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turn_dataset.py
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turn_dataset.py
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import torch.utils.data as data
import numpy as np
import random
import os
class turnTestDataset(data.Dataset):
def __init__(self, ctx_num, feat_dir, test_clip_path, batch_size,
unit_size, unit_feature_dim, data_preparation=None):
self.ctx_num = ctx_num
self.feat_dir = feat_dir
self.test_clip_path = test_clip_path
self.batch_size = batch_size
self.unit_size = unit_size
self.unit_feature_dim = unit_feature_dim
self.data_preparation = data_preparation
print "Reading testing sliding window list from: " + test_clip_path + '\n'
self.test_samples = list()
with open(test_clip_path) as f:
for l in f:
video_name = l.rstrip().split(" ")[0]
clip_start = float(l.rstrip().split(" ")[1])
clip_end = float(l.rstrip().split(" ")[2])
self.test_samples.append((video_name, clip_start, clip_end))
self.num_samples = len(self.test_samples)
print "total test clips number is: " + str(len(self.test_samples)) + '\n'
def __getitem__(self, index):
video_name = self.test_samples[index][0]
clip_start = self.test_samples[index][1]
clip_end = self.test_samples[index][2]
prop_feat = self.get_centeric_proposal_feature(self.feat_dir, video_name, clip_start, clip_end)
left_feat = self.get_left_context_feature(self.feat_dir, video_name, clip_start, clip_end)
right_feat = self.get_right_context_feature(self.feat_dir, video_name, clip_start, clip_end)
feat = np.hstack((left_feat, prop_feat, right_feat))
feat = self.data_preparation(feat)
return video_name, feat, clip_start, clip_end
def get_right_context_feature(self, feat_dir, movie_name, start, end):
swin_step = self.unit_size
all_feat = np.zeros([0, self.unit_feature_dim], dtype=np.float32)
count = 0
current_pos = end
context_ext = False
while count < self.ctx_num:
swin_start = current_pos
swin_end = current_pos + swin_step
if os.path.exists(feat_dir + movie_name + ".mp4" + "_" + str(swin_start) + "_" + str(swin_end) + ".npy"):
feat = np.load(feat_dir + movie_name + ".mp4" + "_" + str(swin_start) + "_" + str(swin_end) + ".npy")
all_feat = np.vstack((all_feat, feat))
context_ext = True
current_pos += swin_step
count += 1
if context_ext:
pool_feat = np.mean(all_feat, axis=0)
else:
pool_feat = np.zeros([self.unit_feature_dim], dtype=np.float32)
return pool_feat
def get_left_context_feature(self, feat_dir, movie_name, start, end):
swin_step = self.unit_size
all_feat = np.zeros([0, self.unit_feature_dim], dtype=np.float32)
count = 0
current_pos = start
context_ext = False
while count < self.ctx_num:
swin_start = current_pos - swin_step
swin_end = current_pos
if os.path.exists(feat_dir + movie_name + ".mp4" + "_" + str(swin_start) + "_" + str(swin_end) + ".npy"):
feat = np.load(feat_dir + movie_name + ".mp4" + "_" + str(swin_start) + "_" + str(swin_end) + ".npy")
all_feat = np.vstack((all_feat, feat))
context_ext = True
current_pos -= swin_step
count += 1
if context_ext:
pool_feat = np.mean(all_feat, axis=0)
else:
pool_feat = np.zeros([self.unit_feature_dim], dtype=np.float32)
return pool_feat
def get_centeric_proposal_feature(self, feat_dir, movie_name, start, end):
swin_step = self.unit_size
all_feat = np.zeros([0, self.unit_feature_dim], dtype=np.float32)
current_pos = start
while current_pos < end:
swin_start = current_pos
swin_end = swin_start + swin_step
feat = np.load(feat_dir + movie_name + ".mp4" + "_" + str(swin_start) + "_" + str(swin_end) + ".npy")
all_feat = np.vstack((all_feat, feat))
current_pos += swin_step
pool_feat = np.mean(all_feat, axis=0)
return pool_feat
def __len__(self):
return len(self.test_samples)
class turnTrainDataset(data.Dataset):
def __init__(self, ctx_num, unit_feature_dim, unit_size,
batch_size, video_length_info, feat_dir,
clip_gt_path, background_path, epoch_multiplier=1,
data_preparation=None):
self.ctx_num = ctx_num
self.unit_feature_dim = unit_feature_dim
self.unit_size = unit_size
self.batch_size = batch_size
self.video_length_info = video_length_info
self.visual_feature_dim = self.unit_feature_dim * 3
self.feat_dir = feat_dir
self.epoch_multiplier = epoch_multiplier
self.data_preparation = data_preparation
# prepare the foreground training sample list
print "Reading foreground training sample list from: " + clip_gt_path + '\n'
self.training_samples = list()
with open(clip_gt_path) as f:
for l in f:
movie_name = l.rstrip().split(" ")[0]
clip_start = float(l.rstrip().split(" ")[1])
clip_end = float(l.rstrip().split(" ")[2])
gt_start = float(l.rstrip().split(" ")[3])
gt_end = float(l.rstrip().split(" ")[4])
round_gt_start = np.round(gt_start / unit_size) * self.unit_size + 1
round_gt_end = np.round(gt_end / unit_size) * self.unit_size + 1
self.training_samples.append(
(movie_name, clip_start, clip_end, gt_start, gt_end, round_gt_start, round_gt_end, 1))
print str(len(self.training_samples)) + " foreground training samples are read" + '\n'
# prepare the background training sample list
print "Reading background training sample list from: " + background_path + '\n'
positive_num = len(self.training_samples) * 1.0
with open(background_path) as f:
for l in f:
# control the ratio between background samples and positive samples to be 10:1
if random.random() > 10.0 * positive_num / 270000: continue
movie_name = l.rstrip().split(" ")[0]
clip_start = float(l.rstrip().split(" ")[1])
clip_end = float(l.rstrip().split(" ")[2])
self.training_samples.append((movie_name, clip_start, clip_end, 0, 0, 0, 0, 0))
self.num_samples = len(self.training_samples)
print str(len(self.training_samples)) + " background training samples are read" + '\n'
def __getitem__(self, index):
real_index = index % len(self.training_samples)
video_name = self.training_samples[real_index][0]
clip_start = self.training_samples[real_index][1]
clip_end = self.training_samples[real_index][2]
round_gt_start = self.training_samples[real_index][5]
round_gt_end = self.training_samples[real_index][6]
label = self.training_samples[real_index][7]
start_offset, end_offset = self.calculate_regoffset(clip_start, clip_end, round_gt_start, round_gt_end)
prop_feat = self.get_centeric_proposal_feature(self.feat_dir, video_name, clip_start, clip_end)
left_feat = self.get_left_context_feature(self.feat_dir, video_name, clip_start, clip_end)
right_feat = self.get_right_context_feature(self.feat_dir, video_name, clip_start, clip_end)
feat = np.hstack((left_feat, prop_feat, right_feat))
feat = self.data_preparation(feat)
return feat, label, start_offset, end_offset
def calculate_regoffset(self, clip_start, clip_end, round_gt_start, round_gt_end):
start_offset = (round_gt_start - clip_start) / self.unit_size
end_offset = (round_gt_end - clip_end) / self.unit_size
return start_offset, end_offset
def get_right_context_feature(self,feat_dir,movie_name,start,end):
swin_step = self.unit_size
all_feat = np.zeros([0, self.unit_feature_dim], dtype=np.float32)
count = 0
current_pos = end
context_ext = False
while count < self.ctx_num:
swin_start = current_pos
swin_end = current_pos + swin_step
if os.path.exists(feat_dir + movie_name + ".mp4" + "_" + str(swin_start) + "_" + str(swin_end) + ".npy"):
feat = np.load(feat_dir + movie_name + ".mp4" + "_" + str(swin_start) + "_" + str(swin_end) + ".npy")
all_feat = np.vstack((all_feat, feat))
context_ext = True
current_pos += swin_step
count += 1
if context_ext:
pool_feat = np.mean(all_feat, axis=0)
else:
pool_feat = np.zeros([self.unit_feature_dim], dtype=np.float32)
return pool_feat
def get_left_context_feature(self,feat_dir,movie_name,start,end):
swin_step = self.unit_size
all_feat = np.zeros([0, self.unit_feature_dim], dtype=np.float32)
count = 0
current_pos = start
context_ext = False
while count < self.ctx_num:
swin_start = current_pos - swin_step
swin_end = current_pos
if os.path.exists(feat_dir + movie_name + ".mp4" + "_" + str(swin_start) + "_" + str(swin_end) + ".npy"):
feat = np.load(feat_dir + movie_name + ".mp4" + "_" + str(swin_start) + "_" + str(swin_end) + ".npy")
all_feat = np.vstack((all_feat, feat))
context_ext = True
current_pos -= swin_step
count += 1
if context_ext:
pool_feat = np.mean(all_feat, axis=0)
else:
pool_feat = np.zeros([self.unit_feature_dim], dtype=np.float32)
return pool_feat
def get_centeric_proposal_feature(self,feat_dir,movie_name,start,end):
swin_step = self.unit_size
all_feat = np.zeros([0, self.unit_feature_dim], dtype=np.float32)
current_pos = start
while current_pos < end:
swin_start = current_pos
swin_end = swin_start + swin_step
feat = np.load(feat_dir + movie_name + ".mp4" + "_" + str(swin_start) + "_" + str(swin_end) + ".npy")
all_feat = np.vstack((all_feat, feat))
current_pos += swin_step
pool_feat = np.mean(all_feat, axis=0)
return pool_feat
def __len__(self):
return len(self.training_samples) * self.epoch_multiplier