File size: 2,652 Bytes
36c95ba |
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 |
import pytest
import torch
from torch.autograd import gradcheck
import kornia.testing as utils # test utils
from kornia.feature.siftdesc import get_sift_bin_ksize_stride_pad, get_sift_pooling_kernel, SIFTDescriptor
from kornia.testing import assert_close
@pytest.mark.parametrize("ksize", [5, 13, 25])
def test_get_sift_pooling_kernel(ksize):
kernel = get_sift_pooling_kernel(ksize)
assert kernel.shape == (ksize, ksize)
@pytest.mark.parametrize("ps,n_bins,ksize,stride,pad", [(41, 3, 20, 13, 5), (32, 4, 12, 8, 3)])
def test_get_sift_bin_ksize_stride_pad(ps, n_bins, ksize, stride, pad):
out = get_sift_bin_ksize_stride_pad(ps, n_bins)
assert out == (ksize, stride, pad)
class TestSIFTDescriptor:
def test_shape(self, device, dtype):
inp = torch.ones(1, 1, 32, 32, device=device, dtype=dtype)
sift = SIFTDescriptor(32).to(device, dtype)
out = sift(inp)
assert out.shape == (1, 128)
def test_batch_shape(self, device, dtype):
inp = torch.ones(2, 1, 15, 15, device=device, dtype=dtype)
sift = SIFTDescriptor(15).to(device, dtype)
out = sift(inp)
assert out.shape == (2, 128)
def test_batch_shape_non_std(self, device, dtype):
inp = torch.ones(3, 1, 19, 19, device=device, dtype=dtype)
sift = SIFTDescriptor(19, 5, 3).to(device, dtype)
out = sift(inp)
assert out.shape == (3, (3 ** 2) * 5)
def test_toy(self, device, dtype):
patch = torch.ones(1, 1, 6, 6, device=device, dtype=dtype)
patch[0, 0, :, 3:] = 0
sift = SIFTDescriptor(6, num_ang_bins=4, num_spatial_bins=1, clipval=0.2, rootsift=False).to(device, dtype)
out = sift(patch)
expected = torch.tensor([[0, 0, 1.0, 0]], device=device, dtype=dtype)
assert_close(out, expected, atol=1e-3, rtol=1e-3)
def test_gradcheck(self, device):
dtype = torch.float64
batch_size, channels, height, width = 1, 1, 15, 15
patches = torch.rand(batch_size, channels, height, width, device=device, dtype=dtype)
patches = utils.tensor_to_gradcheck_var(patches) # to var
sift = SIFTDescriptor(15).to(device, dtype)
assert gradcheck(sift, (patches,), raise_exception=True, nondet_tol=1e-4)
@pytest.mark.skip("Compiled functions can't take variable number")
def test_jit(self, device, dtype):
B, C, H, W = 1, 1, 32, 32
patches = torch.ones(B, C, H, W, device=device, dtype=dtype)
model = SIFTDescriptor(32).to(patches.device, patches.dtype).eval()
model_jit = torch.jit.script(model)
assert_close(model(patches), model_jit(patches))
|