diff --git a/iccv19-challenge/gen_video_feature.py b/iccv19-challenge/gen_video_feature.py
new file mode 100644
index 0000000..0451741
--- /dev/null
+++ b/iccv19-challenge/gen_video_feature.py
@@ -0,0 +1,204 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+from datetime import datetime
+import os.path
+from easydict import EasyDict as edict
+import time
+import json
+import glob
+import sys
+import numpy as np
+import importlib
+import itertools
+import argparse
+import struct
+import cv2
+import sklearn
+from sklearn.preprocessing import normalize
+import mxnet as mx
+from mxnet import ndarray as nd
+
+image_shape = None
+net = None
+data_size = 203848
+emb_size = 0
+use_flip = False
+ctx_num = 0
+
+
+
+def do_flip(data):
+  for idx in xrange(data.shape[0]):
+    data[idx,:,:] = np.fliplr(data[idx,:,:])
+
+def get_feature(buffer):
+  global emb_size
+  input_count = len(buffer)
+  if use_flip:
+    input_count *= 2
+  network_count = input_count
+  if input_count%ctx_num!=0:
+    network_count = (input_count//ctx_num+1)*ctx_num
+
+  input_blob = np.zeros( (network_count, 3, image_shape[1], image_shape[2]), dtype=np.float32)
+  idx = 0
+  for item in buffer:
+    img = cv2.imread(item)[:,:,::-1] #to rgb
+    img = np.transpose( img, (2,0,1) )
+    attempts = [0,1] if use_flip else [0]
+    for flipid in attempts:
+      _img = np.copy(img)
+      if flipid==1:
+        do_flip(_img)
+      input_blob[idx] = _img
+      idx+=1
+  data = mx.nd.array(input_blob)
+  db = mx.io.DataBatch(data=(data,))
+  net.model.forward(db, is_train=False)
+  _embedding = net.model.get_outputs()[0].asnumpy()
+  _embedding = _embedding[0:input_count]
+  if emb_size==0:
+    emb_size = _embedding.shape[1]
+    print('set emb_size to ', emb_size)
+  embedding = np.zeros( (len(buffer), emb_size), dtype=np.float32 )
+  if use_flip:
+    embedding1 = _embedding[0::2]
+    embedding2 = _embedding[1::2]
+    embedding = embedding1+embedding2
+  else:
+    embedding = _embedding
+  embedding = sklearn.preprocessing.normalize(embedding)
+  return embedding
+
+def write_bin(path, m):
+  rows, cols = m.shape
+  with open(path, 'wb') as f:
+    f.write(struct.pack('4i', rows,cols,cols*4,5))
+    f.write(m.data)
+
+def main(args):
+  global image_shape
+  global net
+  global ctx_num
+
+  print(args)
+  ctx = []
+  cvd = os.environ['CUDA_VISIBLE_DEVICES'].strip()
+  if len(cvd)>0:
+    for i in xrange(len(cvd.split(','))):
+      ctx.append(mx.gpu(i))
+  if len(ctx)==0:
+    ctx = [mx.cpu()]
+    print('use cpu')
+  else:
+    print('gpu num:', len(ctx))
+  ctx_num = len(ctx)
+  image_shape = [int(x) for x in args.image_size.split(',')]
+  vec = args.model.split(',')
+  assert len(vec)>1
+  prefix = vec[0]
+  epoch = int(vec[1])
+  print('loading',prefix, epoch)
+  net = edict()
+  net.ctx = ctx
+  net.sym, net.arg_params, net.aux_params = mx.model.load_checkpoint(prefix, epoch)
+  #net.arg_params, net.aux_params = ch_dev(net.arg_params, net.aux_params, net.ctx)
+  all_layers = net.sym.get_internals()
+  net.sym = all_layers['fc1_output']
+  net.model = mx.mod.Module(symbol=net.sym, context=net.ctx, label_names = None)
+  net.model.bind(data_shapes=[('data', (args.batch_size, 3, image_shape[1], image_shape[2]))])
+  net.model.set_params(net.arg_params, net.aux_params)
+
+  features_all = None
+
+  i = 0
+  filelist = os.path.join(args.input, 'filelist.txt')
+  #print(filelist)
+  buffer_images = []
+  buffer_embedding = np.zeros( (0,0), dtype=np.float32)
+  aggr_nums = []
+  row_idx = 0
+  for line in open(filelist, 'r'):
+    if i%1000==0:
+      print("processing ",i)
+    i+=1
+    #print('stat', i, len(buffer_images), buffer_embedding.shape, aggr_nums, row_idx)
+    videoname = line.strip().split()[0]
+    images = glob.glob("%s/%s/*.jpg"%(args.input, videoname))
+    assert len(images)>0
+    image_features = []
+    for image_path in images:
+      buffer_images.append(image_path)
+    aggr_nums.append(len(images))
+    while len(buffer_images)>=args.batch_size:
+      embedding = get_feature(buffer_images[0:args.batch_size])
+      buffer_images = buffer_images[args.batch_size:]
+      if buffer_embedding.shape[1]==0:
+        buffer_embedding = embedding.copy()
+      else:
+        buffer_embedding = np.concatenate( (buffer_embedding, embedding), axis=0)
+    buffer_idx = 0
+    acount = 0
+    for anum in aggr_nums:
+      if buffer_embedding.shape[0]>=anum+buffer_idx:
+        image_features = buffer_embedding[buffer_idx:buffer_idx+anum]
+        video_feature = np.sum(image_features, axis=0, keepdims=True)
+        video_feature = sklearn.preprocessing.normalize(video_feature)
+        if features_all is None:
+          features_all = np.zeros( (data_size, video_feature.shape[1]), dtype=np.float32 )
+        #print('write to', row_idx, anum, buffer_embedding.shape)
+        features_all[row_idx] = video_feature.flatten()
+        row_idx+=1
+        buffer_idx += anum
+        acount+=1
+      else:
+        break
+    aggr_nums = aggr_nums[acount:]
+    buffer_embedding = buffer_embedding[buffer_idx:]
+
+  if len(buffer_images)>0:
+    embedding = get_feature(buffer_images)
+    buffer_images = buffer_images[args.batch_size:]
+    buffer_embedding = np.concatenate( (buffer_embedding, embedding), axis=0)
+  buffer_idx = 0
+  acount = 0
+  for anum in aggr_nums:
+    assert buffer_embedding.shape[0]>=anum+buffer_idx
+    image_features = buffer_embedding[buffer_idx:buffer_idx+anum]
+    video_feature = np.sum(image_features, axis=0, keepdims=True)
+    video_feature = sklearn.preprocessing.normalize(video_feature)
+    #print('last write to', row_idx, anum, buffer_embedding.shape)
+    features_all[row_idx] = video_feature.flatten()
+    row_idx+=1
+    buffer_idx += anum
+    acount+=1
+
+  aggr_nums = aggr_nums[acount:]
+  buffer_embedding = buffer_embedding[buffer_idx:]
+  assert len(aggr_nums)==0
+  assert buffer_embedding.shape[0]==0
+
+  write_bin(args.output, features_all)
+  print(row_idx, features_all.shape)
+  #os.system("bypy upload %s"%args.output)
+
+
+
+def parse_arguments(argv):
+  parser = argparse.ArgumentParser()
+  
+  parser.add_argument('--batch_size', type=int, help='', default=32)
+  parser.add_argument('--image_size', type=str, help='', default='3,112,112')
+  parser.add_argument('--input', type=str, help='', default='./testdata-video')
+  parser.add_argument('--output', type=str, help='', default='')
+  parser.add_argument('--model', type=str, help='', default='')
+  return parser.parse_args(argv)
+
+if __name__ == '__main__':
+  main(parse_arguments(sys.argv[1:]))
+
+