Fix fileds fields

python/paddle/io/dataloader/collate.py

@@ -70,7 +70,7 @@ def default_collate_fn(batch):
         sample_fields_num = len(sample)
         if not all(len(sample) == sample_fields_num for sample in iter(batch)):
             raise RuntimeError(
-                "fileds number not same among samples in a batch"
+                "fields number not same among samples in a batch"
             )
         return [default_collate_fn(fields) for fields in zip(*batch)]
 
@@ -88,8 +88,8 @@ def default_convert_fn(batch):
     dictionary, string, number, numpy array and paddle.Tensor.
 
     .. note::
-        This function is default :attr:`collate_fn` in **Distable
-        automatic batching** mode, for **Distable automatic batching**
+        This function is default :attr:`collate_fn` in **Disable
+        automatic batching** mode, for **Disable automatic batching**
         mode, please ses :attr:`paddle.io.DataLoader`
 
     Args:

python/paddle/io/dataloader/dataloader_iter.py

@@ -164,7 +164,7 @@ def __init__(self, loader):
             self._drop_last,
         )
 
-        # NOTE: _structrue_infos used to record the data structure of
+        # NOTE: _structure_infos used to record the data structure of
         # batch to restore batch structure after reading Tensor
         # from blocking_queue in single-process mode. Note that
         # only single process is used in single-process mode, we

python/paddle/io/dataloader/dataset.py

@@ -325,7 +325,7 @@ class ComposeDataset(Dataset):
     """
     A Dataset which composes fields of multiple datasets.
 
-    This dataset is used for composing fileds of multiple map-style
+    This dataset is used for composing fields of multiple map-style
     datasets of same length.
 
     Args:

python/paddle/io/dataloader/flat.py

@@ -96,7 +96,7 @@ def _flatten(batch, flat_batch, structure, field_idx):
 def _restore_batch(flat_batch, structure):
     """
     After reading list of Tensor data from lod_blocking_queue outputs,
-    use this function to restore the batch data structrue, replace
+    use this function to restore the batch data structure, replace
     :attr:`_paddle_field_x` with data from flat_batch
     """
 

python/paddle/io/dataloader/sampler.py

@@ -288,7 +288,7 @@ def _weighted_sample(weights, num_samples, replacement=True):
 
 class WeightedRandomSampler(Sampler):
     """
-    Random sample with given weights (probabilities), sampe index will be in range
+    Random sample with given weights (probabilities), sample index will be in range
     [0, len(weights) - 1], if :attr:`replacement` is True, index can be sampled
     multiple times.
 

python/paddle/io/dataloader/worker.py

@@ -85,7 +85,7 @@ def get_worker_info():
 
     :attr:`num_workers`: total worker process number, see `paddle.io.DataLoader`
 
-    :attr:`id`: the worker processs id, count from 0 to :attr:`num_workers - 1`
+    :attr:`id`: the worker process id, count from 0 to :attr:`num_workers - 1`
 
     :attr:`dataset`: the dataset object in this worker process
 

python/paddle/quantization/imperative/ptq_quantizer.py

@@ -69,14 +69,14 @@ def combine_abs_max_and_hist(
         # bin_width = origin_max / (bins * upsample_bins)
         #           = new_max / (bins * downsample_bins)
         bin_width = origin_max / (bins * upsample_bins)
-        downsampe_bins = int(math.ceil(new_max / (bins * bin_width)))
-        new_max = bins * bin_width * downsampe_bins
+        downsample_bins = int(math.ceil(new_max / (bins * bin_width)))
+        new_max = bins * bin_width * downsample_bins
 
         upsampled_hist = np.repeat(origin_hist, upsample_bins)
-        expanded_hist = np.zeros((bins * downsampe_bins), dtype=np.float32)
+        expanded_hist = np.zeros((bins * downsample_bins), dtype=np.float32)
         expanded_hist[0 : bins * upsample_bins] = upsampled_hist
         cumsumed_hist = np.cumsum(expanded_hist, dtype=np.float64)[
-            downsampe_bins - 1 :: downsampe_bins
+            downsample_bins - 1 :: downsample_bins
         ]
         shift_cumsumed_hist = np.zeros((bins), dtype=np.float64)
         shift_cumsumed_hist[1:] = cumsumed_hist[0:-1]