Add dataloader doc_string

neural_compressor/experimental/data/__init__.py

@@ -14,8 +14,9 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+# ==============================================================================
+"""Built-in dataloaders, datasets, transforms, filters for multiple framework backends."""
 
-"""Built-in datasets class for multiple framework backends."""
 
 from .datasets import DATASETS, Dataset, IterableDataset, dataset_registry
 from .transforms import TRANSFORMS, BaseTransform, transform_registry

neural_compressor/experimental/data/dataloaders/__init__.py

@@ -14,6 +14,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+# ==============================================================================
+"""Built-in dataloaders for multiple framework backends."""
 
 from .dataloader import DATALOADERS
 

neural_compressor/experimental/data/dataloaders/base_dataloader.py

@@ -14,21 +14,40 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+# ==============================================================================
+"""BaseDataloder of all dataloaders."""
 
 from abc import abstractmethod
 
 
-class BaseDataLoader(object):
-    """Base class for all DataLoaders. _generate_dataloader is needed to create a dataloader object
-       from the general params like batch_size and sampler. The dynamic batching is just to
-       generate a new dataloader by setting batch_size and last_batch.
+class BaseDataLoader:
+    """Base class for all DataLoaders.
 
-    """
+    _generate_dataloader is needed to create a dataloader object
+    from the general params like batch_size and sampler. The dynamic batching is just to
+    generate a new dataloader by setting batch_size and last_batch.
 
+    """
+
     def __init__(self, dataset, batch_size=1, last_batch='rollover', collate_fn=None,
                  sampler=None, batch_sampler=None, num_workers=0, pin_memory=False,
                  shuffle=False, distributed=False):
+        """Initialize BaseDataLoader.
 
+        Args:
+            dataset (object): dataset from which to load the data
+            batch_size (int, optional): number of samples per batch. Defaults to 1.
+            last_batch (str, optional): whether to drop the last batch if it is incomplete.
+                                        Support ['rollover', 'discard'], rollover means False, discard means True.
+                                        Defaults to 'rollover'.
+            collate_fn (callable, optional): merge data with outer dimension batch size. Defaults to None.
+            sampler (Sampler, optional): Sampler object to sample data. Defaults to None.
+            batch_sampler (BatchSampler, optional): BatchSampler object to generate batch of indices. Defaults to None.
+            num_workers (int, optional): number of subprocesses to use for data loading. Defaults to 0.
+            pin_memory (bool, optional): whether to copy data into pinned memory before returning. Defaults to False.
+            shuffle (bool, optional): whether to shuffle data. Defaults to False.
+            distributed (bool, optional): whether the dataloader is distributed. Defaults to False.
+        """
         self.dataset = dataset
         self.collate_fn = collate_fn
         self.sampler = sampler
@@ -54,6 +73,14 @@ def __init__(self, dataset, batch_size=1, last_batch='rollover', collate_fn=None
             distributed=distributed)
 
     def batch(self, batch_size, last_batch=None):
+        """Set batch size for dataloader.
+
+        Args:
+            batch_size (int): number of samples per batch.
+            last_batch (str, optional): whether to drop the last batch if it is incomplete.
+                                        Support ['rollover', 'discard'], rollover means False, discard means True.
+                                        Defaults to None.
+        """
         self._batch_size = batch_size
         if last_batch is not None:
             self.last_batch = last_batch
@@ -71,9 +98,19 @@ def batch(self, batch_size, last_batch=None):
 
     @property
     def batch_size(self):
+        """Get dataloader's batch_size.
+
+        Returns:
+            int: batch_size
+        """
         return self._batch_size
 
     def __iter__(self):
+        """Yield data in iterative order.
+
+        Returns:
+            iterator: iterator for dataloder
+        """
         return iter(self.dataloader)
 
     @abstractmethod

neural_compressor/experimental/data/dataloaders/dataloader.py

@@ -14,6 +14,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+# ==============================================================================
+"""Built-in dataloaders for multiple framework backends."""
 
 from .tensorflow_dataloader import TensorflowDataLoader
 from .mxnet_dataloader import MXNetDataLoader

neural_compressor/experimental/data/dataloaders/default_dataloader.py

@@ -14,6 +14,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+# ==============================================================================
+"""Default dataloader for multiple framework backends."""
 
 import collections
 import numpy as np
@@ -24,7 +26,7 @@
 from .base_dataloader import BaseDataLoader
 
 def default_collate(batch):
-    """Puts each data field into a pd frame with outer dimension batch size"""
+    """Merge data with outer dimension batch size."""
     elem = batch[0]
     if isinstance(elem, collections.abc.Mapping):
         return {key: default_collate([d[key] for d in batch]) for key in elem}
@@ -40,13 +42,27 @@ def default_collate(batch):
         return batch
 
 class DefaultDataLoader(BaseDataLoader):
-    """DefaultDataLoader
-
-    """
-
+    """DefaultDataLoader for multiple framework backends."""
+
     def __init__(self, dataset, batch_size=1, last_batch='rollover', collate_fn=None,
                  sampler=None, batch_sampler=None, num_workers=0, pin_memory=False,
                  shuffle=False, distributed=False):
+        """Initialize DefaultDataLoader.
+
+        Args:
+            dataset (object): dataset from which to load the data
+            batch_size (int, optional): number of samples per batch. Defaults to 1.
+            last_batch (str, optional): whether to drop the last batch if it is incomplete.
+                                        Support ['rollover', 'discard'], rollover means False, discard means True.
+                                        Defaults to 'rollover'.
+            collate_fn (callable, optional): merge data with outer dimension batch size. Defaults to None.
+            sampler (Sampler, optional): Sampler object to sample data. Defaults to None.
+            batch_sampler (BatchSampler, optional): BatchSampler object to generate batch of indices. Defaults to None.
+            num_workers (int, optional): number of subprocesses to use for data loading. Defaults to 0.
+            pin_memory (bool, optional): whether to copy data into pinned memory before returning. Defaults to False.
+            shuffle (bool, optional): whether to shuffle data. Defaults to False.
+            distributed (bool, optional): whether the dataloader is distributed. Defaults to False.            
+        """
         self.dataset = dataset
         self.last_batch = last_batch
         self.sampler = sampler
@@ -62,14 +78,17 @@ def __init__(self, dataset, batch_size=1, last_batch='rollover', collate_fn=None
             self.collate_fn = default_collate
 
     def batch(self, batch_size, last_batch='rollover'):
+        """Set batch_size and last_batch."""
         self._batch_size = batch_size
         self.last_batch = last_batch
 
     @property
     def dataloader(self):
+        """Return dataloader."""
         return self
 
     def __iter__(self):
+        """Yield data in iterative order."""
         return self._generate_dataloader(
             self.dataset,
             batch_size=self.batch_size,
@@ -83,6 +102,7 @@ def __iter__(self):
             distributed=self.distributed)
 
     def __len__(self):
+        """Get dataset length."""
         try:
             dataset_len = self.dataset.__len__()
         except (AttributeError, TypeError):

neural_compressor/experimental/data/dataloaders/fetcher.py

@@ -14,21 +14,49 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+# ==============================================================================
+"""Definitions of the methods to fetch data from an iterable-style or list-style dataset."""
 
 from abc import abstractmethod
 
 class Fetcher(object):
+    """Base class for different fetchers."""
+
     def __init__(self, dataset, collate_fn, drop_last):
+        """Initialize Fetcher.
+
+        Args:
+            dataset (object): dataset object from which to get data 
+            collate_fn (callable): merge data with outer dimension batch size
+            drop_last (bool): whether to drop the last batch if it is incomplete
+        """
         self.dataset = dataset
         self.collate_fn = collate_fn
         self.drop_last = drop_last
 
     @abstractmethod
     def __call__(self, batched_indices):
+        """Fetch data.
+
+        Args:
+            batched_indices (list): fetch data according to batched_indices
+
+        """
         raise NotImplementedError
 
 class IterableFetcher(Fetcher):
+    """Iterate to get next batch-size samples as a batch."""
+
     def __init__(self, dataset, collate_fn, drop_last, distributed):
+        """Initialize IterableFetcher.
+
+        Args:
+            dataset (object): dataset object from which to get data
+            collate_fn (callable): merge data with outer dimension batch size
+            drop_last (bool): whether to drop the last batch if it is incomplete
+            distributed (bool): whether the dataloader is distributed
+
+        """
         super(IterableFetcher, self).__init__(dataset, collate_fn, drop_last)
         self.dataset_iter = iter(dataset)
         self.index_whole = 0
@@ -47,6 +75,12 @@ def __init__(self, dataset, collate_fn, drop_last, distributed):
                     " please set 'distributed: True' and launch multiple processes.")
 
     def __call__(self, batched_indices):
+        """Fetch data.
+
+        Args:
+            batched_indices (list): fetch data according to batched_indices
+
+        """
         batch_data = []
         batch_size = len(batched_indices)
         while True:
@@ -64,10 +98,26 @@ def __call__(self, batched_indices):
         return self.collate_fn(batch_data)
 
 class IndexFetcher(Fetcher):
+    """Take single index or a batch of indices to fetch samples as a batch."""
+
     def __init__(self, dataset, collate_fn, drop_last, distributed):
+        """Initialize IndexFetcher.
+
+        Args:
+            dataset (object): dataset object from which to get data
+            collate_fn (callable): merge data with outer dimension batch size
+            drop_last (bool): whether to drop the last batch if it is incomplete
+            distributed (bool): whether the dataloader is distributed
+        """
         super(IndexFetcher, self).__init__(dataset, collate_fn, drop_last)
 
     def __call__(self, batched_indices):
+        """Fetch data.
+
+        Args:
+            batched_indices (list): fetch data according to batched_indices
+
+        """
         data = [self.dataset[idx] for idx in batched_indices]
         return self.collate_fn(data)
 

neural_compressor/experimental/data/dataloaders/onnxrt_dataloader.py

@@ -14,6 +14,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+# ==============================================================================
+"""Built-in dataloaders for onnxruntime framework backends."""
 
 from neural_compressor.utils.utility import LazyImport
 from .base_dataloader import BaseDataLoader
@@ -23,6 +25,8 @@
 torch = LazyImport('torch')
 
 class ONNXRTBertDataLoader(DefaultDataLoader):
+    """Built-in dataloader for onnx bert model and its varients."""
+
     def _generate_dataloader(self, dataset, batch_size, last_batch, collate_fn,
                              sampler, batch_sampler, num_workers, pin_memory,
                              shuffle, distributed):
@@ -59,6 +63,8 @@ def _generate_dataloader(self, dataset, batch_size, last_batch, collate_fn,
                 return
 
 class ONNXRTDataLoader(BaseDataLoader):
+    """Built-in dataloader for onnxruntime framework backends."""
+
     def _generate_dataloader(self, dataset, batch_size, last_batch, collate_fn,
                              sampler, batch_sampler, num_workers, pin_memory,
                              shuffle, distributed):