Remove all instances of for x in set(y)

This pattern is replaced with `for x in unique_ever_seen(y)`, which also
removes duplicate elements, but is guaranteed to always produce elements
in the same order as in the input list.

Signed-off-by: Kale Kundert <[email protected]>

escnn/kernels/steerable_basis.py

@@ -5,6 +5,7 @@
 from escnn.group import Group
 from escnn.group import IrreducibleRepresentation
 from escnn.group import Representation
+from escnn.utils import unique_ever_seen
 
 import torch
 
@@ -238,9 +239,9 @@ def __init__(self,
         js = set()
 
         # loop over all input irreps
-        for i_irrep_id in set(in_repr.irreps):
+        for i_irrep_id in unique_ever_seen(in_repr.irreps):
             # loop over all output irreps
-            for o_irrep_id in set(out_repr.irreps):
+            for o_irrep_id in unique_ever_seen(out_repr.irreps):
                 try:
                     # retrieve the irrep intertwiner basis
                     intertwiner_basis = irreps_basis._generator(basis, i_irrep_id, o_irrep_id, **kwargs)

escnn/kernels/wignereckart_solver.py

@@ -4,6 +4,7 @@
 from .steerable_filters_basis import SteerableFiltersBasis
 
 from escnn.group import *
+from escnn.utils import unique_ever_seen
 
 import torch
 
@@ -302,7 +303,7 @@ def __init__(self,
         _js_restriction = defaultdict(list)
 
         # for each harmonic j' to consider
-        for _j in set(_j for _j, _ in basis.js):
+        for _j in unique_ever_seen(_j for _j, _ in basis.js):
             if basis.multiplicity(_j) == 0:
                 continue
 

escnn/nn/modules/basismanager/basisexpansion_blocks.py

@@ -2,7 +2,7 @@
 from escnn.kernels import KernelBasis, EmptyBasisException
 from escnn.group import Representation
 from escnn.nn.modules import utils
-from escnn.nn.modules.utils import unique_ever_seen
+from escnn.utils import unique_ever_seen
 
 from .basismanager import BasisManager
 from .basisexpansion_singleblock import block_basisexpansion

escnn/nn/modules/basismanager/basissampler_blocks.py

@@ -1,13 +1,13 @@
 
 from escnn.group import Representation
 from escnn.kernels import KernelBasis, EmptyBasisException
+from escnn.utils import unique_ever_seen
 
 
 from escnn.nn.modules.basismanager import retrieve_indices
 from .basismanager import BasisManager
 
 from escnn.nn.modules.basismanager.basissampler_singleblock import block_basissampler
-from escnn.nn.modules.utils import unique_ever_seen
 
 from typing import Callable, Tuple, Dict, List, Iterable, Union
 from collections import defaultdict

escnn/nn/modules/batchnormalization/gnorm.py

@@ -5,7 +5,7 @@
 from escnn.gspaces import *
 from escnn.nn import FieldType
 from escnn.nn import GeometricTensor
-from escnn.nn.modules.utils import unique_ever_seen
+from escnn.utils import unique_ever_seen
 
 from ..equivariant_module import EquivariantModule
 

escnn/nn/modules/batchnormalization/iid.py

@@ -5,7 +5,7 @@
 from escnn.gspaces import *
 from escnn.nn import FieldType
 from escnn.nn import GeometricTensor
-from escnn.nn.modules.utils import unique_ever_seen
+from escnn.utils import unique_ever_seen
 
 from ..equivariant_module import EquivariantModule
 

escnn/nn/modules/utils.py

@@ -1,6 +1,6 @@
 
 from escnn.nn import FieldType
-from typing import List, Dict, Tuple, Iterable
+from typing import List, Dict, Tuple
 from collections import defaultdict
 
 
@@ -55,14 +55,3 @@ def indexes_from_labels(in_type: FieldType, labels: List[str]) -> Dict[str, Tupl
 
     return groups
 
-
-def unique_ever_seen(iterable: Iterable) -> Iterable:
-    already_seen = set()
-
-    for item in iterable:
-        if item in already_seen:
-            continue
-        else:
-            already_seen.add(item)
-            yield item
-

escnn/utils.py

@@ -0,0 +1,13 @@
+from typing import Iterable
+
+def unique_ever_seen(iterable: Iterable) -> Iterable:
+    already_seen = set()
+
+    for item in iterable:
+        if item in already_seen:
+            continue
+        else:
+            already_seen.add(item)
+            yield item
+
+

test/nn/test_basisexpansion.py

@@ -135,17 +135,17 @@ def compare(self, basis: BlocksBasisExpansion):
 
         for i, attr1 in enumerate(basis.get_basis_info()):
             attr2 = basis.get_element_info(i)
-            self.assertEquals(attr1, attr2)
-            self.assertEquals(attr1['id'], i)
+            self.assertEqual(attr1, attr2)
+            self.assertEqual(attr1['id'], i)
 
         for _ in range(5):
             w = torch.randn(basis.dimension())
 
             f1 = basis(w)
             f2 = basis(w)
             assert torch.allclose(f1, f2)
-            self.assertEquals(f1.shape[1], basis._input_size)
-            self.assertEquals(f1.shape[0], basis._output_size)
+            self.assertEqual(f1.shape[1], basis._input_size)
+            self.assertEqual(f1.shape[0], basis._output_size)
 
 
     def test_checkpoint_meshgrid(self):

test/nn/test_basissampler.py

@@ -126,8 +126,8 @@ def compare(self, basis: BlocksBasisSampler, d: int):
 
         for i, attr1 in enumerate(basis.get_basis_info()):
             attr2 = basis.get_element_info(i)
-            self.assertEquals(attr1, attr2)
-            self.assertEquals(attr1['id'], i)
+            self.assertEqual(attr1, attr2)
+            self.assertEqual(attr1['id'], i)
 
         for _ in range(5):
             P = 20
@@ -150,8 +150,8 @@ def compare(self, basis: BlocksBasisSampler, d: int):
             f1 = basis(w, edge_delta)
             f2 = basis(w, edge_delta)
             self.assertTrue(torch.allclose(f1, f2))
-            self.assertEquals(f1.shape[2], basis._input_size)
-            self.assertEquals(f1.shape[1], basis._output_size)
+            self.assertEqual(f1.shape[2], basis._input_size)
+            self.assertEqual(f1.shape[1], basis._output_size)
 
             y1 = basis.compute_messages(w, x_j, edge_delta, conv_first=False)
             y2 = basis.compute_messages(w, x_j, edge_delta, conv_first=True)