Source code for akida_models.imagenet.model_akidanet18

#!/usr/bin/env python
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# Copyright 2023 Brainchip Holdings Ltd.
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AkidaNet18 model definition for ImageNet classification.

AkidaNet18 architecture is inspired both from AkidaNet and ResNet18: same depth and dimensions than
ResNet18 but without skip connections and using SeparableConvolution layers.


__all__ = ["akidanet18_imagenet", "akidanet18_imagenet_pretrained"]

from keras import Model, regularizers
from keras.layers import Input, Rescaling

from .imagenet_utils import obtain_input_shape
from ..layer_blocks import conv_block, separable_conv_block, dense_block
from ..utils import fetch_file
from ..model_io import load_model, get_model_path

[docs]def akidanet18_imagenet(input_shape=None, include_top=True, pooling=None, classes=1000, depths=(4, 4, 4, 4), dimensions=(64, 128, 256, 512), input_scaling=(128, -1)): """Instantiates the AkidaNet18 architecture. Note: input preprocessing is included as part of the model (as a Rescaling layer). This model expects inputs to be float tensors of pixels with values in the [0, 255] range. Args: input_shape (tuple, optional): shape tuple. Defaults to None. include_top (bool, optional): whether to include the fully-connected layer 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. classes (int, optional): optional number of classes to classify images into, only to be specified if `include_top` is `True`. Defaults to 1000. depth (tuple, optional): number of layers in each stages of the model. The length of the tuple defines the number of stages. Defaults to (4, 4, 4, 4). dimensions (tuple, optional): number of filters in each stage on the model. The length of the tuple must be equal to the length of the `depth` tuple. Defaults to (64, 128, 256, 512). 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 divisor. Returns: keras.Model: a Keras model for AkidaNet/ImageNet. Raises: ValueError: in case of invalid input shape or mismatching `depth` and `dimensions`. """ # Sanity checks stages = len(depths) if len(dimensions) != stages: raise ValueError(f"'depth' and 'dimensions' must be of the same length, received: {depths} " f"and {dimensions}.") # Define weight regularization, will apply to the convolutional layers and # to all pointwise weights of separable convolutional layers. weight_regularizer = regularizers.l2(4e-5) # Determine proper input shape and default size. if input_shape is None: default_size = 224 else: rows = input_shape[0] cols = input_shape[1] if rows == cols and rows in [128, 160, 192, 224]: default_size = rows else: default_size = 224 input_shape = obtain_input_shape(input_shape, default_size=default_size, min_size=32, include_top=include_top) img_input = Input(shape=input_shape, name="input") if input_scaling is None: x = img_input else: scale, offset = input_scaling x = Rescaling(1. / scale, offset, name="rescaling")(img_input) # ConvNext stem layer: 4x4 kernel with stride 4 x = conv_block(x, filters=int(dimensions[0]), name='convnext_stem', kernel_size=(4, 4), padding='same', use_bias=False, strides=4, add_batchnorm=True, relu_activation='ReLU7.5', kernel_regularizer=weight_regularizer) # Define the stages for stage in range(stages): # Like for AkidaNet, early layers (first 2 stages) are defined as standard Convolutional and # next layers are SeparableConvolutional layers if stage < 2: current_block = conv_block kwarg = {"kernel_regularizer": weight_regularizer} else: current_block = separable_conv_block kwarg = {"pointwise_regularizer": weight_regularizer, "fused": False} strides = 2 if stage > 0 else 1 for i in range(depths[stage]): # First layer in stage comes with strides 2 except in first stage where strides is # handled by the previous stem strides = 2 if i == 0 and stage > 0 else 1 # Handle final pooling in last layer of last stage if stage == stages - 1 and i == depths[stage] - 1: pool = 'global_avg' if include_top or pooling == 'avg' else None else: pool = None x = current_block(x, filters=int(dimensions[stage]), name=f'stage_{stage}/conv_{i}', kernel_size=(3, 3), strides=strides, padding='same', use_bias=False, pooling=pool, add_batchnorm=True, relu_activation='ReLU7.5', post_relu_gap=True, **kwarg) # Classification layer if include_top: x = dense_block(x, classes, add_batchnorm=False, relu_activation=False, kernel_initializer="he_normal", name='classifier', kernel_regularizer=weight_regularizer) # Create model return Model(img_input, x, name='akidanet18_%s_%s' % (input_shape[0], classes))
[docs]def akidanet18_imagenet_pretrained(quantized=True): """ Helper method to retrieve an `akidanet18_imagenet` model that was trained on ImageNet dataset. 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. """ # Note: cannot be converted to v1 so we should ultimately removed v1 support and only keep v2 if quantized: model_name_v2 = 'akidanet18_imagenet_224_i8_w8_a8.h5' file_hash_v2 = '736d07851c9a3967f61ba2efcdde1f224fc0ca8b7f6fc1110417bd6de14979bf' else: model_name_v2 = 'akidanet18_imagenet_224.h5' file_hash_v2 = 'e52cf2a20b786a488b75f371b8b89f0c95a383dafc1331eea51b2356552d9704' model_path, model_name, file_hash = get_model_path("akidanet18", model_name_v2=model_name_v2, file_hash_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)