[Doc] Update GraphDataLoader in our docs (#2504)

* update graphdataloader in docs

* fix

* update examples

* fix sagpool
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>

docs/source/guide/data-loadogb.rst

@@ -26,7 +26,7 @@ Prediction* tasks.
     import dgl
     import torch
     from ogb.graphproppred import DglGraphPropPredDataset
-    from torch.utils.data import DataLoader
+    from dgl.dataloading import GraphDataLoader
     
     
     def _collate_fn(batch):
@@ -41,9 +41,9 @@ Prediction* tasks.
     dataset = DglGraphPropPredDataset(name='ogbg-molhiv')
     split_idx = dataset.get_idx_split()
     # dataloader
-    train_loader = DataLoader(dataset[split_idx["train"]], batch_size=32, shuffle=True, collate_fn=_collate_fn)
-    valid_loader = DataLoader(dataset[split_idx["valid"]], batch_size=32, shuffle=False, collate_fn=_collate_fn)
-    test_loader = DataLoader(dataset[split_idx["test"]], batch_size=32, shuffle=False, collate_fn=_collate_fn)
+    train_loader = GraphDataLoader(dataset[split_idx["train"]], batch_size=32, shuffle=True, collate_fn=_collate_fn)
+    valid_loader = GraphDataLoader(dataset[split_idx["valid"]], batch_size=32, shuffle=False, collate_fn=_collate_fn)
+    test_loader = GraphDataLoader(dataset[split_idx["test"]], batch_size=32, shuffle=False, collate_fn=_collate_fn)
 
 Loading *Node Property Prediction* datasets is similar, but note that
 there is only one graph object in this kind of dataset.

docs/source/guide/data-process.rst

@@ -96,21 +96,14 @@ follows:
     import dgl
     import torch
 
-    from torch.utils.data import DataLoader
+    from dgl.dataloading import GraphDataLoader
     
     # load data
     dataset = QM7bDataset()
     num_labels = dataset.num_labels
     
-    # create collate_fn
-    def _collate_fn(batch):
-        graphs, labels = batch
-        g = dgl.batch(graphs)
-        labels = torch.tensor(labels, dtype=torch.long)
-        return g, labels
-    
     # create dataloaders
-    dataloader = DataLoader(dataset, batch_size=1, shuffle=True, collate_fn=_collate_fn)
+    dataloader = GraphDataLoader(dataset, batch_size=1, shuffle=True)
     
     # training
     for epoch in range(100):

docs/source/guide/training-graph.rst

@@ -193,27 +193,15 @@ Assuming that one have a graph classification dataset as introduced in
 
 Each item in the graph classification dataset is a pair of a graph and
 its label. One can speed up the data loading process by taking advantage
-of the DataLoader, by customizing the collate function to batch the
-graphs:
-
-.. code:: python
-
-    def collate(samples):
-        graphs, labels = map(list, zip(*samples))
-        batched_graph = dgl.batch(graphs)
-        batched_labels = torch.tensor(labels)
-        return batched_graph, batched_labels
-
-Then one can create a DataLoader that iterates over the dataset of
+of the GraphDataLoader to iterate over the dataset of
 graphs in mini-batches.
 
 .. code:: python
 
-    from torch.utils.data import DataLoader
-    dataloader = DataLoader(
+    from dgl.dataloading import GraphDataLoader
+    dataloader = GraphDataLoader(
         dataset,
         batch_size=1024,
-        collate_fn=collate,
         drop_last=False,
         shuffle=True)
 

docs/source/guide_cn/data-loadogb.rst

@@ -24,7 +24,7 @@
     import dgl
     import torch
     from ogb.graphproppred import DglGraphPropPredDataset
-    from torch.utils.data import DataLoader
+    from dgl.dataloading import GraphDataLoader
     
     def _collate_fn(batch):
         # 小批次是一个元组(graph, label)列表
@@ -38,9 +38,9 @@
     dataset = DglGraphPropPredDataset(name='ogbg-molhiv')
     split_idx = dataset.get_idx_split()
     # dataloader
-    train_loader = DataLoader(dataset[split_idx["train"]], batch_size=32, shuffle=True, collate_fn=_collate_fn)
-    valid_loader = DataLoader(dataset[split_idx["valid"]], batch_size=32, shuffle=False, collate_fn=_collate_fn)
-    test_loader = DataLoader(dataset[split_idx["test"]], batch_size=32, shuffle=False, collate_fn=_collate_fn)
+    train_loader = GraphDataLoader(dataset[split_idx["train"]], batch_size=32, shuffle=True, collate_fn=_collate_fn)
+    valid_loader = GraphDataLoader(dataset[split_idx["valid"]], batch_size=32, shuffle=False, collate_fn=_collate_fn)
+    test_loader = GraphDataLoader(dataset[split_idx["test"]], batch_size=32, shuffle=False, collate_fn=_collate_fn)
 
 加载 *Node Property Prediction* 数据集类似，但要注意的是这种数据集只有一个图对象。
 

docs/source/guide_cn/data-process.rst

@@ -82,21 +82,14 @@ DGL建议让 ``__getitem__(idx)`` 返回如上面代码所示的元组 ``(图，
     import dgl
     import torch
 
-    from torch.utils.data import DataLoader
+    from dgl.dataloading import GraphDataLoader
     
     # 数据导入
     dataset = QM7bDataset()
     num_labels = dataset.num_labels
     
-    # 创建collate_fn函数
-    def _collate_fn(batch):
-        graphs, labels = batch
-        g = dgl.batch(graphs)
-        labels = torch.tensor(labels, dtype=torch.long)
-        return g, labels
-    
     # 创建 dataloaders
-    dataloader = DataLoader(dataset, batch_size=1, shuffle=True, collate_fn=_collate_fn)
+    dataloader = GraphDataLoader(dataset, batch_size=1, shuffle=True)
     
     # 训练
     for epoch in range(100):

docs/source/guide_cn/training-graph.rst

@@ -143,25 +143,14 @@ DGL内置了常见的图读出函数，例如 :func:`dgl.readout_nodes` 就实
     dataset = dgl.data.GINDataset('MUTAG', False)
 
 整图分类数据集里的每个数据点是一个图和它对应标签的元组。为提升数据加载速度，
-用户可以在DataLoader里自定义collate函数。
+用户可以调用GraphDataLoader，从而以小批次遍历整个图数据集。
 
 .. code:: python
 
-    def collate(samples):
-        graphs, labels = map(list, zip(*samples))
-        batched_graph = dgl.batch(graphs)
-        batched_labels = torch.tensor(labels)
-        return batched_graph, batched_labels
-
-随后用户可以创建一个以小批次遍历整个图数据集的DataLoader。
-
-.. code:: python
-
-    from torch.utils.data import DataLoader
-    dataloader = DataLoader(
+    from dgl.dataloading import GraphDataLoader
+    dataloader = GraphDataLoader(
         dataset,
         batch_size=1024,
-        collate_fn=collate,
         drop_last=False,
         shuffle=True)
 

examples/pytorch/diffpool/train.py

@@ -110,12 +110,11 @@ def prepare_data(dataset, prog_args, train=False, pre_process=None):
         pre_process(dataset, prog_args)
 
     # dataset.set_fold(fold)
-    return torch.utils.data.DataLoader(dataset,
-                                       batch_size=prog_args.batch_size,
-                                       shuffle=shuffle,
-                                       collate_fn=collate_fn,
-                                       drop_last=True,
-                                       num_workers=prog_args.n_worker)
+    return dgl.dataloading.GraphDataLoader(dataset,
+                                           batch_size=prog_args.batch_size,
+                                           shuffle=shuffle,
+                                           drop_last=True,
+                                           num_workers=prog_args.n_worker)
 
 
 def graph_classify_task(prog_args):
@@ -191,26 +190,6 @@ def graph_classify_task(prog_args):
     print("test  accuracy {}%".format(result * 100))
 
 
-def collate_fn(batch):
-    '''
-    collate_fn for dataset batching
-    transform ndata to tensor (in gpu is available)
-    '''
-    graphs, labels = map(list, zip(*batch))
-    #cuda = torch.cuda.is_available()
-
-    # batch graphs and cast to PyTorch tensor
-    for graph in graphs:
-        for (key, value) in graph.ndata.items():
-            graph.ndata[key] = value.float()
-    batched_graphs = dgl.batch(graphs)
-
-    # cast to PyTorch tensor
-    batched_labels = torch.LongTensor(np.array(labels))
-
-    return batched_graphs, batched_labels
-
-
 def train(dataset, model, prog_args, same_feat=True, val_dataset=None):
     '''
     training function
@@ -233,6 +212,9 @@ def train(dataset, model, prog_args, same_feat=True, val_dataset=None):
         print("EPOCH ###### {} ######".format(epoch))
         computation_time = 0.0
         for (batch_idx, (batch_graph, graph_labels)) in enumerate(dataloader):
+            for (key, value) in batch_graph.ndata.items():
+                batch_graph.ndata[key] = value.float()
+            graph_labels = graph_labels.long()
             if torch.cuda.is_available():
                 batch_graph = batch_graph.to(torch.cuda.current_device())
                 graph_labels = graph_labels.cuda()
@@ -283,6 +265,9 @@ def evaluate(dataloader, model, prog_args, logger=None):
     correct_label = 0
     with torch.no_grad():
         for batch_idx, (batch_graph, graph_labels) in enumerate(dataloader):
+            for (key, value) in batch_graph.ndata.items():
+                batch_graph.ndata[key] = value.float()
+            graph_labels = graph_labels.long()
             if torch.cuda.is_available():
                 batch_graph = batch_graph.to(torch.cuda.current_device())
                 graph_labels = graph_labels.cuda()

examples/pytorch/gat/train_ppi.py

@@ -18,11 +18,7 @@ import argparse
 from sklearn.metrics import f1_score
 from gat import GAT
 from dgl.data.ppi import PPIDataset
-from torch.utils.data import DataLoader
-
-def collate(graphs):
-    graph = dgl.batch(graphs)
-    return graph
+from dgl.dataloading import GraphDataLoader
 
 def evaluate(feats, model, subgraph, labels, loss_fcn):
     with torch.no_grad():
@@ -54,9 +50,9 @@ def main(args):
     train_dataset = PPIDataset(mode='train')
     valid_dataset = PPIDataset(mode='valid')
     test_dataset = PPIDataset(mode='test')
-    train_dataloader = DataLoader(train_dataset, batch_size=batch_size, collate_fn=collate)
-    valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, collate_fn=collate)
-    test_dataloader = DataLoader(test_dataset, batch_size=batch_size, collate_fn=collate)
+    train_dataloader = GraphDataLoader(train_dataset, batch_size=batch_size)
+    valid_dataloader = GraphDataLoader(valid_dataset, batch_size=batch_size)
+    test_dataloader = GraphDataLoader(test_dataset, batch_size=batch_size)
     g = train_dataset[0]
     n_classes = train_dataset.num_labels
     num_feats = g.ndata['feat'].shape[1]

examples/pytorch/gin/dataloader.py

@@ -6,32 +6,18 @@ 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
+from dgl.dataloading import GraphDataLoader
 
 
-# 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 key in g.node_attr_schemes().keys():
-            g.ndata[key] = g.ndata[key].float()
-        # no edge feats
-    batched_graph = dgl.batch(graphs)
-    labels = torch.tensor(labels)
-    return batched_graph, labels
-
-
-class GraphDataLoader():
+class GINDataLoader():
     def __init__(self,
                  dataset,
                  batch_size,
                  device,
-                 collate_fn=collate,
+                 collate_fn=None,
                  seed=0,
                  shuffle=True,
                  split_name='fold10',
@@ -56,10 +42,10 @@ class GraphDataLoader():
         train_sampler = SubsetRandomSampler(train_idx)
         valid_sampler = SubsetRandomSampler(valid_idx)
 
-        self.train_loader = DataLoader(
+        self.train_loader = GraphDataLoader(
             dataset, sampler=train_sampler,
             batch_size=batch_size, collate_fn=collate_fn, **self.kwargs)
-        self.valid_loader = DataLoader(
+        self.valid_loader = GraphDataLoader(
             dataset, sampler=valid_sampler,
             batch_size=batch_size, collate_fn=collate_fn, **self.kwargs)
 

examples/pytorch/gin/main.py

@@ -7,7 +7,7 @@ import torch.nn as nn
 import torch.optim as optim
 
 from dgl.data.gindt import GINDataset
-from dataloader import GraphDataLoader, collate
+from dataloader import GINDataLoader
 from parser import Parser
 from gin import GIN
 
@@ -22,6 +22,8 @@ def train(args, net, trainloader, optimizer, criterion, epoch):
 
     for pos, (graphs, labels) in zip(bar, trainloader):
         # batch graphs will be shipped to device in forward part of model
+        for key in graphs.node_attr_schemes().keys():
+            graphs.ndata[key] = graphs.ndata[key].float()
         labels = labels.to(args.device)
         feat = graphs.ndata.pop('attr').to(args.device)
         graphs = graphs.to(args.device)
@@ -53,6 +55,8 @@ def eval_net(args, net, dataloader, criterion):
 
     for data in dataloader:
         graphs, labels = data
+        for key in graphs.node_attr_schemes().keys():
+            graphs.ndata[key] = graphs.ndata[key].float()
         feat = graphs.ndata.pop('attr').to(args.device)
         graphs = graphs.to(args.device)
         labels = labels.to(args.device)
@@ -88,9 +92,9 @@ def main(args):
 
     dataset = GINDataset(args.dataset, not args.learn_eps)
 
-    trainloader, validloader = GraphDataLoader(
+    trainloader, validloader = GINDataLoader(
         dataset, batch_size=args.batch_size, device=args.device,
-        collate_fn=collate, seed=args.seed, shuffle=True,
+        seed=args.seed, shuffle=True,
         split_name='fold10', fold_idx=args.fold_idx).train_valid_loader()
     # or split_name='rand', split_ratio=0.7
 

examples/pytorch/sagpool/dataloader.py

-import torch.utils.data
-from torch.utils.data.dataloader import DataLoader
-import dgl
-import numpy as np
-
-
-def collate_fn(batch):
-    """
-    collate_fn for dataset batching
-    transform ndata to tensor (in gpu is available)
-    """
-    graphs, labels = map(list, zip(*batch))
-
-    # batch graphs and cast to PyTorch tensor
-    for graph in graphs:
-        for (key, value) in graph.ndata.items():
-            graph.ndata[key] = value.float()
-    batched_graphs = dgl.batch(graphs)
-
-    # cast to PyTorch tensor
-    batched_labels = torch.LongTensor(np.array(labels))
-
-    return batched_graphs, batched_labels
-
-
-class GraphDataLoader(DataLoader):
-    def __init__(self, dataset, batch_size=1, shuffle=False, **kwargs):
-        super(GraphDataLoader, self).__init__(dataset, batch_size, shuffle,
-                                              collate_fn=collate_fn, **kwargs)

examples/pytorch/sagpool/main.py

@@ -8,9 +8,9 @@ import torch
 import torch.nn
 import torch.nn.functional as F
 from dgl.data import LegacyTUDataset
+from dgl.dataloading import GraphDataLoader
 from torch.utils.data import random_split
 
-from dataloader import GraphDataLoader
 from network import get_sag_network
 from utils import get_stats
 
@@ -78,11 +78,14 @@ def parse_args():
 def train(model:torch.nn.Module, optimizer, trainloader, device):
     model.train()
     total_loss = 0.
+    num_batches = len(trainloader)
     for batch in trainloader:
         optimizer.zero_grad()
         batch_graphs, batch_labels = batch
+        for (key, value) in batch_graphs.ndata.items():
+            batch_graphs.ndata[key] = value.float()
         batch_graphs = batch_graphs.to(device)
-        batch_labels = batch_labels.to(device)
+        batch_labels = batch_labels.long().to(device)
         out = model(batch_graphs)
         loss = F.nll_loss(out, batch_labels)
         loss.backward()
@@ -90,7 +93,7 @@ def train(model:torch.nn.Module, optimizer, trainloader, device):
 
         total_loss += loss.item()
     
-    return total_loss / len(trainloader.dataset)
+    return total_loss / num_batches
 
 
 @torch.no_grad()
@@ -98,11 +101,13 @@ def test(model:torch.nn.Module, loader, device):
     model.eval()
     correct = 0.
     loss = 0.
-    num_graphs = len(loader.dataset)
+    num_graphs = len(loader)
     for batch in loader:
         batch_graphs, batch_labels = batch
+        for (key, value) in batch_graphs.ndata.items():
+            batch_graphs.ndata[key] = value.float()
         batch_graphs = batch_graphs.to(device)
-        batch_labels = batch_labels.to(device)
+        batch_labels = batch_labels.long().to(device)
         out = model(batch_graphs)
         pred = out.argmax(dim=1)
         loss += F.nll_loss(out, batch_labels, reduction="sum").item()

python/dgl/dataloading/pytorch/__init__.py

@@ -427,7 +427,7 @@ class GraphDataLoader:
 
     Parameters
     ----------
-    collate : Function, default is None
+    collate_fn : Function, default is None
         The customized collate function. Will use the default collate
         function if not given.
     kwargs : dict
@@ -445,7 +445,7 @@ class GraphDataLoader:
     """
     collator_arglist = inspect.getfullargspec(GraphCollator).args
 
-    def __init__(self, dataset, collate=None, **kwargs):
+    def __init__(self, dataset, collate_fn=None, **kwargs):
         collator_kwargs = {}
         dataloader_kwargs = {}
         for k, v in kwargs.items():
@@ -454,10 +454,10 @@ class GraphDataLoader:
             else:
                 dataloader_kwargs[k] = v
 
-        if collate is None:
+        if collate_fn is None:
             self.collate = GraphCollator(**collator_kwargs).collate
         else:
-            self.collate = collate
+            self.collate = collate_fn
 
         self.dataloader = DataLoader(dataset=dataset,
                                      collate_fn=self.collate,

tutorials/basics/4_batch.py

@@ -36,6 +36,8 @@ networks to this problem has been a popular approach recently. This can be seen
 # Implement a synthetic dataset :class:`data.MiniGCDataset` in DGL. The dataset has eight 
 # different types of graphs and each class has the same number of graph samples.
 
+import dgl
+import torch
 from dgl.data import MiniGCDataset
 import matplotlib.pyplot as plt
 import networkx as nx
@@ -68,20 +70,6 @@ plt.show()
 #     :width: 400pt
 #     :align: center
 #
-# Define the following ``collate`` function to form a mini-batch from a given
-# list of graph and label pairs.
-
-import dgl
-import torch
-
-def collate(samples):
-    # The input `samples` is a list of pairs
-    #  (graph, label).
-    graphs, labels = map(list, zip(*samples))
-    batched_graph = dgl.batch(graphs)
-    return batched_graph, torch.tensor(labels)
-
-###############################################################################
 # The return type of :func:`dgl.batch` is still a graph. In the same way, 
 # a batch of tensors is still a tensor. This means that any code that works
 # for one graph immediately works for a batch of graphs. More importantly,
@@ -149,15 +137,14 @@ class Classifier(nn.Module):
 # :math:`80` graphs constitute a test set.
 
 import torch.optim as optim
-from torch.utils.data import DataLoader
+from dgl.dataloading import GraphDataLoader
 
 # Create training and test sets.
 trainset = MiniGCDataset(320, 10, 20)
 testset = MiniGCDataset(80, 10, 20)
-# Use PyTorch's DataLoader and the collate function
-# defined before.
-data_loader = DataLoader(trainset, batch_size=32, shuffle=True,
-                         collate_fn=collate)
+# Use DGL's GraphDataLoader. It by default handles the 
+# graph batching operation for every mini-batch.
+data_loader = GraphDataLoader(trainset, batch_size=32, shuffle=True)
 
 # Create model
 model = Classifier(1, 256, trainset.num_classes)