Fix yapf for readers.py and losses.py

losses.py

@@ -92,11 +92,11 @@ def calculate_loss(self, predictions, labels, **unused_params):
       epsilon = 10e-8
       float_labels = tf.cast(labels, tf.float32)
       # l1 normalization (labels are no less than 0)
-      label_rowsum = tf.maximum(
-          tf.reduce_sum(float_labels, 1, keep_dims=True), epsilon)
+      label_rowsum = tf.maximum(tf.reduce_sum(float_labels, 1, keep_dims=True),
+                                epsilon)
       norm_float_labels = tf.div(float_labels, label_rowsum)
       softmax_outputs = tf.nn.softmax(predictions)
       softmax_loss = tf.negative(
-          tf.reduce_sum(
-              tf.multiply(norm_float_labels, tf.log(softmax_outputs)), 1))
+          tf.reduce_sum(tf.multiply(norm_float_labels, tf.log(softmax_outputs)),
+                        1))
     return tf.reduce_mean(softmax_loss)

readers.py

@@ -286,22 +286,21 @@ def prepare_serialized_examples(self,
       start_times = contexts["segment_start_times"].values
       # Here we assume all the segments that started at the same start time has
       # the same segment_size.
-      uniq_start_times, seg_idxs = tf.unique(
-          start_times, out_idx=tf.dtypes.int64)
+      uniq_start_times, seg_idxs = tf.unique(start_times,
+                                             out_idx=tf.dtypes.int64)
       # TODO(zhengxu): Ensure the segment_sizes are all same.
       segment_size = self.segment_size
       # Range gather matrix, e.g., [[0,1,2],[1,2,3]] for segment_size == 3.
-      range_mtx = tf.expand_dims(
-          uniq_start_times, axis=-1) + tf.expand_dims(
-              tf.range(0, segment_size, dtype=tf.int64), axis=0)
+      range_mtx = tf.expand_dims(uniq_start_times, axis=-1) + tf.expand_dims(
+          tf.range(0, segment_size, dtype=tf.int64), axis=0)
       # Shape: [num_segment, segment_size, feature_dim].
       batch_video_matrix = tf.gather_nd(video_matrix,
                                         tf.expand_dims(range_mtx, axis=-1))
       num_segment = tf.shape(batch_video_matrix)[0]
-      batch_video_ids = tf.reshape(
-          tf.tile([contexts["id"]], [num_segment]), (num_segment,))
-      batch_frames = tf.reshape(
-          tf.tile([segment_size], [num_segment]), (num_segment,))
+      batch_video_ids = tf.reshape(tf.tile([contexts["id"]], [num_segment]),
+                                   (num_segment,))
+      batch_frames = tf.reshape(tf.tile([segment_size], [num_segment]),
+                                (num_segment,))
 
       # For segment labels, all labels are not exhausively rated. So we only
       # evaluate the rated labels.
@@ -317,17 +316,18 @@ def prepare_serialized_examples(self,
       sparse_label_weights = tf.sparse.SparseTensor(
           label_indices, tf.ones_like(label_values, dtype=tf.float32),
           (num_segment, self.num_classes))
-      batch_label_weights = tf.sparse.to_dense(
-          sparse_label_weights, validate_indices=False)
+      batch_label_weights = tf.sparse.to_dense(sparse_label_weights,
+                                               validate_indices=False)
     else:
       # Process video-level labels.
       label_indices = contexts["labels"].values
       sparse_labels = tf.sparse.SparseTensor(
           tf.expand_dims(label_indices, axis=-1),
           tf.ones_like(contexts["labels"].values, dtype=tf.bool),
           (self.num_classes,))
-      labels = tf.sparse.to_dense(
-          sparse_labels, default_value=False, validate_indices=False)
+      labels = tf.sparse.to_dense(sparse_labels,
+                                  default_value=False,
+                                  validate_indices=False)
       # convert to batch format.
       batch_video_ids = tf.expand_dims(contexts["id"], 0)
       batch_video_matrix = tf.expand_dims(video_matrix, 0)