コード例 #1
0
def build_model(opt, vocab, checkpoint, gpu_id=None):
    print('Building model...')
    use_gpu = len(opt.gpu_ranks) > 0
    if use_gpu and gpu_id is not None:
        device = torch.device("cuda", gpu_id)
    elif use_gpu and not gpu_id:
        device = torch.device("cuda")
    elif not use_gpu:
        device = torch.device("cpu")

    #  model = MLP([1000, 500, 500], len(vocab))
    model = CNN(len(vocab), opt.dropout)

    if checkpoint is not None:
        # end of patch for backward compatibility
        if opt.train_from:
            print("Loading model parameters from checkpoint...")
            model.load_state_dict(checkpoint['model'], strict=False)
        else:
            print("Loading model parameters from checkpoint...")
            cur_state_dict = model.state_dict()
            for key in cur_state_dict.keys():
                if key in checkpoint['model'] and cur_state_dict[key].size(
                ) == checkpoint['model'][key].size():
                    cur_state_dict[key] = checkpoint['model'][key]
                #  elif key in checkpoint['model'] and cur_state_dict[key].size() != checkpoint['model'][key].size():
                #  print("***" , key)
            model.load_state_dict(cur_state_dict, strict=False)
    if len(opt.gpu_ranks) > 1:
        model = nn.DataParallel(model, opt.gpu_ranks)
    model.to(device)
    return model, device
コード例 #2
0
def build_model(opt, vocab, checkpoint, gpu_id=None):
    print('Building model...')
    if opt.gpu == -1:
        device = torch.device("cpu")
    else:
        device = torch.device("cuda", gpu_id)
    #  model = MLP([1000, 500, 500], len(vocab))
    model = CNN(len(vocab))

    if checkpoint is not None:
        # end of patch for backward compatibility
        print("Loading model parameters from checkpoint...")
        model.load_state_dict(checkpoint['model'], strict=False)
    model.to(device)
    return model, device
コード例 #3
0
def load_model(opt):
    global model
    global device
    global idx2label

    print('Loading checkpoint from %s' % opt.model)
    checkpoint = torch.load(opt.model,
                            map_location=lambda storage, loc: storage)
    print('Loading vocab from checkpoint at %s' % opt.model)
    vocab = checkpoint['vocab']
    idx2label = {v: k for k, v in vocab.items()}

    print('Building model...')
    if opt.gpu == -1:
        device = torch.device("cpu")
    else:
        device = torch.device("cuda", opt.gpu)
    model = CNN(len(vocab))

    # end of patch for backward compatibility
    print("Loading model parameters from checkpoint...")
    model.load_state_dict(checkpoint['model'], strict=False)
    model.to(device)
    model.eval()