fastNLP/test/core/test_tester.py

import unittest
import numpy as np
from torch import nn
import time
from fastNLP import DataSet
from fastNLP import Instance
from fastNLP import AccuracyMetric
from fastNLP import Tester

data_name = "pku_training.utf8"
pickle_path = "data_for_tests"


def prepare_fake_dataset():
    mean = np.array([-3, -3])
    cov = np.array([[1, 0], [0, 1]])
    class_A = np.random.multivariate_normal(mean, cov, size=(1000,))
    
    mean = np.array([3, 3])
    cov = np.array([[1, 0], [0, 1]])
    class_B = np.random.multivariate_normal(mean, cov, size=(1000,))
    
    data_set = DataSet([Instance(x=[float(item[0]), float(item[1])], y=[0.0]) for item in class_A] +
                       [Instance(x=[float(item[0]), float(item[1])], y=[1.0]) for item in class_B])
    return data_set


def prepare_fake_dataset2(*args, size=100):
    ys = np.random.randint(4, size=100, dtype=np.int64)
    data = {'y': ys}
    for arg in args:
        data[arg] = np.random.randn(size, 5)
    return DataSet(data=data)


class TestTester(unittest.TestCase):
    def test_case_1(self):
        # 检查报错提示能否正确提醒用户
        dataset = prepare_fake_dataset2('x1', 'x_unused')
        dataset.rename_field('x_unused', 'x2')
        dataset.set_input('x1', 'x2')
        dataset.set_target('y', 'x1')
        
        class Model(nn.Module):
            def __init__(self):
                super().__init__()
                self.fc = nn.Linear(5, 4)
            
            def forward(self, x1, x2):
                x1 = self.fc(x1)
                x2 = self.fc(x2)
                x = x1 + x2
                time.sleep(0.1)
                # loss = F.cross_entropy(x, y)
                return {'preds': x}
        
        model = Model()
        with self.assertRaises(NameError):
            tester = Tester(
                data=dataset,
                model=model,
                metrics=AccuracyMetric())
            tester.test()