def test_rectangular(self):
t = torch.Tensor([[[
[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, 0.6, 0.1, 0.0, 0.0],
[0.0, 0.0, 0.1, 0.0, 0.0, 0.0],
]]])
actual = average_loss(variance_reg_losses(t, 2.0))
self.assertEqual(28.88, actual.item())
def test_exact(self):
t = torch.Tensor([[[
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.1, 0.0],
[0.0, 0.1, 0.6, 0.1],
[0.0, 0.0, 0.1, 0.0],
]]])
actual = average_loss(variance_reg_losses(Variable(t), 2.0))
self.assertEqual(28.88, actual.data[0])
def test_variance_rectangular():
t = torch.Tensor([[[
[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, 0.6, 0.1, 0.0, 0.0],
[0.0, 0.0, 0.1, 0.0, 0.0, 0.0],
]]])
actual = average_loss(variance_reg_losses(t, 2.0))
assert actual.item() == 28.88
def test_batch(self):
t = torch.Tensor([[[
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.1, 0.0],
[0.0, 0.1, 0.6, 0.1],
[0.0, 0.0, 0.1, 0.0],
]],
[[
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.2, 0.0, 0.0],
[0.1, 0.5, 0.1, 0.0],
[0.0, 0.1, 0.0, 0.0],
]]])
actual = average_loss(variance_reg_losses(t, 2.0))
self.assertEqual(28.54205, actual.item())
def test_variance_batch():
t = torch.Tensor([[[
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.1, 0.0],
[0.0, 0.1, 0.6, 0.1],
[0.0, 0.0, 0.1, 0.0],
]],
[[
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.2, 0.0, 0.0],
[0.1, 0.5, 0.1, 0.0],
[0.0, 0.1, 0.0, 0.0],
]]])
actual = average_loss(variance_reg_losses(t, 2.0))
assert actual.item() == approx(28.54205)
def test_3d(self):
t = torch.Tensor([[[[
[0.000035, 0.000002, 0.000000],
[0.009165, 0.000570, 0.000002],
[0.147403, 0.009165, 0.000035],
],
[
[0.000142, 0.000009, 0.000000],
[0.036755, 0.002285, 0.000009],
[0.591145, 0.036755, 0.000142],
],
[
[0.000035, 0.000002, 0.000000],
[0.009165, 0.000570, 0.000002],
[0.147403, 0.009165, 0.000035],
]]]])
actual = average_loss(variance_reg_losses(Variable(t), 0.6))
self.assertEqual(0.18564102213775013, actual.data[0])
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_variance_3d():
t = torch.Tensor([[[[
[0.000035, 0.000002, 0.000000],
[0.009165, 0.000570, 0.000002],
[0.147403, 0.009165, 0.000035],
],
[
[0.000142, 0.000009, 0.000000],
[0.036755, 0.002285, 0.000009],
[0.591145, 0.036755, 0.000142],
],
[
[0.000035, 0.000002, 0.000000],
[0.009165, 0.000570, 0.000002],
[0.147403, 0.009165, 0.000035],
]]]])
actual = average_loss(variance_reg_losses(t, 0.6))
assert actual.item() == approx(0.18564102213775013)
import torch
import numpy
from dsntnn import variance_reg_losses
if __name__ == "__main__":
b, c, h, w = 5, 1, 256, 512
batch = torch.rand(b, c, h, w)
heatmaps = torch.nn.Softmax(dim=2)(
batch.reshape(b, c, -1)
).reshape(b, c, h, w)
var_loss = variance_reg_losses(heatmaps, 1.5)
print(var_loss)
