Merge pull request #1037 from theakshaypant/akshay/fix-read_shard_fro…

…m_default_path

Modifying the `shard_num` read for templates to indicate that `data_path` flag needs to be passed as an integer

openfl-workspace/keras_cnn_mnist/src/tfmnist_inmemory.py

@@ -26,6 +26,14 @@ def __init__(self, data_path, batch_size, **kwargs):
         #  what index/rank is this collaborator.
         # Then we have a way to automatically shard based on rank and size of
         # collaborator list.
+        try:
+            int(data_path)
+        except:
+            raise ValueError(
+                "Expected `%s` to be representable as `int`, as it refers to the data shard " +
+                "number used by the collaborator.",
+                data_path
+            )
 
         _, num_classes, X_train, y_train, X_valid, y_valid = load_mnist_shard(
             shard_num=int(data_path), **kwargs

openfl-workspace/keras_cnn_with_compression/src/tfmnist_inmemory.py

@@ -26,11 +26,18 @@ def __init__(self, data_path, batch_size, **kwargs):
         #  what index/rank is this collaborator.
         # Then we have a way to automatically shard based on rank and size of
         # collaborator list.
+        try:
+            int(data_path)
+        except:
+            raise ValueError(
+                "Expected `%s` to be representable as `int`, as it refers to the data shard " +
+                "number used by the collaborator.",
+                data_path
+            )
 
         _, num_classes, X_train, y_train, X_valid, y_valid = load_mnist_shard(
             shard_num=int(data_path), **kwargs
         )
-
         self.X_train = X_train
         self.y_train = y_train
         self.X_valid = X_valid

openfl-workspace/torch_cnn_histology/src/dataloader.py

@@ -37,9 +37,18 @@ def __init__(self, data_path, batch_size, **kwargs):
         """
         super().__init__(batch_size, random_seed=0, **kwargs)
 
-        _, num_classes, X_train, y_train, X_valid, y_valid = load_histology_shard(
-            shard_num=int(data_path), **kwargs)
+        try:
+            int(data_path)
+        except:
+            raise ValueError(
+                "Expected `%s` to be representable as `int`, as it refers to the data shard " +
+                "number used by the collaborator.",
+                data_path
+            )
 
+        _, num_classes, X_train, y_train, X_valid, y_valid = load_histology_shard(
+            shard_num=int(data_path), **kwargs
+        )
         self.X_train = X_train
         self.y_train = y_train
         self.X_valid = X_valid

openfl-workspace/torch_cnn_histology_gramine_ready/src/pthistology_inmemory.py

@@ -21,8 +21,18 @@ def __init__(self, data_path, batch_size, **kwargs):
         """
         super().__init__(batch_size, random_seed=0, **kwargs)
 
+        try:
+            int(data_path)
+        except:
+            raise ValueError(
+                "Expected `%s` to be representable as `int`, as it refers to the data shard " +
+                "number used by the collaborator.",
+                data_path
+            )
+
         _, num_classes, X_train, y_train, X_valid, y_valid = load_histology_shard(
-            shard_num=int(data_path), **kwargs)
+            shard_num=int(data_path), **kwargs
+        )
 
         self.X_train = X_train
         self.y_train = y_train

openfl-workspace/torch_cnn_mnist/src/dataloader.py

@@ -26,10 +26,18 @@ def __init__(self, data_path, batch_size, **kwargs):
         """
         super().__init__(batch_size, **kwargs)
 
+        try:
+            int(data_path)
+        except:
+            raise ValueError(
+                "Expected `%s` to be representable as `int`, as it refers to the data shard " +
+                "number used by the collaborator.",
+                data_path
+            )
+
         num_classes, X_train, y_train, X_valid, y_valid = load_mnist_shard(
             shard_num=int(data_path), **kwargs
         )
-
         self.X_train = X_train
         self.y_train = y_train
         self.train_loader = self.get_train_loader()

openfl-workspace/torch_cnn_mnist_eden_compression/src/ptmnist_inmemory.py

@@ -27,9 +27,18 @@ def __init__(self, data_path, batch_size, **kwargs):
         # Then we have a way to automatically shard based on rank and size
         # of collaborator list.
 
-        num_classes, X_train, y_train, X_valid, y_valid = load_mnist_shard(
-            shard_num=int(data_path), **kwargs)
+        try:
+            int(data_path)
+        except:
+            raise ValueError(
+                "Expected `%s` to be representable as `int`, as it refers to the data shard " +
+                "number used by the collaborator.",
+                data_path
+            )
 
+        num_classes, X_train, y_train, X_valid, y_valid = load_mnist_shard(
+            shard_num=int(data_path), **kwargs
+        )
         self.X_train = X_train
         self.y_train = y_train
         self.train_loader = self.get_train_loader()

openfl-workspace/torch_cnn_mnist_fed_eval/src/ptmnist_inmemory.py

@@ -27,9 +27,18 @@ def __init__(self, data_path, batch_size, **kwargs):
         # Then we have a way to automatically shard based on rank and size
         # of collaborator list.
 
-        num_classes, X_train, y_train, X_valid, y_valid = load_mnist_shard(
-            shard_num=int(data_path), **kwargs)
+        try:
+            int(data_path)
+        except:
+            raise ValueError(
+                "Expected `%s` to be representable as `int`, as it refers to the data shard " +
+                "number used by the collaborator.",
+                data_path
+            )
 
+        num_classes, X_train, y_train, X_valid, y_valid = load_mnist_shard(
+            shard_num=int(data_path), **kwargs
+        )
         self.X_train = X_train
         self.y_train = y_train
         self.train_loader = self.get_train_loader()

openfl-workspace/torch_cnn_mnist_straggler_check/src/ptmnist_inmemory.py

@@ -26,10 +26,18 @@ def __init__(self, data_path, batch_size, **kwargs):
         #  what index/rank is this collaborator.
         # Then we have a way to automatically shard based on rank and size
         # of collaborator list.
+        try:
+            int(data_path)
+        except:
+            raise ValueError(
+                "Expected `%s` to be representable as `int`, as it refers to the data shard " +
+                "number used by the collaborator.",
+                data_path
+            )
 
         num_classes, X_train, y_train, X_valid, y_valid = load_mnist_shard(
-            shard_num=int(data_path), **kwargs)
-
+            shard_num=int(data_path), **kwargs
+        )
         self.X_train = X_train
         self.y_train = y_train
         self.train_loader = self.get_train_loader()

openfl-workspace/torch_unet_kvasir/src/data_loader.py

@@ -7,6 +7,7 @@
 from os import listdir
 from pathlib import Path
 
+
 import numpy as np
 import PIL
 from skimage import io
@@ -121,6 +122,15 @@ def __init__(self, data_path, batch_size, **kwargs):
         """
         super().__init__(batch_size, **kwargs)
 
+        try:
+            int(data_path)
+        except:
+            raise ValueError(
+                "Expected `%s` to be representable as `int`, as it refers to the data shard " +
+                "number used by the collaborator.",
+                data_path
+            )
+
         load_kvasir_dataset()
         self.valid_dataset = KvasirDataset(True, shard_num=int(data_path), **kwargs)
         self.train_dataset = KvasirDataset(False, shard_num=int(data_path), **kwargs)

openfl-workspace/torch_unet_kvasir_gramine_ready/src/data_loader.py

@@ -7,6 +7,7 @@
 from os import listdir
 from pathlib import Path
 
+
 import numpy as np
 import PIL
 from skimage import io
@@ -121,9 +122,19 @@ def __init__(self, data_path, batch_size, **kwargs):
         """
         super().__init__(batch_size, **kwargs)
 
+        try:
+            int(data_path)
+        except:
+            raise ValueError(
+                "Expected `%s` to be representable as `int`, as it refers to the data shard " +
+                "number used by the collaborator.",
+                data_path
+            )
+
         load_kvasir_dataset()
         self.valid_dataset = KvasirDataset(True, shard_num=int(data_path), **kwargs)
         self.train_dataset = KvasirDataset(False, shard_num=int(data_path), **kwargs)
+
         self.train_loader = self.get_train_loader()
         self.val_loader = self.get_valid_loader()
         self.batch_size = batch_size