Fix typo in RN50 PyTorch readme, remove unneeded lines for DALI

Signed-off-by: Janusz Lisiecki <jlisiecki@nvidia.com>
NVIDIA · Jun 3, 2019 · 20f0977 · 20f0977
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diff --git a/PyTorch/Classification/RN50v1.5/README.md b/PyTorch/Classification/RN50v1.5/README.md
@@ -245,9 +245,9 @@ Each of this scripts will run 100 iterations and save results in benchmark.json
 
 | **number of GPUs** | **mixed precision training time** | **FP32 training time** |
 |:------------------:|:---------------------------------:|:----------------------:|
-| 1                  | ~24h                              | ~45h                   |
-| 4                  | ~8h                               | ~13h                   |
-| 8                  | ~4h                               | ~7h                    |
+| 2                  | ~24h                              | ~45h                   |
+| 8                  | ~8h                               | ~13h                   |
+| 16                  | ~4h                               | ~7h                    |
 
 
 ### Example plots (250 Epochs configuration on DGX2)
@@ -269,7 +269,7 @@ Each of this scripts will run 100 iterations and save results in benchmark.json
 | 4                  | 2886.9                    | 1375.5         | 2.1                         | 3.86                             | 3.79                  |
 | 8                  | 5815.8                    | 2857.9         | 2.03                        | 7.78                             | 7.87                  |
 
-### NVIDIA DGX1V (16x V100 32G)
+### NVIDIA DGX2V (16x V100 32G)
 
 | **number of GPUs** | **mixed precision img/s** | **FP32 img/s** | **mixed precision speedup** |
 |:------------------:|:-------------------------:|:--------------:|:---------------------------:|

diff --git a/PyTorch/Classification/RN50v1.5/image_classification/dataloaders.py b/PyTorch/Classification/RN50v1.5/image_classification/dataloaders.py
@@ -134,7 +134,6 @@ def gdtl(data_path, batch_size, num_classes, one_hot, workers=5, _worker_init_fn
                 data_dir = traindir, crop = 224, dali_cpu=dali_cpu)
 
         pipe.build()
-        test_run = pipe.run()
         train_loader = DALIClassificationIterator(pipe, size = int(pipe.epoch_size("Reader") / world_size))
 
         return DALIWrapper(train_loader, num_classes, one_hot), int(pipe.epoch_size("Reader") / (world_size * batch_size))
@@ -158,7 +157,6 @@ def gdvl(data_path, batch_size, num_classes, one_hot, workers=5, _worker_init_fn
                 data_dir = valdir,
                 crop = 224, size = 256)
         pipe.build()
-        test_run = pipe.run()
         val_loader = DALIClassificationIterator(pipe, size = int(pipe.epoch_size("Reader") / world_size))
 
         return DALIWrapper(val_loader, num_classes, one_hot), int(pipe.epoch_size("Reader") / (world_size * batch_size))