[README] Update speed numbers in README (#661)

* update speed numbers in readme

* numbers in examples/pytorch/readme

README.md

@@ -15,11 +15,11 @@ A summary of the model accuracy and training speed with the Pytorch backend (on
 
 | Model                                                            | Reported <br> Accuracy | DGL <br> Accuracy | Author's training speed (epoch time)                                          | DGL speed (epoch time) | Improvement |
 | -----                                                            | -----------------      | ------------      | ------------------------------------                                          | ---------------------- | ----------- |
-| [GCN](https://arxiv.org/abs/1609.02907)                          | 81.5%                  | 81.0%             | [0.0051s (TF)](https://github.com/tkipf/gcn)                                  | 0.0038s                | 1.34x       |
-| [GAT](https://arxiv.org/abs/1710.10903)                          | 83.0%                  | 83.9%             | [0.0982s (TF)](https://github.com/PetarV-/GAT)                                | 0.0076s                | 12.9x       |
+| [GCN](https://arxiv.org/abs/1609.02907)                          | 81.5%                  | 81.0%             | [0.0051s (TF)](https://github.com/tkipf/gcn)                                  | 0.0031s                | 1.64x       |
+| [GAT](https://arxiv.org/abs/1710.10903)                          | 83.0%                  | 83.9%             | [0.0982s (TF)](https://github.com/PetarV-/GAT)                                | 0.0113s                | 8.69x       |
 | [SGC](https://arxiv.org/abs/1902.07153)                          | 81.0%                  | 81.9%             | n/a                                                                           | 0.0008s                | n/a         |
 | [TreeLSTM](http://arxiv.org/abs/1503.00075)                      | 51.0%                  | 51.72%            | [14.02s (DyNet)](https://github.com/clab/dynet/tree/master/examples/treelstm) | 3.18s                  | 4.3x        |
-| [R-GCN <br> (classification)](https://arxiv.org/abs/1703.06103)  | 73.23%                 | 73.53%            | [0.2853s (Theano)](https://github.com/tkipf/relational-gcn)                   | 0.0097s                | 29.4x       |
+| [R-GCN <br> (classification)](https://arxiv.org/abs/1703.06103)  | 73.23%                 | 73.53%            | [0.2853s (Theano)](https://github.com/tkipf/relational-gcn)                   | 0.0075s                | 38.2x       |
 | [R-GCN <br> (link prediction)](https://arxiv.org/abs/1703.06103) | 0.158                  | 0.151             | [2.204s (TF)](https://github.com/MichSchli/RelationPrediction)                | 0.453s                 | 4.86x       |
 | [JTNN](https://arxiv.org/abs/1802.04364)                         | 96.44%                 | 96.44%            | [1826s (Pytorch)](https://github.com/wengong-jin/icml18-jtnn)                 | 743s                   | 2.5x        |
 | [LGNN](https://arxiv.org/abs/1705.08415)                         | 94%                    | 94%               | n/a                                                                           | 1.45s                  | n/a         |

examples/pytorch/README.md

@@ -12,11 +12,12 @@ Here is a summary of the model accuracy and training speed. Our testbed is Amazo
 
 | Model | Reported <br> Accuracy | DGL <br> Accuracy | Author's training speed (epoch time) | DGL speed (epoch time) | Improvement |
 | ----- | ----------------- | ------------ | ------------------------------------ | ---------------------- | ----------- |
-| [GCN](https://arxiv.org/abs/1609.02907)  | 81.5% | 81.0% | [0.0051s (TF)](https://github.com/tkipf/gcn) | 0.0042s | 1.17x |
+| [GCN](https://arxiv.org/abs/1609.02907)  | 81.5% | 81.0% | [0.0051s (TF)](https://github.com/tkipf/gcn) | 0.0031s | 1.64x |
+| [GAT](https://arxiv.org/abs/1710.10903)  | 83.0% | 83.9% | [0.0982s (TF)](https://github.com/PetarV-/GAT) | 0.0113s | 8.69x |
 | [SGC](https://arxiv.org/abs/1902.07153) | 81.0% | 81.9% | n/a | 0.0008s | n/a |
 | [TreeLSTM](http://arxiv.org/abs/1503.00075) | 51.0% | 51.72% | [14.02s (DyNet)](https://github.com/clab/dynet/tree/master/examples/treelstm) | 3.18s | 4.3x |
-| [R-GCN <br> (classification)](https://arxiv.org/abs/1703.06103) | 73.23% | 73.53% | [0.2853s (Theano)](https://github.com/tkipf/relational-gcn) | 0.0273s | 10.4x |
-| [R-GCN <br> (link prediction)](https://arxiv.org/abs/1703.06103) | 0.158 | 0.151 | [2.204s (TF)](https://github.com/MichSchli/RelationPrediction) | 0.633s | 3.5x |
+| [R-GCN <br> (classification)](https://arxiv.org/abs/1703.06103) | 73.23% | 73.53% | [0.2853s (Theano)](https://github.com/tkipf/relational-gcn) | 0.0075s | 38.2x |
+| [R-GCN <br> (link prediction)](https://arxiv.org/abs/1703.06103) | 0.158 | 0.151 | [2.204s (TF)](https://github.com/MichSchli/RelationPrediction) | 0.453s | 4.86x |
 | [JTNN](https://arxiv.org/abs/1802.04364) | 96.44% | 96.44% | [1826s (Pytorch)](https://github.com/wengong-jin/icml18-jtnn) | 743s | 2.5x |
 | [LGNN](https://arxiv.org/abs/1705.08415) | 94% | 94% | n/a | 1.45s | n/a |
 | [DGMG](https://arxiv.org/pdf/1803.03324.pdf) | 84% | 90% | n/a | 238s | n/a |