#!/usr/bin/env python
# ******************************************************************************
# 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
#
# 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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"""
CenterNet model definition for detection
"""
__all__ = ["centernet_base", "centernet_voc_pretrained"]
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,
sepconv_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(quantized=True):
"""
Helper method to retrieve an `centernet_base` model that was trained on VOC detection 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.
"""
if quantized:
model_name_v2 = 'centernet_akidanet_voc_i8_w8_a8.h5'
file_hash_v2 = 'e5d37942e28e8dac80a36238dba1ee39c8ce59c577bf79a569a8e48b786aa2af'
else:
model_name_v2 = 'centernet_akidanet_voc.h5'
file_hash_v2 = 'e8dfbb5af77c24c386eda4250a5f765014f92ac41091e6d23dbe7713b55e48e9'
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)