modeling_convnet.py

from transformers import PreTrainedModel
import torch
import torch.nn as nn
from .configuration_convnet import ConNetConfig

# Convolutional neural network (two convolutional layers)
class ConvNet(nn.Module):
    def __init__(self, num_classes=10):
        super(ConvNet, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.fc = nn.Linear(7*7*32, num_classes)
        
    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        return out




class ConvNetModel(PreTrainedModel):
    config_class = ConNetConfig
    
    def __init__(self, config):
        super().__init__(config)
        self.model = ConvNet(num_classes=config.num_classes)
    
    def forward(self, x):
        out = self.model(x)

        return out


if __name__=="__main__":
    resnet50d_config = ConNetConfig(num_classes=10)
    resnet50d = ConvNetModel(resnet50d_config)
    resnet50d.save_pretrained("my_models")
    pass