fix docstring

Signed-off-by: changwa1 <[email protected]>

Author: changwangss

neural_compressor/data/datasets/imagenet_dataset.py

@@ -153,7 +153,7 @@ class TensorflowImagenetDataset(IterableDataset):
     """Configuration for Imagenet dataset."""
 
     def __new__(cls, root, subset='validation', num_cores=28, transform=None, filter=None):
-        """New a imagenet dataset for tensorflow"""
+        """New a imagenet dataset for tensorflow."""
         assert subset in ('validation', 'train'), \
             'only support subset (validation, train)'
         logger.warning("This api is going to be deprecated, "
@@ -187,6 +187,7 @@ class ONNXRTImagenetDataset(Dataset):
     """Configuration for Imagenet dataset."""
 
     def __init__(self, root, subset='val', num_cores=28, transform=None, filter=None):
+        """Initialize `ONNXRTImagenetDataset` class."""
         self.val_dir = os.path.join(root, subset)
         assert os.path.exists(self.val_dir), "find no val dir in {}".format(root) + \
             "please make sure there are train/val subfolders"
@@ -204,7 +205,7 @@ def __init__(self, root, subset='val', num_cores=28, transform=None, filter=None
                 self.image_list.append((img, idx))
 
     def __len__(self):
-        """Return the number of images"""
+        """Return the number of images."""
         return len(self.image_list)
 
     def __getitem__(self, index):

neural_compressor/data/transforms/coco_transform.py

@@ -37,8 +37,10 @@
 @transform_registry(transform_type="ParseDecodeCoco", \
                     process="preprocess", framework="tensorflow")
 class ParseDecodeCocoTransform(BaseTransform):
-
+    """Coco decoding will be performed automatically from Neural Compressor v1.4.
+    """
     def __call__(self, sample):
+        """Convert `ParseDecodeCocoTransform` feature."""
         logger.warning("This transform is going to be deprecated, " \
             "coco decoding will be performed automatically from Neural Compressor v1.4.")
         return sample

neural_compressor/data/transforms/imagenet_transform.py

@@ -138,14 +138,14 @@ def __call__(self, sample):
 @transform_registry(transform_type="ParseDecodeImagenet", \
                     process="preprocess", framework="tensorflow")
 class ParseDecodeImagenetTransform(BaseTransform):
-    """imagenet decoding will be performed automatically from Neural Compressor v1.4.
+    """Imagenet decoding will be performed automatically from Neural Compressor v1.4.
 
-       Returns:
-            sample
+    Returns:
+        sample
     """
 
     def __call__(self, sample):
-        """Return sample"""
+        """Convert `ParseDecodeImagenetTransform` feature."""
         logger.warning("This transform is going to be deprecated, " \
             "imagenet decoding will be performed automatically from Neural Compressor v1.4.")
         return sample
@@ -202,15 +202,6 @@ def __call__(self, sample):
         new_height = tf.cast(tf.math.rint(height*scale), dtype=tf.int32)
         new_width = tf.cast(tf.math.rint(width*scale), dtype=tf.int32)
 
-        # image = tf.cond(pred=tf.greater(shape[0], shape[1]), \
-        #                 false_fn=lambda: tf.image.resize(image, \
-        #                     tf.convert_to_tensor(value=[self.resize_side*shape[0]/shape[1], \
-        #                         self.resize_side], dtype=tf.int32)),
-        #                 true_fn=lambda: tf.image.resize(image, \
-        #                     tf.convert_to_tensor(value=[self.resize_side, \
-        #                         self.resize_side * shape[1] / shape[0]], dtype=tf.int32)),
-        #                )
-
         if self.subpixels=='BGR' and self.data_format=='channels_first':
             # 'RGB'->'BGR'
             image = tf.cond(tf.equal(tf.rank(image), 3),

neural_compressor/experimental/data/datasets/imagenet_dataset.py

@@ -36,6 +36,7 @@
 import numpy as np
 from PIL import Image
 from neural_compressor.utils.utility import LazyImport
+from neural_compressor.utils import logger
 from .dataset import dataset_registry, IterableDataset, Dataset
 tf = LazyImport('tensorflow')
 mx = LazyImport('mxnet')
@@ -152,7 +153,7 @@ class TensorflowImagenetDataset(IterableDataset):
     """Configuration for Imagenet dataset."""
 
     def __new__(cls, root, subset='validation', num_cores=28, transform=None, filter=None):
-        """New a imagenet dataset for tensorflow"""
+        """New a imagenet dataset for tensorflow."""
         assert subset in ('validation', 'train'), \
             'only support subset (validation, train)'
         logger.warning("This api is going to be deprecated, "
@@ -186,6 +187,7 @@ class ONNXRTImagenetDataset(Dataset):
     """Configuration for Imagenet dataset."""
 
     def __init__(self, root, subset='val', num_cores=28, transform=None, filter=None):
+        """Initialize `ONNXRTImagenetDataset` class."""
         self.val_dir = os.path.join(root, subset)
         assert os.path.exists(self.val_dir), "find no val dir in {}".format(root) + \
             "please make sure there are train/val subfolders"
@@ -203,7 +205,7 @@ def __init__(self, root, subset='val', num_cores=28, transform=None, filter=None
                 self.image_list.append((img, idx))
 
     def __len__(self):
-        """Return the number of images"""
+        """Return the number of images."""
         return len(self.image_list)
 
     def __getitem__(self, index):

neural_compressor/experimental/data/transforms/imagenet_transform.py

@@ -137,14 +137,14 @@ def __call__(self, sample):
 @transform_registry(transform_type="ParseDecodeImagenet", \
                     process="preprocess", framework="tensorflow")
 class ParseDecodeImagenetTransform(BaseTransform):
-    """imagenet decoding will be performed automatically from Neural Compressor v1.4.
+    """Imagenet decoding will be performed automatically from Neural Compressor v1.4.
 
-       Returns:
-            sample
+    Returns:
+        sample
     """
 
     def __call__(self, sample):
-        """Return sample"""
+        """Convert `ParseDecodeImagenetTransform` feature."""
         logger.warning("This transform is going to be deprecated, " \
             "imagenet decoding will be performed automatically from Neural Compressor v1.4.")
         return sample
@@ -201,15 +201,6 @@ def __call__(self, sample):
         new_height = tf.cast(tf.math.rint(height*scale), dtype=tf.int32)
         new_width = tf.cast(tf.math.rint(width*scale), dtype=tf.int32)
 
-        # image = tf.cond(pred=tf.greater(shape[0], shape[1]), \
-        #                 false_fn=lambda: tf.image.resize(image, \
-        #                     tf.convert_to_tensor(value=[self.resize_side*shape[0]/shape[1], \
-        #                         self.resize_side], dtype=tf.int32)),
-        #                 true_fn=lambda: tf.image.resize(image, \
-        #                     tf.convert_to_tensor(value=[self.resize_side, \
-        #                         self.resize_side * shape[1] / shape[0]], dtype=tf.int32)),
-        #                )
-
         if self.subpixels=='BGR' and self.data_format=='channels_first':
             # 'RGB'->'BGR'
             image = tf.cond(tf.equal(tf.rank(image), 3),
@@ -426,4 +417,4 @@ def __call__(self, sample):
             h = new_height
             w = int(new_width * width / height)
         img = cv2.resize(img, (w, h), interpolation=self.inter_pol)
-        return img, label
+        return img, label