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kinetics_multigrid.py
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kinetics_multigrid.py
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import torch
import torchvision
import torch.utils.data as data
from PIL import Image
import os
import math
import functools
import json
import copy
import numpy as np
import random
import cycle_batch_sampler as cbs
def load_value_file(file_path):
with open(file_path, 'r') as input_file:
value = float(input_file.read().rstrip('\n\r'))
return value
def pil_loader(path):
with open(path, 'rb') as f:
with Image.open(f) as img:
return img.convert('RGB')
def accimage_loader(path):
try:
import accimage
return accimage.Image(path)
except IOError:
# Potentially a decoding problem, fall back to PIL.Image
return pil_loader(path)
def get_default_image_loader():
torchvision.set_image_backend('accimage')
from torchvision import get_image_backend
if get_image_backend() == 'accimage':
return accimage_loader
else:
return pil_loader
def video_loader(video_dir_path, frame_indices, image_loader):
video = []
for i in frame_indices:
image_path = os.path.join(video_dir_path, 'frame_{:05d}.jpg'.format(i))
if os.path.exists(image_path):
video.append(image_loader(image_path))
else:
return video
return video
def get_default_video_loader():
image_loader = get_default_image_loader()
return functools.partial(video_loader, image_loader=image_loader)
def load_annotation_data(data_file_path):
with open(data_file_path, 'r') as data_file:
return json.load(data_file)
def get_class_labels(data):
class_labels_map = {}
index = 0
data = open(data).read().splitlines()
for class_label in data: #['labels']
class_labels_map[class_label] = index
index += 1
return class_labels_map
def get_video_names_and_annotations(data, subset):
video_names = []
annotations = []
for key, value in data.items():
this_subset = value['subset']
if this_subset == subset:
if subset == 'testing':
video_names.append('test/{}'.format(key))
elif subset == 'train':
st = int(value['annotations']['segment'][0])
end = int(value['annotations']['segment'][1])
label = value['annotations']['label'].replace(' ','_')
video_names.append('{}/{}_{}_{}'.format(label, key, str(st).zfill(6), str(end).zfill(6)))
annotations.append(value['annotations'])
else:
label = value['annotations']['label'].replace(' ','_')
video_names.append('{}/{}'.format(label, key))
annotations.append(value['annotations'])
return video_names, annotations
def make_dataset(root_path, annotation_path, class_labels, subset, n_samples_for_each_video, sample_duration):
data = load_annotation_data(annotation_path)
video_names, annotations = get_video_names_and_annotations(data, subset)
class_to_idx = get_class_labels(class_labels)
idx_to_class = {}
for name, label in class_to_idx.items():
idx_to_class[label] = name
pre_saved_dataset = os.path.join(root_path, 'labeldata_80.npy')
if os.path.exists(pre_saved_dataset):
print('{} exists'.format(pre_saved_dataset))
dataset = np.load(pre_saved_dataset, allow_pickle=True)
else:
dataset = []
na = 0
for i in range(len(video_names)):
if i % 1000 == 0:
print('dataset loading [{}/{}] N/A {}'.format(i, len(video_names), na))
video_path = os.path.join(root_path, video_names[i])
if not os.path.exists(video_path):
na += 1
continue
n_frames = len(os.listdir(video_path))
if n_frames <= 80+1:
na += 1
continue
begin_t = 1
end_t = n_frames
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'n_frames': n_frames,
'video_id': video_names[i].split('/')[1]
}
if len(annotations) != 0:
sample['label'] = class_to_idx[annotations[i]['label']]
else:
sample['label'] = -1
if n_samples_for_each_video == 1:
sample['frame_indices'] = list(range(1, n_frames + 1))
dataset.append(sample)
else:
if n_samples_for_each_video > 1:
step = max(1,
math.ceil((n_frames - 1 - sample_duration) /
(n_samples_for_each_video - 1)))
else:
step = sample_duration
for j in range(1, n_frames, step):
sample_j = copy.deepcopy(sample)
sample_j['frame_indices'] = list(
range(j, min(n_frames + 1, j + sample_duration)))
dataset.append(sample_j)
np.save(pre_saved_dataset, dataset)
return dataset, idx_to_class
class Kinetics(data.Dataset):
"""
Args:
root (string): Root directory path.
spatial_transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
temporal_transform (callable, optional): A function/transform that takes in a list of frame indices
and returns a transformed version
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
loader (callable, optional): A function to load an video given its path and frame indices.
Attributes:
classes (list): List of the class names.
class_to_idx (dict): Dict with items (class_name, class_index).
imgs (list): List of (image path, class_index) tuples
"""
def __init__(self,
root_path,
annotation_path,
class_labels,
subset,
n_samples_for_each_video=1,
spatial_transform=None,
temporal_transform=None,
target_transform=None,
sample_duration=16,
crop_size = 224,
get_loader=get_default_video_loader):
self.data, self.class_names = make_dataset(
root_path, annotation_path, class_labels, subset, n_samples_for_each_video,
sample_duration)
self.spatial_transform = spatial_transform
self.temporal_transform = temporal_transform
self.target_transform = target_transform
self.loader = get_loader()
self.sample_duration = sample_duration
self.crop_size = crop_size
self.long_cycles = [
(self.sample_duration//4, int(np.floor(self.crop_size/np.sqrt(2)))),
(self.sample_duration//2, int(np.floor(self.crop_size/np.sqrt(2)))),
(self.sample_duration//2, self.crop_size),
(self.sample_duration, self.crop_size)]
#print(torchvision.__version__, torchvision.version.cuda)
#print(torch.__version__, torch.version.cuda)
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is class_index of the target class.
"""
iteration = index[0]
index, long_cycle_state = index[1]
sample_duration, crop_size = self.long_cycles[long_cycle_state]
stats = (sample_duration, crop_size//2, int(np.floor(crop_size/np.sqrt(2))), crop_size)
if long_cycle_state in [0,1]:
short_cycle_state = iteration % 2
if short_cycle_state == 0:
crop_size = int(np.floor(crop_size/np.sqrt(2)))
else:
short_cycle_state = iteration % 3
if short_cycle_state == 0:
crop_size = crop_size//2
elif short_cycle_state == 1:
crop_size = int(np.floor(crop_size/np.sqrt(2)))
path = self.data[index]['video']
frame_indices = self.data[index]['frame_indices']
if self.temporal_transform is not None:
t_stride = random.randint(1,max(1,self.sample_duration//sample_duration))
frame_indices = self.temporal_transform(frame_indices, t_stride, sample_duration)
clip = self.loader(path, frame_indices)
if self.spatial_transform is not None:
self.spatial_transform.randomize_parameters(crop_size)
clip = [self.spatial_transform(img) for img in clip]
clip = torch.stack(clip, 0).permute(1, 0, 2, 3)
target = self.data[index]
if self.target_transform is not None:
target = self.target_transform(target)
return clip, target, long_cycle_state, stats
def __len__(self):
return len(self.data)