| |
| import numpy as np |
| import torch |
| from torch.autograd import gradcheck |
|
|
| from mmdet.models.utils import interpolate_as, sigmoid_geometric_mean |
|
|
|
|
| def test_interpolate_as(): |
| source = torch.rand((1, 5, 4, 4)) |
| target = torch.rand((1, 1, 16, 16)) |
|
|
| |
| result = interpolate_as(source, target) |
| assert result.shape == torch.Size((1, 5, 16, 16)) |
|
|
| |
| result = interpolate_as(source, target.squeeze(0)) |
| assert result.shape == torch.Size((1, 5, 16, 16)) |
|
|
| |
| result = interpolate_as(source.squeeze(0), target) |
| assert result.shape == torch.Size((5, 16, 16)) |
|
|
| |
| target = np.random.rand(16, 16) |
| result = interpolate_as(source.squeeze(0), target) |
| assert result.shape == torch.Size((5, 16, 16)) |
|
|
|
|
| def test_sigmoid_geometric_mean(): |
| x = torch.randn(20, 20, dtype=torch.double, requires_grad=True) |
| y = torch.randn(20, 20, dtype=torch.double, requires_grad=True) |
| inputs = (x, y) |
| test = gradcheck(sigmoid_geometric_mean, inputs, eps=1e-6, atol=1e-4) |
| assert test |
|
|
