KonCept.py

from inceptionresnetv2 import inceptionresnetv2
import torch.nn as nn
class KonCept(nn.Module):
    def __init__(self, config):
        super(KonCept,self).__init__()
        base_model = inceptionresnetv2(num_classes=1000, pretrained='imagenet')
        self.base= nn.Sequential(*list(base_model.children())[:-2])
        self.avg_pool = nn.AdaptiveAvgPool2d(output_size=(1, 1))
        self.fc = nn.Sequential(
            nn.Linear(1536, 2048),
            nn.ReLU(inplace=True),
            nn.BatchNorm1d(2048),
            nn.Dropout(p=0.25),
            nn.Linear(2048, 1024),
            nn.ReLU(inplace=True),
            nn.BatchNorm1d(1024),
            nn.Dropout(p=0.25),
            nn.Linear(1024, 256),
            nn.ReLU(inplace=True),
            nn.BatchNorm1d(256),
            nn.Dropout(p=0.5),
            nn.Linear(256, 1),
        )
        if config.fc:
            for param in self.base.parameters():
                param.requires_grad = False

    def forward(self, x):
        x = self.base(x)
        x = self.avg_pool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        output = []
        output.append(x)
        return output, x