revert to naive branching resolution

requirements.txt

@@ -8,4 +8,3 @@ tqdm
 networkx
 ninja
 jinja2
-class-resolver

torchdrug/layers/__init__.py

@@ -3,7 +3,7 @@
 from .conv import MessagePassingBase, GraphConv, GraphAttentionConv, RelationalGraphConv, GraphIsomorphismConv, \
     NeuralFingerprintConv, ContinuousFilterConv, MessagePassing, ChebyshevConv
 from .pool import DiffPool, MinCutPool
-from .readout import MeanReadout, SumReadout, MaxReadout, Softmax, Set2Set, Sort, readout_resolver, Readout
+from .readout import MeanReadout, SumReadout, MaxReadout, Softmax, Set2Set, Sort
 from .flow import ConditionalFlow
 from .sampler import NodeSampler, EdgeSampler
 from . import distribution, functional
@@ -23,7 +23,7 @@
     "MessagePassingBase", "GraphConv", "GraphAttentionConv", "RelationalGraphConv", "GraphIsomorphismConv",
     "NeuralFingerprintConv", "ContinuousFilterConv", "MessagePassing", "ChebyshevConv",
     "DiffPool", "MinCutPool",
-    "MeanReadout", "SumReadout", "MaxReadout", "Softmax", "Set2Set", "Sort", "readout_resolver", "Readout",
+    "MeanReadout", "SumReadout", "MaxReadout", "Softmax", "Set2Set", "Sort",
     "ConditionalFlow",
     "NodeSampler", "EdgeSampler",
     "distribution", "functional",

torchdrug/layers/readout.py

@@ -1,14 +1,9 @@
 import torch
 from torch import nn
 from torch_scatter import scatter_mean, scatter_add, scatter_max
-from class_resolver import ClassResolver
 
 
-class Readout(nn.Module):
-    """A base class for readouts."""
-
-
-class MeanReadout(Readout):
+class MeanReadout(nn.Module):
     """Mean readout operator over graphs with variadic sizes."""
 
     def forward(self, graph, input):
@@ -26,7 +21,7 @@ def forward(self, graph, input):
         return output
 
 
-class SumReadout(Readout):
+class SumReadout(nn.Module):
     """Sum readout operator over graphs with variadic sizes."""
 
     def forward(self, graph, input):
@@ -44,7 +39,7 @@ def forward(self, graph, input):
         return output
 
 
-class MaxReadout(Readout):
+class MaxReadout(nn.Module):
     """Max readout operator over graphs with variadic sizes."""
 
     def forward(self, graph, input):
@@ -62,12 +57,6 @@ def forward(self, graph, input):
         return output
 
 
-readout_resolver = ClassResolver.from_subclasses(
-    Readout,
-    default=SumReadout,
-)
-
-
 class Softmax(nn.Module):
     """Softmax operator over graphs with variadic sizes."""
 

torchdrug/models/chebnet.py

@@ -1,12 +1,10 @@
 from collections.abc import Sequence
 
 import torch
-from class_resolver import Hint
 from torch import nn
 
 from torchdrug import core, layers
 from torchdrug.core import Registry as R
-from torchdrug.layers import Readout, readout_resolver
 
 
 @R.register("models.ChebNet")
@@ -31,7 +29,7 @@ class ChebyshevConvolutionalNetwork(nn.Module, core.Configurable):
     """
 
     def __init__(self, input_dim, hidden_dims, edge_input_dim=None, k=1, short_cut=False, batch_norm=False,
-                 activation="relu", concat_hidden=False, readout: Hint[Readout] = "sum"):
+                 activation="relu", concat_hidden=False, readout="sum"):
         super(ChebyshevConvolutionalNetwork, self).__init__()
 
         if not isinstance(hidden_dims, Sequence):
@@ -47,7 +45,14 @@ def __init__(self, input_dim, hidden_dims, edge_input_dim=None, k=1, short_cut=F
             self.layers.append(layers.ChebyshevConv(self.dims[i], self.dims[i + 1], edge_input_dim, k,
                                                     batch_norm, activation))
 
-        self.readout = readout_resolver.make(readout)
+        if readout == "sum":
+            self.readout = layers.SumReadout()
+        elif readout == "mean":
+            self.readout = layers.MeanReadout()
+        elif readout == "max":
+            self.readout = layers.MaxReadout()
+        else:
+            raise ValueError("Unknown readout `%s`" % readout)
 
     def forward(self, graph, input, all_loss=None, metric=None):
         """

torchdrug/models/gat.py

@@ -1,12 +1,10 @@
 from collections.abc import Sequence
 
 import torch
-from class_resolver import Hint
 from torch import nn
 
 from torchdrug import core, layers
 from torchdrug.core import Registry as R
-from torchdrug.layers import Readout, readout_resolver
 
 
 @R.register("models.GAT")
@@ -31,7 +29,7 @@ class GraphAttentionNetwork(nn.Module, core.Configurable):
     """
 
     def __init__(self, input_dim, hidden_dims, edge_input_dim=None, num_head=1, negative_slope=0.2, short_cut=False,
-                 batch_norm=False, activation="relu", concat_hidden=False, readout: Hint[Readout] = "sum"):
+                 batch_norm=False, activation="relu", concat_hidden=False, readout="sum"):
         super(GraphAttentionNetwork, self).__init__()
 
         if not isinstance(hidden_dims, Sequence):
@@ -47,7 +45,14 @@ def __init__(self, input_dim, hidden_dims, edge_input_dim=None, num_head=1, nega
             self.layers.append(layers.GraphAttentionConv(self.dims[i], self.dims[i + 1], edge_input_dim, num_head,
                                                          negative_slope, batch_norm, activation))
 
-        self.readout = readout_resolver.make(readout)
+        if readout == "sum":
+            self.readout = layers.SumReadout()
+        elif readout == "mean":
+            self.readout = layers.MeanReadout()
+        elif readout == "max":
+            self.readout = layers.MaxReadout()
+        else:
+            raise ValueError("Unknown readout `%s`" % readout)
 
     def forward(self, graph, input, all_loss=None, metric=None):
         """

torchdrug/models/gcn.py

@@ -1,12 +1,10 @@
 from collections.abc import Sequence
 
 import torch
-from class_resolver import Hint
 from torch import nn
 
 from torchdrug import core, layers
 from torchdrug.core import Registry as R
-from torchdrug.layers import Readout, readout_resolver
 
 
 @R.register("models.GCN")
@@ -29,7 +27,7 @@ class GraphConvolutionalNetwork(nn.Module, core.Configurable):
     """
 
     def __init__(self, input_dim, hidden_dims, edge_input_dim=None, short_cut=False, batch_norm=False,
-                 activation="relu", concat_hidden=False, readout: Hint[Readout] = "sum"):
+                 activation="relu", concat_hidden=False, readout="sum"):
         super(GraphConvolutionalNetwork, self).__init__()
 
         if not isinstance(hidden_dims, Sequence):
@@ -44,7 +42,14 @@ def __init__(self, input_dim, hidden_dims, edge_input_dim=None, short_cut=False,
         for i in range(len(self.dims) - 1):
             self.layers.append(layers.GraphConv(self.dims[i], self.dims[i + 1], edge_input_dim, batch_norm, activation))
 
-        self.readout = readout_resolver.make(readout)
+        if readout == "sum":
+            self.readout = layers.SumReadout()
+        elif readout == "mean":
+            self.readout = layers.MeanReadout()
+        elif readout == "max":
+            self.readout = layers.MaxReadout()
+        else:
+            raise ValueError("Unknown readout `%s`" % readout)
 
     def forward(self, graph, input, all_loss=None, metric=None):
         """

torchdrug/models/gin.py

@@ -1,12 +1,10 @@
 from collections.abc import Sequence
 
 import torch
-from class_resolver import Hint
 from torch import nn
 
 from torchdrug import core, layers
 from torchdrug.core import Registry as R
-from torchdrug.layers import Readout, readout_resolver
 
 
 @R.register("models.GIN")
@@ -32,8 +30,7 @@ class GraphIsomorphismNetwork(nn.Module, core.Configurable):
     """
 
     def __init__(self, input_dim=None, hidden_dims=None, edge_input_dim=None, num_mlp_layer=2, eps=0, learn_eps=False,
-                 short_cut=False, batch_norm=False, activation="relu", concat_hidden=False,
-                 readout: Hint[Readout] = "sum"):
+                 short_cut=False, batch_norm=False, activation="relu", concat_hidden=False, readout="sum"):
         super(GraphIsomorphismNetwork, self).__init__()
 
         if not isinstance(hidden_dims, Sequence):
@@ -50,7 +47,14 @@ def __init__(self, input_dim=None, hidden_dims=None, edge_input_dim=None, num_ml
             self.layers.append(layers.GraphIsomorphismConv(self.dims[i], self.dims[i + 1], edge_input_dim,
                                                            layer_hidden_dims, eps, learn_eps, batch_norm, activation))
 
-        self.readout = readout_resolver.make(readout)
+        if readout == "sum":
+            self.readout = layers.SumReadout()
+        elif readout == "mean":
+            self.readout = layers.MeanReadout()
+        elif readout == "max":
+            self.readout = layers.MaxReadout()
+        else:
+            raise ValueError("Unknown readout `%s`" % readout)
 
     def forward(self, graph, input, all_loss=None, metric=None):
         """

torchdrug/models/neuralfp.py

@@ -1,13 +1,11 @@
 from collections.abc import Sequence
 
 import torch
-from class_resolver import Hint
 from torch import nn
 from torch.nn import functional as F
 
 from torchdrug import core, layers
 from torchdrug.core import Registry as R
-from torchdrug.layers import Readout, readout_resolver
 
 
 @R.register("models.NeuralFP")
@@ -31,7 +29,7 @@ class NeuralFingerprint(nn.Module, core.Configurable):
     """
 
     def __init__(self, input_dim, output_dim, hidden_dims, edge_input_dim=None, short_cut=False, batch_norm=False,
-                 activation="relu", concat_hidden=False, readout: Hint[Readout] = "sum"):
+                 activation="relu", concat_hidden=False, readout="sum"):
         super(NeuralFingerprint, self).__init__()
 
         if not isinstance(hidden_dims, Sequence):
@@ -49,7 +47,14 @@ def __init__(self, input_dim, output_dim, hidden_dims, edge_input_dim=None, shor
                                                             batch_norm, activation))
             self.linears.append(nn.Linear(self.dims[i + 1], output_dim))
 
-        self.readout = readout_resolver.make(readout)
+        if readout == "sum":
+            self.readout = layers.SumReadout()
+        elif readout == "mean":
+            self.readout = layers.MeanReadout()
+        elif readout == "max":
+            self.readout = layers.MaxReadout()
+        else:
+            raise ValueError("Unknown readout `%s`" % readout)
 
     def forward(self, graph, input, all_loss=None, metric=None):
         """

torchdrug/models/schnet.py

@@ -1,12 +1,10 @@
 from collections.abc import Sequence
 
 import torch
-from class_resolver import Hint
 from torch import nn
 
 from torchdrug import core, layers
 from torchdrug.core import Registry as R
-from torchdrug.layers import Readout, readout_resolver
 
 
 @R.register("models.SchNet")
@@ -31,7 +29,7 @@ class SchNet(nn.Module, core.Configurable):
     """
 
     def __init__(self, input_dim, hidden_dims, edge_input_dim=None, cutoff=5, num_gaussian=100, short_cut=True,
-                 batch_norm=False, activation="shifted_softplus", concat_hidden=False, readout: Hint[Readout] = "sum"):
+                 batch_norm=False, activation="shifted_softplus", concat_hidden=False, readout="sum"):
         super(SchNet, self).__init__()
 
         if not isinstance(hidden_dims, Sequence):
@@ -47,7 +45,14 @@ def __init__(self, input_dim, hidden_dims, edge_input_dim=None, cutoff=5, num_ga
             self.layers.append(layers.ContinuousFilterConv(self.dims[i], self.dims[i + 1], edge_input_dim, None, cutoff,
                                                            num_gaussian, batch_norm, activation))
 
-        self.readout = readout_resolver.make(readout)
+        if readout == "sum":
+            self.readout = layers.SumReadout()
+        elif readout == "mean":
+            self.readout = layers.MeanReadout()
+        elif readout == "max":
+            self.readout = layers.MaxReadout()
+        else:
+            raise ValueError("Unknown readout `%s`" % readout)
 
     def forward(self, graph, input, all_loss=None, metric=None):
         """