[Bugfix] Fix dgk/graph2vec/gdc/grace

README.md

@@ -4,6 +4,7 @@
 [![PyPI Latest Release](https://badge.fury.io/py/cogdl.svg)](https://pypi.org/project/cogdl/)
 [![Build Status](https://travis-ci.org/THUDM/cogdl.svg?branch=master)](https://travis-ci.org/THUDM/cogdl)
 [![Documentation Status](https://readthedocs.org/projects/cogdl/badge/?version=latest)](https://cogdl.readthedocs.io/en/latest/?badge=latest)
+[![Downloads](https://pepy.tech/badge/cogdl)](https://pepy.tech/project/cogdl)
 [![Coverage Status](https://coveralls.io/repos/github/THUDM/cogdl/badge.svg?branch=master)](https://coveralls.io/github/THUDM/cogdl?branch=master)
 [![License](https://img.shields.io/github/license/thudm/cogdl)](https://github.com/THUDM/cogdl/blob/master/LICENSE)
 [![Code Style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)

README_CN.md

@@ -3,8 +3,9 @@
 
 [![PyPI Latest Release](https://badge.fury.io/py/cogdl.svg)](https://pypi.org/project/cogdl/)
 [![Build Status](https://travis-ci.org/THUDM/cogdl.svg?branch=master)](https://travis-ci.org/THUDM/cogdl)
-[![Coverage Status](https://coveralls.io/repos/github/THUDM/cogdl/badge.svg?branch=master)](https://coveralls.io/github/THUDM/cogdl?branch=master)
 [![Documentation Status](https://readthedocs.org/projects/cogdl/badge/?version=latest)](https://cogdl.readthedocs.io/en/latest/?badge=latest)
+[![Downloads](https://pepy.tech/badge/cogdl)](https://pepy.tech/project/cogdl)
+[![Coverage Status](https://coveralls.io/repos/github/THUDM/cogdl/badge.svg?branch=master)](https://coveralls.io/github/THUDM/cogdl?branch=master)
 [![License](https://img.shields.io/github/license/thudm/cogdl)](https://github.com/THUDM/cogdl/blob/master/LICENSE)
 [![Code Style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
 

cogdl/models/emb/dgk.py

@@ -60,7 +60,7 @@ def wl_iterations(graph, features, rounds):
                 neighbors = graph.neighbors(node)
                 neigh_feats = [features[x] for x in neighbors]
                 neigh_feats = [features[node]] + sorted(neigh_feats)
-                hash_feat = hashlib.md5("_".join(neigh_feats).encode())
+                hash_feat = hashlib.md5("_".join([str(x) for x in neigh_feats]).encode())
                 hash_feat = hash_feat.hexdigest()
                 new_feats[node] = hash_feat
             wl_features = wl_features + list(new_feats.values())

cogdl/models/emb/graph2vec.py

@@ -70,7 +70,7 @@ def wl_iterations(graph, features, rounds):
                 neighbors = graph.neighbors(node)
                 neigh_feats = [features[x] for x in neighbors]
                 neigh_feats = [features[node]] + sorted(neigh_feats)
-                hash_feat = hashlib.md5("_".join(neigh_feats).encode())
+                hash_feat = hashlib.md5("_".join([str(x) for x in neigh_feats]).encode())
                 hash_feat = hash_feat.hexdigest()
                 new_feats[node] = hash_feat
             wl_features = wl_features + list(new_feats.values())

cogdl/models/nn/gdc_gcn.py

@@ -39,7 +39,6 @@ def add_args(parser):
         parser.add_argument("--t", type=float, default=5.0)
         parser.add_argument("--k", type=int, default=128)
         parser.add_argument("--eps", type=float, default=0.01)
-        parser.add_argument("--dataset", default=None)
         parser.add_argument("--gdc-type", default="ppr")
         # fmt: on
 
@@ -76,8 +75,12 @@ def __init__(self, nfeat, nhid, nclass, dropout, alpha, t, k, eps, gdctype):
         self.dropout = dropout
 
     def forward(self, graph):
+        if self.data is None:
+            self.reset_data(graph)
+        graph = self.data
         x = graph.x
-        graph.sym_norm()
+        if self.gdc_type == "none":
+            graph.sym_norm()
 
         x = F.dropout(x, self.dropout, training=self.training)
         x = F.relu(self.gc1(graph, x))

cogdl/models/nn/grace.py

@@ -82,9 +82,6 @@ def __init__(
         )
         self.encoder = GraceEncoder(in_feats, hidden_size, num_layers, activation)
 
-    def augment(self, graph):
-        pass
-
     def forward(
         self,
         graph: Graph,
@@ -95,76 +92,6 @@ def forward(
         graph.sym_norm()
         return self.encoder(graph, x)
 
-    def prop(
-        self,
-        graph: Graph,
-        x: torch.Tensor,
-        drop_feature_rate: float = 0.0,
-        drop_edge_rate: float = 0.0,
-    ):
-        x = self.drop_feature(x, drop_feature_rate)
-        with graph.local_graph():
-            graph = self.drop_adj(graph, drop_edge_rate)
-            return self.forward(graph, x)
-
-    def contrastive_loss(self, z1: torch.Tensor, z2: torch.Tensor):
-        z1 = F.normalize(z1, p=2, dim=-1)
-        z2 = F.normalize(z2, p=2, dim=-1)
-
-        def score_func(emb1, emb2):
-            scores = torch.matmul(emb1, emb2.t())
-            scores = torch.exp(scores / self.tau)
-            return scores
-
-        intro_scores = score_func(z1, z1)
-        inter_scores = score_func(z1, z2)
-
-        _loss = -torch.log(intro_scores.diag() / (intro_scores.sum(1) - intro_scores.diag() + inter_scores.sum(1)))
-        return torch.mean(_loss)
-
-    def batched_loss(
-        self,
-        z1: torch.Tensor,
-        z2: torch.Tensor,
-        batch_size: int,
-    ):
-        num_nodes = z1.shape[0]
-        num_batches = (num_nodes - 1) // batch_size + 1
-
-        losses = []
-        indices = torch.arange(num_nodes).to(z1.device)
-        for i in range(num_batches):
-            train_indices = indices[i * batch_size : (i + 1) * batch_size]
-            _loss = self.contrastive_loss(z1[train_indices], z2)
-            losses.append(_loss)
-        return sum(losses) / len(losses)
-
     def embed(self, data):
         pred = self.forward(data, data.x)
         return pred
-
-    def drop_adj(self, graph: Graph, drop_rate: float = 0.5):
-        if drop_rate < 0.0 or drop_rate > 1.0:
-            raise ValueError("Dropout probability has to be between 0 and 1, " "but got {}".format(drop_rate))
-        if not self.training:
-            return graph
-
-        num_edges = graph.num_edges
-        mask = torch.full((num_edges,), 1 - drop_rate, dtype=torch.float)
-        mask = torch.bernoulli(mask).to(torch.bool)
-        row, col = graph.edge_index
-        row = row[mask]
-        col = col[mask]
-        edge_weight = graph.edge_weight[mask]
-        graph.edge_index = (row, col)
-        graph.edge_weight = edge_weight
-        return graph
-
-    def drop_feature(self, x: torch.Tensor, droprate: float):
-        n = x.shape[1]
-        drop_rates = torch.ones(n) * droprate
-        if self.training:
-            masks = torch.bernoulli(1.0 - drop_rates).view(1, -1).expand_as(x)
-            masks = masks.to(x.device)
-            x = masks * x
-        return x