Source code for akida_models.imagenet.model_akidanet_edge

#!/usr/bin/env python
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# Copyright 2021 Brainchip Holdings Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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"""
This model is an adaptation of the `akidanet_imagenet` model for edge
applications. It is based on AkidaNet with top layers replaced by a quantized
spike extractor and a classification layer.
"""

__all__ = ["akidanet_edge_imagenet", "akidanet_edge_imagenet_pretrained",
           "akidanet_faceidentification_edge_pretrained"]

from keras import Model
from keras.layers import Reshape

from cnn2snn import quantize_layer

from ..layer_blocks import separable_conv_block, dense_block
from ..utils import fetch_file, get_params_by_version
from ..model_io import load_model, get_model_path


[docs]def akidanet_edge_imagenet(base_model, classes, base_layer="classifier"): """Instantiates an AkidaNet-edge architecture. Args: base_model (str/keras.Model): an akidanet_imagenet base model. classes (int): the number of classes for the edge classifier. base_layer (str, optional): the last base layer. Defaults to "classifier". Returns: keras.Model: a Keras Model instance. """ if isinstance(base_model, str): base_model = load_model(base_model) try: # Identify the base model classifier base_classifier = base_model.get_layer(base_layer) # Determine if the base model is quantized with CNN2SNN is_base_quantized_cnn2snn = str(base_classifier.__module__).startswith("cnn2snn") if is_base_quantized_cnn2snn: # Remember the classifier weight bitwidth wq = base_classifier.quantizer.bitwidth # Check the base model is not quantized with QuantizeML is_base_quantized_quantizeml = str(base_classifier.__module__).startswith("quantizeml") if is_base_quantized_quantizeml: raise ValueError("A QuantizeML quantized backbone is not supported, " "use a float model and quantize with QuantizeML.") except Exception as e: raise ValueError("The base model is not an expected AkidaNet/Imagenet model.") from e # Model version management fused, _, _ = get_params_by_version() # Recreate a model with all layers up to the classifier x = base_classifier.input x = Reshape((1, 1, x.shape[-1]))(x) # Add the new end layer with kernel_size (3, 3) instead of (1, 1) for # hardware compatibility reasons # Because it will be quantized to 1 bit, the ReLU max_value should be set to 1 x = separable_conv_block(x, filters=2048, kernel_size=(3, 3), padding='same', use_bias=False, add_batchnorm=True, name='spike_generator', fused=fused, relu_activation='ReLU1') # Then add the Akida edge learning layer that will be dropped after x = dense_block(x, classes, name="classification_layer", relu_activation=False, add_batchnorm=False, use_bias=False) x = Reshape((classes,), name="reshape_3")(x) # Create model model = Model(inputs=base_model.input, outputs=x, name=f"{base_model.name}_edge") # When targeting Akida V1 and with a quantized base model, edge layers can be quantized with # CNN2SNN and tuned which is not the case with a quantizeml quantization. # (float head training is required). if is_base_quantized_cnn2snn: # Quantize edge layers model = quantize_layer(model, 'spike_generator', wq) model = quantize_layer(model, 'spike_generator/relu', 1) # NOTE: quantization set to 2 here, to be as close as # possible to the Akida native layer that will replace this one, # with binary weights. model = quantize_layer(model, 'classification_layer', 2) return model
[docs]def akidanet_edge_imagenet_pretrained(quantized=True): """ Helper method to retrieve a `akidanet_edge_imagenet` model that was trained on ImageNet dataset. Args: quantized (bool): a boolean indicating whether the model should be loaded quantized or not Returns: keras.Model: a Keras Model instance. """ if quantized: model_name_v1 = 'akidanet_imagenet_224_alpha_50_edge_iq8_wq4_aq4.h5' file_hash_v1 = '71ffc3acb09e5682e479505f6b288bd2736311ce46d3974bdf7b2c02916e52a8' model_name_v2 = 'akidanet_imagenet_224_alpha_0.5_edge_i8_w8_a8.h5' file_hash_v2 = '13e55373858a1136a1580b7a9ee16855ebe2fee4b76554bf03d64b0aaee3e3eb' else: model_name_v1 = 'akidanet_imagenet_224_alpha_50_edge.h5' file_hash_v1 = '586ab1fb1baf9e171c54bbc6f4e7ef9fd6910bb5b169ef5b9dd5a29122683d31' model_name_v2 = 'akidanet_imagenet_224_alpha_0.5_edge.h5' file_hash_v2 = '9763ff8d95d2be88766339171c5ffb6f407f706802bb5939d39c87e5218521ed' model_path, model_name, file_hash = get_model_path("akidanet_edge", model_name_v1, file_hash_v1, model_name_v2, file_hash_v2) model_path = fetch_file(model_path, fname=model_name, file_hash=file_hash, cache_subdir='models') return load_model(model_path)
[docs]def akidanet_faceidentification_edge_pretrained(quantized=True): """ Helper method to retrieve an `akidanet_edge_imagenet` model that was trained on CASIA Webface dataset and that performs face identification. Args: quantized (bool, optional): a boolean indicating whether the model should be loaded quantized or not. Defaults to True. Returns: keras.Model: a Keras Model instance. """ if quantized: model_name_v1 = 'akidanet_faceidentification_edge_iq8_wq4_aq4.h5' file_hash_v1 = '61838682cc88cec6dc9a347f1a301bfa9e94fbcbc7a52a273789259de07d3104' model_name_v2 = 'akidanet_faceidentification_edge_i8_w4_a4.h5' file_hash_v2 = '2b0fed9bb88674ebba2925cdd76c26232c418d43b7e05b8ba8121861fcc49137' else: model_name_v1 = 'akidanet_faceidentification_edge.h5' file_hash_v1 = 'fc0e6f06078dcafb503ec8944a871c781297bc120ef6335b17a17f6f8b316bbf' model_name_v2 = 'akidanet_faceidentification_edge.h5' file_hash_v2 = 'bc5054c3370c8194d27380df0f99c5ac285ce160d04c7776e665436668fe416f' model_path, model_name, file_hash = get_model_path("akidanet_edge", model_name_v1, file_hash_v1, model_name_v2, file_hash_v2) model_path = fetch_file(model_path, fname=model_name, file_hash=file_hash, cache_subdir='models') return load_model(model_path)