def DenseLayer(growth_rate, name=''):
"""
The basic normalization, convolution and activation combination for dense connection
Args:
growth_rate (int):The growth rate regulates how much new information each layer contributes to the global state
name (str): None of this dense layer
Returns:
An instrance of dense layer.
"""
items = OrderedDict()
items['norm'] = BatchNorm2d()
items['relu'] = Relu()
items['conv1'] = Conv2d_Block((1, 1),
num_filters=4 * growth_rate,
strides=1,
activation='relu',
auto_pad=True,
padding_mode='zero',
use_bias=False,
normalization='batch')
items['conv2'] = Conv2d((3, 3),
num_filters=growth_rate,
strides=1,
auto_pad=True,
padding_mode='zero',
use_bias=False)
return Sequential(items)
def Transition(reduction, name=''):
"""
The block for transition-down, down-sampling by average pooling
Args:
reduction (float): The depth_multiplier to transition-down the dense features
name (str): Name of the transition-down process
Returns:
An instrance of transition-down .
"""
items = OrderedDict()
items['norm'] = BatchNorm2d()
items['relu'] = Relu()
items['conv1'] = Conv2d((1, 1),
num_filters=None,
depth_multiplier=reduction,
strides=1,
auto_pad=True,
padding_mode='zero',
use_bias=False)
items['pool'] = AvgPool2d(2, 2, auto_pad=True)
return Sequential(items, name=name)
def DenseNet(blocks,
growth_rate=32,
initial_filters=64,
include_top=True,
pretrained=True,
input_shape=(224, 224, 3),
num_classes=1000,
name='',
**kwargs):
"""'
Instantiates the DenseNet architecture.
Optionally loads weights pre-trained on ImageNet.
Args
blocks (tuple/ list of int ): numbers of building blocks for the dense layers.
growth_rate (int):The growth rate regulates how much new information each layer contributes to the global state
initial_filters (int): the channel of the first convolution layer
pretrained (bool): If True, returns a model pre-trained on ImageNet.
input_shape (tuple or list): the default input image size in CHW order (C, H, W)
num_classes (int): number of classes
name (string): anme of the model
Returns
A trident image classification model instance.
"""
densenet = Sequential()
densenet.add_module(
'conv1/conv',
Conv2d_Block((7, 7),
num_filters=initial_filters,
strides=2,
use_bias=False,
auto_pad=True,
padding_mode='zero',
activation='relu',
normalization='batch',
name='conv1/conv'))
densenet.add_module('maxpool', (MaxPool2d(
(3, 3), strides=2, auto_pad=True, padding_mode='zero')))
densenet.add_module('denseblock1',
DenseBlock(blocks[0], growth_rate=growth_rate))
densenet.add_module('transitiondown1', Transition(0.5))
densenet.add_module('denseblock2',
DenseBlock(blocks[1], growth_rate=growth_rate))
densenet.add_module('transitiondown2', Transition(0.5))
densenet.add_module('denseblock3',
DenseBlock(blocks[2], growth_rate=growth_rate))
densenet.add_module('transitiondown3', Transition(0.5))
densenet.add_module('denseblock4',
DenseBlock(blocks[3], growth_rate=growth_rate))
densenet.add_module('classifier_norm', BatchNorm2d(name='classifier_norm'))
densenet.add_module('classifier_relu', Relu(name='classifier_relu'))
densenet.add_module('avg_pool', GlobalAvgPool2d(name='avg_pool'))
if include_top:
densenet.add_module(
'classifier', Dense(num_classes,
activation=None,
name='classifier'))
densenet.add_module('softmax', SoftMax(name='softmax'))
densenet.name = name
model = ImageClassificationModel(input_shape=input_shape, output=densenet)
with open(os.path.join(os.path.dirname(os.path.abspath(__file__)),
'imagenet_labels1.txt'),
'r',
encoding='utf-8-sig') as f:
labels = [l.rstrip() for l in f]
model.class_names = labels
model.preprocess_flow = [
resize((input_shape[0], input_shape[1]), keep_aspect=True),
normalize(0, 255),
normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
# model.summary()
return model