def check_equivariance(self,
atol: float = 1e-7,
rtol: float = 1e-5) -> List[Tuple[Any, float]]:
_, parent_mapping, _ = self.in_type.gspace.restrict(self._id)
c = self.in_type.size
x = torch.randn(3, c, 10, 10)
x = GeometricTensor(x, self.in_type)
errors = []
for el in self.out_type.testing_elements:
print(el)
out1 = self(x).transform(el).tensor.detach().numpy()
out2 = self(x.transform(
parent_mapping(el))).tensor.detach().numpy()
errs = out1 - out2
errs = np.abs(errs).reshape(-1)
print(el, errs.max(), errs.mean(), errs.var())
assert np.allclose(out1, out2, atol=atol, rtol=rtol), \
'The error found during equivariance check with element "{}" is too high: max = {}, mean = {} var ={}' \
.format(el, errs.max(), errs.mean(), errs.var())
errors.append((el, errs.mean()))
return errors
def check_equivariance(self,
atol: float = 1e-7,
rtol: float = 1e-5) -> List[Tuple[Any, float]]:
r"""
Method that automatically tests the equivariance of the current module.
The default implementation of this method relies on :meth:`e2cnn.nn.GeometricTensor.transform` and uses the
the group elements in :attr:`~e2cnn.nn.FieldType.testing_elements`.
This method can be overwritten for custom tests.
Returns:
a list containing containing for each testing element a pair with that element and the corresponding
equivariance error
"""
c = self.in_type.size
x = torch.randn(3, c, 10, 10)
x = GeometricTensor(x, self.in_type)
errors = []
for el in self.out_type.testing_elements:
print(el)
out1 = self(x).transform(el).tensor.detach().numpy()
out2 = self(x.transform(el)).tensor.detach().numpy()
errs = out1 - out2
errs = np.abs(errs).reshape(-1)
print(el, errs.max(), errs.mean(), errs.var())
assert np.allclose(out1, out2, atol=atol, rtol=rtol), \
'The error found during equivariance check with element "{}" is too high: max = {}, mean = {} var ={}'\
.format(el, errs.max(), errs.mean(), errs.var())
errors.append((el, errs.mean()))
return errors
def check_equivariance(self,
atol: float = 0.1,
rtol: float = 0.1,
assertion: bool = True,
verbose: bool = True):
# np.set_printoptions(precision=5, threshold=30 *self.in_type.size**2, suppress=False, linewidth=30 *self.in_type.size**2)
feature_map_size = 33
last_downsampling = 5
first_downsampling = 5
initial_size = (feature_map_size * last_downsampling - 1 +
self.kernel_size) * first_downsampling
c = self.in_type.size
import matplotlib.image as mpimg
from skimage.measure import block_reduce
from skimage.transform import resize
x = mpimg.imread('../group/testimage.jpeg').transpose(
(2, 0, 1))[np.newaxis, 0:c, :, :]
x = resize(x, (x.shape[0], x.shape[1], initial_size, initial_size),
anti_aliasing=True)
x = x / 255.0 - 0.5
if x.shape[1] < c:
to_stack = [x for i in range(c // x.shape[1])]
if c % x.shape[1] > 0:
to_stack += [x[:, :(c % x.shape[1]), ...]]
x = np.concatenate(to_stack, axis=1)
x = GeometricTensor(torch.FloatTensor(x), self.in_type)
def shrink(t: GeometricTensor, s) -> GeometricTensor:
return GeometricTensor(
torch.FloatTensor(
block_reduce(t.tensor.detach().numpy(), s, func=np.mean)),
t.type)
errors = []
for el in self.space.testing_elements:
out1 = self(shrink(
x, (1, 1, 5, 5))).transform(el).tensor.detach().numpy()
out2 = self(shrink(x.transform(el),
(1, 1, 5, 5))).tensor.detach().numpy()
out1 = block_reduce(out1, (1, 1, 5, 5), func=np.mean)
out2 = block_reduce(out2, (1, 1, 5, 5), func=np.mean)
b, c, h, w = out2.shape
center_mask = np.zeros((2, h, w))
center_mask[1, :, :] = np.arange(0, w) - w / 2
center_mask[0, :, :] = np.arange(0, h) - h / 2
center_mask[0, :, :] = center_mask[0, :, :].T
center_mask = center_mask[0, :, :]**2 + center_mask[1, :, :]**2 < (
h / 4)**2
out1 = out1[..., center_mask]
out2 = out2[..., center_mask]
out1 = out1.reshape(-1)
out2 = out2.reshape(-1)
errs = np.abs(out1 - out2)
esum = np.maximum(np.abs(out1), np.abs(out2))
esum[esum == 0.0] = 1
relerr = errs / esum
if verbose:
print(el, relerr.max(), relerr.mean(), relerr.var(),
errs.max(), errs.mean(), errs.var())
tol = rtol * esum + atol
if np.any(errs > tol) and verbose:
print(out1[errs > tol])
print(out2[errs > tol])
print(tol[errs > tol])
if assertion:
assert np.all(
errs < tol
), 'The error found during equivariance check with element "{}" is too high: max = {}, mean = {} var ={}'.format(
el, errs.max(), errs.mean(), errs.var())
errors.append((el, errs.mean()))
return errors
def check_equivariance(self, atol: float = 0.1, rtol: float = 0.1):
initial_size = 55
c = self.in_type.size
# x = torch.randn(3, c, initial_size, initial_size)
import matplotlib.image as mpimg
from skimage.transform import resize
x = mpimg.imread('../group/testimage.jpeg').transpose(
(2, 0, 1))[np.newaxis, 0:c, :, :]
x = x / 255.0
x = resize(x, (x.shape[0], x.shape[1], initial_size, initial_size),
anti_aliasing=True)
if x.shape[1] < c:
to_stack = [x for i in range(c // x.shape[1])]
if c % x.shape[1] > 0:
to_stack += [x[:, :(c % x.shape[1]), ...]]
x = np.concatenate(to_stack, axis=1)
x = GeometricTensor(torch.FloatTensor(x), self.in_type)
errors = []
for el in self.space.testing_elements:
out1 = self(x).transform(el).tensor.detach().numpy()
out2 = self(x.transform(el)).tensor.detach().numpy()
b, c, h, w = out2.shape
center_mask = np.zeros((2, h, w))
center_mask[1, :, :] = np.arange(0, w) - w / 2
center_mask[0, :, :] = np.arange(0, h) - h / 2
center_mask[0, :, :] = center_mask[0, :, :].T
center_mask = center_mask[0, :, :]**2 + center_mask[1, :, :]**2 < (
h * 0.4)**2
out1 = out1[..., center_mask]
out2 = out2[..., center_mask]
out1 = out1.reshape(-1)
out2 = out2.reshape(-1)
errs = np.abs(out1 - out2)
esum = np.maximum(np.abs(out1), np.abs(out2))
esum[esum == 0.0] = 1
relerr = errs / esum
# print(el, relerr.max(), relerr.mean(), relerr.var(), errs.max(), errs.mean(), errs.var())
# tol = rtol*(np.abs(out1) + np.abs(out2)) + atol
tol = rtol * esum + atol
if np.any(errs > tol):
print(el, relerr.max(), relerr.mean(), relerr.var(),
errs.max(), errs.mean(), errs.var())
# print(errs[errs > tol])
print(out1[errs > tol])
print(out2[errs > tol])
print(tol[errs > tol])
# assert np.all(np.abs(out1 - out2) < tol), 'The error found during equivariance check with element "{}" is too high: max = {}, mean = {} var ={}'.format(el, errs.max(), errs.mean(), errs.var())
assert np.all(
errs < tol
), 'The error found during equivariance check with element "{}" is too high: max = {}, mean = {} var ={}'.format(
el, errs.max(), errs.mean(), errs.var())
errors.append((el, errs.mean()))
return errors
