faceserver/vectorizer/recognition/nets.py

# -*- coding: utf-8 -*-
"""
   Copyright 2019 Petr Masopust, Aprar s.r.o.

   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

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.

   Adopted code from https://github.com/ronghuaiyang/arcface-pytorch

   Created on 18-5-21 下午5:26

   @author: ronghuaiyang
"""

import torchvision.models as models
from torch import nn


def resnet18(pretrained=False, **kwargs):
    """Constructs a ResNet-18 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = models.resnet18(num_classes=512, **kwargs)
    return model


def resnet34(pretrained=False, **kwargs):
    """Constructs a ResNet-34 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = models.resnet34(num_classes=512, **kwargs)
    return model


def resnet50(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = models.resnet50(num_classes=512, **kwargs)
    return model


def resnet101(pretrained=False, **kwargs):
    """Constructs a ResNet-101 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = models.resnet101(num_classes=512, **kwargs)
    return model


def resnet152(pretrained=False, **kwargs):
    """Constructs a ResNet-152 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = models.resnet152(num_classes=512, **kwargs)
    return model


def sphere20():
    return sphere20a()


def get_net_by_depth(depth):
    if depth == 18:
        model = resnet18()
    elif depth == 20:
        model = sphere20()
    elif depth == 34:
        model = resnet34()
    elif depth == 50:
        model = resnet50()
    elif depth == 101:
        model = resnet101()
    elif depth == 152:
        model = resnet152()
    else:
        raise ValueError('Unsupported model depth %d, must be one of 18, 34, 50, 101, 152' % depth)

    return model


class sphere20a(nn.Module):
    def __init__(self):
        super(sphere20a, self).__init__()
        # input = B*3*112*96
        self.conv1_1 = nn.Conv2d(3, 64, 3, 2, 1)  # =>B*64*56*48
        self.relu1_1 = nn.PReLU(64)
        self.conv1_2 = nn.Conv2d(64, 64, 3, 1, 1)
        self.relu1_2 = nn.PReLU(64)
        self.conv1_3 = nn.Conv2d(64, 64, 3, 1, 1)
        self.relu1_3 = nn.PReLU(64)

        self.conv2_1 = nn.Conv2d(64, 128, 3, 2, 1)  # =>B*128*28*24
        self.relu2_1 = nn.PReLU(128)
        self.conv2_2 = nn.Conv2d(128, 128, 3, 1, 1)
        self.relu2_2 = nn.PReLU(128)
        self.conv2_3 = nn.Conv2d(128, 128, 3, 1, 1)
        self.relu2_3 = nn.PReLU(128)

        self.conv2_4 = nn.Conv2d(128, 128, 3, 1, 1)  # =>B*128*28*24
        self.relu2_4 = nn.PReLU(128)
        self.conv2_5 = nn.Conv2d(128, 128, 3, 1, 1)
        self.relu2_5 = nn.PReLU(128)

        self.conv3_1 = nn.Conv2d(128, 256, 3, 2, 1)  # =>B*256*14*12
        self.relu3_1 = nn.PReLU(256)
        self.conv3_2 = nn.Conv2d(256, 256, 3, 1, 1)
        self.relu3_2 = nn.PReLU(256)
        self.conv3_3 = nn.Conv2d(256, 256, 3, 1, 1)
        self.relu3_3 = nn.PReLU(256)

        self.conv3_4 = nn.Conv2d(256, 256, 3, 1, 1)  # =>B*256*14*12
        self.relu3_4 = nn.PReLU(256)
        self.conv3_5 = nn.Conv2d(256, 256, 3, 1, 1)
        self.relu3_5 = nn.PReLU(256)

        self.conv3_6 = nn.Conv2d(256, 256, 3, 1, 1)  # =>B*256*14*12
        self.relu3_6 = nn.PReLU(256)
        self.conv3_7 = nn.Conv2d(256, 256, 3, 1, 1)
        self.relu3_7 = nn.PReLU(256)

        self.conv3_8 = nn.Conv2d(256, 256, 3, 1, 1)  # =>B*256*14*12
        self.relu3_8 = nn.PReLU(256)
        self.conv3_9 = nn.Conv2d(256, 256, 3, 1, 1)
        self.relu3_9 = nn.PReLU(256)

        self.conv4_1 = nn.Conv2d(256, 512, 3, 2, 1)  # =>B*512*7*6
        self.relu4_1 = nn.PReLU(512)
        self.conv4_2 = nn.Conv2d(512, 512, 3, 1, 1)
        self.relu4_2 = nn.PReLU(512)
        self.conv4_3 = nn.Conv2d(512, 512, 3, 1, 1)
        self.relu4_3 = nn.PReLU(512)

        self.fc5 = nn.Linear(512 * 14 * 14, 512)
        # ORIGINAL for 112x96: self.fc5 = nn.Linear(512*7*6,512)

    def forward(self, x):
        x = self.relu1_1(self.conv1_1(x))
        x = x + self.relu1_3(self.conv1_3(self.relu1_2(self.conv1_2(x))))

        x = self.relu2_1(self.conv2_1(x))
        x = x + self.relu2_3(self.conv2_3(self.relu2_2(self.conv2_2(x))))
        x = x + self.relu2_5(self.conv2_5(self.relu2_4(self.conv2_4(x))))

        x = self.relu3_1(self.conv3_1(x))
        x = x + self.relu3_3(self.conv3_3(self.relu3_2(self.conv3_2(x))))
        x = x + self.relu3_5(self.conv3_5(self.relu3_4(self.conv3_4(x))))
        x = x + self.relu3_7(self.conv3_7(self.relu3_6(self.conv3_6(x))))
        x = x + self.relu3_9(self.conv3_9(self.relu3_8(self.conv3_8(x))))

        x = self.relu4_1(self.conv4_1(x))
        x = x + self.relu4_3(self.conv4_3(self.relu4_2(self.conv4_2(x))))

        x = x.view(x.size(0), -1)
        x = self.fc5(x)
        return x