[Bug fix] Misc Fix for Transforms and NN Modules (#4038)

* Update module.py

* Update utils.py

* Update utils.py

* Update utils.py

* Update module.py

* Update

* Update

* Update

python/dgl/nn/pytorch/utils.py

@@ -403,8 +403,8 @@ class LabelPropagation(nn.Module):
     Propagation <http://mlg.eng.cam.ac.uk/zoubin/papers/CMU-CALD-02-107.pdf>`__
 
     .. math::
-    \mathbf{Y}^{(t+1)} = \alpha \cdot \tilde{A} \mathbf{Y}^{(t)} + (1 - \alpha)
-    \mathbf{Y}^{(0)},
+
+        \mathbf{Y}^{(t+1)} = \alpha \tilde{A} \mathbf{Y}^{(t)} + (1 - \alpha) \mathbf{Y}^{(0)}
 
     where unlabeled data is initially set to zero and inferred from labeled data via
     propagation. :math:`\alpha` is a weight parameter for balancing between updated labels

python/dgl/transforms/module.py

@@ -121,11 +121,11 @@ class RowFeatNormalizer(BaseTransform):
         Subtraction will make all values non-negative. If all values are negative, after
         normalisation, the sum of each row of the feature tensor will be 1.
     node_feat_names : list[str], optional
-        The names of the node features to be normalized. Default: `None`.
-        If None, all node features will be normalized.
+        The names of the node feature tensors to be row-normalized. Default: `None`, which will
+        not normalize any node feature tensor.
     edge_feat_names : list[str], optional
-        The names of the edge features to be normalized. Default: `None`.
-        If None, all edge features will be normalized.
+        The names of the edge feature tensors to be row-normalized. Default: `None`, which will
+        not normalize any edge feature tensor.
 
     Example
     -------
@@ -138,51 +138,40 @@ class RowFeatNormalizer(BaseTransform):
 
     Case1: Row normalize features of a homogeneous graph.
 
-    >>> transform = RowFeatNormalizer()
+    >>> transform = RowFeatNormalizer(subtract_min=True,
+    ...                               node_feat_names=['h'], edge_feat_names=['w'])
     >>> g = dgl.rand_graph(5, 20)
     >>> g.ndata['h'] = torch.randn((g.num_nodes(), 5))
-    >>> print(g.ndata['h'].sum(1))
-    tensor([-3.0586,  3.4974,  3.1509,  1.5805,  0.4890])
-    >>> g.edata['w'] = torch.randn((g.num_edges, 5))
-    >>> print(g.edata['w'].sum(1))
-    tensor([ 2.9284, -3.8341,  1.5087,  0.8673, -2.5115,  2.4751,  0.1427,  1.7180,
-            2.7705,  1.0600, -0.7126,  0.1072, -0.8159,  1.2082,  2.0327, -2.3323,
-            -1.2495,  1.9458, -1.4240, -1.3575])
+    >>> g.edata['w'] = torch.randn((g.num_edges(), 5))
     >>> g = transform(g)
     >>> print(g.ndata['h'].sum(1))
-    tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000])
+    tensor([1., 1., 1., 1., 1.])
     >>> print(g.edata['w'].sum(1))
-    tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
-            1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
-            1.0000, 1.0000])
+    tensor([1., 1., 1., 1., 1., 1., 1., 1., 1.,
+            1., 1., 1., 1., 1., 1., 1., 1., 1.,
+            1., 1.])
 
     Case2: Row normalize features of a heterogeneous graph.
 
-    >>> transform = RowFeatNormalizer()
     >>> g = dgl.heterograph({
     ...     ('user', 'follows', 'user'): (torch.tensor([1, 2]), torch.tensor([3, 4])),
     ...     ('player', 'plays', 'game'): (torch.tensor([2, 2]), torch.tensor([1, 1]))
     ... })
     >>> g.ndata['h'] = {'game': torch.randn(2, 5), 'player': torch.randn(3, 5)}
-    >>> print(g.ndata['h']['game'].sum(1), g.ndata['h']['player'].sum(1))
-    tensor([ 4.4201, -4.0683]) tensor([-2.2460, -1.1204, -2.0254])
     >>> g.edata['w'] = {
     ...     ('user', 'follows', 'user'): torch.randn(2, 5),
     ...     ('player', 'plays', 'game'): torch.randn(2, 5)
     ... }
-    >>> print(g.edata['w'][('user', 'follows', 'user')].sum(1),
-    ...     g.edata['w'][('player', 'plays', 'game')].sum(1))
-    tensor([-1.2663,  3.3789]) tensor([4.1371, 1.4743])
     >>> g = transform(g)
     >>> print(g.ndata['h']['game'].sum(1), g.ndata['h']['player'].sum(1))
-    tensor([1.0000, 1.0000]) tensor([1.0000, 1.0000, 1.0000])
+    tensor([1., 1.]) tensor([1., 1., 1.])
     >>> print(g.edata['w'][('user', 'follows', 'user')].sum(1),
     ...     g.edata['w'][('player', 'plays', 'game')].sum(1))
-    tensor([1.0000, 1.0000]) tensor([1.0000, 1.0000])
+    tensor([1., 1.]) tensor([1., 1.])
     """
     def __init__(self, subtract_min=False, node_feat_names=None, edge_feat_names=None):
-        self.node_feat_names = node_feat_names
-        self.edge_feat_names = edge_feat_names
+        self.node_feat_names = [] if node_feat_names is None else node_feat_names
+        self.edge_feat_names = [] if edge_feat_names is None else edge_feat_names
         self.subtract_min = subtract_min
 
     def row_normalize(self, feat):
@@ -208,12 +197,6 @@ class RowFeatNormalizer(BaseTransform):
         return feat
 
     def __call__(self, g):
-        if self.node_feat_names is None:
-            self.node_feat_names = g.ndata.keys()
-
-        if self.edge_feat_names is None:
-            self.edge_feat_names = g.edata.keys()
-
         for node_feat_name in self.node_feat_names:
             if isinstance(g.ndata[node_feat_name], torch.Tensor):
                 g.ndata[node_feat_name] = self.row_normalize(g.ndata[node_feat_name])
@@ -233,21 +216,19 @@ class RowFeatNormalizer(BaseTransform):
         return g
 
 class FeatMask(BaseTransform):
-    r"""Randomly mask columns of the node and edge feature tensors, as described in `An Empirical
-    Study of Graph Contrastive Learning <https://arxiv.org/abs/2109.01116>`__.
+    r"""Randomly mask columns of the node and edge feature tensors, as described in `Graph
+    Contrastive Learning with Augmentations <https://arxiv.org/abs/2010.13902>`__.
 
     Parameters
     ----------
     p : float, optional
         Probability of masking a column of a feature tensor. Default: `0.5`.
     node_feat_names : list[str], optional
-        The names of the node feature tensors to be masked. Default: `None`.
-        If None, all node feature tensors will be randomly mask some columns according to
-        probability :attr:`p`.
+        The names of the node feature tensors to be masked. Default: `None`, which will
+        not mask any node feature tensor.
     edge_feat_names : list[str], optional
-        The names of the edge features to be masked. Default: `None`.
-        If None, all edge feature tensors will be randomly mask some columns according to
-        probability :attr:`p`.
+        The names of the edge features to be masked. Default: `None`, which will not mask
+        any edge feature tensor.
 
     Example
     -------
@@ -260,7 +241,7 @@ class FeatMask(BaseTransform):
 
     Case1 : Mask node and edge feature tensors of a homogeneous graph.
 
-    >>> transform = FeatMask()
+    >>> transform = FeatMask(node_feat_names=['h'], edge_feat_names=['w'])
     >>> g = dgl.rand_graph(5, 10)
     >>> g.ndata['h'] = torch.ones((g.num_nodes(), 10))
     >>> g.edata['w'] = torch.ones((g.num_edges(), 10))
@@ -268,53 +249,48 @@ class FeatMask(BaseTransform):
     >>> g = transform(g)
     >>> print(g.ndata['h'])
     tensor([[0., 0., 1., 1., 0., 0., 1., 1., 1., 0.],
-        [0., 0., 1., 1., 0., 0., 1., 1., 1., 0.],
-        [0., 0., 1., 1., 0., 0., 1., 1., 1., 0.],
-        [0., 0., 1., 1., 0., 0., 1., 1., 1., 0.],
-        [0., 0., 1., 1., 0., 0., 1., 1., 1., 0.]])
+            [0., 0., 1., 1., 0., 0., 1., 1., 1., 0.],
+            [0., 0., 1., 1., 0., 0., 1., 1., 1., 0.],
+            [0., 0., 1., 1., 0., 0., 1., 1., 1., 0.],
+            [0., 0., 1., 1., 0., 0., 1., 1., 1., 0.]])
     >>> print(g.edata['w'])
     tensor([[1., 1., 0., 1., 0., 1., 0., 0., 0., 1.],
-        [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.],
-        [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.],
-        [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.],
-        [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.],
-        [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.],
-        [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.],
-        [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.],
-        [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.],
-        [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.]])
+            [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.],
+            [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.],
+            [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.],
+            [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.],
+            [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.],
+            [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.],
+            [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.],
+            [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.],
+            [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.]])
 
     Case2 : Mask node and edge feature tensors of a heterogeneous graph.
 
-    >>> transform = FeatMask()
     >>> g = dgl.heterograph({
     ...     ('user', 'follows', 'user'): (torch.tensor([1, 2]), torch.tensor([3, 4])),
     ...     ('player', 'plays', 'game'): (torch.tensor([2, 2]), torch.tensor([1, 1]))
     ... })
     >>> g.ndata['h'] = {'game': torch.ones(2, 5), 'player': torch.ones(3, 5)}
     >>> g.edata['w'] = {('user', 'follows', 'user'): torch.ones(2, 5)}
-    >>> print(g.ndata['h']['game'], g.ndata['h']['player'])
+    >>> print(g.ndata['h']['game'])
     tensor([[1., 1., 1., 1., 1.],
-        [1., 1., 1., 1., 1.]]) tensor([[1., 1., 1., 1., 1.],
-        [1., 1., 1., 1., 1.],
-        [1., 1., 1., 1., 1.]])
+            [1., 1., 1., 1., 1.]])
     >>> print(g.edata['w'][('user', 'follows', 'user')])
     tensor([[1., 1., 1., 1., 1.],
-        [1., 1., 1., 1., 1.]])
+            [1., 1., 1., 1., 1.]])
     >>> g = transform(g)
-    >>> print(g.ndata['h']['game'], g.ndata['h']['player'])
+    >>> print(g.ndata['h']['game'])
     tensor([[1., 1., 0., 1., 0.],
-        [1., 1., 0., 1., 0.]]) tensor([[0., 0., 0., 0., 1.],
-        [0., 0., 0., 0., 1.],
-        [0., 0., 0., 0., 1.]])
+            [1., 1., 0., 1., 0.]])
     >>> print(g.edata['w'][('user', 'follows', 'user')])
     tensor([[0., 1., 0., 1., 0.],
-        [0., 1., 0., 1., 0.]])
+            [0., 1., 0., 1., 0.]])
     """
     def __init__(self, p=0.5, node_feat_names=None, edge_feat_names=None):
         self.p = p
-        self.node_feat_names = node_feat_names
-        self.edge_feat_names = edge_feat_names
+        self.node_feat_names = [] if node_feat_names is None else node_feat_names
+        self.edge_feat_names = [] if edge_feat_names is None else edge_feat_names
         self.dist = Bernoulli(p)
 
     def __call__(self, g):
@@ -322,12 +298,6 @@ class FeatMask(BaseTransform):
         if self.p == 0:
             return g
 
-        if self.node_feat_names is None:
-            self.node_feat_names = g.ndata.keys()
-
-        if self.edge_feat_names is None:
-            self.edge_feat_names = g.edata.keys()
-
         for node_feat_name in self.node_feat_names:
             if isinstance(g.ndata[node_feat_name], torch.Tensor):
                 feat_mask = self.dist.sample(torch.Size([g.ndata[node_feat_name].shape[-1], ]))
@@ -1592,6 +1562,7 @@ class SIGNDiffusion(BaseTransform):
 
         for i in range(1, self.k + 1):
             g.ndata[self.out_feat_name + '_' + str(i)] = feat_list[i - 1]
+        return g
 
     def raw(self, g):
         use_eweight = False

tests/compute/test_transform.py

@@ -2370,54 +2370,56 @@ def test_module_sign(g):
 
     # raw
     transform = dgl.SIGNDiffusion(k=1, in_feat_name='h', diffuse_op='raw')
-    transform(g)
+    g = transform(g)
     assert torch.allclose(g.ndata['out_feat_1'], torch.matmul(adj, g.ndata['h']))
 
     transform = dgl.SIGNDiffusion(k=1, in_feat_name='h', eweight_name='scalar_w', diffuse_op='raw')
-    transform(g)
+    g = transform(g)
     assert torch.allclose(g.ndata['out_feat_1'], torch.matmul(weight_adj, g.ndata['h']))
 
     # rw
     adj_rw = torch.matmul(torch.diag(1 / adj.sum(dim=1)), adj)
     transform = dgl.SIGNDiffusion(k=1, in_feat_name='h', diffuse_op='rw')
-    transform(g)
+    g = transform(g)
     assert torch.allclose(g.ndata['out_feat_1'], torch.matmul(adj_rw, g.ndata['h']))
 
     weight_adj_rw = torch.matmul(torch.diag(1 / weight_adj.sum(dim=1)), weight_adj)
     transform = dgl.SIGNDiffusion(k=1, in_feat_name='h', eweight_name='scalar_w', diffuse_op='rw')
-    transform(g)
+    g = transform(g)
     assert torch.allclose(g.ndata['out_feat_1'], torch.matmul(weight_adj_rw, g.ndata['h']))
 
     # gcn
     raw_eweight = g.edata['scalar_w']
     gcn_norm = dgl.GCNNorm()
-    gcn_norm(g)
+    g = gcn_norm(g)
     adj_gcn = adj.clone()
     adj_gcn[dst, src] = g.edata.pop('w')
     transform = dgl.SIGNDiffusion(k=1, in_feat_name='h', diffuse_op='gcn')
-    transform(g)
-    assert torch.allclose(g.ndata['out_feat_1'], torch.matmul(adj_gcn, g.ndata['h']))
+    g = transform(g)
+    target = torch.matmul(adj_gcn, g.ndata['h'])
+    assert torch.allclose(g.ndata['out_feat_1'], target)
 
     gcn_norm = dgl.GCNNorm('scalar_w')
-    gcn_norm(g)
+    g = gcn_norm(g)
     weight_adj_gcn = weight_adj.clone()
     weight_adj_gcn[dst, src] = g.edata['scalar_w']
     g.edata['scalar_w'] = raw_eweight
     transform = dgl.SIGNDiffusion(k=1, in_feat_name='h',
                                   eweight_name='scalar_w', diffuse_op='gcn')
-    transform(g)
-    assert torch.allclose(g.ndata['out_feat_1'], torch.matmul(weight_adj_gcn, g.ndata['h']))
+    g = transform(g)
+    target = torch.matmul(weight_adj_gcn, g.ndata['h'])
+    assert torch.allclose(g.ndata['out_feat_1'], target)
 
     # ppr
     alpha = 0.2
     transform = dgl.SIGNDiffusion(k=1, in_feat_name='h', diffuse_op='ppr', alpha=alpha)
-    transform(g)
+    g = transform(g)
     target = (1 - alpha) * torch.matmul(adj_gcn, g.ndata['h']) + alpha * g.ndata['h']
     assert torch.allclose(g.ndata['out_feat_1'], target)
 
     transform = dgl.SIGNDiffusion(k=1, in_feat_name='h', eweight_name='scalar_w',
                                   diffuse_op='ppr', alpha=alpha)
-    transform(g)
+    g = transform(g)
     target = (1 - alpha) * torch.matmul(weight_adj_gcn, g.ndata['h']) + alpha * g.ndata['h']
     assert torch.allclose(g.ndata['out_feat_1'], target)
 
@@ -2425,7 +2427,8 @@ def test_module_sign(g):
 @parametrize_idtype
 def test_module_row_feat_normalizer(idtype):
     # Case1: Normalize features of a homogeneous graph.
-    transform = dgl.RowFeatNormalizer(subtract_min=True)
+    transform = dgl.RowFeatNormalizer(subtract_min=True,
+                                      node_feat_names=['h'], edge_feat_names=['w'])
     g = dgl.rand_graph(5, 5, idtype=idtype, device=F.ctx())
     g.ndata['h'] = F.randn((g.num_nodes(), 128))
     g.edata['w'] = F.randn((g.num_edges(), 128))
@@ -2436,7 +2439,8 @@ def test_module_row_feat_normalizer(idtype):
     assert F.allclose(g.edata['w'].sum(1), F.tensor([1.0, 1.0, 1.0, 1.0, 1.0]))
 
     # Case2: Normalize features of a heterogeneous graph.
-    transform = dgl.RowFeatNormalizer(subtract_min=True)
+    transform = dgl.RowFeatNormalizer(subtract_min=True,
+                                      node_feat_names=['h', 'h2'], edge_feat_names=['w'])
     g = dgl.heterograph({
         ('user', 'follows', 'user'): (F.tensor([1, 2]), F.tensor([3, 4])),
         ('player', 'plays', 'game'): (F.tensor([2, 2]), F.tensor([1, 1]))
@@ -2460,7 +2464,7 @@ def test_module_row_feat_normalizer(idtype):
 @parametrize_idtype
 def test_module_feat_mask(idtype):
     # Case1: Mask node and edge feature tensors of a homogeneous graph.
-    transform = dgl.FeatMask()
+    transform = dgl.FeatMask(node_feat_names=['h'], edge_feat_names=['w'])
     g = dgl.rand_graph(5, 20, idtype=idtype, device=F.ctx())
     g.ndata['h'] = F.ones((g.num_nodes(), 10))
     g.edata['w'] = F.ones((g.num_edges(), 20))
@@ -2471,7 +2475,6 @@ def test_module_feat_mask(idtype):
     assert g.edata['w'].shape == (g.num_edges(), 20)
 
     # Case2: Mask node and edge feature tensors of a heterogeneous graph.
-    transform = dgl.FeatMask()
     g = dgl.heterograph({
         ('user', 'follows', 'user'): (F.tensor([1, 2]), F.tensor([3, 4])),
         ('player', 'plays', 'game'): (F.tensor([2, 2]), F.tensor([1, 1]))