# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os.path as osp
import copy
import random
import numpy as np
try:
import lmdb
except ImportError as e:
print(
f"Warning! {e}, [lmdb] package and it's dependencies is required for ActBERT."
)
import pickle
try:
from paddlenlp.transformers import BertTokenizer
except ImportError as e:
print(
f"Warning! {e}, [paddlenlp] package and it's dependencies is required for ActBERT."
)
from ..registry import DATASETS
from .base import BaseDataset
from ...utils import get_logger
logger = get_logger("paddlevideo")
@DATASETS.register()
class MSRVTTDataset(BaseDataset):
"""MSR-VTT dataset for text-video clip retrieval.
"""
def __init__(
self,
file_path,
pipeline,
features_path,
bert_model="bert-base-uncased",
padding_index=0,
max_seq_length=36,
max_region_num=36,
max_action_num=5,
vision_feature_dim=2048,
action_feature_dim=2048,
spatials_dim=5,
data_prefix=None,
test_mode=False,
):
self.features_path = features_path
self.bert_model = bert_model
self.padding_index = padding_index
self.max_seq_length = max_seq_length
self.max_region_num = max_region_num
self._max_action_num = max_action_num
self.vision_feature_dim = vision_feature_dim
self.action_feature_dim = action_feature_dim
self.spatials_dim = spatials_dim
self._tokenizer = BertTokenizer.from_pretrained(bert_model,
do_lower_case=True)
super().__init__(file_path, pipeline, data_prefix, test_mode)
self.tokenize()
self.gen_feature()
def load_file(self):
"""Load index file to get video information."""
with open(self.file_path) as fin:
self.image_entries = []
self.caption_entries = []
for line in fin.readlines():
line = line.strip()
vid_id = line.split(',')[0]
self.image_entries.append(vid_id)
self.caption_entries.append({
"caption": line.split(',')[1],
"vid_id": vid_id
})
self.env = lmdb.open(self.features_path)
def tokenize(self):
for entry in self.caption_entries:
tokens = []
tokens.append("[CLS]")
for token in self._tokenizer.tokenize(entry["caption"]):
tokens.append(token)
tokens.append("[SEP]")
tokens = self._tokenizer.convert_tokens_to_ids(tokens)
segment_ids = [0] * len(tokens)
input_mask = [1] * len(tokens)
if len(tokens) < self.max_seq_length:
padding = [self.padding_index
] * (self.max_seq_length - len(tokens))
tokens = tokens + padding
input_mask += padding
segment_ids += padding
entry["token"] = np.array(tokens).astype('int64')
entry["input_mask"] = np.array(input_mask)
entry["segment_ids"] = np.array(segment_ids).astype('int64')
def get_image_feature(self, video_id):
video_id = str(video_id).encode()
with self.env.begin(write=False) as txn:
item = pickle.loads(txn.get(video_id))
video_id = item["video_id"]
image_h = int(item["image_h"])
image_w = int(item["image_w"])
features = item["features"].reshape(-1, self.vision_feature_dim)
boxes = item["boxes"].reshape(-1, 4)
num_boxes = features.shape[0]
g_feat = np.sum(features, axis=0) / num_boxes
num_boxes = num_boxes + 1
features = np.concatenate(
[np.expand_dims(g_feat, axis=0), features], axis=0)
action_features = item["action_features"].reshape(
-1, self.action_feature_dim)
image_location = np.zeros((boxes.shape[0], self.spatials_dim),
dtype=np.float32)
image_location[:, :4] = boxes
image_location[:,
4] = ((image_location[:, 3] - image_location[:, 1]) *
(image_location[:, 2] - image_location[:, 0]) /
(float(image_w) * float(image_h)))
image_location[:, 0] = image_location[:, 0] / float(image_w)
image_location[:, 1] = image_location[:, 1] / float(image_h)
image_location[:, 2] = image_location[:, 2] / float(image_w)
image_location[:, 3] = image_location[:, 3] / float(image_h)
g_location = np.array([0, 0, 1, 1, 1])
image_location = np.concatenate(
[np.expand_dims(g_location, axis=0), image_location], axis=0)
return features, num_boxes, image_location, action_features
def gen_feature(self):
num_inst = len(self.image_entries) #1000
self.features_all = np.zeros(
(num_inst, self.max_region_num, self.vision_feature_dim))
self.action_features_all = np.zeros(
(num_inst, self._max_action_num, self.action_feature_dim))
self.spatials_all = np.zeros(
(num_inst, self.max_region_num, self.spatials_dim))
self.image_mask_all = np.zeros((num_inst, self.max_region_num))
self.action_mask_all = np.zeros((num_inst, self._max_action_num))
for i, image_id in enumerate(self.image_entries):
features, num_boxes, boxes, action_features = self.get_image_feature(
image_id)
mix_num_boxes = min(int(num_boxes), self.max_region_num)
mix_boxes_pad = np.zeros((self.max_region_num, self.spatials_dim))
mix_features_pad = np.zeros(
(self.max_region_num, self.vision_feature_dim))
image_mask = [1] * (int(mix_num_boxes))
while len(image_mask) < self.max_region_num:
image_mask.append(0)
action_mask = [1] * (self._max_action_num)
while len(action_mask) < self._max_action_num:
action_mask.append(0)
mix_boxes_pad[:mix_num_boxes] = boxes[:mix_num_boxes]
mix_features_pad[:mix_num_boxes] = features[:mix_num_boxes]
self.features_all[i] = mix_features_pad
x = action_features.shape[0]
self.action_features_all[i][:x] = action_features[:]
self.image_mask_all[i] = np.array(image_mask)
self.action_mask_all[i] = np.array(action_mask)
self.spatials_all[i] = mix_boxes_pad
self.features_all = self.features_all.astype("float32")
self.action_features_all = self.action_features_all.astype("float32")
self.image_mask_all = self.image_mask_all.astype("int64")
self.action_mask_all = self.action_mask_all.astype("int64")
self.spatials_all = self.spatials_all.astype("float32")
def prepare_train(self, idx):
pass
def prepare_test(self, idx):
entry = self.caption_entries[idx]
caption = entry["token"]
input_mask = entry["input_mask"]
segment_ids = entry["segment_ids"]
target_all = np.zeros(1000)
for i, image_id in enumerate(self.image_entries):
if image_id == entry["vid_id"]:
target_all[i] = 1
return (
caption,
self.action_features_all,
self.features_all,
self.spatials_all,
segment_ids,
input_mask,
self.image_mask_all,
self.action_mask_all,
target_all,
)
def __len__(self):
return len(self.caption_entries)