def eval_model()

in src/nli/inference_debug.py [0:0]

• 34 lines of code
• 4 McCabe index (conditional complexity)

def eval_model(model, dev_dataloader, device_num, args):
    model.eval()

    uid_list = []
    y_list = []
    pred_list = []
    logits_list = []

    with torch.no_grad():
        for i, batch in enumerate(dev_dataloader, 0):
            batch = move_to_device(batch, device_num)

            if args.model_class_name in ["distilbert", "bart-large"]:
                outputs = model(batch['input_ids'],
                                attention_mask=batch['attention_mask'],
                                labels=None)
            else:
                outputs = model(batch['input_ids'],
                                attention_mask=batch['attention_mask'],
                                token_type_ids=batch['token_type_ids'],
                                labels=None)

            # print(outputs)
            logits = outputs[0]

            uid_list.extend(list(batch['uid']))
            y_list.extend(batch['y'].tolist())
            pred_list.extend(torch.max(logits, 1)[1].view(logits.size(0)).tolist())
            logits_list.extend(logits.tolist())

    assert len(pred_list) == len(logits_list)
    assert len(pred_list) == len(logits_list)

    result_items_list = []
    for i in range(len(uid_list)):
        r_item = dict()
        r_item['uid'] = uid_list[i]
        r_item['logits'] = logits_list[i]
        r_item['probability'] = softmax(r_item['logits'])
        r_item['predicted_label'] = id2label[pred_list[i]]

        result_items_list.append(r_item)

    return result_items_list