In [41]:

    

import random
import numpy as np

from work.config import STORED_FEATURES_PATH
from work.dataset.activitynet import ActivityNetDataset

length = 16
sequence_length = 20
batch_size = 256
# Loading dataset
print('Loading dataset')
dataset = ActivityNetDataset(
    videos_path='../dataset/videos.json',
    labels_path='../dataset/labels.txt'
)
nb_instances = sum([video.num_frames // length for video in dataset.videos])
print('Number of instances: %d' % nb_instances)


stacks = []
for _ in range(batch_size):
    stacks.append([])
stacks_size = np.zeros(batch_size)

videos = dataset.videos
random.shuffle(videos)

for video in videos:
    pos = np.argmin(stacks_size)
    stacks[pos] += [video]
    stacks_size[pos] += video.num_frames//16


    

    




Loading dataset
Number of instances: 4078435

In [42]:

    

print(np.argmin(stacks_size))
print(np.min(stacks_size))


    

    




49
15797.0

In [43]:

    

print(np.argmax(stacks_size))
print(np.max(stacks_size))


    

    




91
16256.0

In [18]:

    

import numpy as np

data = np.zeros((batch_size, max_seq, 4096))


    

In [20]:

    

data.shape


    

    
Out[20]:





(256, 16410, 4096)

In [27]:

    

# How to reshape all the data loaded

a = np.array([[1,2,3,4],[2,5,7,8], [9,8,7,6]])
print(a)
print(a.shape)
b=a.reshape((3,2,2))
b=b.transpose(1,0,2)
print(b)
print(b.shape)


    

    




[[1 2 3 4]
 [2 5 7 8]
 [9 8 7 6]]
(3, 4)
[[[1 2]
  [2 5]
  [9 8]]

 [[3 4]
  [7 8]
  [7 6]]]
(2, 3, 2)