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"""
Dataset for clip model
"""
import logging
import torch
from torch.utils.data import Dataset
import numpy as np
import h5py
from utils.basic_utils import load_jsonl, load_json, l2_normalize_np_array, flat_list_of_lists, merge_dicts
from utils.tensor_utils import pad_sequences_1d
logger = logging.getLogger(__name__)
class RetrievalDataset(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,
vid_feat_path_or_handler, max_desc_len, max_ctx_len, 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.max_desc_len = max_desc_len
self.max_ctx_len = max_ctx_len
self.desc_bert_path_or_handler = desc_bert_path_or_handler
self.sub_bert_path_or_handler = sub_bert_path_or_handler
self.vid_feat_path_or_handler = vid_feat_path_or_handler
self.ctx_mode = ctx_mode
# prepare desc data
self.data = load_jsonl(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 __getitem__(self, index):
raw_data = self.data[index]
# initialize with basic data
meta = dict(
query_id=raw_data["query_id"],
desc=raw_data["desc"],
vid_name=raw_data["vid_name"],
duration=raw_data["duration"],
)
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 = np.mean(self.vid_feat_h5[meta["vid_name"]][:self.max_ctx_len], axis=0) # (D, )
if self.normalize_vfeat:
video_feat = l2_normalize_np_array(video_feat)
model_inputs["video_feat"] = torch.from_numpy(video_feat)
else:
model_inputs["video_feat"] = torch.zeros(2)
if self.use_sub: # no need for ctx feature, as the features are already contextulized
sub_feat = np.mean(self.sub_bert_h5[meta["vid_name"]][:self.max_ctx_len], axis=0) # (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)
else:
model_inputs["sub_feat"] = torch.zeros(2)
return dict(meta=meta, model_inputs=model_inputs)
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 RetrievalEvalDataset(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, dset_name, eval_split_name, 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, ctx_mode="video", data_mode="context",
h5driver=None, data_ratio=1.0, normalize_vfeat=True, normalize_tfeat=True):
self.dset_name = dset_name
self.eval_split_name = eval_split_name
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.query_data = load_jsonl(data_path)
if data_ratio != 1:
n_examples = int(len(self.query_data) * data_ratio)
self.query_data = self.query_data[:n_examples]
logger.info("Using {}% of the data: {} examples".format(data_ratio * 100, n_examples))
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.eval_split_name]
self.video_data = [{"vid_name": k, "duration": v[0]} for k, v in video_data.items()]
self.video2idx = {k: v[1] for k, v in video_data.items()}
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 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"""
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.query_data)
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.query_data[index]
meta = dict(
query_id=raw_data["query_id"],
desc=raw_data["desc"],
vid_name=raw_data["vid_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_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()
if self.use_video:
video_feat = np.mean(self.vid_feat_h5[meta["vid_name"]][:self.max_ctx_len], axis=0) # (1, D)
if self.normalize_vfeat:
video_feat = l2_normalize_np_array(video_feat)
model_inputs["video_feat"] = torch.from_numpy(video_feat)
else:
model_inputs["video_feat"] = torch.zeros(2)
if self.use_sub: # no need for ctx feature, as the features are already contextulized
sub_feat = np.mean(self.sub_bert_h5[meta["vid_name"]][:self.max_ctx_len], axis=0)
if self.normalize_tfeat:
sub_feat = l2_normalize_np_array(sub_feat)
model_inputs["sub_feat"] = torch.from_numpy(sub_feat)
else:
model_inputs["sub_feat"] = torch.zeros(2)
return dict(meta=meta, model_inputs=model_inputs)
def retrieval_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 k == "query_feat":
batched_data[k] = pad_sequences_1d(
[e["model_inputs"][k] for e in batch], dtype=torch.float32, fixed_length=None)
elif "feat" in k:
batched_data[k] = torch.stack([e["model_inputs"][k] for e in batch])
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 k == "query_feat":
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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