[Example] Rename NodeDataLoader to DataLoader in GraphSAGE example (#3972)

* rename

* Update node_classification.py

* more fixes...
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

examples/pytorch/rgcn-hetero/entity_classify_mb.py

@@ -97,14 +97,14 @@ def main(args):
 
     # train sampler
     sampler = dgl.dataloading.MultiLayerNeighborSampler([args.fanout] * args.n_layers)
-    loader = dgl.dataloading.NodeDataLoader(
+    loader = dgl.dataloading.DataLoader(
         g, {category: train_idx}, sampler,
         batch_size=args.batch_size, shuffle=True, num_workers=0)
 
     # validation sampler
     # we do not use full neighbor to save computation resources
     val_sampler = dgl.dataloading.MultiLayerNeighborSampler([args.fanout] * args.n_layers)
-    val_loader = dgl.dataloading.NodeDataLoader(
+    val_loader = dgl.dataloading.DataLoader(
         g, {category: val_idx}, val_sampler,
         batch_size=args.batch_size, shuffle=True, num_workers=0)
 

examples/pytorch/rgcn-hetero/model.py

@@ -350,7 +350,7 @@ class EntityClassify(nn.Module):
                 for k in g.ntypes}
 
             sampler = dgl.dataloading.MultiLayerFullNeighborSampler(1)
-            dataloader = dgl.dataloading.NodeDataLoader(
+            dataloader = dgl.dataloading.DataLoader(
                 g,
                 {k: th.arange(g.number_of_nodes(k)) for k in g.ntypes},
                 sampler,
@@ -445,7 +445,7 @@ class EntityClassify_HeteroAPI(nn.Module):
                 for k in g.ntypes}
 
             sampler = dgl.dataloading.MultiLayerFullNeighborSampler(1)
-            dataloader = dgl.dataloading.NodeDataLoader(
+            dataloader = dgl.dataloading.DataLoader(
                 g,
                 {k: th.arange(g.number_of_nodes(k)) for k in g.ntypes},
                 sampler,

examples/pytorch/rgcn/entity_sample.py

@@ -8,7 +8,7 @@ import torch as th
 import torch.nn.functional as F
 import dgl
 
-from dgl.dataloading import MultiLayerNeighborSampler, NodeDataLoader
+from dgl.dataloading import MultiLayerNeighborSampler, DataLoader
 from torchmetrics.functional import accuracy
 from tqdm import tqdm
 
@@ -19,7 +19,7 @@ def init_dataloaders(args, g, train_idx, test_idx, target_idx, device, use_ddp=F
     fanouts = [int(fanout) for fanout in args.fanout.split(',')]
     sampler = MultiLayerNeighborSampler(fanouts)
 
-    train_loader = NodeDataLoader(
+    train_loader = DataLoader(
         g,
         target_idx[train_idx],
         sampler,
@@ -30,7 +30,7 @@ def init_dataloaders(args, g, train_idx, test_idx, target_idx, device, use_ddp=F
         drop_last=False)
 
     # The datasets do not have a validation subset, use the train subset
-    val_loader = NodeDataLoader(
+    val_loader = DataLoader(
         g,
         target_idx[train_idx],
         sampler,
@@ -42,7 +42,7 @@ def init_dataloaders(args, g, train_idx, test_idx, target_idx, device, use_ddp=F
 
     # -1 for sampling all neighbors
     test_sampler = MultiLayerNeighborSampler([-1] * len(fanouts))
-    test_loader = NodeDataLoader(
+    test_loader = DataLoader(
         g,
         target_idx[test_idx],
         test_sampler,

examples/pytorch/rgcn/entity_utils.py

@@ -38,7 +38,7 @@ def load_data(data_name, get_norm=False, inv_target=False):
     category_id = hg.ntypes.index(category)
 
     g = dgl.to_homogeneous(hg, edata=edata)
-    # Rename the fields as they can be changed by for example NodeDataLoader
+    # Rename the fields as they can be changed by for example DataLoader
     g.ndata['ntype'] = g.ndata.pop(dgl.NTYPE)
     g.ndata['type_id'] = g.ndata.pop(dgl.NID)
     node_ids = th.arange(g.num_nodes())

examples/pytorch/tgn/dataloading.py

 import torch
 import dgl
 
-from dgl.dataloading.dataloader import EdgeCollator
-from dgl.dataloading import BlockSampler
-from dgl.dataloading.pytorch import _pop_subgraph_storage, _pop_storages
+from dgl._dataloading.dataloader import EdgeCollator
+from dgl._dataloading import BlockSampler
+from dgl._dataloading.pytorch import _pop_subgraph_storage, _pop_storages
 from dgl.base import DGLError
 
 from functools import partial