed.
get_features_for_wav(wav_filename)Applies the feature transformation process to the input_wav.
maybe_download_and_extract_dataset(data_url, ...)Download and extract data set tar file.
Searches a folder for background noise audio, and loads it into
prepare_data_index(silence_percentage, ...)Prepares a list of the samples organized by set and label.
Builds a TensorFlow graph to apply the input distortions.
- get_augmented_data_for_wav(wav_filename, background_frequency, background_volume_range, time_shift, num_augmented_samples=1)[source]
- Applies the feature transformation process to a wav audio file,
adding data augmentation (background noise and time shifting).
- Parameters
wav_filename (str) – The path to the input audio file.
background_frequency – How many clips will have background noise, 0.0 to 1.0.
background_volume_range – How loud the background noise will be.
time_shift – How much to randomly shift the clips by in time.
num_augmented_samples – How many samples will be generated using data augmentation.
- Returns
- Numpy data array containing the generated features for every augmented
sample.
- get_data(how_many, offset, background_frequency, background_volume_range, time_shift, mode)[source]
Gather samples from the data set, applying transformations as needed.
When the mode is ‘training’, a random selection of samples will be returned, otherwise the first N clips in the partition will be used. This ensures that validation always uses the same samples, reducing noise in the metrics.
- Parameters
how_many – Desired number of samples to return. -1 means the entire contents of this partition.
offset – Where to start when fetching deterministically.
background_frequency – How many clips will have background noise, 0.0 to 1.0.
background_volume_range – How loud the background noise will be.
time_shift – How much to randomly shift the clips by in time.
mode – Which partition to use, must be ‘training’, ‘validation’, or ‘testing’.
- Returns
List of sample data for the transformed samples, and list of label indexes
- Raises
ValueError – If background samples are too short.
- get_features_for_wav(wav_filename)[source]
Applies the feature transformation process to the input_wav.
Runs the feature generation process (generally producing a spectrogram from the input samples) on the WAV file. This can be useful for testing and verifying implementations being run on other platforms.
- Parameters
wav_filename – The path to the input audio file.
- Returns
Numpy data array containing the generated features.
- static maybe_download_and_extract_dataset(data_url, dest_directory)[source]
Download and extract data set tar file.
If the data set we’re using doesn’t already exist, this function downloads it from the TensorFlow.org website and unpacks it into a directory. If the data_url is none, don’t download anything and expect the data directory to contain the correct files already.
- Parameters
data_url – Web location of the tar file containing the data set.
dest_directory – File path to extract data to.
- prepare_background_data()[source]
- Searches a folder for background noise audio, and loads it into
memory.
It’s expected that the background audio samples will be in a subdirectory named ‘_background_noise_’ inside the ‘data_dir’ folder, as .wavs that match the sample rate of the training data, but can be much longer in duration.
If the ‘_background_noise_’ folder doesn’t exist at all, this isn’t an error, it’s just taken to mean that no background noise augmentation should be used. If the folder does exist, but it’s empty, that’s treated as an error.
- Returns
List of raw PCM-encoded audio samples of background noise.
- Raises
Exception – If files aren’t found in the folder.
- prepare_data_index(silence_percentage, unknown_percentage, wanted_words, validation_percentage, testing_percentage)[source]
Prepares a list of the samples organized by set and label.
The training loop needs a list of all the available data, organized by which partition it should belong to, and with ground truth labels attached. This function analyzes the folders below the data_dir, figures out the right labels for each file based on the name of the subdirectory it belongs to, and uses a stable hash to assign it to a data set partition.
- Parameters
silence_percentage – How much of the resulting data should be background.
unknown_percentage – How much should be audio outside the wanted classes.
wanted_words – Labels of the classes we want to be able to recognize.
validation_percentage – How much of the data set to use for validation.
testing_percentage – How much of the data set to use for testing.
- Returns
Dictionary containing a list of file information for each set partition, and a lookup map for each class to determine its numeric index.
- Raises
Exception – If expected files are not found.
VGG
CIFAR-10
- akida_models.vgg_cifar10(input_shape=(32, 32, 3), classes=10, weight_quantization=0, activ_quantization=0, input_weight_quantization=None, input_scaling=(255, 0))[source]
Instantiates a VGG-like model for the “CIFAR-10” example. This model is based on the VGG architecture, mainly with fewer layers. The weights and activations are quantized such that it can be converted into an Akida model.
- Parameters
input_shape (tuple) – input shape tuple of the model
classes (int) – number of classes to classify images into
weight_quantization (int) –
sets all weights in the model to have a particular quantization bitwidth except for the weights in the first layer.
’0’ implements floating point 32-bit weights
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
activ_quantization (int) –
sets all activations in the model to have a. particular activation quantization bitwidth.
’0’ implements floating point 32-bit activations.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
input_weight_quantization (int) –
sets weight quantization in the first layer. Defaults to weight_quantization value.
’None’ implements the same bitwidth as the other weights.
’0’ implements floating point 32-bit weights.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
input_scaling (tuple, optional) – scale factor and offset to apply to inputs. Defaults to (255, 0). Note that following Akida convention, the scale factor is an integer used as a divider.
- Returns
a Keras model for VGG/CIFAR-10
- Return type
keras.Model
ImageNet
- akida_models.vgg_imagenet(input_shape=(224, 224, 3), classes=1000, include_top=True, pooling=None, weight_quantization=0, activ_quantization=0, input_weight_quantization=None, input_scaling=(128, - 1))[source]
Instantiates a VGG11 architecture with reduced number of filters in convolutional layers (i.e. a quarter of the filters of the original implementation of https://arxiv.org/pdf/1409.1556.pdf).
- Parameters
input_shape (tuple, optional) – input shape tuple. Defaults to (224, 224, 3).
classes (int, optional) – optional number of classes to classify images into. Defaults to 1000.
include_top (bool, optional) – whether to include the classification layers at the top of the model. Defaults to True.
pooling (str, optional) –
Optional pooling mode for feature extraction when include_top is False. Defaults to None.
None means that the output of the model will be the 4D tensor output of the last convolutional block.
avg means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor.
weight_quantization (int, optional) –
sets all weights in the model to have a particular quantization bitwidth except for the weights in the first layer. Defaults to 0.
’0’ implements floating point 32-bit weights.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
activ_quantization (int, optional) –
sets all activations in the model to have a particular activation quantization bitwidth. Defaults to 0.
’0’ implements floating point 32-bit activations.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
input_weight_quantization (int, optional) –
sets weight quantization in the first layer. Defaults to weight_quantization value.
’0’ implements floating point 32-bit weights.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
input_scaling (tuple, optional) – scale factor and offset to apply to inputs. Defaults to (128, -1). Note that following Akida convention, the scale factor is an integer used as a divider.
- Returns
a Keras model for VGG/ImageNet
- Return type
keras.Model
- akida_models.vgg_imagenet_pretrained()[source]
Helper method to retrieve a vgg_imagenet model that was trained on ImageNet dataset.
- Returns
a Keras Model instance.
- Return type
keras.Model
- akida_models.vgg_melanoma_pretrained()[source]
Helper method to retrieve a vgg_imagenet model that was trained on SIIM-ISIC Melanoma Classification dataset.
- Returns
a Keras Model instance.
- Return type
keras.Model
- akida_models.vgg_odir5k_pretrained()[source]
Helper method to retrieve a vgg_imagenet model that was trained on ODIR-5K dataset.
The model focuses on the following classes that are a part of the original dataset: normal, cataract, AMD (age related macular degeneration) and pathological myopia.
- Returns
a Keras Model instance.
- Return type
keras.Model
UTK Face
- akida_models.vgg_utk_face(input_shape=(32, 32, 3), weight_quantization=0, activ_quantization=0, input_weight_quantization=None, input_scaling=(127, - 1))[source]
Instantiates a VGG-like model for the regression example on age estimation using UTKFace dataset.
- Parameters
input_shape (tuple) – input shape tuple of the model
weight_quantization (int) –
sets all weights in the model to have a particular quantization bitwidth except for the weights in the first layer.
’0’ implements floating point 32-bit weights.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
activ_quantization (int) –
sets all activations in the model to have a particular activation quantization bitwidth.
’0’ implements floating point 32-bit activations.
’1’ through ‘8’ implements n-bit weights where n is from 1-8 bits.
input_weight_quantization (int) –
sets weight quantization in the first layer. Defaults to weight_quantization value.
’None’ implements the same bitwidth as the other weights.
’0’ implements floating point 32-bit weights.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
input_scaling (tuple, optional) – scale factor and offset to apply to inputs. Defaults to (127, -1). Note that following Akida convention, the scale factor is an integer used as a divider.
- Returns
a Keras model for VGG/UTKFace
- Return type
keras.Model
- akida_models.vgg_utk_face_pretrained()[source]
Helper method to retrieve a vgg_utk_face model that was trained on UTK Face dataset.
- Returns
a Keras Model instance.
- Return type
keras.Model
Preprocessing
YOLO
- akida_models.yolo_base(input_shape=(224, 224, 3), classes=1, nb_box=5, alpha=1.0, dropout=0.001, use_stride2=False, weight_quantization=0, activ_quantization=0, input_weight_quantization=None, input_scaling=(127.5, - 1))[source]
Instantiates the YOLOv2 architecture.
- Parameters
input_shape (tuple) – input shape tuple
classes (int) – number of classes to classify images into
nb_box (int) – number of anchors boxes to use
alpha (float) – controls the width of the model
dropout (float) – dropout rate
use_stride2 (bool) – optional, replace max pooling operations by stride 2 convolutions in layers separable 2, 4, 6 and 12.
weight_quantization (int) –
sets all weights in the model to have a particular quantization bitwidth except for the weights in the first layer.
’0’ implements floating point 32-bit weights.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
activ_quantization –
sets all activations in the model to have a particular activation quantization bitwidth.
’0’ implements floating point 32-bit activations.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
input_weight_quantization –
sets weight quantization in the first layer. Defaults to weight_quantization value.
’0’ implements floating point 32-bit weights.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
input_scaling (tuple, optional) – scale factor and offset to apply to inputs. Defaults to (127.5, -1). Note that following Akida convention, the scale factor is a number used as a divider.
- Returns
a Keras Model instance.
- Return type
keras.Model
- akida_models.yolo_widerface_pretrained()[source]
Helper method to retrieve a yolo_base model that was trained on WiderFace dataset and the anchors that are needed to interpet the model output.
- Returns
a Keras Model instance and a list of anchors.
- Return type
keras.Model, list
- akida_models.yolo_voc_pretrained()[source]
Helper method to retrieve a yolo_base model that was trained on PASCAL VOC2012 dataset for ‘person’ and ‘car’ classes only, and the anchors that are needed to interpet the model output.
- Returns
a Keras Model instance and a list of anchors.
- Return type
keras.Model, list
YOLO Toolkit
Processing
- akida_models.detection.processing.load_image(image_path)[source]
Loads an image from a path.
- Parameters
image_path (string) – full path of the image to load
- Returns
a Tensorflow image Tensor
- akida_models.detection.processing.preprocess_image(image_buffer, output_size)[source]
Preprocess an image for YOLO inference.
- Parameters
image_buffer (tf.Tensor) – image to preprocess
output_size (tuple) – shape of the image after preprocessing
- Returns
A resized and normalized image as a Numpy array.
- akida_models.detection.processing.decode_output(output, anchors, nb_classes, obj_threshold=0.5, nms_threshold=0.5)[source]
Decodes a YOLO model output.
- Parameters
output (tf.Tensor) – model output to decode
anchors (list) – list of anchors boxes
nb_classes (int) – number of classes
obj_threshold (float) – confidence threshold for a box
nms_threshold (float) – non-maximal supression threshold
- Returns
List of BoundingBox objects
- akida_models.detection.processing.parse_voc_annotations(gt_folder, image_folder, file_path, labels)[source]
Loads PASCAL-VOC data.
Data is loaded using the groundtruth informations and stored in a dictionary.
- Parameters
gt_folder (str) – path to the folder containing ground truth files
image_folder (str) – path to the folder containing the images
file_path (str) – file containing the list of files to parse
labels (list) – list of labels of interest
- Returns
a dictionnary containing all data present in the ground truth file
- Return type
dict
- akida_models.detection.processing.parse_widerface_annotations(gt_file, image_folder)[source]
Loads WiderFace data.
Data is loaded using the groundtruth informations and stored in a dictionary.
- Parameters
gt_file (str) – path to the ground truth file
image_folder (str) – path to the directory containing the images
- Returns
a dictionnary containing all data present in the ground truth file
- Return type
dict
- class akida_models.detection.processing.BoundingBox(x1, y1, x2, y2, score=- 1, classes=None)[source]
Utility class to represent a bounding box.
The box is defined by its top left corner (x1, y1), bottom right corner (x2, y2), label, score and classes.
Methods:
Returns the label for this bounding box.
Returns the score for this bounding box.
iou(other)Computes intersection over union ratio between this bounding box and another one.
- get_label()[source]
Returns the label for this bounding box.
- Returns
Index of the label as an integer.
- iou(other)[source]
Computes intersection over union ratio between this bounding box and another one.
- Parameters
other (BoundingBox) – the other bounding box for IOU computation
- Returns
IOU value as a float
Performances
- class akida_models.detection.map_evaluation.MapEvaluation(model, val_data, labels, anchors, period=1, obj_threshold=0.5, nms_threshold=0.5, max_box_per_image=10, is_keras_model=True)[source]
Evaluate a given dataset using a given model. Code originally from https://github.com/fizyr/keras-retinanet.
- Parameters
model (keras.Model) – model to evaluate.
val_data (dict) – dictionary containing validation data as obtained using preprocess_widerface.py module
labels (list) – list of labels as strings
anchors (list) – list of anchors boxes
period (int, optional) – periodicity the precision is printed, defaults to once per epoch.
obj_threshold (float, optional) – confidence threshold for a box
nms_threshold (float, optional) – non-maximal supression threshold
max_box_per_image (int, optional) – maximum number of detections per image
is_keras_model (bool, optional) – indicated if the model is a Keras model (True) or an Akida model (False)
- Returns
A dict mapping class names to mAP scores.
Methods:
Evaluates current mAP score on the model.
on_epoch_end(epoch[, logs])Keras callback called at the end of an epoch.
- evaluate_map()[source]
Evaluates current mAP score on the model.
- Returns
global mAP score and dictionnary of label and mAP for each class.
- Return type
tuple
- on_epoch_end(epoch, logs=None)[source]
Keras callback called at the end of an epoch.
- Parameters
epoch (int) – index of epoch.
logs (dict, optional) – metric results for this training epoch, and for the validation epoch if validation is performed. Validation result keys are prefixed with val. For training epoch, the values of the Model’s metrics are returned. Example : {‘loss’: 0.2, ‘acc’: 0.7}. Defaults to None.
Anchors
- akida_models.detection.generate_anchors.generate_anchors(annotations_data, num_anchors=5, grid_size=(7, 7))[source]
Creates anchors by clustering dimensions of the ground truth boxes from the training dataset.
- Parameters
annotations_data (dict) – dictionnary of preprocessed VOC data
num_anchors (int, optional) – number of anchors
grid_size (tuple, optional) – size of the YOLO grid
- Returns
the computed anchors
- Return type
list
ConvTiny
CWRU
PointNet++
ModelNet40
- akida_models.pointnet_plus_modelnet40(selected_points=128, features=3, knn_points=64, classes=40, alpha=1.0, weight_quantization=0, activ_quantization=0)[source]
Instantiates a PointNet++ model for the ModelNet40 classification.
This example implements the point cloud deep learning paper PointNet (Qi et al., 2017). For a detailed introduction on PointNet see this blog post.
PointNet++ is conceived as a repeated series of operations: sampling and grouping of points, followed by the trainable convnet itself. Those operations are then repeated at increased scale. Each of the selected points is taken as the centroid of the K-nearest neighbours. This defines a localized group.
- Parameters
selected_points (int, optional) – the number of points to process per sample. Default is 128.
features (int, optional) – the number of features. Expected values are 1 or 3. Default is 3.
knn_points (int, optional) – the number of points to include in each localised group. Must be a power of 2, and ideally an integer square (so 64, or 16 for a deliberately small network, or 256 for large). Default is 64.
classes (int, optional) – the number of classes for the classifier. Default is 40.
alpha (float, optional) – network filters multiplier. Default is 1.0.
weight_quantization (int, optional) –
sets all weights in the model to have a particular quantization bitwidth except for the weights in the first layer. Defaults to 0.
’0’ implements floating point 32-bit weights.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
activ_quantization (int, optional) –
sets all activations in the model to have a particular activation quantization bitwidth. Defaults to 0.
’0’ implements floating point 32-bit activations.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
- Returns
a quantized Keras model for PointNet++/ModelNet40.
- Return type
keras.Model
- akida_models.pointnet_plus_modelnet40_pretrained()[source]
Helper method to retrieve a pointnet_plus model that was trained on ModelNet40 dataset.
- Returns
a Keras Model instance.
- Return type
keras.Model
Processing
- akida_models.modelnet40.preprocessing.get_modelnet_from_file(num_points, filename='ModelNet40.zip')[source]
Load the ModelNet data from file.
First parse through the ModelNet data folders. Each mesh is loaded and sampled into a point cloud before being added to a standard python list and converted to a numpy array. We also store the current enumerate index value as the object label and use a dictionary to recall this later.
- Parameters
num_points (int) – number of points with which mesh is sample.
filename (str) – the dataset file to load if the npz file was not generated yet. Defaults to “ModelNet40.zip”.
- Returns
- train set, train labels,
test set, test labels as numpy arrays and dict containing class folder name.
- Return type
np.array, np.array, np.array, np.array, dict
- akida_models.modelnet40.preprocessing.get_modelnet(train_points, train_labels, test_points, test_labels, batch_size, selected_points=128, knn_points=64)[source]
Obtains the ModelNet dataset.
- Parameters
train_points (numpy.array) – train set.
train_labels (numpy.array) – train labels.
test_points (numpy.array) – test set.
test_labels (numpy.array) – test labels.
batch_size (int) – size of the batch.
selected_points (int) – num points to process per sample. Default is 512.
knn_points (int) – number of points to include in each localised group. Must be a power of 2, and ideally an integer square (so 64, or 16 for a deliberately small network, or 256 for large).
- Returns
- train and test point with data
augmentation.
- Return type
tf.data.Dataset, tf.data.Dataset
GXNOR
MNIST
- akida_models.gxnor_mnist(weight_quantization=0, activ_quantization=0, input_weight_quantization=None)[source]
Instantiates a Keras GXNOR model with an additional dense layer to make better classification.
The paper describing the original model can be found here.
- Parameters
weight_quantization (int, optional) –
sets all weights in the model to have a particular quantization bitwidth except for the weights in the first layer. Defaults to 0.
’0’ implements floating point 32-bit weights.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
activ_quantization (int, optional) –
sets all activations in the model to have a particular activation quantization bitwidth. Defaults to 0.
’0’ implements floating point 32-bit activations.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
input_weight_quantization (int, optional) –
sets weight quantization in the first layer. Defaults to weight_quantization value.
’0’ implements floating point 32-bit weights.
’2’ through ‘8’ implements n-bit weights where n is from 2-8 bits.
- Returns
a Keras model for GXNOR/MNIST
- Return type
keras.Model
- akida_models.gxnor_mnist_pretrained()[source]
Helper method to retrieve a gxnor_mnist model that was trained on MNIST dataset.
This model was trained with the distillation knowledge method, using the EfficientNet model from this repository and the Distiller class from the knowledge distillation toolkit (akida_models.distiller).
The float training was done for 30 epochs with a learning rate of 1e-4 After that we gradually quantize the model from: 8-4-4 –> 4-4-4 –> 4-4-2 –> 2-2-2 –> 2-2-1 tuning the model at each step with the same distillation training method for 5 epochs and a learning rate of 5e-5.
- Returns
a Keras Model instance.
- Return type
keras.Model