Inception Resnet V2

irv2.py

"""
Inception ResNet V2

Official Tensorflow Implementation 
    @ https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py

Paper 
    @ http://arxiv.org/abs/1602.07261

Naming convention in the official TF-slim implementation
    - Inception-ResNet-A: `block_type='block35'`
    - Inception-ResNet-B: `block_type='block17'`
    - Inception-ResNet-C: `block_type='block8'`

Input image size = (299, 299)
"""

import torch
import torch.nn as nn

class BasicConv2d(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride, padding=0):
        super(BasicConv2d, self).__init__()
        self.conv = nn.Conv2d(
                in_channels=in_channels, 
                out_channels=out_channels, 
                kernel_size=kernel_size, 
                stride=stride, 
                padding=padding, 
                bias=False)
        self.bn = nn.BatchNorm2d(num_features=out_channels, eps=0.001, momentum=0, affine=True)
        self.relu = nn.ReLU(inplace=False)

    def forward(self, x):
        out = self.conv(x)
        out = self.bn(out)
        out = self.relu(out)
        return out


class Mixed_5b(nn.Module):
    """
    In channels: 192
    Out channels: 320
    """
    
    def __init__(self):
        super(Mixed_5b, self).__init__()
        self.branch0 = BasicConv2d(192, 96, kernel_size=1, stride=1)
        self.branch1 = nn.Sequential(
            BasicConv2d(192, 48, kernel_size=1, stride=1),
            BasicConv2d(48, 64, kernel_size=5, stride=1, padding=2))
        self.branch2 = nn.Sequential(
            BasicConv2d(192, 64, kernel_size=1, stride=1),
            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
            BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1))
        self.branch3 = nn.Sequential(
            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
            BasicConv2d(192, 64, kernel_size=1, stride=1))

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        x3 = self.branch3(x)
        out = torch.cat((x0, x1, x2, x3), dim=1)
        return out


class Block35(nn.Module):
    
    def __init__(self, scale=1.0):
        super(Block35, self).__init__()
        self.scale = scale
        self.branch0 = BasicConv2d(320, 32, kernel_size=1, stride=1)
        self.branch1 = nn.Sequential(
            BasicConv2d(320, 32, kernel_size=1, stride=1),
            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1))
        self.branch2 = nn.Sequential(
            BasicConv2d(320, 32, kernel_size=1, stride=1),
            BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1),
            BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1))
        self.conv2d = nn.Conv2d(128, 320, kernel_size=1, stride=1)
        self.relu = nn.ReLU(inplace=False)

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        out = torch.cat((x0, x1, x2), dim=1)
        out = self.conv2d(out)
        out = out * self.scale + x
        out = self.relu(out)
        return out


class Mixed_6a(nn.Module):
    """
    In channels: 320
    Out channels: 1024
    """
    
    def __init__(self):
        super(Mixed_6a, self).__init__()
        self.branch0 = BasicConv2d(320, 384, kernel_size=3, stride=2)
        self.branch1 = nn.Sequential(
            BasicConv2d(320, 256, kernel_size=1, stride=1),
            BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
            BasicConv2d(256, 384, kernel_size=3, stride=2))
        self.branch2 = nn.MaxPool2d(3, stride=2)

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        out = torch.cat((x0, x1, x2), dim=1)
        return out


class Block17(nn.Module):
    
    def __init__(self, scale=1.0):
        super(Block17, self).__init__()
        self.scale = scale
        self.branch0 = BasicConv2d(1088, 192, kernel_size=1, stride=1)
        self.branch1 = nn.Sequential(
            BasicConv2d(1088, 128, kernel_size=1, stride=1),
            BasicConv2d(128, 160, kernel_size=(1, 7), stride=1, padding=(0, 3)),
            BasicConv2d(160, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)))
        self.conv2d = nn.Conv2d(384, 1088, kernel_size=1, stride=1)
        self.relu = nn.ReLU(inplace=False)

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        out = torch.cat((x0, x1), dim=1)
        out = self.conv2d(out)
        out = out * self.scale + x
        out = self.relu(out)
        return out


class Mixed_7a(nn.Module):
    
    def __init__(self):
        super(Mixed_7a, self).__init__()
        self.branch0 = nn.Sequential(
            BasicConv2d(1088, 256, kernel_size=1, stride=1),
            BasicConv2d(256, 384, kernel_size=3, stride=2))
        self.branch1 = nn.Sequential(
            BasicConv2d(1088, 256, kernel_size=1, stride=1),
            BasicConv2d(256, 288, kernel_size=3, stride=2))
        self.branch2 = nn.Sequential(
            BasicConv2d(1088, 256, kernel_size=1, stride=1),
            BasicConv2d(256, 288, kernel_size=3, stride=1, padding=1),
            BasicConv2d(288, 320, kernel_size=3, stride=2))
        self.branch3 = nn.MaxPool2d(3, stride=2)
    
    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        x3 = self.branch3(x)
        out = torch.cat((x0, x1, x2, x3), 1)
        return out


class Block8(nn.Module):
    
    def __init__(self, scale=1.0, noReLU=False):
        super(Block8, self).__init__()
        self.scale = scale
        self.noReLU = noReLU
        self.branch0 = BasicConv2d(2080, 192, kernel_size=1, stride=1)
        self.branch1 = nn.Sequential(
            BasicConv2d(2080, 192, kernel_size=1, stride=1),
            BasicConv2d(192, 224, kernel_size=(1, 3), stride=1, padding=(0, 1)),
            BasicConv2d(224, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)))
        self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1)
        if not self.noReLU:
            self.relu = nn.ReLU(inplace=False)

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        out = torch.cat((x0, x1), 1)
        out = self.conv2d(out)
        out = out * self.scale + x
        if not self.noReLU:
            out = self.relu(out)
        return out


class InceptionResnetV2(nn.Module):

    def __init__(self, num_classes=8631, num_embeddings=512):
        super(InceptionResnetV2, self).__init__()
        self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
        self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
        self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.maxpool_3a = nn.MaxPool2d(3, stride=2)
        self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
        self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
        self.maxpool_5a = nn.MaxPool2d(3, stride=2)
        self.mixed_5b = Mixed_5b()
        self.repeat = nn.Sequential(
            Block35(scale=0.17),
            Block35(scale=0.17),
            Block35(scale=0.17),
            Block35(scale=0.17),
            Block35(scale=0.17),
            Block35(scale=0.17),
            Block35(scale=0.17),
            Block35(scale=0.17),
            Block35(scale=0.17),
            Block35(scale=0.17))
        self.mixed_6a = Mixed_6a()
        self.repeat_1 = nn.Sequential(
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10))
        self.mixed_7a = Mixed_7a()
        self.repeat_2 = nn.Sequential(
            Block8(scale=0.20),
            Block8(scale=0.20),
            Block8(scale=0.20),
            Block8(scale=0.20),
            Block8(scale=0.20),
            Block8(scale=0.20),
            Block8(scale=0.20),
            Block8(scale=0.20),
            Block8(scale=0.20))
        self.block8 = Block8(noReLU=True)
        self.conv2d_7b = BasicConv2d(2080, 1536, kernel_size=1, stride=1)
        self.avgpool_1a = nn.AvgPool2d(8, count_include_pad=False)

        self.fc = nn.Linear(1536, num_embeddings)
        self.classifier = nn.Linear(num_embeddings, num_classes)

    def forward(self, x):
        x = self.conv2d_1a(x)
        x = self.conv2d_2a(x)
        x = self.conv2d_2b(x)
        x = self.maxpool_3a(x)
        x = self.conv2d_3b(x)
        x = self.conv2d_4a(x)
        x = self.maxpool_5a(x)
        x = self.mixed_5b(x)
        x = self.repeat(x)
        x = self.mixed_6a(x)
        x = self.repeat_1(x)
        x = self.mixed_7a(x)
        x = self.repeat_2(x)
        x = self.block8(x)
        x = self.conv2d_7b(x)
        x = self.avgpool_1a(x)
        x = x.view(x.size(0), -1)

        # inject features to the net, will be accessd by center loss later
        # Get embeddings and output from classifier
        x = self.fc(x)
        self.features = x   # embeddings
        x = self.classifier(x)
        return x

if __name__ == "__main__":
    from torchsummary import summary
    net = InceptionResnetV2()
    
    summary(net, (3, 299, 299))