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dataloader_charades_SL.py
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dataloader_charades_SL.py
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" Dataloader of charades-STA dataset for Supervised Learning based methods"
import torch
import torch.utils.data
import os
import pickle
import numpy as np
import math
from utils import *
import random
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 * 3
self.sent_vec_dim = 4800
self.clip_softmax_dim = 400
self.softmax_unit_size = 32
self.spacy_vec_dim = 300
self.train_softmax_dir = './Dataset/Charades/train_softmax/'
self.sliding_clip_path = "./Dataset/Charades/all_fc6_unit16_overlap0.5/"
self.clip_sentence_pairs_iou = pickle.load(open("./Dataset/Charades/ref_info/charades_sta_train_semantic_sentence_VP_sub_obj.pkl"))
self.num_videos = len(self.clip_sentence_pairs_iou) # 5182
# get the number of self.clip_sentence_pairs_iou
self.clip_sentence_pairs_iou_all = []
for ii in self.clip_sentence_pairs_iou:
for iii in self.clip_sentence_pairs_iou[ii]:
for iiii in range(len(self.clip_sentence_pairs_iou[ii][iii])):
self.clip_sentence_pairs_iou_all.append(self.clip_sentence_pairs_iou[ii][iii][iiii])
self.num_samples_iou = len(self.clip_sentence_pairs_iou_all)
print(self.num_samples_iou, "iou clip-sentence pairs are readed") # 49442
# print self.clip_sentence_pairs_iou
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_unit_level_feats(self, clip_name):
# read unit level feats by just passing the start and end number
movie_name = clip_name.split("_")[0]
start = int(clip_name.split("_")[1])
end = int(clip_name.split("_")[2])
num_units = (end - start) / self.unit_size
# print(start, end, num_units)
curr_start = start
start_end_list = []
while (curr_start + self.unit_size <= end):
start_end_list.append((curr_start, curr_start + self.unit_size))
curr_start += self.unit_size
original_feats = np.zeros([num_units, self.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
return np.mean(original_feats, axis=0)
def read_unit_level_softmax(self, clip_name):
# read unit level softmax by just passing the start and end number
movie_name = clip_name.split("_")[0]
start = int(clip_name.split("_")[1])
end = int(clip_name.split("_")[2])
num_units = (end - start) / self.unit_size - (self.softmax_unit_size / self.unit_size) + 1
_is_clip_shorter_than_unit_size = False
if num_units <= 0:
num_units = 1
_is_clip_shorter_than_unit_size = True
softmax_feats = np.zeros([num_units, self.clip_softmax_dim], dtype=np.float32)
if _is_clip_shorter_than_unit_size:
_start_here = start
_end_here = end
_npy_file_path_this = self.train_softmax_dir + movie_name + ".mp4_" + str(curr_s) + "_" + str(
curr_e) + ".npy"
if not os.path.exists(_npy_file_path_this):
_npy_file_path_this = self.train_softmax_dir + movie_name + ".mp4_" + str(curr_s) + "_" + str(
curr_e) + ".npy"
one_feat = np.load(_npy_file_path_this)
softmax_feats[0] = one_feat
else:
curr_start = start
start_end_list = []
while (curr_start + self.softmax_unit_size <= end):
start_end_list.append((curr_start, curr_start + self.softmax_unit_size))
curr_start += self.unit_size
for k, (curr_s, curr_e) in enumerate(start_end_list):
one_feat = np.load(
self.train_softmax_dir + movie_name + ".mp4_" + str(curr_s) + "_" + str(curr_e) + ".npy")
softmax_feats[k] = one_feat
return np.mean(softmax_feats, axis=0)
def feat_exists(self, clip_name):
# judge the feats is existed or not
movie_name = clip_name.split("_")[0]
start = int(clip_name.split("_")[1])
end = int(clip_name.split("_")[2])
return os.path.exists(
self.sliding_clip_path + movie_name + "_" + str(end - 16) + ".0_" + str(end) + ".0.npy") and \
os.path.exists(
self.sliding_clip_path + movie_name + "_" + str(start) + ".0_" + str(start + 16) + ".0.npy")
def get_context_window(self, clip_name, win_length):
# compute left (pre) and right (post) context features based on read_unit_level_feats().
movie_name = clip_name.split("_")[0]
start = int(clip_name.split("_")[1])
end = int(clip_name.split("_")[2])
clip_length = self.context_size
left_context_feats = np.zeros([win_length, self.feats_dimen], dtype=np.float32)
right_context_feats = np.zeros([win_length, self.feats_dimen], dtype=np.float32)
last_left_feat = self.read_unit_level_feats(clip_name)
last_right_feat = self.read_unit_level_feats(clip_name)
for k in range(win_length):
left_context_start = start - clip_length * (k + 1)
left_context_end = start - clip_length * k
right_context_start = end + clip_length * k
right_context_end = end + clip_length * (k + 1)
left_context_name = movie_name + "_" + str(left_context_start) + "_" + str(left_context_end)
right_context_name = movie_name + "_" + str(right_context_start) + "_" + str(right_context_end)
if self.feat_exists(left_context_name):
left_context_feat = self.read_unit_level_feats(left_context_name)
last_left_feat = left_context_feat
else:
left_context_feat = last_left_feat
if self.feat_exists(right_context_name):
right_context_feat = self.read_unit_level_feats(right_context_name)
last_right_feat = right_context_feat
else:
right_context_feat = last_right_feat
left_context_feats[k] = left_context_feat
right_context_feats[k] = right_context_feat
return np.mean(left_context_feats, axis=0), np.mean(right_context_feats, axis=0)
def __getitem__(self, index):
offset = np.zeros(2, dtype=np.float32)
VP_spacy = np.zeros(self.spacy_vec_dim*2, dtype=np.float32)
# get this clip's: sentence vector, swin, p_offest, l_offset, sentence, Vps
dict_3rd = self.clip_sentence_pairs_iou_all[index]
# read visual feats
featmap = self.read_unit_level_feats(dict_3rd['proposal_or_sliding_window'])
left_context_feat, right_context_feat = self.get_context_window(dict_3rd['proposal_or_sliding_window'], self.context_num)
image = np.hstack((left_context_feat, featmap, right_context_feat))
# read softmax batch
softmax_center_clip = self.read_unit_level_softmax(dict_3rd['proposal_or_sliding_window'])
# sentence batch
sentence = dict_3rd['sent_skip_thought_vec'][0][0, :self.sent_vec_dim]
if len(dict_3rd['dobj_or_VP']) != 0:
VP_spacy_one_by_one_this_ = dict_3rd['VP_spacy_vec_one_by_one_word'][random.choice(xrange(len(dict_3rd['dobj_or_VP'])))]
if len(VP_spacy_one_by_one_this_) == 1:
VP_spacy[:self.spacy_vec_dim] = VP_spacy_one_by_one_this_[0]
else:
VP_spacy = np.concatenate((VP_spacy_one_by_one_this_[0], VP_spacy_one_by_one_this_[1]))
# offest
p_offset = dict_3rd['offset_start']
l_offset = dict_3rd['offset_end']
offset[0] = p_offset
offset[1] = l_offset
return image, sentence, offset, softmax_center_clip, VP_spacy
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 * 3
self.feats_dimen = 4096
self.unit_size = 16
self.context_size = 128
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.clip_softmax_dim = 400
self.softmax_unit_size = 32
self.test_softmax_dir = './Dataset/Charades/test_softmax/'
self.epochs_completed = 0
self.test_swin_txt_path = "./Dataset/Charades/ref_info/charades_sta_test_swin_props_num_36364.txt"
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.sliding_clip_names = []
with open(self.test_swin_txt_path) as f:
for l in f:
self.sliding_clip_names.append(l.rstrip().replace(" ", "_"))
print "sliding clips number for test: " + str(len(self.sliding_clip_names)) # 36364
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_unit_level_feats(self, clip_name):
# read unit level feats by just passing the start and end number
movie_name = clip_name.split("_")[0]
start = int(clip_name.split("_")[1])
end = int(clip_name.split("_")[2])
num_units = (end - start) / self.unit_size
curr_start = start
start_end_list = []
while (curr_start + self.unit_size <= end):
start_end_list.append((curr_start, curr_start + self.unit_size))
curr_start += self.unit_size
original_feats = np.zeros([num_units, self.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
return np.mean(original_feats, axis=0)
def read_unit_level_softmax(self, clip_name):
# read unit level softmax by just passing the start and end number
movie_name = clip_name.split("_")[0]
start = int(clip_name.split("_")[1])
end = int(clip_name.split("_")[2])
num_units = (end - start) / self.unit_size - (self.softmax_unit_size / self.unit_size) + 1
_is_clip_shorter_than_unit_size = False
if num_units <= 0:
num_units = 1
_is_clip_shorter_than_unit_size = True
softmax_feats = np.zeros([num_units, self.clip_softmax_dim], dtype=np.float32)
if _is_clip_shorter_than_unit_size:
_start_here = start
_end_here = end
_npy_file_path_this = self.test_softmax_dir + movie_name + ".mp4_" + str(curr_s) + "_" + str(
curr_e) + ".npy"
if not os.path.exists(_npy_file_path_this):
_npy_file_path_this = self.test_softmax_dir + movie_name + ".mp4_" + str(curr_s) + "_" + str(
curr_e) + ".npy"
one_feat = np.load(_npy_file_path_this)
softmax_feats[0] = one_feat
else:
curr_start = start
start_end_list = []
while (curr_start + self.softmax_unit_size <= end):
start_end_list.append((curr_start, curr_start + self.softmax_unit_size))
curr_start += self.unit_size
for k, (curr_s, curr_e) in enumerate(start_end_list):
one_feat = np.load(
self.test_softmax_dir + movie_name + ".mp4_" + str(curr_s) + "_" + str(curr_e) + ".npy")
softmax_feats[k] = one_feat
return np.mean(softmax_feats, axis=0)
def feat_exists(self, clip_name):
# judge the feats is existed or not
movie_name = clip_name.split("_")[0]
start = int(clip_name.split("_")[1])
end = int(clip_name.split("_")[2])
return os.path.exists(
self.sliding_clip_path + movie_name + "_" + str(end - 16) + ".0_" + str(end) + ".0.npy") and \
os.path.exists(
self.sliding_clip_path + movie_name + "_" + str(start) + ".0_" + str(start + 16) + ".0.npy")
def get_context_window(self, clip_name, win_length):
# compute left (pre) and right (post) context features based on read_unit_level_feats().
movie_name = clip_name.split("_")[0]
start = int(clip_name.split("_")[1])
end = int(clip_name.split("_")[2])
clip_length = self.context_size
left_context_feats = np.zeros([win_length, self.feats_dimen], dtype=np.float32)
right_context_feats = np.zeros([win_length, self.feats_dimen], dtype=np.float32)
last_left_feat = self.read_unit_level_feats(clip_name)
last_right_feat = self.read_unit_level_feats(clip_name)
for k in range(win_length):
left_context_start = start - clip_length * (k + 1)
left_context_end = start - clip_length * k
right_context_start = end + clip_length * k
right_context_end = end + clip_length * (k + 1)
left_context_name = movie_name + "_" + str(left_context_start) + "_" + str(left_context_end)
right_context_name = movie_name + "_" + str(right_context_start) + "_" + str(right_context_end)
if self.feat_exists(left_context_name):
left_context_feat = self.read_unit_level_feats(left_context_name)
last_left_feat = left_context_feat
else:
left_context_feat = last_left_feat
if self.feat_exists(right_context_name):
right_context_feat = self.read_unit_level_feats(right_context_name)
last_right_feat = right_context_feat
else:
right_context_feat = last_right_feat
left_context_feats[k] = left_context_feat
right_context_feats[k] = right_context_feat
return np.mean(left_context_feats, axis=0), np.mean(right_context_feats, axis=0)
def load_movie_slidingclip(self, movie_name, sample_num):
# load unit level feats and sentence vector
movie_clip_sentences = []
movie_clip_featmap = []
for dict_2nd in self.clip_sentence_pairs[movie_name]:
for dict_3rd in self.clip_sentence_pairs[movie_name][dict_2nd]:
VP_spacy_vec_ = np.zeros(self.spacy_vec_dim * 2)
subj_spacy_vec_ = np.zeros(self.spacy_vec_dim)
obj_spacy_vec_ = np.zeros(self.spacy_vec_dim)
if len(dict_3rd['dobj_or_VP']) != 0:
VP_spacy_one_by_one_this_ = dict_3rd['VP_spacy_vec_one_by_one_word'][
random.choice(xrange(len(dict_3rd['dobj_or_VP'])))]
if len(VP_spacy_one_by_one_this_) == 1:
VP_spacy_vec_[:self.spacy_vec_dim] = VP_spacy_one_by_one_this_[0]
else:
VP_spacy_vec_ = np.concatenate((VP_spacy_one_by_one_this_[0], VP_spacy_one_by_one_this_[1]))
if len(dict_3rd['subj']) != 0:
subj_spacy_vec_ = dict_3rd['subj_spacy_vec'][random.choice(xrange(len(dict_3rd['subj'])))]
if len(dict_3rd['obj']) != 0:
obj_spacy_vec_ = dict_3rd['obj_spacy_vec'][random.choice(xrange(len(dict_3rd['obj'])))]
sentence_vec_ = dict_3rd['sent_skip_thought_vec'][0][0, :self.sent_vec_dim]
movie_clip_sentences.append((dict_2nd, sentence_vec_, VP_spacy_vec_, subj_spacy_vec_, obj_spacy_vec_))
for k in xrange(len(self.sliding_clip_names)):
if movie_name in self.sliding_clip_names[k]:
left_context_feat, right_context_feat = self.get_context_window(self.sliding_clip_names[k],
self.context_num)
feature_data = self.read_unit_level_feats(self.sliding_clip_names[k])
# read softmax batch
softmax_center_clip = self.read_unit_level_softmax(self.sliding_clip_names[k])
comb_feat = np.hstack((left_context_feat, feature_data, right_context_feat))
movie_clip_featmap.append((self.sliding_clip_names[k], comb_feat, softmax_center_clip))
# movie_clip_featmap.append((self.sliding_clip_na
return movie_clip_featmap, movie_clip_sentences