Add E2E vision_resnet50 model with CIFAR10 input data (#1345)
Summary:
This PR adds resnet50 model training using the CIFAR10 input dataset.
The CIFAR10 input dataset is about 164MB. The train set has 50000 images and test set has 10000 images. Each image is 32x32 and belongs to one in 10 classes, with 6000 images per class.
It uses the default upstream parameter, which has batch size 128 and 200 epochs.
On my local reproduction, I get 83.66% accuracy after 200 epochs of training.
Upstream training code: https://github.com/kuangliu/pytorch-cifar
Pull Request resolved: https://github.com/pytorch/benchmark/pull/1345
Test Plan:
```
$ python run_e2e.py vision_resnet50 -t train
Training epoch: 100%|██████████████| 200/200 [57:24<00:00, 17.22s/it]
{"device": "cuda", "device_num": 1, "test": "train", "num_examples": 50000, "num_epochs": 200, "batch_size": 128, "result": {"latency": 4183.559438905, "qps": 2390.309052861787, "accuracy": 83.66}}
```
Reviewed By: yanboliang
Differential Revision: D42048225
Pulled By: xuzhao9
fbshipit-source-id: 1e463e82e83a904fc8ca7e86b101ad2fe6ed4861
