Adding CIFAR data providers.

mlp/data_providers.py

@@ -131,7 +131,38 @@ class DataProvider(object):
         return inputs_batch, targets_batch
 
 
-class MNISTDataProvider(DataProvider):
+class OneOfKDataProvider(DataProvider):
+    """1-of-K classification target data provider.
+
+    Transforms integer target labels to binary 1-of-K encoded targets.
+    """
+
+    def next(self):
+        """Returns next data batch or raises `StopIteration` if at end."""
+        inputs_batch, targets_batch = super(OneOfKDataProvider, self).next()
+        return inputs_batch, self.to_one_of_k(targets_batch)
+
+    def to_one_of_k(self, int_targets):
+        """Converts integer coded class target to 1-of-K coded targets.
+
+        Args:
+            int_targets (ndarray): Array of integer coded class targets (i.e.
+                where an integer from 0 to `num_classes` - 1 is used to
+                indicate which is the correct class). This should be of shape
+                (num_data,).
+
+        Returns:
+            Array of 1-of-K coded targets i.e. an array of shape
+            (num_data, num_classes) where for each row all elements are equal
+            to zero except for the column corresponding to the correct class
+            which is equal to one.
+        """
+        one_of_k_targets = np.zeros((int_targets.shape[0], self.num_classes))
+        one_of_k_targets[range(int_targets.shape[0]), int_targets] = 1
+        return one_of_k_targets
+
+
+class MNISTDataProvider(OneOfKDataProvider):
     """Data provider for MNIST handwritten digit images."""
 
     def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
@@ -173,29 +204,102 @@ class MNISTDataProvider(DataProvider):
         super(MNISTDataProvider, self).__init__(
             inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
 
-    def next(self):
-        """Returns next data batch or raises `StopIteration` if at end."""
-        inputs_batch, targets_batch = super(MNISTDataProvider, self).next()
-        return inputs_batch, self.to_one_of_k(targets_batch)
 
-    def to_one_of_k(self, int_targets):
-        """Converts integer coded class target to 1 of K coded targets.
+class CIFAR10DataProvider(OneOfKDataProvider):
+    """Data provider for CIFAR-10 object images."""
+
+    def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
+                 shuffle_order=True, rng=None):
+        """Create a new CIFAR-10 data provider object.
 
         Args:
-            int_targets (ndarray): Array of integer coded class targets (i.e.
-                where an integer from 0 to `num_classes` - 1 is used to
-                indicate which is the correct class). This should be of shape
-                (num_data,).
-
-        Returns:
-            Array of 1 of K coded targets i.e. an array of shape
-            (num_data, num_classes) where for each row all elements are equal
-            to zero except for the column corresponding to the correct class
-            which is equal to one.
+            which_set: One of 'train' or 'valid'. Determines which
+                portion of the CIFAR-10 data this object should provide.
+            batch_size (int): Number of data points to include in each batch.
+            max_num_batches (int): Maximum number of batches to iterate over
+                in an epoch. If `max_num_batches * batch_size > num_data` then
+                only as many batches as the data can be split into will be
+                used. If set to -1 all of the data will be used.
+            shuffle_order (bool): Whether to randomly permute the order of
+                the data before each epoch.
+            rng (RandomState): A seeded random number generator.
         """
-        one_of_k_targets = np.zeros((int_targets.shape[0], self.num_classes))
-        one_of_k_targets[range(int_targets.shape[0]), int_targets] = 1
-        return one_of_k_targets
+        # check a valid which_set was provided
+        assert which_set in ['train', 'valid'], (
+            'Expected which_set to be either train or valid. '
+            'Got {0}'.format(which_set)
+        )
+        self.which_set = which_set
+        self.num_classes = 10
+        # construct path to data using os.path.join to ensure the correct path
+        # separator for the current platform / OS is used
+        # MLP_DATA_DIR environment variable should point to the data directory
+        data_path = os.path.join(
+            os.environ['MLP_DATA_DIR'], 'cifar-10-{0}.npz'.format(which_set))
+        assert os.path.isfile(data_path), (
+            'Data file does not exist at expected path: ' + data_path
+        )
+        # load data from compressed numpy file
+        loaded = np.load(data_path)
+        inputs, targets = loaded['inputs'], loaded['targets']
+        # label map gives strings corresponding to integer label targets
+        self.label_map = loaded['label_map']
+        # pass the loaded data to the parent class __init__
+        super(CIFAR10DataProvider, self).__init__(
+            inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
+
+
+class CIFAR100DataProvider(OneOfKDataProvider):
+    """Data provider for CIFAR-100 object images."""
+
+    def __init__(self, which_set='train', use_coarse_targets=False,
+                 batch_size=100, max_num_batches=-1,
+                 shuffle_order=True, rng=None):
+        """Create a new CIFAR-100 data provider object.
+
+        Args:
+            which_set: One of 'train' or 'valid'. Determines which
+                portion of the CIFAR-100 data this object should provide.
+            use_coarse_targets: Whether to use coarse 'superclass' labels as
+                targets instead of standard class labels.
+            batch_size (int): Number of data points to include in each batch.
+            max_num_batches (int): Maximum number of batches to iterate over
+                in an epoch. If `max_num_batches * batch_size > num_data` then
+                only as many batches as the data can be split into will be
+                used. If set to -1 all of the data will be used.
+            shuffle_order (bool): Whether to randomly permute the order of
+                the data before each epoch.
+            rng (RandomState): A seeded random number generator.
+        """
+        # check a valid which_set was provided
+        assert which_set in ['train', 'valid'], (
+            'Expected which_set to be either train or valid. '
+            'Got {0}'.format(which_set)
+        )
+        self.which_set = which_set
+        self.use_coarse_targets = use_coarse_targets
+        self.num_classes = 20 if use_coarse_targets else 100
+        # construct path to data using os.path.join to ensure the correct path
+        # separator for the current platform / OS is used
+        # MLP_DATA_DIR environment variable should point to the data directory
+        data_path = os.path.join(
+            os.environ['MLP_DATA_DIR'], 'cifar-100-{0}.npz'.format(which_set))
+        assert os.path.isfile(data_path), (
+            'Data file does not exist at expected path: ' + data_path
+        )
+        # load data from compressed numpy file
+        loaded = np.load(data_path)
+        inputs, targets = loaded['inputs'], loaded['targets']
+        targets = targets[:, 1] if use_coarse_targets else targets[:, 0]
+        # label map gives strings corresponding to integer label targets
+        self.label_map = (
+            loaded['coarse_label_map']
+            if use_coarse_targets else
+            loaded['fine_label_map']
+        )
+        # pass the loaded data to the parent class __init__
+        super(CIFAR100DataProvider, self).__init__(
+            inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
 
 
 class MetOfficeDataProvider(DataProvider):