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