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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 |