[Model] Add GIN Model (dmlc#471)

* add gin model

* convert dataset.py to data_ont_the_fly way and put it into dgl.data module

* convert dataset.py to data_ont_the_fly way and put it into dgl.data module
python code checked

* modified document and reference TUDataset; checked python part and bypass cpp part due to error

* change tensor to numpy in dataset and transform in collate@Dataloader

* Change minor format issue

Change minor format issue

* moved logging; adjusted tqdm etc

.gitignore

@@ -1,3 +1,6 @@
+# IDE
+.idea
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

CONTRIBUTORS.md

@@ -15,6 +15,7 @@ Contributors
 * [Yizhi Liu](https://github.com/yzhliu): RGCN in MXNet
 * [@hbsun2113](https://github.com/hbsun2113): GraphSAGE in Pytorch
 * [Tianyi Zhang](https://github.com/Tiiiger): SGC in Pytorch
+* [Jun Chen](https://github.com/kitaev-chen): GIN in Pytorch
 
 Other improvement
 * [Brett Koonce](https://github.com/brettkoonce)

docs/source/api/python/data.rst

@@ -33,6 +33,7 @@ Mini graph classification dataset
 .. autoclass:: MiniGCDataset
     :members: __getitem__, __len__, num_classes
 
+
 Graph kernel dataset
 ````````````````````
 
@@ -41,6 +42,16 @@ For more information about the dataset, see `Benchmark Data Sets for Graph Kerne
 .. autoclass:: TUDataset
     :members: __getitem__, __len__
 
+
+Graph isomorphism network dataset
+```````````````````````````````````
+
+A compact subset of graph kernel dataset
+
+.. autoclass:: GINDataset
+    :members: __getitem__, __len__
+
+
 Protein-Protein Interaction dataset
 ```````````````````````````````````
 

examples/pytorch/gin/README.md

@@ -0,0 +1,45 @@
+Graph Isomorphism Network (GIN)
+============
+
+- Paper link: [arXiv](https://arxiv.org/abs/1810.00826) [OpenReview](https://openreview.net/forum?id=ryGs6iA5Km) 
+- Author's code repo: [https://github.com/weihua916/powerful-gnns](https://github.com/weihua916/powerful-gnns).
+
+Dependencies
+------------
+- PyTorch 1.0.1+
+- sklearn
+- tqdm
+
+``bash
+pip install torch sklearn tqdm
+``
+
+How to run
+----------
+
+An experiment on the GIN in default settings can be run with
+
+```bash
+python main.py
+```
+
+An experiment on the GIN in customized settings can be run with
+```bash
+python main.py [--device 0 | --disable-cuda] --dataset COLLAB \
+               --graph_pooling_type max --neighbor_pooling_type sum
+```
+
+Results
+-------
+
+Run with following with the double SUM pooling way:
+(tested dataset: "MUTAG"(default), "COLLAB", "IMDBBINARY", "IMDBMULTI")
+```bash
+python train.py --dataset MUTAB --device 0  \
+                --graph_pooling_type sum --neighbor_pooling_type sum
+```
+
+* MUTAG: 0.85 (paper: ~0.89)
+* COLLAB: 0.89 (paper: ~0.80)
+* IMDBBINARY: 0.76 (paper: ~0.75)
+* IMDBMULTI: 0.51 (paper: ~0.52)

examples/pytorch/gin/dataloader.py

@@ -0,0 +1,105 @@
+"""
+PyTorch compatible dataloader
+
+"""
+
+
+import math
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+from torch.utils.data.sampler import SubsetRandomSampler
+from sklearn.model_selection import StratifiedKFold
+import dgl
+
+
+# default collate function
+def collate(samples):
+    # The input `samples` is a list of pairs (graph, label).
+    graphs, labels = map(list, zip(*samples))
+    for g in graphs:
+        # deal with node feats
+        for feat in g.node_attr_schemes().keys():
+            # TODO torch.Tensor is not recommended
+            # torch.DoubleTensor and torch.tensor
+            # will meet error in executor.py@runtime line 472, tensor.py@backend line 147
+            # RuntimeError: expected type torch.cuda.DoubleTensor but got torch.cuda.FloatTensor
+            g.ndata[feat] = torch.Tensor(g.ndata[feat])
+        # no edge feats
+    batched_graph = dgl.batch(graphs)
+    labels = torch.tensor(labels)
+    return batched_graph, labels
+
+
+class GraphDataLoader():
+    def __init__(self,
+                 dataset,
+                 batch_size,
+                 device,
+                 collate_fn=collate,
+                 seed=0,
+                 shuffle=True,
+                 split_name='fold10',
+                 fold_idx=0,
+                 split_ratio=0.7):
+
+        self.shuffle = shuffle
+        self.seed = seed
+        self.kwargs = {'pin_memory': True} if 'cuda' in device.type else {}
+
+        labels = [l for _, l in dataset]
+
+        if split_name == 'fold10':
+            train_idx, valid_idx = self._split_fold10(
+                labels, fold_idx, seed, shuffle)
+        elif split_name == 'rand':
+            train_idx, valid_idx = self._split_rand(
+                labels, split_ratio, seed, shuffle)
+        else:
+            raise NotImplementedError()
+
+        train_sampler = SubsetRandomSampler(train_idx)
+        valid_sampler = SubsetRandomSampler(valid_idx)
+
+        self.train_loader = DataLoader(
+            dataset, sampler=train_sampler,
+            batch_size=batch_size, collate_fn=collate, **self.kwargs)
+        self.valid_loader = DataLoader(
+            dataset, sampler=valid_sampler,
+            batch_size=batch_size, collate_fn=collate, **self.kwargs)
+
+    def train_valid_loader(self):
+        return self.train_loader, self.valid_loader
+
+    def _split_fold10(self, labels, fold_idx=0, seed=0, shuffle=True):
+        ''' 10 flod '''
+        assert 0 <= fold_idx and fold_idx < 10, print(
+            "fold_idx must be from 0 to 9.")
+
+        idx_list = []
+        skf = StratifiedKFold(n_splits=10, shuffle=shuffle, random_state=seed)
+        idx_list = []
+        for idx in skf.split(np.zeros(len(labels)), labels):    # split(x, y)
+            idx_list.append(idx)
+        train_idx, valid_idx = idx_list[fold_idx]
+
+        print(
+            "train_set : test_set = %d : %d",
+            len(train_idx), len(valid_idx))
+
+        return train_idx, valid_idx
+
+    def _split_rand(self, labels, split_ratio=0.7, seed=0, shuffle=True):
+        num_entries = len(labels)
+        indices = list(range(num_entries))
+        np.random.seed(seed)
+        np.random.shuffle(indices)
+        split = int(math.floor(split_ratio * num_entries))
+        train_idx, valid_idx = indices[:split], indices[split:]
+
+        print(
+            "train_set : test_set = %d : %d",
+            len(train_idx), len(valid_idx))
+
+        return train_idx, valid_idx
+