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data.py
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data.py
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import os
import csv
import random
import numpy as np
from data_augmenter import Frame_Level_Augmenter, Video_Level_Augmenter
import torch
import torch.utils.data as data
def read_videopair(input_file):
print 'reading data from:', input_file
videopairlist = []
reader = csv.reader(open(input_file, 'r'))
for data in reader:
if data[1] == "":
continue
video = data[0]
for video2 in data[1:]:
videopairlist.append((video, video2))
return videopairlist
def collate_fn(data):
videos_1, videos_2, inds = zip(*data)
# Merge videos (convert tuple of 2D tensor to 3D tensor)
videos_1 = torch.stack(videos_1, 0)
videos_2 = torch.stack(videos_2, 0)
return videos_1, videos_2, inds
# using data argumentation on the fly (training is too slow, so we discard it)
# class Dataset_frame_da(data.Dataset):
# def __init__(self, data_path, frame_feats, video2frames, stride=2):
# self.videopairlist = read_videopair(data_path)
# self.frame_feats = frame_feats
# self.video2frames = video2frames
# self.sub_length = len(self.videopairlist)
# if type(stride) is int:
# self.length = self.sub_length * stride
# else:
# self.length = self.sub_length * sum(stride)
# self.f_auger = Frame_Level_Augmenter(stride)
# def get_aug_pool_feat(self, vidoe_id):
# frm_feat = [self.frame_feats.read_one(fid) for fid in self.video2frames[vidoe_id]]
# frm_feat = self.f_auger.aug_feat_choice(frm_feat)
# return np.array(frm_feat).mean(axis=0)
# def __getitem__(self, index):
# vidoe_id_1, video_id_2 = self.videopairlist[index%self.sub_length]
# video_1 = torch.Tensor(self.get_aug_pool_feat(vidoe_id_1))
# video_2 = torch.Tensor(self.get_aug_pool_feat(video_id_2))
# return video_1, video_2, index
# def __len__(self):
# return self.length
# def get_frame_da_loader(data_path, frame_feats, opt, batch_size=100, shuffle=True, num_workers=2, video2frames=None, stride=5):
# """Returns torch.utils.data.DataLoader for custom coco dataset."""
# dset = Dataset_frame_da(data_path, frame_feats, video2frames, stride)
# data_loader = torch.utils.data.DataLoader(dataset=dset,
# batch_size=batch_size,
# shuffle=shuffle,
# pin_memory=True,
# collate_fn=collate_fn)
# return data_loader
class PrecompDataset_video_da(data.Dataset):
def __init__(self, data_path, video_feats, video2subvideo, n_subs, aug_prob=0, perturb_intensity=0.01, perturb_prob=0.5, feat_path=None):
self.videopairlist = read_videopair(data_path)
self.video_feats = video_feats
self.sub_length = len(self.videopairlist)
self.video2subvideo = video2subvideo
self.length = self.sub_length * n_subs
self.n_subs = n_subs
self.aug_prob = aug_prob
self.perturb_intensity = perturb_intensity
self.perturb_prob = perturb_prob
if self.aug_prob > 0:
self.length = int(self.length / aug_prob)
self.v_auger = Video_Level_Augmenter(feat_path, video_feats, perturb_intensity=perturb_intensity, perturb_prob=perturb_prob)
def __getitem__(self, index):
vidoe_id_1, video_id_2 = self.videopairlist[index%self.sub_length]
if self.n_subs > 1:
vidoe_id_1 = random.choice(self.video2subvideo[vidoe_id_1])
video_id_2 = random.choice(self.video2subvideo[video_id_2])
video_1 = self.video_feats.read_one(vidoe_id_1)
video_2 = self.video_feats.read_one(video_id_2)
if self.aug_prob > 0: # Adding tiny perturbations for data argumentation
if random.random() < self.aug_prob:
video_1 = self.v_auger.get_aug_feat(video_1)
video_2 = self.v_auger.get_aug_feat(video_2)
video_1 = torch.Tensor(video_1)
video_2 = torch.Tensor(video_2)
return video_1, video_2, index
def __len__(self):
return self.length
def get_video_da_loader(data_path, video_feats, opt, batch_size=100, shuffle=True, num_workers=2, video2subvideo=None, n_subs=1, feat_path=""):
dset = PrecompDataset_video_da(data_path, video_feats, video2subvideo, n_subs,
aug_prob=opt.aug_prob, perturb_intensity=opt.perturb_intensity, perturb_prob=opt.perturb_prob, feat_path=feat_path)
data_loader = torch.utils.data.DataLoader(dataset=dset,
batch_size=batch_size,
shuffle=shuffle,
pin_memory=True,
collate_fn=collate_fn)
return data_loader
# for validation and test
class FeatDataset(data.Dataset):
"""
Load precomputed video features
"""
def __init__(self, videolist, video_feats):
self.video_feats = video_feats
self.videolist = videolist
self.length = len(videolist)
def __getitem__(self, index):
vidoe_id = self.videolist[index]
video = torch.Tensor(self.video_feats.read_one(vidoe_id))
return video, vidoe_id, index
def __len__(self):
return self.length
def collate_fn_feat(data):
videos, ids, idxs = zip(*data)
# Merge videos (convert tuple of 2D tensor to 3D tensor)
videos = torch.stack(videos, 0)
return videos, ids, idxs
def get_feat_loader(videolist, video_feats, batch_size=100, shuffle=False, num_workers=2):
dset = FeatDataset(videolist, video_feats)
data_loader = torch.utils.data.DataLoader(dataset=dset,
batch_size=batch_size,
shuffle=shuffle,
pin_memory=True,
collate_fn=collate_fn_feat)
return data_loader