#!/usr/bin/env python
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# Copyright 2020 Brainchip Holdings Ltd.
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"""
YOLO model definition for detection.
"""
import pickle
import numpy as np
from keras import Model
from keras.utils.data_utils import get_file
from cnn2snn import load_quantized_model, quantize
from ..layer_blocks import separable_conv_block
from ..imagenet.model_mobilenet import mobilenet_imagenet
[docs]def yolo_base(input_shape=(224, 224, 3),
classes=1,
nb_box=5,
alpha=1.0,
dropout=1e-3,
use_stride2=False,
weight_quantization=0,
activ_quantization=0,
input_weight_quantization=None,
input_scaling=(127.5, -1)):
""" Instantiates the YOLOv2 architecture.
Args:
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:
keras.Model: a Keras Model instance.
"""
# Overrides input weight quantization if None
if input_weight_quantization is None:
input_weight_quantization = weight_quantization
# Create a MobileNet network without top layers
base_model = mobilenet_imagenet(
input_shape=input_shape,
alpha=alpha,
dropout=dropout,
include_top=False,
use_stride2=use_stride2,
weight_quantization=weight_quantization,
activ_quantization=activ_quantization,
input_weight_quantization=input_weight_quantization,
input_scaling=input_scaling)
# Add YOLO top layers to the base model
input_shape = base_model.input_shape
x = base_model.layers[-1].output
x = separable_conv_block(x,
filters=1024,
name='1conv',
kernel_size=(3, 3),
padding='same',
use_bias=False,
add_activation=True,
add_batchnorm=True)
x = separable_conv_block(x,
filters=1024,
name='2conv',
kernel_size=(3, 3),
padding='same',
use_bias=False,
add_activation=True,
add_batchnorm=True)
x = separable_conv_block(x,
filters=1024,
name='3conv',
kernel_size=(3, 3),
padding='same',
use_bias=False,
add_activation=True,
add_batchnorm=True)
x = separable_conv_block(x,
filters=(nb_box * (4 + 1 + classes)),
name='detection_layer',
kernel_size=(3, 3),
padding='same',
use_bias=True,
add_activation=False,
add_batchnorm=False)
model = Model(inputs=base_model.input, outputs=x, name='yolo_base')
# Initialize detection layer weights
layer = model.get_layer("detection_layer")
detection_weights = layer.get_weights()
mu, sigma = 0, 0.1
grid_size = model.output_shape[1:3]
grid_area = grid_size[0] * grid_size[1]
dw_kernel = np.random.normal(mu, sigma,
size=detection_weights[0].shape) / grid_area
pw_kernel = np.random.normal(mu, sigma,
size=detection_weights[1].shape) / grid_area
bias = np.random.normal(mu, sigma,
size=detection_weights[2].shape) / grid_area
layer.set_weights([dw_kernel, pw_kernel, bias])
if ((weight_quantization != 0) or (activ_quantization != 0) or
(input_weight_quantization != 0)):
return quantize(model, weight_quantization, activ_quantization,
input_weight_quantization)
return model
[docs]def yolo_widerface_pretrained():
"""
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:
keras.Model, list: a Keras Model instance and a list of anchors.
"""
anchors_name = 'widerface_anchors.pkl'
anchors_path = get_file(
fname=anchors_name,
origin='http://data.brainchip.com/dataset-mirror/widerface/' +
anchors_name,
cache_subdir='datasets/widerface')
with open(anchors_path, 'rb') as handle:
anchors = pickle.load(handle)
model_name = 'yolo_widerface_iq8_wq4_aq4.h5'
file_hash = 'ac7da503de7753326a9bca35295f576aabc29d4916250385252646df1430eb94'
model_path = get_file(fname=model_name,
origin='http://data.brainchip.com/models/yolo/' +
model_name,
file_hash=file_hash,
cache_subdir='models')
return load_quantized_model(model_path), anchors
[docs]def yolo_voc_pretrained():
"""
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:
keras.Model, list: a Keras Model instance and a list of anchors.
"""
anchors_name = 'voc_anchors.pkl'
anchors_path = get_file(
fname=anchors_name,
origin='http://data.brainchip.com/dataset-mirror/voc/' + anchors_name,
cache_subdir='datasets/voc')
with open(anchors_path, 'rb') as handle:
anchors = pickle.load(handle)
model_name = 'yolo_voc_iq8_wq4_aq4.h5'
file_hash = 'aaeb2900b6c5e66b2dbddb307dee2fd813a06f792cad8a60d001dff32097a473'
model_path = get_file(fname=model_name,
origin='http://data.brainchip.com/models/yolo/' +
model_name,
file_hash=file_hash,
cache_subdir='models')
return load_quantized_model(model_path), anchors