PyTorch implementation of LS-CNN architecture from the paper
- LS-CNN: Characterizing Local Patches at Multiple Scales for Face Recognition by Qiangchang Wang and Guodong Guo
Since similar discriminative face regions may occur at different scales, a new backbone network HSNet which extracts multi-scale features has been proposed in the paper. HSNet excracts multi-scale features from two harmonious perspectives.
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Different kernels in a single layer, concatenation of features from different layers.
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To identify local patches from which facial features can be extracted, new spatial attention is used in the paper.
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Also in a CNN, channels in high-level layers represent high-level representations. So, a channel attention is also used in the paper to emphasize important channels and suppress less informative ones automatically.
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This spatial and channel attention is called Dual Face Attention (DFA)
The goal of this package is to provide a nice and simple object-oriented implementation of the architecture. The individual submodules are cleanly separated into self-contained blocks, that come with documentation and typings, and that are therefore easy to import and reuse.
This project is based on Python 3.6 and PyTorch 1.7.0
Within the correct environment, install the package from the repository:
pip install git https://github.com/Ksuryateja/LS-CNN Either load the predefined network from model.py
from model import LSCNN
net = LSCNN()Or use the modules for custom architecture:
from torch.nn import Sequential
from LSCNN import TransitionBlock, DenseBlock
class Module(Sequential):
def __init__(self, in_channels, out_channels):
super(Module, self).__init__()
self.dense = DenseBlock(in_channels, growth_rate=4, num_layers=2,
concat_input=True, dense_layer_params={'dropout': 0.2})
self.transition = TransitionBlock(self.dense.out_channels, out_channels)
net = Module(10, 4)Module((dense): DenseBlock(60, 1*30 60=90)(
(DenseBlock_0): Sequential(
(DFA_0): DFA(60, 16)(
(LANet): LALayer(
(spatial_atten): Sequential(
(0): Conv2d(60, 3, kernel_size=(1, 1), stride=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(3, 1, kernel_size=(1, 1), stride=(1, 1))
(3): Sigmoid()
)
)
(SENet): SELayer(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Linear(in_features=60, out_features=3, bias=False)
(relu): ReLU(inplace=True)
(fc2): Linear(in_features=3, out_features=60, bias=False)
(sigmoid): Sigmoid()
)
)
(DenseLayer_0): DenseLayer(60, 30)(
(branch1_1x1): BasicConv2d(
(conv): Conv2d(60, 120, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(120, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
(branch2_1x1): BasicConv2d(
(conv): Conv2d(60, 120, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(120, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
(branch3_1x1): BasicConv2d(
(conv): Conv2d(60, 30, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(30, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
(branch1_3x3): BasicConv2d(
(conv): Conv2d(120, 30, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(30, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
(branch1_3x3_2): BasicConv2d(
(conv): Conv2d(30, 30, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(30, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
(branch2_3x3): BasicConv2d(
(conv): Conv2d(120, 30, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(30, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
(output): BasicConv2d(
(conv): Conv2d(90, 30, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(30, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
(transition): TransitionBlock()(
(transition_block): Sequential(
(DFA): DFA(90, 16)(
(LANet): LALayer(
(spatial_atten): Sequential(
(0): Conv2d(90, 5, kernel_size=(1, 1), stride=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(5, 1, kernel_size=(1, 1), stride=(1, 1))
(3): Sigmoid()
)
)
(SENet): SELayer(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Linear(in_features=90, out_features=5, bias=False)
(relu): ReLU(inplace=True)
(fc2): Linear(in_features=5, out_features=90, bias=False)
(sigmoid): Sigmoid()
)
)
(Transition layer): TransitionLayer(90, 45)(
(branch1_1x1): BasicConv2d(
(conv): Conv2d(90, 45, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(45, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
(branch2_1x1): BasicConv2d(
(conv): Conv2d(90, 45, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(45, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
(branch3_1x1): BasicConv2d(
(conv): Conv2d(90, 45, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(45, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
(branch1_3x3): BasicConv2d(
(conv): Conv2d(45, 45, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(45, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
(branch1_3x3_2): BasicConv2d(
(conv): Conv2d(45, 45, kernel_size=(3, 3), stride=(2, 2), bias=False)
(bn): BatchNorm2d(45, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
(branch2_3x3): BasicConv2d(
(conv): Conv2d(45, 45, kernel_size=(3, 3), stride=(2, 2), bias=False)
(bn): BatchNorm2d(45, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
(branch3_3x3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(output): BasicConv2d(
(conv): Conv2d(135, 45, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(45, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
)