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"""
Dataset for clip model
"""
import logging
import torch
from torch.utils.data import Dataset
import numpy as np
import h5py
import time
import math
import random
from tqdm import tqdm
from utils.basic_utils import load_json, load_json, l2_normalize_np_array, flat_list_of_lists, merge_dicts
from utils.tensor_utils import pad_sequences_1d
from baselines.clip_alignment_with_language.local_utils.compute_proposal_upper_bound import \
get_didemo_agreed_ts
import pandas as pd
logger = logging.getLogger(__name__)
class StartEndDataset(Dataset):
"""
Args:
dset_name, str, ["tvr"]
ctx_mode: str,
Return:
a dict: {
"meta": {
"query_id": int,
"desc": str,
"vid_name": str,
"duration": float,
"ts": [st (float), ed (float)], seconds, ground_truth timestamps
}
"model_inputs": {
"query_feat": torch.tensor, (L, D_q)
"video_feat": torch.tensor, (n_clip_in_moment, D_video)
"sub_feat": torch.tensor, (n_clip_in_moment, D_sub)
"st_ed_indices": torch.LongTensor, (2, )
}
}
"""
def __init__(self, dset_name, data_path, desc_bert_path_or_handler, sub_bert_path_or_handler,
max_desc_len, max_ctx_len,
vid_feat_path_or_handler, clip_length, ctx_mode="video",
normalize_vfeat=True, normalize_tfeat=True, h5driver=None, data_ratio=1.0):
self.dset_name = dset_name
self.data_path = data_path
self.data_ratio = data_ratio
self.desc_bert_path_or_handler = desc_bert_path_or_handler
self.max_desc_len = max_desc_len
self.sub_bert_path_or_handler = sub_bert_path_or_handler
self.max_ctx_len = max_ctx_len
self.vid_feat_path_or_handler = vid_feat_path_or_handler
self.clip_length = clip_length
self.ctx_mode = ctx_mode
# prepare desc data
self.data = self.expand_annotations(load_json(data_path))
if self.data_ratio != 1:
n_examples = int(len(self.data) * data_ratio)
self.data = self.data[:n_examples]
logger.info("Using {}% of the data: {} examples".format(data_ratio * 100, n_examples))
self.use_video = "video" in self.ctx_mode
self.use_sub = "sub" in self.ctx_mode
self.use_tef = "tef" in self.ctx_mode
if self.use_video:
if isinstance(vid_feat_path_or_handler, h5py.File):
self.vid_feat_h5 = vid_feat_path_or_handler
else: # str path
self.vid_feat_h5 = h5py.File(vid_feat_path_or_handler, "r", driver=h5driver)
if isinstance(desc_bert_path_or_handler, h5py.File):
self.desc_bert_h5 = desc_bert_path_or_handler
else:
self.desc_bert_h5 = h5py.File(desc_bert_path_or_handler, "r", driver=h5driver)
if self.use_sub:
if isinstance(sub_bert_path_or_handler, h5py.File):
self.sub_bert_h5 = sub_bert_path_or_handler
else: # str path
self.sub_bert_h5 = h5py.File(sub_bert_path_or_handler, "r", driver=h5driver)
self.normalize_vfeat = normalize_vfeat
self.normalize_tfeat = normalize_tfeat
def __len__(self):
return len(self.data)
def expand_annotations(self, annotations):
new_annotations = []
for i in annotations:
query = i["query"]
query_id = i["query_id"]
for moment in i["relevant_moment"]:
moment.update({'query': query, 'query_id': query_id})
new_annotations.append(moment)
return new_annotations
def __getitem__(self, index):
raw_data = self.data[index]
# initialize with basic data
meta = dict(
query_id=raw_data["query_id"],
desc=raw_data["query"],
vid_name=raw_data["video_name"],
duration=raw_data["duration"],
ts=raw_data["timestamp"] ,
)
model_inputs = dict()
model_inputs["query_feat"] = self.get_query_feat_by_query_id(meta["query_id"])
ctx_l = 0
if self.use_video:
video_feat = self.vid_feat_h5[meta["vid_name"]][:self.max_ctx_len] # (N_clip, D)
if self.normalize_vfeat:
video_feat = l2_normalize_np_array(video_feat)
model_inputs["video_feat"] = torch.from_numpy(video_feat)
ctx_l = len(video_feat)
else:
model_inputs["video_feat"] = torch.zeros((2, 2))
if self.use_sub: # no need for ctx feature, as the features are already contextulized
sub_feat = self.sub_bert_h5[meta["vid_name"]][:self.max_ctx_len] # (N_clips, D_t)
if self.normalize_tfeat:
sub_feat = l2_normalize_np_array(sub_feat)
model_inputs["sub_feat"] = torch.from_numpy(sub_feat)
ctx_l = len(sub_feat)
else:
model_inputs["sub_feat"] = torch.zeros((2, 2))
if self.use_tef:
# note the tef features here are normalized clip indices (1.5 secs), instead of the original time (1 sec)
ctx_l = meta["duration"] // self.clip_length + 1 if ctx_l == 0 else ctx_l
tef_st = torch.arange(0, ctx_l, 1.0) / ctx_l
tef_ed = tef_st + 1.0 / ctx_l
tef = torch.stack([tef_st, tef_ed], dim=1) # (N_clips, 2)
model_inputs["tef_feat"] = tef
else:
model_inputs["tef_feat"] = torch.zeros((2, 2))
if self.use_video and self.use_tef:
model_inputs["video_feat"] = torch.cat(
[model_inputs["video_feat"], model_inputs["tef_feat"]], dim=1) # (N_clips, D+2)
if self.use_sub and self.use_tef:
model_inputs["sub_feat"] = torch.cat(
[model_inputs["sub_feat"], model_inputs["tef_feat"]], dim=1) # (N_clips, D_t+2)
model_inputs["st_ed_indices"] = self.get_st_ed_label(meta["ts"], max_idx=ctx_l-1)
return dict(meta=meta, model_inputs=model_inputs)
def get_st_ed_label(self, ts, max_idx):
"""
Args:
ts: [st (float), ed (float)] in seconds, ed > st
max_idx: length of the video
Returns:
[st_idx, ed_idx]: int,
Given ts = [3.2, 7.6], st_idx = 2, ed_idx = 6,
clips should be indexed as [2: 6), the translated back ts should be [3:9].
# TODO which one is better, [2: 5] or [2: 6)
"""
st_idx = min(math.floor(ts[0] / self.clip_length), max_idx)
ed_idx = min(math.ceil(ts[1] / self.clip_length), max_idx)
return torch.LongTensor([st_idx, ed_idx])
def get_query_feat_by_query_id(self, query_id):
query_feat = self.desc_bert_h5[str(query_id)][:self.max_desc_len]
if self.normalize_tfeat:
query_feat = l2_normalize_np_array(query_feat)
return torch.from_numpy(query_feat)
class StartEndEvalDataset(Dataset):
"""
init_data_mode: `video_query` or `video_only` or `query_only`,
it indicates which data to load when initialize the Dataset object.
data_mode: `context` or `query`, it indicates which data to return for self.__get_item__()
desc_bert_path_or_handler: h5py.File object or str path
vid_feat_path_or_handler: h5py.File object or str path
eval_proposal_bsz: the proposals for a single video will be sorted in length and batched here with
max batch size to be eval_proposal_bsz. A single video might have multiple batches of proposals.
load_gt_video: load GroundTruth Video, useful when evaluating single video moment retrieval.
data_ratio: percentage of query data to use.
"""
def __init__(self, data_path=None,
desc_bert_path_or_handler=None, max_desc_len=None, max_ctx_len=None,
sub_bert_path_or_handler=None, vid_feat_path_or_handler=None,
corpus_path=None, clip_length=None,
ctx_mode="video", data_mode="context",
h5driver=None, data_ratio=1.0, normalize_vfeat=True, normalize_tfeat=True):
self.ctx_mode = ctx_mode
self.load_gt_video = False
self.data_ratio = data_ratio # only affect query data
self.normalize_vfeat = normalize_vfeat
self.normalize_tfeat = normalize_tfeat
self.data_mode = None
self.set_data_mode(data_mode)
self.max_desc_len = max_desc_len
self.max_ctx_len = max_ctx_len
self.data_path = data_path
self.annotations = load_json(data_path)
self.ground_truth = self.get_relevant_moment_gt()
if isinstance(desc_bert_path_or_handler, h5py.File):
self.desc_bert_h5 = desc_bert_path_or_handler
else:
self.desc_bert_h5 = h5py.File(desc_bert_path_or_handler, "r", driver=h5driver)
video_data = load_json(corpus_path)
self.video_data = [{"vid_name": k, "duration": v} for k, v in video_data.items()]
self.video2idx = {k: v for k, v in video_data.items()}
self.clip_length = clip_length
self.use_video = "video" in self.ctx_mode
self.use_sub = "sub" in self.ctx_mode
self.use_tef = "tef" in self.ctx_mode
if self.use_video:
if isinstance(vid_feat_path_or_handler, h5py.File):
self.vid_feat_h5 = vid_feat_path_or_handler
else: # str path
self.vid_feat_h5 = h5py.File(vid_feat_path_or_handler, "r", driver=h5driver)
if self.use_sub:
if isinstance(sub_bert_path_or_handler, h5py.File):
self.sub_bert_h5 = sub_bert_path_or_handler
else: # str path
self.sub_bert_h5 = h5py.File(sub_bert_path_or_handler, "r", driver=h5driver)
def get_relevant_moment_gt(self):
gt_all = {}
for data in self.annotations:
gt_all[data["query_id"]] = data["relevant_moment"]
return gt_all
def set_data_mode(self, data_mode):
"""context or query"""
assert data_mode in ["context", "query"]
self.data_mode = data_mode
# def load_gt_vid_name_for_query(self, load_gt_video):
# """load_gt_video: bool, affect the returned value of self._get_item_query"""
# if load_gt_video:
# assert "vid_name" in self.query_data[0]
# self.load_gt_video = load_gt_video
def __len__(self):
if self.data_mode == "context":
return len(self.video_data)
else:
return len(self.annotations)
def __getitem__(self, index):
if self.data_mode == "context":
return self._get_item_context(index)
else:
return self._get_item_query(index)
def get_query_feat_by_query_id(self, query_id):
query_feat = self.desc_bert_h5[str(query_id)][:self.max_desc_len]
if self.normalize_tfeat:
query_feat = l2_normalize_np_array(query_feat)
return torch.from_numpy(query_feat)
def _get_item_query(self, index):
"""Need to batch"""
raw_data = self.annotations[index]
meta = dict(
query_id=raw_data["query_id"],
desc=raw_data["query"],
vid_name=raw_data["video_name"] if self.load_gt_video else None
)
model_inputs = dict()
model_inputs["query_feat"] = self.get_query_feat_by_query_id(meta["query_id"])
return dict(meta=meta, model_inputs=model_inputs)
def get_st_ed_label(self, ts, max_idx):
"""
Args:
ts: [st (float), ed (float)] in seconds, ed > st
max_idx: length of the video
Returns:
[st_idx, ed_idx]: int,
Given ts = [3.2, 7.6], st_idx = 2, ed_idx = 6,
clips should be indexed as [2: 6), the translated back ts should be [3:9].
Given ts = [5, 9], st_idx = 3, ed_idx = 6,
clips should be indexed as [3: 6), the translated back ts should be [4.5:9].
# TODO which one is better, [2: 5] or [2: 6)
"""
# TODO ed_idx -= 1, should also modify relevant code in inference.py
st_idx = min(math.floor(ts[0] / self.clip_length), max_idx)
ed_idx = min(math.ceil(ts[1] / self.clip_length) - 1, max_idx) # st_idx could be the same as ed_idx
return torch.LongTensor([st_idx, ed_idx])
def _get_item_context(self, index):
"""No need to batch, since it has already been batched here"""
raw_data = self.video_data[index]
# initialize with basic data
meta = dict(
vid_name=raw_data["vid_name"],
duration=raw_data["duration"],
)
model_inputs = dict()
ctx_l = 0
if self.use_video:
video_feat = self.vid_feat_h5[meta["vid_name"]][:self.max_ctx_len] # (N_clip, D)
if self.normalize_vfeat:
video_feat = l2_normalize_np_array(video_feat)
model_inputs["video_feat"] = torch.from_numpy(video_feat)
ctx_l = len(video_feat)
else:
model_inputs["video_feat"] = torch.zeros((2, 2))
if self.use_sub: # no need for ctx feature, as the features are already contextulized
sub_feat = self.sub_bert_h5[meta["vid_name"]][:self.max_ctx_len] # (N_clips, D_t)
if self.normalize_tfeat:
sub_feat = l2_normalize_np_array(sub_feat)
model_inputs["sub_feat"] = torch.from_numpy(sub_feat)
ctx_l = len(sub_feat)
else:
model_inputs["sub_feat"] = torch.zeros((2, 2))
if self.use_tef:
ctx_l = meta["duration"] // self.clip_length + 1 if ctx_l == 0 else ctx_l
tef_st = torch.arange(0, ctx_l, 1.0) / ctx_l
tef_ed = tef_st + 1.0 / ctx_l
tef = torch.stack([tef_st, tef_ed], dim=1) # (N_clips, 2)
model_inputs["tef_feat"] = tef
else:
model_inputs["tef_feat"] = torch.zeros((2, 2))
if self.use_video and self.use_tef:
model_inputs["video_feat"] = torch.cat(
[model_inputs["video_feat"], model_inputs["tef_feat"]], dim=1) # (N_clips, D+2)
if self.use_sub and self.use_tef:
model_inputs["sub_feat"] = torch.cat(
[model_inputs["sub_feat"], model_inputs["tef_feat"]], dim=1) # (N_clips, D_t+2)
return dict(meta=meta, model_inputs=model_inputs)
def start_end_collate(batch):
batch_meta = [e["meta"] for e in batch] # seems no need to collate ?
model_inputs_keys = batch[0]["model_inputs"].keys()
batched_data = dict()
for k in model_inputs_keys:
if "feat" in k:
batched_data[k] = pad_sequences_1d(
[e["model_inputs"][k] for e in batch], dtype=torch.float32, fixed_length=None)
if "st_ed_indices" in model_inputs_keys:
batched_data["st_ed_indices"] = torch.stack(
[e["model_inputs"]["st_ed_indices"] for e in batch], dim=0)
return batch_meta, batched_data
def prepare_batch_inputs(batched_model_inputs, device, non_blocking=False):
model_inputs = {}
for k, v in batched_model_inputs.items():
if "feat" in k:
model_inputs[k] = v[0].to(device, non_blocking=non_blocking)
model_inputs[k.replace("feat", "mask")] = v[1].to(device, non_blocking=non_blocking)
else:
model_inputs[k] = v.to(device, non_blocking=non_blocking)
return model_inputs
if __name__ == '__main__':
from baselines.crossmodal_moment_localization.config import BaseOptions
options = BaseOptions().parse()
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