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Update kth.py #19

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Update kth.py #19

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24 changes: 15 additions & 9 deletions data/kth.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,9 @@
import socket
import torch
from scipy import misc
from torch.utils.serialization import load_lua
from imageio import imread
# from torch.utils.serialization import load_lua
import torchfile

class KTH(object):

Expand All @@ -26,7 +28,8 @@ def __init__(self, train, data_root, seq_len = 20, image_size=64):

self.data= {}
for c in self.classes:
self.data[c] = load_lua('%s/%s/%s_meta%dx%d.t7' % (self.data_root, c, data_type, image_size, image_size))
self.data[c] = torchfile.load('%s/%s/%s_meta%dx%d.t7' % (self.data_root, c, data_type, image_size, image_size))
# self.data[c] = load_lua('%s/%s/%s_meta%dx%d.t7' % (self.data_root, c, data_type, image_size, image_size))


self.seed_set = False
Expand All @@ -38,17 +41,21 @@ def get_sequence(self):
c = self.classes[c_idx]
vid_idx = np.random.randint(len(self.data[c]))
vid = self.data[c][vid_idx]
seq_idx = np.random.randint(len(vid['files']))
if len(vid['files'][seq_idx]) - t >= 0:
seq_idx = np.random.randint(len(vid[b'files']))
if len(vid[b'files'][seq_idx]) - t >= 0:
break
dname = '%s/%s/%s' % (self.data_root, c, vid['vid'])
st = random.randint(0, len(vid['files'][seq_idx])-t)
# dname = '%s/%s/%s' % (self.data_root, c, vid[b'vid'])
dname = '%s/%s/%s' % (self.data_root, c, vid[b'vid'].decode())
# st = random.randint(0, len(vid[b'files'][seq_idx])-t)
st = random.randint(0, len(vid[b'files'][seq_idx])-t)


seq = []
for i in range(st, st+t):
fname = '%s/%s' % (dname, vid['files'][seq_idx][i])
im = misc.imread(fname)/255.
# fname = '%s/%s' % (dname, vid[b'files'][seq_idx][i])
fname = '%s/%s' % (dname, vid[b'files'][seq_idx][i].decode())
# im = misc.imread(fname)/255.
im = imread(fname)/255.
seq.append(im[:, :, 0].reshape(self.image_size, self.image_size, 1))
return np.array(seq)

Expand All @@ -62,4 +69,3 @@ def __getitem__(self, index):

def __len__(self):
return len(self.dirs)*36*5 # arbitrary