fix video bug in reader/metric/benchmark (#4918)

* fix video bug and slowfast benchmark bug

* refine bmn doc

PaddleCV/video/metrics/youtube8m/eval_util.py

@@ -37,7 +37,7 @@ def calculate_hit_at_one(predictions, actuals):
     float: The average hit at one across the entire batch.
   """
     top_prediction = numpy.argmax(predictions, 1)
-    hits = actuals[:, top_prediction]
+    hits = actuals[numpy.arange(actuals.shape[0]), top_prediction]
     return numpy.average(hits)
 
 

PaddleCV/video/models/bmn/bmn.py

@@ -27,7 +27,15 @@ __all__ = ["BMN"]
 
 
 class BMN(ModelBase):
-    """BMN model"""
+    """BMN model from
+    `"BMN: Boundary-Matching Network for Temporal Action Proposal Generation" <https://arxiv.org/abs/1907.09702>`_
+    Args:
+        tscale (int): sequence length, default 100.
+        dscale (int): max duration length, default 100.
+        prop_boundary_ratio (float): ratio of expanded temporal region in proposal boundary, default 0.5. 
+        num_sample (int): number of samples betweent starting boundary and ending boundary of each propoasl, default 32.
+        num_sample_perbin (int):  number of selected points in each sample, default 3.
+    """
 
     def __init__(self, name, cfg, mode='train'):
         super(BMN, self).__init__(name, cfg, mode=mode)

PaddleCV/video/reader/ctcn_reader.py

@@ -425,9 +425,9 @@ class CTCNReader(DataReader):
                     tmp_list = fl[i * file_num:]
                 reader_lists[i] = tmp_list
 
-            queue = multiprocessing.Queue(queue_size)
+            manager = multiprocessing.Manager()
+            queue = manager.Queue(queue_size)
             p_list = [None] * len(reader_lists)
-            # for reader_list in reader_lists:
             for i in range(len(reader_lists)):
                 reader_list = reader_lists[i]
                 p_list[i] = multiprocessing.Process(

dygraph/slowfast/README.md

@@ -68,31 +68,8 @@ SlowFast Overview
 
 ## 数据准备
 
-SlowFast模型的训练数据采用Kinetics400数据集，直接输入mp4文件进行训练，数据准备方式如下:
+SlowFast模型的训练数据采用Kinetics400数据集，数据下载及准备请参考[数据说明](../PaddleCV/video/data/dataset/README.md)
 
-### mp4视频下载
-
-在Code\_Root目录下创建文件夹
-
-```
-    cd $Code_Root/ && mkdir data
-    cd data && mkdir data_k400 && cd data_k400
-    mkdir train_mp4 && mkdir val_mp4
-```
-
-ActivityNet官方提供了Kinetics的下载工具，具体参考其[官方repo](https://github.com/activitynet/ActivityNet/tree/master/Crawler/Kinetics)即可下载Kinetics400的mp4视频集合。将kinetics400的训练与验证集合分别下载到data/data\_k400/train\_mp4与data/data\_k400/val\_mp4。
-
-### 生成训练和验证集list
-
-验证(Validation)和评估(Evaluation)使用同一份数据集，预测(Predict)可以从验证数据集中挑选少量样本进行预测
-
-```
-    cd $Code_Root/data/
-    ls $Code_Root/data/data_k400/train_mp4/* > train.csv
-    ls $Code_Root/data/data_k400/val_mp4/* > val.csv
-    ls $Code_Root/data/data_k400/val_mp4/* > test.csv
-    ls $Code_Root/data/data_k400/val_mp4/* > infer.csv
-```
 
 ## 模型训练
 

dygraph/slowfast/train.py

@@ -268,7 +268,7 @@ def train(args):
 
     random.seed(0)
     np.random.seed(0)
-    paddle.framework.manual_seed(0)
+    paddle.framework.seed(0)
     with fluid.dygraph.guard(place):
         # 1. init net
         if args.use_data_parallel: