dataset.py
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# Code for "TSM: Temporal Shift Module for Efficient Video Understanding"
# arXiv:1811.08383
# Ji Lin*, Chuang Gan, Song Han
# {jilin, songhan}@mit.edu, ganchuang@csail.mit.edu
import torch.utils.data as data
from PIL import Image
import os
import numpy as np
from numpy.random import randint
class VideoRecord(object):
def __init__(self, row):
self._data = row
@property
def path(self):
return self._data[0]
@property
def num_frames(self):
return int(self._data[1])
class TSNDataSet(data.Dataset):
def __init__(self, root_path, list_file,
num_segments=3, new_length=1, modality='RGB',
image_tmpl='img_{:05d}.jpg', transform=None,
random_shift=True, test_mode=False,
remove_missing=False, dense_sample=False, twice_sample=False):
self.root_path = root_path
self.list_file = list_file
self.num_segments = num_segments
self.new_length = new_length
self.modality = modality
self.image_tmpl = image_tmpl
self.transform = transform
self.random_shift = random_shift
self.test_mode = test_mode
self.remove_missing = remove_missing
self.dense_sample = dense_sample # using dense sample as I3D
self.twice_sample = twice_sample # twice sample for more validation
if self.dense_sample:
print('=> Using dense sample for the dataset...')
if self.twice_sample:
print('=> Using twice sample for the dataset...')
if self.modality == 'RGBDiff':
self.new_length += 1 # Diff needs one more image to calculate diff
self._parse_list()
def _load_image(self, directory, idx):
if self.modality == 'RGB' or self.modality == 'RGBDiff':
try:
return [Image.open(os.path.join(self.root_path, directory, self.image_tmpl.format(idx))).convert('RGB')]
except Exception:
print('error loading image:', os.path.join(self.root_path, directory, self.image_tmpl.format(idx)))
return [Image.open(os.path.join(self.root_path, directory, self.image_tmpl.format(1))).convert('RGB')]
elif self.modality == 'Flow':
if self.image_tmpl == 'flow_{}_{:05d}.jpg': # ucf
x_img = Image.open(os.path.join(self.root_path, directory, self.image_tmpl.format('x', idx))).convert(
'L')
y_img = Image.open(os.path.join(self.root_path, directory, self.image_tmpl.format('y', idx))).convert(
'L')
elif self.image_tmpl == '{:06d}-{}_{:05d}.jpg': # something v1 flow
x_img = Image.open(os.path.join(self.root_path, '{:06d}'.format(int(directory)), self.image_tmpl.
format(int(directory), 'x', idx))).convert('L')
y_img = Image.open(os.path.join(self.root_path, '{:06d}'.format(int(directory)), self.image_tmpl.
format(int(directory), 'y', idx))).convert('L')
else:
try:
# idx_skip = 1 + (idx-1)*5
flow = Image.open(os.path.join(self.root_path, directory, self.image_tmpl.format(idx))).convert(
'RGB')
except Exception:
print('error loading flow file:',
os.path.join(self.root_path, directory, self.image_tmpl.format(idx)))
flow = Image.open(os.path.join(self.root_path, directory, self.image_tmpl.format(1))).convert('RGB')
# the input flow file is RGB image with (flow_x, flow_y, blank) for each channel
flow_x, flow_y, _ = flow.split()
x_img = flow_x.convert('L')
y_img = flow_y.convert('L')
return [x_img, y_img]
def _parse_list(self):
# check the frame number is large >3:
tmp = [x.strip().split(' ') for x in open(self.list_file)]
if not self.test_mode or self.remove_missing:
tmp = [item for item in tmp if int(item[1]) >= 3]
self.video_list = [VideoRecord(item) for item in tmp]
if self.image_tmpl == '{:06d}-{}_{:05d}.jpg':
for v in self.video_list:
v._data[1] = int(v._data[1]) / 2
print('video number:%d' % (len(self.video_list)))
def _sample_indices(self, record):
"""
:param record: VideoRecord
:return: list
"""
if self.dense_sample: # i3d dense sample
sample_pos = max(1, 1 + record.num_frames - 64)
t_stride = 64 // self.num_segments
start_idx = 0 if sample_pos == 1 else np.random.randint(0, sample_pos - 1)
offsets = [(idx * t_stride + start_idx) % record.num_frames for idx in range(self.num_segments)]
return np.array(offsets) + 1
else: # normal sample
average_duration = (record.num_frames - self.new_length + 1) // self.num_segments
if average_duration > 0:
offsets = np.multiply(list(range(self.num_segments)), average_duration) + randint(average_duration,
size=self.num_segments)
elif record.num_frames > self.num_segments:
offsets = np.sort(randint(record.num_frames - self.new_length + 1, size=self.num_segments))
else:
offsets = np.zeros((self.num_segments,))
return offsets + 1
def _get_val_indices(self, record):
if self.dense_sample: # i3d dense sample
sample_pos = max(1, 1 + record.num_frames - 64)
t_stride = 64 // self.num_segments
start_idx = 0 if sample_pos == 1 else np.random.randint(0, sample_pos - 1)
offsets = [(idx * t_stride + start_idx) % record.num_frames for idx in range(self.num_segments)]
return np.array(offsets) + 1
else:
if record.num_frames > self.num_segments + self.new_length - 1:
tick = (record.num_frames - self.new_length + 1) / float(self.num_segments)
offsets = np.array([int(tick / 2.0 + tick * x) for x in range(self.num_segments)])
else:
offsets = np.zeros((self.num_segments,))
return offsets + 1
def _get_test_indices(self, record):
if self.dense_sample:
sample_pos = max(1, 1 + record.num_frames - 64)
t_stride = 64 // self.num_segments
start_list = np.linspace(0, sample_pos - 1, num=10, dtype=int)
offsets = []
for start_idx in start_list.tolist():
offsets += [(idx * t_stride + start_idx) % record.num_frames for idx in range(self.num_segments)]
return np.array(offsets) + 1
elif self.twice_sample:
tick = (record.num_frames - self.new_length + 1) / float(self.num_segments)
offsets = np.array([int(tick / 2.0 + tick * x) for x in range(self.num_segments)] +
[int(tick * x) for x in range(self.num_segments)])
return offsets + 1
else:
tick = (record.num_frames - self.new_length + 1) / float(self.num_segments)
offsets = np.array([int(tick / 2.0 + tick * x) for x in range(self.num_segments)])
return offsets + 1
def __getitem__(self, index):
record = self.video_list[index]
# check this is a legit video folder
if self.image_tmpl == 'flow_{}_{:05d}.jpg':
file_name = self.image_tmpl.format('x', 1)
full_path = os.path.join(self.root_path, record.path, file_name)
elif self.image_tmpl == '{:06d}-{}_{:05d}.jpg':
file_name = self.image_tmpl.format(int(record.path), 'x', 1)
full_path = os.path.join(self.root_path, '{:06d}'.format(int(record.path)), file_name)
else:
file_name = self.image_tmpl.format(1)
full_path = os.path.join(self.root_path, record.path, file_name)
while not os.path.exists(full_path):
print('################## Not Found:', os.path.join(self.root_path, record.path, file_name))
index = np.random.randint(len(self.video_list))
record = self.video_list[index]
if self.image_tmpl == 'flow_{}_{:05d}.jpg':
file_name = self.image_tmpl.format('x', 1)
full_path = os.path.join(self.root_path, record.path, file_name)
elif self.image_tmpl == '{:06d}-{}_{:05d}.jpg':
file_name = self.image_tmpl.format(int(record.path), 'x', 1)
full_path = os.path.join(self.root_path, '{:06d}'.format(int(record.path)), file_name)
else:
file_name = self.image_tmpl.format(1)
full_path = os.path.join(self.root_path, record.path, file_name)
if not self.test_mode:
segment_indices = self._sample_indices(record) if self.random_shift else self._get_val_indices(record)
else:
segment_indices = self._get_test_indices(record)
return self.get(record, segment_indices)
def get(self, record, indices):
images = list()
for seg_ind in indices:
p = int(seg_ind)
for i in range(self.new_length):
seg_imgs = self._load_image(record.path, p)
images.extend(seg_imgs)
if p < record.num_frames:
p += 1
process_data = self.transform(images)
return record.path, process_data
def __len__(self):
return len(self.video_list)