Source code for akida_models.centernet.model_centernet

#!/usr/bin/env python
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# Copyright 2023 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
# Unless required by applicable law or agreed to in writing, software
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CenterNet model definition for detection
import numpy as np

from keras import Model, initializers
from keras.layers import Conv2D

from cnn2snn import set_akida_version, AkidaVersion

from .. import akidanet_imagenet
from ..layer_blocks import (separable_conv_block, conv_block, conv_transpose_block,
from ..utils import fetch_file
from ..model_io import load_model, get_model_path

[docs]def centernet_base(input_shape=(224, 224, 3), classes=2, alpha=0.5, input_scaling=(127, -1)): """ A Keras Model implementing the CenterNet architecture, on top of an AkidaNet backbone Args: input_shape (tuple, optional): input shape. Defaults to (224, 224, 3). classes (int, optional): number of output classes. Defaults to 2. alpha (float, optional): controls the width of the model. Defaults to 0.5. input_scaling (tuple, optional): input scaling. Defaults to (127, -1). Returns: keras.Model: a Keras Model instance. """ # This model is only available for akida 2.0 with set_akida_version(AkidaVersion.v2): # Create an AkidaNet network without top layers base_model = akidanet_imagenet( input_shape=input_shape, alpha=alpha, include_top=False, input_scaling=input_scaling) x = base_model.layers[-1].output # Build the neck with up convolutions num_deconv_filters = [256, 128, 64] for i, (n_filt) in enumerate(num_deconv_filters): if n_filt <= 128: curr_block = conv_block block_type = "conv" kwargs = {} transpose_block = conv_transpose_block else: curr_block = separable_conv_block block_type = "sepconv" kwargs = {'fused': False} transpose_block = sepconv_transpose_block x = curr_block(x, filters=n_filt, name=f'neck_{block_type}_{i}', kernel_size=(3, 3), padding='same', use_bias=False, relu_activation='ReLU7.5', add_batchnorm=True, **kwargs) x = transpose_block(x, filters=n_filt, name=f"neck_transpose_{block_type}_{i}", kernel_size=(4, 4), padding="same", strides=(2, 2), use_bias=False, relu_activation='ReLU7.5', add_batchnorm=True) # Build the head which is composed of 2 consecutive convs bias_initializer = initializers.Constant(float(-np.log((1 - 0.1) / 0.1))) init_kernel = initializers.RandomNormal(stddev=0.001, seed=6) # In the legacy model there is 3 branches of 64 filters each one. # This could be merged in one branch x = conv_block(x, 3 * 64, (3, 3), add_batchnorm=True, use_bias=False, relu_activation='ReLU7.5', padding="same", name="head_conv_1", kernel_initializer=init_kernel) # The output is built by #classes and box coordinates in xywh x = Conv2D(classes + 4, (1, 1), padding="same", use_bias=True, name="head_conv_2", bias_initializer=bias_initializer, kernel_initializer=init_kernel)(x) # Build the model return Model(inputs=base_model.input, outputs=x, name='centernet_base')
[docs]def centernet_voc_pretrained(): """ Helper method to retrieve an `centernet_base` model that was trained on VOC detection dataset. Returns: keras.Model: a Keras Model instance. """ model_name_v2 = 'centernet_akidanet_voc_i8_w8_a8.h5' file_hash_v2 = '5705206ecb5b006afd33838ada04d69d7dea0beda34727dee2a4bc0a80b9fd8b' model_path, model_name, file_hash = get_model_path("centernet", 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)