[Convnets/PyT] 'syntetic' typo fix

Author: shakandrew

PyTorch/Classification/ConvNets/configs.yml

@@ -41,7 +41,7 @@ mode:
     benchmark_training_short:
         <<: *benchmark_training
         epochs: 1
-        data_backend: syntetic
+        data_backend: synthetic
         prof: 100
     benchmark_inference: &benchmark_inference
         print_freq: 1

PyTorch/Classification/ConvNets/image_classification/dataloaders.py

@@ -37,7 +37,7 @@
 
 from image_classification.autoaugment import AutoaugmentImageNetPolicy
 
-DATA_BACKEND_CHOICES = ["pytorch", "syntetic"]
+DATA_BACKEND_CHOICES = ["pytorch", "synthetic"]
 try:
     from nvidia.dali.plugin.pytorch import DALIClassificationIterator
     from nvidia.dali.pipeline import Pipeline
@@ -542,7 +542,7 @@ def __iter__(self):
             yield self.input_data, self.input_target
 
 
-def get_syntetic_loader(
+def get_synthetic_loader(
     data_path,
     image_size,
     batch_size,

PyTorch/Classification/ConvNets/main.py

@@ -501,9 +501,9 @@ def _worker_init_fn(id):
     elif args.data_backend == "dali-cpu":
         get_train_loader = get_dali_train_loader(dali_cpu=True)
         get_val_loader = get_dali_val_loader()
-    elif args.data_backend == "syntetic":
-        get_val_loader = get_syntetic_loader
-        get_train_loader = get_syntetic_loader
+    elif args.data_backend == "synthetic":
+        get_val_loader = get_synthetic_loader
+        get_train_loader = get_synthetic_loader
     else:
         print("Bad databackend picked")
         exit(1)

PyTorch/Classification/ConvNets/model2onnx.py

@@ -67,10 +67,10 @@ def final_name(base_name):
 
 def get_dataloader(image_size, bs, num_classes):
     """return dataloader for inference"""
-    from image_classification.dataloaders import get_syntetic_loader
+    from image_classification.dataloaders import get_synthetic_loader
 
     def data_loader():
-        loader, _ = get_syntetic_loader(None, image_size, bs, num_classes, False)
+        loader, _ = get_synthetic_loader(None, image_size, bs, num_classes, False)
         for inp, _ in loader:
             yield inp
             break

PyTorch/Classification/ConvNets/resnet50v1.5/README.md

@@ -352,7 +352,7 @@ positional arguments:
 optional arguments:
   -h, --help            show this help message and exit
   --data-backend BACKEND
-                        data backend: pytorch | syntetic | dali-gpu | dali-cpu
+                        data backend: pytorch | synthetic | dali-gpu | dali-cpu
                         (default: dali-cpu)
   --arch ARCH, -a ARCH  model architecture: resnet18 | resnet34 | resnet50 |
                         resnet101 | resnet152 | resnext50-32x4d |

PyTorch/Classification/ConvNets/resnext101-32x4d/README.md

@@ -335,7 +335,7 @@ positional arguments:
 optional arguments:
   -h, --help            show this help message and exit
   --data-backend BACKEND
-                        data backend: pytorch | syntetic | dali-gpu | dali-cpu
+                        data backend: pytorch | synthetic | dali-gpu | dali-cpu
                         (default: dali-cpu)
   --arch ARCH, -a ARCH  model architecture: resnet18 | resnet34 | resnet50 |
                         resnet101 | resnet152 | resnext50-32x4d |

PyTorch/Classification/ConvNets/se-resnext101-32x4d/README.md

@@ -337,7 +337,7 @@ positional arguments:
 optional arguments:
   -h, --help            show this help message and exit
   --data-backend BACKEND
-                        data backend: pytorch | syntetic | dali-gpu | dali-cpu
+                        data backend: pytorch | synthetic | dali-gpu | dali-cpu
                         (default: dali-cpu)
   --arch ARCH, -a ARCH  model architecture: resnet18 | resnet34 | resnet50 |
                         resnet101 | resnet152 | resnext50-32x4d |

PyTorch/Classification/ConvNets/triton/deployer.py

@@ -67,10 +67,10 @@ def initialize_model(args):
 
 def get_dataloader(args):
     """ return dataloader for inference """
-    from image_classification.dataloaders import get_syntetic_loader
+    from image_classification.dataloaders import get_synthetic_loader
 
     def data_loader():
-        loader, _ = get_syntetic_loader(None, 128, 1000, True, fp16=args.fp16)
+        loader, _ = get_synthetic_loader(None, 128, 1000, True, fp16=args.fp16)
         processed = 0
         for inp, _ in loader:
             yield inp