def DarknetConv2D_BN_Leaky(*args, **kwargs):
"""Darknet Convolution2D followed by BatchNormalization and LeakyReLU."""
darknet_conv_kwargs = {'use_bias': False,'normalization':BatchNorm2d(momentum=0.03,eps=1e-4)}
darknet_conv_kwargs['activation']=LeakyRelu(alpha=0.1)
darknet_conv_kwargs['auto_pad'] = False if kwargs.get('strides')==(2,2) else True
darknet_conv_kwargs.update(kwargs)
return Conv2d_Block(*args, **darknet_conv_kwargs)
def BottleNeck_IR_SE(num_filters, strides, keep_filter=True):
blocks = OrderedDict()
blocks['res_layer'] = Sequential(BatchNorm2d(),
Conv2d_Block(
(3, 3),
num_filters=num_filters,
strides=1,
auto_pad=True,
use_bias=False,
activation=PRelu(num_filters)),
Conv2d_Block((3, 3),
num_filters,
strides=strides,
use_bias=False,
activation=None,
normalization='batch'),
SqueezeExcite(num_filters // 16,
num_filters),
name='res_layer')
if keep_filter:
blocks['shortcut_layer'] = MaxPool2d(1,
strides=strides,
name='shortcut_layer')
else:
blocks['shortcut_layer'] = Conv2d_Block((1, 1),
num_filters,
strides=strides,
use_bias=False,
activation=None,
normalization='batch',
name='shortcut_layer')
return ShortCut2d(blocks, mode='add')
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),
4 * growth_rate,
strides=1,
activation='relu',
auto_pad=True,
padding_mode='zero',
use_bias=False,
normalization='batch')
items['conv2'] = Conv2d((3, 3),
growth_rate,
strides=1,
auto_pad=True,
padding_mode='zero',
use_bias=False)
return Sequential(items)
def DenseNet(blocks,
growth_rate=32,
initial_filters=64,
include_top=True,
pretrained=True,
input_shape=(3,224,224),
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),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'))
if include_top:
densenet.add_module('avg_pool', GlobalAvgPool2d(name='avg_pool'))
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)
#model.model.to(_device)
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[2], 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
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 SEResNet_IR(include_top=True,num_layers=50,Bottleneck=BottleNeck_IR_SE,drop_ratio=0.4,feature_dim=128,input_shape=(3,112,112)):
blocks=OrderedDict()
blocks['input_layer']=Conv2d_Block((3,3),64,strides=1,auto_pad=True,use_bias=False,activation=PRelu(64),normalization='batch',name='input_layer')
blocks['body']=Sequential(
get_block(Bottleneck, out_channel=64, num_units=3,keep_filter=True)+
get_block(Bottleneck, out_channel=128, num_units=4,keep_filter=False)+
get_block(Bottleneck, out_channel=256, num_units=14,keep_filter=False)+
get_block(Bottleneck, out_channel=512, num_units=3,keep_filter=False)
)
blocks['output_layer']=Sequential(
BatchNorm2d(),
Dropout(drop_ratio),
Flatten(),
Dense(feature_dim),
BatchNorm(),
name='output_layer'
)
facenet=Sequential(blocks).to(_device)
facenet.name=camel2snake('SEResNet_IR')
model=FaceRecognitionModel(input_shape=input_shape,output=facenet)
model.preprocess_flow=[Resize((input_shape[1],input_shape[2]),keep_aspect=True),Normalize(0,255),Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])]
#model.summary()
return model
