def test_rectangular(self):
t = torch.Tensor([[[
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.1],
[0.0, 0.0, 0.0, 0.0, 0.1, 0.8],
]]])
coords = torch.Tensor([[[1, 1]]])
actual = average_loss(kl_reg_losses(t, coords, 2.0))
self.assertEqual(1.2646753877545842, actual.item())
def test_kl_rectangular():
t = torch.Tensor([[[
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.1],
[0.0, 0.0, 0.0, 0.0, 0.1, 0.8],
]]])
coords = torch.Tensor([[[1, 1]]])
actual = average_loss(kl_reg_losses(t, coords, 2.0))
assert actual.item() == 1.2646753877545842
def test_rectangular(self):
t = torch.Tensor([[[
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.1],
[0.0, 0.0, 0.0, 0.0, 0.1, 0.8],
]]])
coords = torch.Tensor([[[1, 1]]])
actual = average_loss(kl_reg_losses(Variable(t), Variable(coords),
2.0))
self.assertEqual(1.2646753877545842, actual.data[0])
def test_kl_rectangular():
t = torch.tensor([[
[
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.1],
[0.0, 0.0, 0.0, 0.0, 0.1, 0.8],
]
]])
coords = torch.tensor([[[1.0, 1.0]]])
actual = average_loss(kl_reg_losses(t, coords, 2.0))
assert actual.item() == pytest.approx(1.26467538775)
def _calculate_reg_loss(self, target_var, reg, hm_var, hm_sigma):
# Convert sigma (aka standard deviation) from pixels to normalized units
sigma = (2.0 * hm_sigma / hm_var.size(-1))
# Apply a regularisation term relating to the shape of the heatmap.
if reg == 'var':
reg_loss = dsntnn.variance_reg_losses(hm_var, sigma, )
elif reg == 'kl':
reg_loss = dsntnn.kl_reg_losses(hm_var, target_var, sigma)
elif reg == 'js':
reg_loss = dsntnn.js_reg_losses(hm_var, target_var, sigma)
# elif reg == 'mse':
# reg_loss = dsntnn.mse_reg_losses(hm_var, target_var, sigma, mask_var)
else:
reg_loss = 0
return reg_loss
def test_mask(self):
t = torch.Tensor([[[
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.1],
[0.0, 0.0, 0.1, 0.8],
],
[
[0.8, 0.1, 0.0, 0.0],
[0.1, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
]]])
coords = torch.Tensor([[[1, 1], [0, 0]]])
mask = torch.Tensor([[1, 0]])
actual = average_loss(kl_reg_losses(t, coords, 2.0), mask)
self.assertEqual(1.2228811717796824, actual.item())
def test_kl_mask():
t = torch.Tensor([[[
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.1],
[0.0, 0.0, 0.1, 0.8],
],
[
[0.8, 0.1, 0.0, 0.0],
[0.1, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
]]])
coords = torch.Tensor([[[1, 1], [0, 0]]])
mask = torch.Tensor([[1, 0]])
actual = average_loss(kl_reg_losses(t, coords, 2.0), mask)
assert actual.item() == approx(1.2228811717796824)
def test_mask(self):
t = torch.Tensor([[[
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.1],
[0.0, 0.0, 0.1, 0.8],
],
[
[0.8, 0.1, 0.0, 0.0],
[0.1, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
]]])
coords = torch.Tensor([[[1, 1], [0, 0]]])
mask = torch.Tensor([[1, 0]])
actual = average_loss(
kl_reg_losses(Variable(t), Variable(coords), 2.0), Variable(mask))
self.assertEqual(1.2228811717796824, actual.data[0])