fastNLP/test/core/test_loss.py

import math
import unittest

import torch
import torch as tc
import torch.nn.functional as F

import fastNLP.core.losses as loss
from fastNLP.core.losses import LossFunc


class TestLoss(unittest.TestCase):

    def test_case_1(self):
        loss_func = loss.LossFunc(F.nll_loss)
        nll_loss = loss.NLLLoss()
        y = tc.Tensor(
            [
                [.3, .4, .3],
                [.5, .3, .2],
                [.3, .6, .1],
            ]
        )

        gy = tc.LongTensor(
            [
                0,
                1,
                2,
            ]
        )

        y = tc.log(y)
        los = loss_func({'input': y}, {'target': gy})
        losses = nll_loss({'input': y}, {'target': gy})

        r = -math.log(.3) - math.log(.3) - math.log(.1)
        r /= 3
        print("loss = %f" % (los))
        print("r = %f" % (r))
        print("nll_loss = %f" % (losses))

        self.assertEqual(int(los * 1000), int(r * 1000))

    def test_case_2(self):
        # 验证squash()的正确性

        log = math.log
        loss_func = loss.Loss("nll")

        y = tc.Tensor(
            [
                [[.3, .4, .3], [.3, .4, .3], ],
                [[.5, .3, .2], [.1, .2, .7], ],
                [[.3, .6, .1], [.2, .1, .7], ],
            ]
        )

        gy = tc.LongTensor(
            [
                [0, 2],
                [1, 2],
                [2, 1],
            ]
        )

        y = tc.log(y)
        # los = loss_func({'input': y}, {'target': gy})
        los = loss_func(y, gy)

        r = -log(.3) - log(.3) - log(.1) - log(.3) - log(.7) - log(.1)
        r /= 6

        self.assertEqual(int(los * 1000), int(r * 1000))

    def test_case_3(self):
        # 验证pack_padded_sequence()的正确性
        log = math.log
        loss_func = loss.NLLLoss()
        y = tc.Tensor(
            [
                [[.3, .4, .3], [.3, .2, .5], [.4, .5, .1, ], ],
                [[.5, .3, .2], [.1, .2, .7], [.0, .0, .0, ], ],
                [[.3, .6, .1], [.0, .0, .0], [.0, .0, .0, ], ],
            ]
        )

        gy = tc.LongTensor(
            [
                [0, 2, 1, ],
                [1, 2, 0, ],
                [2, 0, 0, ],
            ]
        )

        lens = [3, 2, 1]

        # pdb.set_trace()

        y = tc.log(y)

        yy = tc.nn.utils.rnn.pack_padded_sequence(y, lens, batch_first=True).data
        gyy = tc.nn.utils.rnn.pack_padded_sequence(gy, lens, batch_first=True).data
        los = loss_func({'input': yy}, {'target': gyy})

        r = -log(.3) - log(.5) - log(.5) - log(.3) - log(.7) - log(.1)
        r /= 6

        self.assertEqual(int(los * 1000), int(r * 1000))

    def test_case_4(self):
        # 验证unpad()的正确性
        log = math.log
        y = tc.Tensor(
            [
                [[.3, .4, .3], [.3, .2, .5], [.4, .5, .1, ], [.6, .3, .1, ], ],
                [[.5, .3, .2], [.1, .2, .7], [.0, .0, .0, ], [.0, .0, .0, ], ],
                [[.3, .6, .1], [.0, .0, .0], [.0, .0, .0, ], [.0, .0, .0, ], ],
            ]
        )

        gy = tc.LongTensor(
            [
                [0, 2, 1, 2, ],
                [1, 2, 0, 0, ],
                [2, 0, 0, 0, ],
            ]
        )

        lens = [4, 2, 1]
        y = tc.log(y)

        loss_func = loss.Loss("nll", pre_pro=["unpad"])
        los = loss_func(y, gy, lens=lens)

        r = -log(.1) - log(.3) - log(.5) - log(.5) - log(.3) - log(.7) - log(.1)
        r /= 7

        self.assertEqual(int(los * 1000), int(r * 1000))

    def test_case_5(self):
        # 验证mask()和make_mask()的正确性
        log = math.log

        y = tc.Tensor(
            [
                [[.5, .3, .2], [.1, .2, .7], [.0, .0, .0, ], [.0, .0, .0, ], ],
                [[.5, .4, .1], [.3, .2, .5], [.4, .5, .1, ], [.6, .1, .3, ], ],
                [[.3, .6, .1], [.3, .2, .5], [.0, .0, .0, ], [.0, .0, .0, ], ],
            ]
        )

        gy = tc.LongTensor(
            [
                [1, 2, 0, 0, ],
                [0, 2, 1, 2, ],
                [2, 1, 0, 0, ],
            ]
        )

        mask = tc.ByteTensor(
            [
                [1, 1, 0, 0, ],
                [1, 1, 1, 1, ],
                [1, 1, 0, 0, ],
            ]
        )

        y = tc.log(y)

        lens = [2, 4, 2]

        loss_func = loss.Loss("nll", pre_pro=["mask"])
        los = loss_func(y, gy, mask=mask)

        los2 = loss_func(y, gy, mask=loss.make_mask(lens, gy.size()[-1]))

        r = -log(.3) - log(.7) - log(.5) - log(.5) - log(.5) - log(.3) - log(.1) - log(.2)
        r /= 8

        self.assertEqual(int(los * 1000), int(r * 1000))
        self.assertEqual(int(los2 * 1000), int(r * 1000))

    def test_case_6(self):
        # 验证unpad_mask()的正确性
        log = math.log
        y = tc.Tensor(
            [
                [[.3, .4, .3], [.3, .2, .5], [.4, .5, .1, ], [.6, .3, .1, ], ],
                [[.5, .3, .2], [.1, .2, .7], [.0, .0, .0, ], [.0, .0, .0, ], ],
                [[.3, .6, .1], [.0, .0, .0], [.0, .0, .0, ], [.0, .0, .0, ], ],
            ]
        )

        gy = tc.LongTensor(
            [
                [0, 2, 1, 2, ],
                [1, 2, 0, 0, ],
                [2, 0, 0, 0, ],
            ]
        )

        lens = [4, 2, 1]

        # pdb.set_trace()

        y = tc.log(y)

        loss_func = loss.Loss("nll", pre_pro=["unpad_mask"])
        los = loss_func(y, gy, lens=lens)

        r = -log(.1) - log(.3) - log(.5) - log(.5) - log(.3) - log(.7) - log(.1)
        r /= 7

        self.assertEqual(int(los * 1000), int(r * 1000))

    def test_case_7(self):
        # 验证一些其他东西
        log = math.log
        y = tc.Tensor(
            [
                [[.3, .4, .3], [.3, .2, .5], [.4, .5, .1, ], [.6, .3, .1, ], ],
                [[.5, .3, .2], [.1, .2, .7], [.0, .0, .0, ], [.0, .0, .0, ], ],
                [[.3, .6, .1], [.0, .0, .0], [.0, .0, .0, ], [.0, .0, .0, ], ],
            ]
        )

        gy = tc.LongTensor(
            [
                [0, 2, 1, 2, ],
                [1, 2, 0, 0, ],
                [2, 0, 0, 0, ],
            ]
        )

        lens = [4, 2, 1]
        y = tc.log(y)

        loss_func = loss.Loss("nll", pre_pro=[], weight=tc.Tensor([1, 1, 0]))
        loss_func.add_pre_pro("unpad_mask")
        los = loss_func(y, gy, lens=lens)

        r = - log(.3) - log(.5) - log(.3)
        r /= 3
        self.assertEqual(int(los * 1000), int(r * 1000))

    def test_case_8(self):
        def func(a, b):
            return F.cross_entropy(a, b)

        def func2(a, truth):
            return func(a, truth)

        def func3(predict, truth):
            return func(predict, truth)

        def func4(a, b, c=2):
            return (a + b) * c

        def func6(a, b, **kwargs):
            c = kwargs['c']
            return (a + b) * c

        get_loss = LossFunc(func, {'a': 'predict', 'b': 'truth'})
        predict = torch.randn(5, 3)
        truth = torch.LongTensor([1, 0, 1, 2, 1])
        loss1 = get_loss({'predict': predict}, {'truth': truth})
        get_loss_2 = LossFunc(func2, {'a': 'predict'})
        loss2 = get_loss_2({'predict': predict}, {'truth': truth})
        get_loss_3 = LossFunc(func3)
        loss3 = get_loss_3({'predict': predict}, {'truth': truth})
        assert loss1 == loss2 and loss1 == loss3

        """
        get_loss_4 = LossFunc(func4)
        loss4 = get_loss_4({'a': 1, 'b': 3}, {})
        print(loss4)
        assert loss4 == (1 + 3) * 2

        get_loss_5 = LossFunc(func4)
        loss5 = get_loss_5({'a': 1, 'b': 3}, {'c': 4})
        print(loss5)
        assert loss5 == (1 + 3) * 4

        get_loss_6 = LossFunc(func6)
        loss6 = get_loss_6({'a': 1, 'b': 3}, {'c': 4})
        print(loss6)
        assert loss6 == (1 + 3) * 4

        get_loss_7 = LossFunc(func6, c='cc')
        loss7 = get_loss_7({'a': 1, 'b': 3}, {'cc': 4})
        print(loss7)
        assert loss7 == (1 + 3) * 4
        """


class TestLoss_v2(unittest.TestCase):
    def test_CrossEntropyLoss(self):
        ce = loss.CrossEntropyLoss(input="my_predict", target="my_truth")
        a = torch.randn(3, 5, requires_grad=False)
        b = torch.empty(3, dtype=torch.long).random_(5)
        ans = ce({"my_predict": a}, {"my_truth": b})
        self.assertEqual(ans, torch.nn.functional.cross_entropy(a, b))

    def test_BCELoss(self):
        bce = loss.BCELoss(input="my_predict", target="my_truth")
        a = torch.sigmoid(torch.randn((3, 5), requires_grad=False))
        b = torch.randn((3, 5), requires_grad=False)
        ans = bce({"my_predict": a}, {"my_truth": b})
        self.assertEqual(ans, torch.nn.functional.binary_cross_entropy(a, b))