[Model] Implement model Unimp

examples/unimp/readme.md

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+# Graph Convolutional Networks (GCN)
+
+- Paper link: [https://arxiv.org/abs/2009.03509](https://arxiv.org/abs/2009.03509)
+
+# Dataset Statics
+
+| Dataset  | # Nodes | # Edges | # Classes |
+|----------|---------|---------|-----------|
+| Cora     | 2,708   | 10,556  | 7         |
+| Citeseer | 3,327   | 9,228   | 6         |
+| Pubmed   | 19,717  | 88,651  | 3         |
+
+Refer to [Planetoid](https://gammagl.readthedocs.io/en/latest/api/gammagl.datasets.html#gammagl.datasets.Planetoid).
+
+Results
+-------
+
+```bash
+# available dataset: "cora", "citeseer", "pubmed"
+TL_BACKEND="tensorflow" python unimp_trainer.py --dataset cora 
+TL_BACKEND="tensorflow" python unimp_trainer.py --dataset citeseer 
+TL_BACKEND="tensorflow" python unimp_trainer.py --dataset pubmed 
+TL_BACKEND="torch" python unimp_trainer.py --dataset cora 
+TL_BACKEND="torch" python unimp_trainer.py --dataset citeseer
+TL_BACKEND="torch" python unimp_trainer.py --dataset pubmed
+```
+
+| Dataset  | Our(tf)    | Our(torch)    |
+|----------|------------|------------|
+| cora     | 83.10±1.12 | 82.30±0.67 |
+| citeseer | 79.90±0.68 | 78.53±0.18 |
+| pubmed   | 74.10±1.08 | 73.63±0.12 |

examples/unimp/unimp_trainer.py

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+import os
+os.environ['CUDA_VISIBLE_DEVICES']='0'
+import random
+import argparse
+import tensorlayerx as tlx
+import tensorlayerx.nn as nn
+from gammagl.utils import segment_softmax
+from gammagl.datasets import Planetoid
+from gammagl.layers.conv import MessagePassing
+from gammagl.utils import add_self_loops, mask_to_index
+from tensorlayerx.model import TrainOneStep, WithLoss
+
+class CrossEntropyLoss(WithLoss):
+    def __init__(self, model, loss_func):
+        super(CrossEntropyLoss, self).__init__(model,loss_func)
+
+    def forward(self, data, label):
+        out = self.backbone_network(data['x'], data['edge_index'])
+        out = tlx.gather(out, data['val_idx'])
+        label = tlx.reshape(tlx.gather(label, data['val_idx']),shape=(-1,))
+        #print(out[0])
+        #print(label[0])
+        loss = self._loss_fn(out, label)
+        return loss
+
+
+class MultiHead(MessagePassing):
+    def __init__(self, in_features, out_features, n_heads,num_nodes):
+        super().__init__()
+        self.heads=n_heads
+        self.num_nodes=num_nodes
+        self.out_channels=out_features
+        self.linear = tlx.layers.Linear(out_features=out_features* n_heads,
+                                        in_features=in_features)
+
+        init = tlx.initializers.RandomNormal()
+        self.att_src = init(shape=(1, n_heads, out_features), dtype=tlx.float32)
+        self.att_dst = init(shape=(1, n_heads, out_features), dtype=tlx.float32)
+
+        self.leaky_relu = tlx.layers.LeakyReLU(0.2)
+        self.dropout = tlx.layers.Dropout()
+
+    def message(self, x, edge_index):
+        node_src = edge_index[0, :]
+        node_dst = edge_index[1, :]
+        weight_src = tlx.gather(tlx.reduce_sum(x * self.att_src, -1), node_src)
+        weight_dst = tlx.gather(tlx.reduce_sum(x * self.att_dst, -1), node_dst)
+        weight = self.leaky_relu(weight_src + weight_dst)
+
+        alpha = self.dropout(segment_softmax(weight, node_dst, self.num_nodes))
+        x = tlx.gather(x, node_src) * tlx.expand_dims(alpha, -1)
+        return x
+
+
+    def forward(self, x, edge_index):
+        x = tlx.reshape(self.linear(x), shape=(-1,self.heads, self.out_channels))
+        x = self.propagate(x, edge_index, num_nodes=self.num_nodes)
+        x=tlx.ops.reduce_mean(x,axis=1)
+
+        return x
+
+
+class Unimp(tlx.nn.Module):
+    def __init__(self,dataset):
+        super(Unimp, self).__init__()
+
+        out_layer1=int(dataset.num_node_features/2)
+        self.layer1=MultiHead(dataset.num_node_features+1, out_layer1, 4,dataset[0].num_nodes)
+        self.norm1=nn.LayerNorm(out_layer1)
+        self.relu1=nn.ReLU()
+
+        self.layer2=MultiHead(out_layer1, dataset.num_classes, 4,dataset[0].num_nodes)
+        self.norm2=nn.LayerNorm(dataset.num_classes)
+        self.relu2=nn.ReLU()
+    def forward(self, x, edge_index):
+        out1 = self.layer1(x, edge_index)
+        out2=self.norm1(out1)
+        out3=self.relu1(out2)
+        out4=self.layer2(out3,edge_index)
+        out5 = self.norm2(out4)
+        out6 = self.relu2(out5)
+        return out6
+
+def calculate_acc(logits, y, metrics):
+    metrics.update(logits, y)
+    rst = metrics.result()
+    metrics.reset()
+    return rst
+def get_label_mask(label,node,dtype):
+    mask=[1 for i in range(node['train_node1'])]+[0 for i in range(node['train_node2'])]
+    random.shuffle(mask)
+    label_mask=[]
+    for i in range(node['train_node']):
+        if mask[i]==0:
+            label_mask.append([-1])
+        else:
+            label_mask.append([(int)(label[i])])
+    label_mask+=[[0] for i in range(node['num_node']-node['train_node'])]
+    return tlx.ops.convert_to_tensor(label_mask,dtype=dtype)
+
+def merge_feature_label(label,feature):
+    return tlx.ops.concat([label,feature],axis=1)
+def main(args):
+    dataset = Planetoid(root='./',name=args.dataset)
+    graph=dataset[0]
+    feature=graph.x
+    edge_index=graph.edge_index
+    label=graph.y
+    train_node=int(graph.num_nodes * 0.3)
+    train_node1=int(graph.num_nodes * 0.1)
+    node = {
+        'train_node': train_node,
+        'train_node1': train_node1,
+        'train_node2': train_node-train_node1,
+        'num_node': graph.num_nodes
+    }
+    val_mask = tlx.ops.concat(
+        [tlx.ops.zeros((train_node, 1),dtype=tlx.int32),
+        tlx.ops.ones((train_node-train_node1, 1),dtype=tlx.int32)],axis=0)
+    test_mask=graph.test_mask
+    model=Unimp(dataset)
+    loss = tlx.losses.softmax_cross_entropy_with_logits
+    optimizer = tlx.optimizers.Adam(lr=0.01, weight_decay=5e-4)
+    train_weights = model.trainable_weights
+    loss_func = CrossEntropyLoss(model, loss)
+    train_one_step = TrainOneStep(loss_func, optimizer, train_weights)
+    val_idx = mask_to_index(val_mask)
+    test_idx = mask_to_index(test_mask)
+    metrics = tlx.metrics.Accuracy()
+    data = {
+        "x": feature,
+        "y": label,
+        "edge_index": edge_index,
+        "val_idx":val_idx,
+        "test_idx": test_idx,
+        "num_nodes": graph.num_nodes,
+    }
+
+    epochs=args.epochs
+    best_val_acc=0
+    for epoch in range(epochs):
+        model.set_train()
+        label_mask=get_label_mask(label,node,feature[0].dtype)
+        data['x']=merge_feature_label(label_mask,feature)
+        train_loss = train_one_step(data, graph.y)
+
+        model.set_eval()
+        logits = model(data['x'], data['edge_index'])
+        test_logits = tlx.gather(logits, data['test_idx'])
+        test_y = tlx.gather(data['y'], data['test_idx'])
+        test_acc = calculate_acc(test_logits, test_y, metrics)
+
+        print("Epoch [{:0>3d}]  ".format(epoch + 1)
+              + "   train loss: {:.4f}".format(train_loss.item())
+              + "   val acc: {:.4f}".format(test_acc))
+
+        # save best model on evaluation set
+        if test_acc > best_val_acc:
+            best_val_acc = test_acc
+            model.save_weights('./'+ 'unimp' + ".npz", format='npz_dict')
+    print("The Best ACC : {:.4f}".format(best_val_acc))
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--epochs", type=int, default=200, help="number of epoch")
+    parser.add_argument('--dataset', type=str, default='cora', help='dataset')
+    args = parser.parse_args()
+    main(args)

gammagl/datasets/OgbGraphData.csv

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+,ogbg-molbace,ogbg-molbbbp,ogbg-molclintox,ogbg-molmuv,ogbg-molpcba,ogbg-molsider,ogbg-moltox21,ogbg-moltoxcast,ogbg-molhiv,ogbg-molesol,ogbg-molfreesolv,ogbg-mollipo,ogbg-molchembl,ogbg-ppa,ogbg-code2
+num tasks,1,1,2,17,128,27,12,617,1,1,1,1,1310,1,1
+eval metric,rocauc,rocauc,rocauc,ap,ap,rocauc,rocauc,rocauc,rocauc,rmse,rmse,rmse,rocauc,acc,F1
+download_name,bace,bbbp,clintox,muv,pcba,sider,tox21,toxcast,hiv,esol,freesolv,lipophilicity,chembl,ogbg_ppi_medium,code2
+version,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+url,http://snap.stanford.edu/ogb/data/graphproppred/csv_mol_download/bace.zip,http://snap.stanford.edu/ogb/data/graphproppred/csv_mol_download/bbbp.zip,http://snap.stanford.edu/ogb/data/graphproppred/csv_mol_download/clintox.zip,http://snap.stanford.edu/ogb/data/graphproppred/csv_mol_download/muv.zip,http://snap.stanford.edu/ogb/data/graphproppred/csv_mol_download/pcba.zip,http://snap.stanford.edu/ogb/data/graphproppred/csv_mol_download/sider.zip,http://snap.stanford.edu/ogb/data/graphproppred/csv_mol_download/tox21.zip,http://snap.stanford.edu/ogb/data/graphproppred/csv_mol_download/toxcast.zip,http://snap.stanford.edu/ogb/data/graphproppred/csv_mol_download/hiv.zip,http://snap.stanford.edu/ogb/data/graphproppred/csv_mol_download/esol.zip,http://snap.stanford.edu/ogb/data/graphproppred/csv_mol_download/freesolv.zip,http://snap.stanford.edu/ogb/data/graphproppred/csv_mol_download/lipophilicity.zip,http://snap.stanford.edu/ogb/data/graphproppred/csv_mol_download/chembl.zip,http://snap.stanford.edu/ogb/data/graphproppred/ogbg_ppi_medium.zip,http://snap.stanford.edu/ogb/data/graphproppred/code2.zip
+add_inverse_edge,True,True,True,True,True,True,True,True,True,True,True,True,True,True,False
+data type,mol,mol,mol,mol,mol,mol,mol,mol,mol,mol,mol,mol,mol,,
+has_node_attr,True,True,True,True,True,True,True,True,True,True,True,True,True,False,True
+has_edge_attr,True,True,True,True,True,True,True,True,True,True,True,True,True,True,False
+task type,binary classification,binary classification,binary classification,binary classification,binary classification,binary classification,binary classification,binary classification,binary classification,regression,regression,regression,binary classification,multiclass classification,subtoken prediction
+num classes,2,2,2,2,2,2,2,2,2,-1,-1,-1,2,37,-1
+split,scaffold,scaffold,scaffold,scaffold,scaffold,scaffold,scaffold,scaffold,scaffold,scaffold,scaffold,scaffold,scaffold,species,project
+additional node files,None,None,None,None,None,None,None,None,None,None,None,None,None,None,"node_is_attributed,node_dfs_order,node_depth"
+additional edge files,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None
+binary,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False

gammagl/datasets/OgbLinkData.csv

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+,ogbl-ppa,ogbl-collab,ogbl-citation2,ogbl-wikikg2,ogbl-ddi,ogbl-biokg,ogbl-vessel
+eval metric,hits@100,hits@50,mrr,mrr,hits@20,mrr,rocauc
+task type,link prediction,link prediction,link prediction,KG completion,link prediction,KG completion,link prediction
+download_name,ppassoc,collab,citation-v2,wikikg-v2,ddi,biokg,vessel
+version,1,1,1,1,1,1,1
+url,http://snap.stanford.edu/ogb/data/linkproppred/ppassoc.zip,http://snap.stanford.edu/ogb/data/linkproppred/collab.zip,http://snap.stanford.edu/ogb/data/linkproppred/citation-v2.zip,http://snap.stanford.edu/ogb/data/linkproppred/wikikg-v2.zip,http://snap.stanford.edu/ogb/data/linkproppred/ddi.zip,http://snap.stanford.edu/ogb/data/linkproppred/biokg.zip,http://snap.stanford.edu/ogb/data/linkproppred/vessel.zip
+add_inverse_edge,True,True,False,False,True,False,False
+has_node_attr,True,True,True,False,False,False,True
+has_edge_attr,False,False,False,False,False,False,True
+split,throughput,time,time,time,target,random,spatial
+additional node files,None,None,node_year,None,None,None,None
+additional edge files,None,"edge_weight,edge_year",None,edge_reltype,None,edge_reltype,None
+is hetero,False,False,False,False,False,True,False
+binary,False,False,False,False,False,False,True

gammagl/datasets/OgbNodeData.csv

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+,ogbn-proteins,ogbn-products,ogbn-arxiv,ogbn-mag,ogbn-papers100M
+num tasks,112,1,1,1,1
+num classes,2,47,40,349,172
+eval metric,rocauc,acc,acc,acc,acc
+task type,binary classification,multiclass classification,multiclass classification,multiclass classification,multiclass classification
+download_name,proteins,products,arxiv,mag,papers100M-bin
+version,1,1,1,2,1
+url,http://snap.stanford.edu/ogb/data/nodeproppred/proteins.zip,http://snap.stanford.edu/ogb/data/nodeproppred/products.zip,http://snap.stanford.edu/ogb/data/nodeproppred/arxiv.zip,http://snap.stanford.edu/ogb/data/nodeproppred/mag.zip,http://snap.stanford.edu/ogb/data/nodeproppred/papers100M-bin.zip
+add_inverse_edge,True,True,False,False,False
+has_node_attr,False,True,True,True,True
+has_edge_attr,True,False,False,False,False
+split,species,sales_ranking,time,time,time
+additional node files,node_species,None,node_year,node_year,node_year
+additional edge files,None,None,None,edge_reltype,None
+is hetero,False,False,False,True,False
+binary,False,False,False,False,True