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dataloader_charades_RL.py
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dataloader_charades_RL.py
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" Dataloader of charades-STA dataset for Reinforcenments Learning based methods"
import torch
import torch.utils.data
import os
import pickle
import numpy as np
import math
from utils import *
import random
import glob
class Charades_Train_dataset(torch.utils.data.Dataset):
def __init__(self):
self.unit_size = 16
self.feats_dimen = 4096
self.context_num = 1
self.context_size = 128
self.visual_feature_dim = 4096
self.sent_vec_dim = 4800
self.sliding_clip_path = "./Dataset/Charades/all_fc6_unit16_overlap0.5/"
self.clip_sentence_pairs_iou_all = pickle.load(open("./Dataset/Charades/ref_info/charades_rl_train_feature.pkl"))
self.num_samples_iou = len(self.clip_sentence_pairs_iou_all)
print(self.num_samples_iou, "iou clip-sentence pairs are readed") # 49442
def read_video_level_feats(self, movie_name, end):
# read unit level feats by just passing the start and end number
unit_size = 16
feats_dimen = 4096
start = 1
num_units = (end - start) / unit_size
# print(start, end, num_units)
curr_start = 1
ten_unit = num_units / 10
four_unit = num_units / 4
oneinfour_unit = four_unit
threeinfour_unit = num_units - four_unit
start_end_list = []
while (curr_start + unit_size <= end):
start_end_list.append((curr_start, curr_start + unit_size))
curr_start += unit_size
original_feats = np.zeros([300, feats_dimen], dtype=np.float32)
original_feats_1 = np.zeros([num_units, feats_dimen], dtype=np.float32)
for k, (curr_s, curr_e) in enumerate(start_end_list):
one_feat = np.load(self.sliding_clip_path + movie_name + "_" + str(curr_s) + ".0_" + str(curr_e) + ".0.npy")
original_feats[k] = one_feat
original_feats_1[k] = one_feat
# print(np.shape(original_feats))
global_feature = np.mean(original_feats_1, axis=0)
initial_feature = original_feats[(oneinfour_unit-1):(threeinfour_unit)]
initial_feature = np.mean(initial_feature, axis=0)
initial_offset_start = oneinfour_unit-1
initial_offset_end = threeinfour_unit - 1
initial_offset_start_norm = initial_offset_start / float(num_units-1)
initial_offset_end_norm = initial_offset_end / float(num_units-1)
return global_feature, original_feats, initial_feature, ten_unit, initial_offset_start, initial_offset_end, initial_offset_start_norm, initial_offset_end_norm, num_units
def __getitem__(self, index):
# print(index)
offset = np.zeros(2, dtype=np.float32)
offset_norm = np.zeros(2, dtype=np.float32)
initial_offset = np.zeros(2, dtype=np.float32)
initial_offset_norm = np.zeros(2, dtype=np.float32)
samples = self.clip_sentence_pairs_iou_all[index]
proposal_or_sliding_window = samples['proposal_or_sliding_window']
end = samples['frames_num'] + 1
movie_name = proposal_or_sliding_window.split("_")[0]
global_feature, original_feats, initial_feature, ten_unit, initial_offset_start, initial_offset_end, initial_offset_start_norm, initial_offset_end_norm, num_units\
= self.read_video_level_feats(movie_name, end)
# print(np.shape(global_feature), np.shape(original_feats), np.shape(initial_feature))
sentence = samples['sent_skip_thought_vec'][0][0]
# print(np.shape(sentence))
offset_start = samples['offset_start']
offset_end = samples['offset_end']
offset_start_norm = samples['offset_start_norm']
offset_end_norm = samples['offset_end_norm']
# offest
offset[0] = offset_start
offset[1] = offset_end
offset_norm[0] = offset_start_norm
offset_norm[1] = offset_end_norm
initial_offset[0] = initial_offset_start
initial_offset[1] = initial_offset_end
initial_offset_norm[0] = initial_offset_start_norm
initial_offset_norm[1] = initial_offset_end_norm
return global_feature, original_feats, initial_feature, sentence, offset_norm, initial_offset, initial_offset_norm, ten_unit, num_units
def __len__(self):
return self.num_samples_iou
class Charades_Test_dataset(torch.utils.data.Dataset):
def __init__(self):
# il_path: image_label_file path
self.context_num = 1
self.context_size = 128
self.visual_feature_dim = 4096
self.feats_dimen = 4096
self.unit_size = 16
self.semantic_size = 4800
self.sliding_clip_path = "./Dataset/Charades/all_fc6_unit16_overlap0.5/"
self.index_in_epoch = 0
self.spacy_vec_dim = 300
self.sent_vec_dim = 4800
self.epochs_completed = 0
self.clip_sentence_pairs = pickle.load(open("./Dataset/Charades/ref_info/charades_sta_test_semantic_sentence_VP_sub_obj.pkl"))
print str(len(self.clip_sentence_pairs)) + " test videos are readed" # 1334
movie_names_set = set()
for ii in self.clip_sentence_pairs:
for iii in self.clip_sentence_pairs[ii]:
clip_name = iii
movie_name = ii
if not movie_name in movie_names_set:
movie_names_set.add(movie_name)
self.movie_names = list(movie_names_set)
self.movie_length_dict = {}
with open("./Dataset/Charades/ref_info/charades_movie_length_info.txt") as f:
for l in f:
self.movie_length_dict[l.rstrip().split(" ")[0]] = float(l.rstrip().split(" ")[1])
def read_video_level_feats(self, movie_name, end):
# read unit level feats by just passing the start and end number
unit_size = 16
feats_dimen = 4096
start = 1
num_units = (end - start) / unit_size
# print(start, end, num_units)
curr_start = 1
ten_unit = num_units / 10
four_unit = num_units / 4
oneinfour_unit = four_unit
threeinfour_unit = num_units - four_unit
start_end_list = []
while (curr_start + unit_size <= end):
start_end_list.append((curr_start, curr_start + unit_size))
curr_start += unit_size
# original_feats = np.zeros([num_units, feats_dimen], dtype=np.float32)
original_feats = np.zeros([num_units, feats_dimen], dtype=np.float32)
for k, (curr_s, curr_e) in enumerate(start_end_list):
one_feat = np.load(self.sliding_clip_path + movie_name + "_" + str(curr_s) + ".0_" + str(curr_e) + ".0.npy")
original_feats[k] = one_feat
# print(np.shape(original_feats))
global_feature = np.mean(original_feats, axis=0)
initial_feature = original_feats[(oneinfour_unit-1):(threeinfour_unit)]
initial_feature = np.mean(initial_feature, axis=0)
initial_offset_start = oneinfour_unit-1
initial_offset_end = threeinfour_unit - 1
initial_offset_start_norm = initial_offset_start / float(num_units-1)
initial_offset_end_norm = initial_offset_end / float(num_units-1)
return global_feature, original_feats, initial_feature, ten_unit, initial_offset_start, initial_offset_end, initial_offset_start_norm, initial_offset_end_norm, num_units
def load_movie_slidingclip(self, movie_name):
# load unit level feats and sentence vector
initial_offset = np.zeros(2, dtype=np.float32)
initial_offset_norm = np.zeros(2, dtype=np.float32)
movie_clip_sentences = []
checkpoint_paths = glob.glob(self.sliding_clip_path + movie_name + "_*")
checkpoint_file_name_ints = [int(float(x.split('/')[-1].split('.npy')[0].split('_')[-1]))
for x in checkpoint_paths]
end = max(checkpoint_file_name_ints)
global_feature, original_feats, initial_feature, ten_unit, initial_offset_start, initial_offset_end, initial_offset_start_norm, initial_offset_end_norm, num_units\
= self.read_video_level_feats(movie_name, end)
ten_unit = np.array(ten_unit)
num_units = np.array(num_units)
for dict_2nd in self.clip_sentence_pairs[movie_name]:
for dict_3rd in self.clip_sentence_pairs[movie_name][dict_2nd]:
sentence_vec_ = dict_3rd['sent_skip_thought_vec'][0][0, :self.sent_vec_dim]
movie_clip_sentences.append((dict_2nd, sentence_vec_))
initial_offset[0] = initial_offset_start
initial_offset[1] = initial_offset_end
initial_offset_norm[0] = initial_offset_start_norm
initial_offset_norm[1] = initial_offset_end_norm
return movie_clip_sentences, global_feature, original_feats, initial_feature, initial_offset, initial_offset_norm, ten_unit, num_units