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thumos_features.py
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thumos_features.py
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import torch.utils.data as data
import os
import csv
import json
import numpy as np
import pandas as pd
import torch
import pdb
import time
import random
import utils
import config
class ThumosFeature(data.Dataset):
def __init__(self, data_path, mode, modal, feature_fps, num_segments, sampling, seed=-1, supervision='point'):
if seed >= 0:
utils.set_seed(seed)
self.mode = mode
self.modal = modal
self.feature_fps = feature_fps
self.num_segments = num_segments
self.sampling = sampling
self.supervision = supervision
if self.modal == 'all':
self.feature_path = []
for _modal in ['rgb', 'flow']:
self.feature_path.append(os.path.join(data_path, 'features', self.mode, _modal))
else:
self.feature_path = os.path.join(data_path, 'features', self.mode, self.modal)
split_path = os.path.join(data_path, 'split_{}.txt'.format(self.mode))
split_file = open(split_path, 'r')
self.vid_list = []
for line in split_file:
self.vid_list.append(line.strip())
split_file.close()
self.fps_dict = json.load(open(os.path.join(data_path, 'fps_dict.json')))
anno_path = os.path.join(data_path, 'gt.json')
anno_file = open(anno_path, 'r')
self.anno = json.load(anno_file)
anno_file.close()
self.class_name_to_idx = dict((v, k) for k, v in config.class_dict.items())
self.num_classes = len(self.class_name_to_idx.keys())
if self.supervision == 'point':
self.point_anno = pd.read_csv(os.path.join(data_path, 'point_gaussian', 'point_labels.csv'))
self.stored_info_all = {'new_dense_anno': [-1] * len(self.vid_list), 'sequence_score': [-1] * len(self.vid_list)}
def __len__(self):
return len(self.vid_list)
def __getitem__(self, index):
data, vid_num_seg, sample_idx = self.get_data(index)
label, point_anno = self.get_label(index, vid_num_seg, sample_idx)
stored_info = {'new_dense_anno': self.stored_info_all['new_dense_anno'][index], 'sequence_score': self.stored_info_all['sequence_score'][index]}
return index, data, label, point_anno, stored_info, self.vid_list[index], vid_num_seg
def get_data(self, index):
vid_name = self.vid_list[index]
vid_num_seg = 0
if self.modal == 'all':
rgb_feature = np.load(os.path.join(self.feature_path[0],
vid_name + '.npy')).astype(np.float32)
flow_feature = np.load(os.path.join(self.feature_path[1],
vid_name + '.npy')).astype(np.float32)
vid_num_seg = rgb_feature.shape[0]
if self.sampling == 'random':
sample_idx = self.random_perturb(vid_num_seg)
elif self.sampling == 'uniform':
sample_idx = self.uniform_sampling(vid_num_seg)
else:
raise AssertionError('Not supported sampling !')
rgb_feature = rgb_feature[sample_idx]
flow_feature = flow_feature[sample_idx]
feature = np.concatenate((rgb_feature, flow_feature), axis=1)
else:
feature = np.load(os.path.join(self.feature_path,
vid_name + '.npy')).astype(np.float32)
vid_num_seg = feature.shape[0]
if self.sampling == 'random':
sample_idx = self.random_perturb(vid_num_seg)
elif self.sampling == 'uniform':
sample_idx = self.uniform_sampling(vid_num_seg)
else:
raise AssertionError('Not supported sampling !')
feature = feature[sample_idx]
return torch.from_numpy(feature), vid_num_seg, sample_idx
def get_label(self, index, vid_num_seg, sample_idx):
vid_name = self.vid_list[index]
anno_list = self.anno['database'][vid_name]['annotations']
label = np.zeros([self.num_classes], dtype=np.float32)
classwise_anno = [[]] * self.num_classes
for _anno in anno_list:
label[self.class_name_to_idx[_anno['label']]] = 1
classwise_anno[self.class_name_to_idx[_anno['label']]].append(_anno)
if self.supervision == 'video':
return label, torch.Tensor(0)
elif self.supervision == 'point':
temp_anno = np.zeros([vid_num_seg, self.num_classes], dtype=np.float32)
t_factor = self.feature_fps / (self.fps_dict[vid_name] * 16)
temp_df = self.point_anno[self.point_anno["video_id"] == vid_name][['point', 'class_index']]
for key in temp_df['point'].keys():
point = temp_df['point'][key]
class_idx = temp_df['class_index'][key]
temp_anno[int(point * t_factor)][class_idx] = 1
point_label = temp_anno[sample_idx, :]
return label, torch.from_numpy(point_label)
def random_perturb(self, length):
if self.num_segments == length or self.num_segments == -1:
return np.arange(length).astype(int)
samples = np.arange(self.num_segments) * length / self.num_segments
for i in range(self.num_segments):
if i < self.num_segments - 1:
if int(samples[i]) != int(samples[i + 1]):
samples[i] = np.random.choice(range(int(samples[i]), int(samples[i + 1]) + 1))
else:
samples[i] = int(samples[i])
else:
if int(samples[i]) < length - 1:
samples[i] = np.random.choice(range(int(samples[i]), length))
else:
samples[i] = int(samples[i])
return samples.astype(int)
def uniform_sampling(self, length):
if length <= self.num_segments or self.num_segments == -1:
return np.arange(length).astype(int)
samples = np.arange(self.num_segments) * length / self.num_segments
samples = np.floor(samples)
return samples.astype(int)