Merge remote-tracking branch 'origin/dev' into dev

test/test_autograd_bijections.py

@@ -8,8 +8,10 @@
 from torchflows.bijections.finite.autoregressive.architectures import NICE, RealNVP, CouplingRQNSF, MAF, IAF, \
     InverseAutoregressiveRQNSF, MaskedAutoregressiveRQNSF
 from torchflows.bijections.finite.autoregressive.layers import ElementwiseScale, ElementwiseAffine, ElementwiseShift, \
-    LRSCoupling, LinearRQSCoupling
-from torchflows.bijections.finite.linear import LU, ReversePermutation, LowerTriangular, Orthogonal, QR
+    LRSCoupling, LinearRQSCoupling, ElementwiseRQSpline
+from torchflows.bijections.finite.matrix import HouseholderProductMatrix, LowerTriangularInvertibleMatrix, \
+    UpperTriangularInvertibleMatrix, IdentityMatrix, RandomPermutationMatrix, ReversePermutationMatrix, QRMatrix, \
+    LUMatrix
 from torchflows.bijections.finite.residual.architectures import InvertibleResNet, ResFlow, ProximalResFlow
 from torchflows.utils import get_batch_shape
 from test.constants import __test_constants
@@ -43,19 +45,33 @@ def assert_valid_log_probability_gradient(bijection: Bijection, x: torch.Tensor,
 
 
 @pytest.mark.parametrize('bijection_class', [
-    LU,
-    ReversePermutation,
     ElementwiseScale,
-    LowerTriangular,
-    Orthogonal,
-    QR,
     ElementwiseAffine,
-    ElementwiseShift
+    ElementwiseShift,
+    ElementwiseRQSpline
 ])
 @pytest.mark.parametrize('batch_shape', __test_constants['batch_shape'])
 @pytest.mark.parametrize('event_shape', __test_constants['event_shape'])
 @pytest.mark.parametrize('context_shape', __test_constants['context_shape'])
-def test_linear(bijection_class: Bijection, batch_shape: Tuple, event_shape: Tuple, context_shape: Tuple):
+def test_elementwise(bijection_class: Bijection, batch_shape: Tuple, event_shape: Tuple, context_shape: Tuple):
+    bijection, x, context = setup_data(bijection_class, batch_shape, event_shape, context_shape)
+    assert_valid_log_probability_gradient(bijection, x, context)
+
+
+@pytest.mark.parametrize('bijection_class', [
+    IdentityMatrix,
+    RandomPermutationMatrix,
+    ReversePermutationMatrix,
+    LowerTriangularInvertibleMatrix,
+    UpperTriangularInvertibleMatrix,
+    HouseholderProductMatrix,
+    QRMatrix,
+    LUMatrix,
+])
+@pytest.mark.parametrize('batch_shape', __test_constants['batch_shape'])
+@pytest.mark.parametrize('event_shape', __test_constants['event_shape'])
+@pytest.mark.parametrize('context_shape', __test_constants['context_shape'])
+def test_matrix(bijection_class: Bijection, batch_shape: Tuple, event_shape: Tuple, context_shape: Tuple):
     bijection, x, context = setup_data(bijection_class, batch_shape, event_shape, context_shape)
     assert_valid_log_probability_gradient(bijection, x, context)
 

test/test_deepcopy.py

@@ -1,26 +1,33 @@
 from copy import deepcopy
 
+import pytest
 import torch
 
-from torchflows import RNODE, Flow
+from torchflows import RNODE, Flow, Sylvester, RealNVP
+from torchflows.bijections.base import invert
 
 
-def test_basic():
+@pytest.mark.parametrize('flow_class', [RNODE, Sylvester, RealNVP])
+def test_basic(flow_class):
     torch.manual_seed(0)
-    b = RNODE(event_shape=(10,))
+    b = flow_class(event_shape=(10,))
     deepcopy(b)
 
 
-def test_post_variational_fit():
+@pytest.mark.parametrize('flow_class', [RNODE, Sylvester, RealNVP])
+def test_post_variational_fit(flow_class):
     torch.manual_seed(0)
-    b = RNODE(event_shape=(10,))
+    b = flow_class(event_shape=(10,))
     f = Flow(b)
     f.variational_fit(lambda x: torch.sum(-x ** 2), n_epochs=2)
     deepcopy(b)
 
-def test_post_fit():
+@pytest.mark.parametrize('flow_class', [RNODE, Sylvester, RealNVP])
+def test_post_fit(flow_class):
     torch.manual_seed(0)
-    b = RNODE(event_shape=(10,))
+    b = flow_class(event_shape=(10,))
+    if isinstance(b, Sylvester):
+        b = invert(b)
     f = Flow(b)
     f.fit(x_train=torch.randn(3, *b.event_shape), n_epochs=2)
     deepcopy(b)

test/test_fit.py

@@ -6,15 +6,15 @@
     MaskedAutoregressiveRQNSF
 from torchflows.bijections.finite.autoregressive.layers import ElementwiseScale, ElementwiseAffine, ElementwiseShift, \
     ElementwiseRQSpline
-from torchflows.bijections.finite.linear import LowerTriangular, LU, QR
+from torchflows.bijections.finite.matrix import LowerTriangularInvertibleMatrix, LUMatrix, QRMatrix
 
 
 @pytest.mark.skip(reason='Takes too long, fit quality is architecture-dependent')
 @pytest.mark.parametrize('bijection_class', [
-    LowerTriangular,
+    LowerTriangularInvertibleMatrix,
     ElementwiseScale,
-    LU,
-    QR,
+    LUMatrix,
+    QRMatrix,
     ElementwiseAffine,
     ElementwiseShift,
     ElementwiseRQSpline,
@@ -83,9 +83,9 @@ def test_diagonal_gaussian_elementwise_scale():
 @pytest.mark.skip(reason='Takes too long, fit quality is architecture-dependent')
 @pytest.mark.parametrize('bijection_class',
                          [
-                             LowerTriangular,
-                             LU,
-                             QR,
+                             LowerTriangularInvertibleMatrix,
+                             LUMatrix,
+                             QRMatrix,
                              MaskedAutoregressiveRQNSF,
                              ElementwiseRQSpline,
                              ElementwiseAffine,
@@ -102,7 +102,7 @@ def test_diagonal_gaussian_1(bijection_class):
     x = torch.randn(size=(n_data, n_dim)) * sigma
     bijection = bijection_class(event_shape=(n_dim,))
     flow = Flow(bijection)
-    if isinstance(bijection, LowerTriangular):
+    if isinstance(bijection, LowerTriangularInvertibleMatrix):
         flow.fit(x, n_epochs=100)
     else:
         flow.fit(x, n_epochs=25)

test/test_reconstruction_bijections.py

@@ -12,7 +12,8 @@
     InverseAutoregressiveRQNSF, MaskedAutoregressiveRQNSF
 from torchflows.bijections.finite.autoregressive.layers import ElementwiseScale, ElementwiseAffine, ElementwiseShift, \
     LRSCoupling, LinearRQSCoupling, ActNorm, DenseSigmoidalCoupling, DeepDenseSigmoidalCoupling, DeepSigmoidalCoupling
-from torchflows.bijections.finite.linear import LU, ReversePermutation, LowerTriangular, Orthogonal, QR
+from torchflows.bijections.finite.matrix import LUMatrix, ReversePermutationMatrix, LowerTriangularInvertibleMatrix, \
+    HouseholderProductMatrix, QRMatrix
 from torchflows.bijections.finite.residual.architectures import ResFlow, InvertibleResNet, ProximalResFlow
 from torchflows.bijections.finite.residual.iterative import InvertibleResNetBlock, ResFlowBlock
 from torchflows.bijections.finite.residual.planar import Planar
@@ -122,12 +123,12 @@ def assert_valid_reconstruction_continuous(bijection: ContinuousBijection,
 
 
 @pytest.mark.parametrize('bijection_class', [
-    LU,
-    ReversePermutation,
+    LUMatrix,
+    ReversePermutationMatrix,
     ElementwiseScale,
-    LowerTriangular,
-    Orthogonal,
-    QR,
+    LowerTriangularInvertibleMatrix,
+    HouseholderProductMatrix,
+    QRMatrix,
     ElementwiseAffine,
     ElementwiseShift,
     ActNorm