add data preprocessing of nonlocal model

PaddleCV/video/dataset/nonlocal/change_filelist.py

+# Copyright (c) 2017-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import sys
+import numpy as np
+import random
+
+# src = 'trainlist_download.txt'
+# outlist = 'trainlist.txt'
+# original_folder = '/nfs.yoda/xiaolonw/kinetics/data/train'
+# replace_folder = '/scratch/xiaolonw/kinetics/data/compress/train_256'
+assert (len(sys.argv) == 5)
+
+src = sys.argv[1]
+outlist = sys.argv[2]
+original_folder = sys.argv[3]
+replace_folder = sys.argv[4]
+
+f = open(src, 'r')
+flist = []
+for line in f:
+    flist.append(line)
+f.close()
+
+f2 = open(outlist, 'w')
+
+listlen = len(flist)
+for i in range(listlen):
+    line = flist[i]
+    line = line.replace(original_folder, replace_folder)
+    f2.write(line)
+
+f2.close()

PaddleCV/video/dataset/nonlocal/generate_filelist.py

+import os
+import numpy as np
+import sys
+
+num_classes = 400
+replace_space_by_underliner = True  # whether to replace space by '_' in labels
+
+fn = sys.argv[1]  #'trainlist_download400.txt'
+train_dir = sys.argv[
+    2]  #'/docker_mount/data/k400/Kinetics_trimmed_processed_train'
+val_dir = sys.argv[3]  #'/docker_mount/data/k400/Kinetics_trimmed_processed_val'
+trainlist = sys.argv[4]  #'trainlist.txt'
+vallist = sys.argv[5]  #'vallist.txt'
+
+fl = open(fn).readlines()
+fl = [line.strip() for line in fl if line.strip() != '']
+action_list = []
+
+for line in fl[1:]:
+    act = line.split(',')[0].strip('\"')
+    action_list.append(act)
+
+action_set = set(action_list)
+action_list = list(action_set)
+action_list.sort()
+if replace_space_by_underliner:
+    action_list = [item.replace(' ', '_') for item in action_list]
+
+# assign integer label to each category, abseiling is labeled as 0, 
+# zumba labeled as 399 and so on, sorted by the category name
+action_label_dict = {}
+for i in range(len(action_list)):
+    key = action_list[i]
+    action_label_dict[key] = i
+
+assert len(action_label_dict.keys(
+)) == num_classes, "action num should be {}".format(num_classes)
+
+
+def generate_file(Faction_label_dict, Ftrain_dir, Ftrainlist, Fnum_classes):
+    trainactions = os.listdir(Ftrain_dir)
+    trainactions.sort()
+    assert len(
+        trainactions) == Fnum_classes, "train action num should be {}".format(
+            Fnum_classes)
+
+    train_items = []
+    trainlist_outfile = open(Ftrainlist, 'w')
+    for trainaction in trainactions:
+        assert trainaction in Faction_label_dict.keys(
+        ), "action {} should be in action_dict".format(trainaction)
+        trainaction_dir = os.path.join(Ftrain_dir, trainaction)
+        trainaction_label = Faction_label_dict[trainaction]
+        trainaction_files = os.listdir(trainaction_dir)
+        for f in trainaction_files:
+            fn = os.path.join(trainaction_dir, f)
+            item = fn + ' ' + str(trainaction_label)
+            train_items.append(item)
+            trainlist_outfile.write(item + '\n')
+    trainlist_outfile.flush()
+    trainlist_outfile.close()
+
+
+generate_file(action_label_dict, train_dir, trainlist, num_classes)
+generate_file(action_label_dict, val_dir, vallist, num_classes)

PaddleCV/video/dataset/nonlocal/generate_list.sh

+# Download txt name
+TRAINLIST_DOWNLOAD="kinetics-400_train.csv"
+
+# path of the train and valid data
+TRAIN_DIR="/home/sungaofeng/docker/dockermount/data/compress/train_256"
+VALID_DIR="/home/sungaofeng/docker/dockermount/data/compress/val_256"
+
+python generate_filelist.py $TRAINLIST_DOWNLOAD $TRAIN_DIR $VALID_DIR trainlist.txt vallist.txt
+
+# generate test list
+python generate_testlist_multicrop.py
+

PaddleCV/video/dataset/nonlocal/generate_testlist_multicrop.py

+import os
+
+vallist = 'vallist.txt'
+testlist = 'testlist.txt'
+sampling_times = 10
+cropping_times = 3
+
+fl = open(vallist).readlines()
+fl = [line.strip() for line in fl if line.strip() != '']
+f_test = open(testlist, 'w')
+
+for i in range(len(fl)):
+    line = fl[i].split(' ')
+    fn = line[0]
+    label = line[1]
+    for j in range(sampling_times):
+        for k in range(cropping_times):
+            test_item = fn + ' ' + str(i) + ' ' + str(j) + ' ' + str(k) + '\n'
+            f_test.write(test_item)
+
+f_test.close()