BIDI_ML_INFO_EXTRACTION/BiddingKG/dl/table_head/predict_torch.py

import copy
import os
import sys
import torch
from torch.utils.data import DataLoader

sys.path.append(os.path.abspath(os.path.dirname(__file__) + "/../../../"))
from BiddingKG.dl.table_head.models.model_torch import TableHeadModel
from BiddingKG.dl.table_head.pre_process_torch import CustomDatasetTiny40, set_same_table_head, set_label

device = torch.device("cpu")
model_path = os.path.abspath(os.path.dirname(__file__)) + '/model_40_0.951.pth'
batch_size = 1


def predict(table_text_list):
    if globals().get("model") is None:
        print("="*15, "init table_head model", "="*15)
        # 实例化模型
        model = TableHeadModel()
        model.to(device)
        model.load_state_dict(torch.load(model_path, map_location=torch.device(device)))
        # 将模型设置为评估模式
        model.eval()
        globals()["model"] = model
    else:
        model = globals().get("model")

    if len(table_text_list) <= 0:
        return []

    data_x = copy.deepcopy(table_text_list)
    data_y = [[0 for col in row] for row in data_x]

    row_len = len(data_x)
    col_len = len(data_x[0])

    if col_len >= 50:
        return data_y

    if col_len >= 20:
        batch_row_len = 50
    else:
        batch_row_len = 100

    result_list = []
    for i in range(0, row_len, batch_row_len):
        batch_data_x = data_x[i:i+batch_row_len]
        dataset = CustomDatasetTiny40([batch_data_x], [data_y], mode=0)
        data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=False)
        # 存储预测结果
        with torch.no_grad():
            for data, targets, _ in data_loader:
                data = data.to(device)
                outputs = model(data)
                outputs = set_same_table_head(data, outputs)
                result = torch.zeros_like(outputs)
                result[outputs >= 0.5] = 1
                result = result.numpy().tolist()
        result_list += result

    # 设置一些特定的表头
    for i in range(len(result_list)):
        row = table_text_list[i]
        row_label = result_list[i]
        result_list[i] = set_label(row, row_label)

    return result_list