| # torch packages | |
| import torch | |
| from model.transformer import Transformer | |
| import json | |
| if __name__ == "__main__": | |
| """ | |
| Following parameters are for Multi30K dataset | |
| """ | |
| # Load config containing model input parameters | |
| with open('params.json') as json_data: | |
| config = json.load(json_data) | |
| print(config) | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| # Instantiate model | |
| model = Transformer( | |
| config["dk"], | |
| config["dv"], | |
| config["h"], | |
| config["src_vocab_size"], | |
| config["target_vocab_size"], | |
| config["num_encoders"], | |
| config["num_decoders"], | |
| config["dim_multiplier"], | |
| config["pdropout"], | |
| device = device) | |
| # Load model weights | |
| model.load_state_dict(torch.load('pytorch_transformer_model.pt', | |
| map_location=device)) | |
| print(model) | |