Skip to content

GitLab

Switch branch/tag
Find file Normal viewHistoryPermalink
test_focal_loss.py 4.76 KB
Newer Older
zhuyuanhao's avatar
add ext ops, support parrots (#310)  
zhuyuanhao committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140 
import numpy as np
import torch

_USING_PARROTS = True
try:
    from parrots.autograd import gradcheck
except ImportError:
    from torch.autograd import gradcheck
    _USING_PARROTS = False

# torch.set_printoptions(precision=8, threshold=100)

inputs = [
    ([[1., 0], [0, 1.]], [0, 1]),
    ([[1., 0, -1.], [0, 1., 2.]], [2, 1]),
    ([[1e-6, 2e-6, 3e-6], [4e-6, 5e-5, 6e-4], [7e-3, 8e-2, 9e-1]], [1, 2, 0]),
]

softmax_outputs = [(0.00566451, [[-0.00657264, 0.00657264],
                                 [0.00657264, -0.00657264]]),
                   (0.34956908, [[0.10165970, 0.03739851, -0.13905823],
                                 [0.01227554, -0.10298023, 0.09070466]]),
                   (0.15754992, [[0.02590877, -0.05181759, 0.02590882],
                                 [0.02589641, 0.02589760, -0.05179400],
                                 [-0.07307514, 0.02234372, 0.05073142]])]

sigmoid_outputs = [(0.13562961, [[-0.00657264, 0.11185755],
                                 [0.11185755, -0.00657264]]),
                   (1.10251057, [[0.28808805, 0.11185755, -0.09602935],
                                 [0.11185755, -0.00657264, 0.40376765]]),
                   (0.42287254, [[0.07457182, -0.02485716, 0.07457201],
                                 [0.07457211, 0.07457669, -0.02483728],
                                 [-0.02462499, 0.08277918, 0.18050370]])]


class Testfocalloss(object):

    def _test_softmax(self, dtype=torch.float):
        if not torch.cuda.is_available():
            return
        from mmcv.ops import softmax_focal_loss
        alpha = 0.25
        gamma = 2.0
        for case, output in zip(inputs, softmax_outputs):
            np_x = np.array(case[0])
            np_y = np.array(case[1])
            np_x_grad = np.array(output[1])

            x = torch.from_numpy(np_x).cuda().type(dtype)
            x.requires_grad_()
            y = torch.from_numpy(np_y).cuda().long()

            loss = softmax_focal_loss(x, y, gamma, alpha, None, 'mean')
            loss.backward()

            assert np.allclose(loss.data.cpu().numpy(), output[0], 1e-2)
            assert np.allclose(x.grad.data.cpu(), np_x_grad, 1e-2)

    def _test_sigmoid(self, dtype=torch.float):
        if not torch.cuda.is_available():
            return
        from mmcv.ops import sigmoid_focal_loss
        alpha = 0.25
        gamma = 2.0
        for case, output in zip(inputs, sigmoid_outputs):
            np_x = np.array(case[0])
            np_y = np.array(case[1])
            np_x_grad = np.array(output[1])

            x = torch.from_numpy(np_x).cuda().type(dtype)
            x.requires_grad_()
            y = torch.from_numpy(np_y).cuda().long()

            loss = sigmoid_focal_loss(x, y, gamma, alpha, None, 'mean')
            loss.backward()

            assert np.allclose(loss.data.cpu().numpy(), output[0], 1e-2)
            assert np.allclose(x.grad.data.cpu(), np_x_grad, 1e-2)

    def _test_grad_softmax(self, dtype=torch.float):
        if not torch.cuda.is_available():
            return
        from mmcv.ops import SoftmaxFocalLoss
        alpha = 0.25
        gamma = 2.0
        for case in inputs:
            np_x = np.array(case[0])
            np_y = np.array(case[1])

            x = torch.from_numpy(np_x).cuda().type(dtype)
            x.requires_grad_()
            y = torch.from_numpy(np_y).cuda().long()

            floss = SoftmaxFocalLoss(gamma, alpha)
            if _USING_PARROTS:
                # gradcheck(floss, (x, y),
                #           no_grads=[y])
                pass
            else:
                gradcheck(floss, (x, y), eps=1e-2, atol=1e-2)

    def _test_grad_sigmoid(self, dtype=torch.float):
        if not torch.cuda.is_available():
            return
        from mmcv.ops import SigmoidFocalLoss
        alpha = 0.25
        gamma = 2.0
        for case in inputs:
            np_x = np.array(case[0])
            np_y = np.array(case[1])

            x = torch.from_numpy(np_x).cuda().type(dtype)
            x.requires_grad_()
            y = torch.from_numpy(np_y).cuda().long()

            floss = SigmoidFocalLoss(gamma, alpha)
            if _USING_PARROTS:
                # gradcheck(floss, (x, y),
                #           no_grads=[y])
                pass
            else:
                gradcheck(floss, (x, y), eps=1e-2, atol=1e-2)

    def test_softmax_float(self):
        self._test_softmax(dtype=torch.float)

    def test_softmax_half(self):
        self._test_softmax(dtype=torch.half)

    def test_sigmoid_float(self):
        self._test_sigmoid(dtype=torch.float)

    def test_sigmoid_half(self):
        self._test_sigmoid(dtype=torch.half)

    def test_grad_softmax_float(self):
        self._test_grad_softmax(dtype=torch.float)

    def test_grad_sigmoid_float(self):
        self._test_grad_sigmoid(dtype=torch.float)