| import torch | |
| import torch.nn as nn | |
| from torchvision import models | |
| from transformers import AutoTokenizer, AutoModel | |
| class Net(nn.Module): | |
| def __init__(self): | |
| super(Net, self).__init__() | |
| self.image_encoder = models.resnet18() | |
| self.image_encoder.fc = nn.Identity() | |
| self.image_out = nn.Sequential( | |
| nn.Linear(512, 256), nn.ReLU(), nn.Linear(256, 256) | |
| ) | |
| self.text_encoder = AutoModel.from_pretrained("dbmdz/distilbert-base-turkish-cased") | |
| self.target_token_idx = 0 | |
| self.text_out = nn.Sequential( | |
| nn.Linear(768, 256), nn.ReLU(), nn.Linear(256, 256) | |
| ) | |
| def forward(self, image, text, mask): | |
| image_vec = self.image_encoder(image) | |
| image_vec = self.image_out(image_vec.view(-1,512)) | |
| text_out = self.text_encoder(text, mask) | |
| last_hidden_states = text_out.last_hidden_state | |
| last_hidden_states = last_hidden_states[:,self.target_token_idx,:] | |
| text_vec = self.text_out(last_hidden_states.view(-1,768)) | |
| return image_vec, text_vec | |