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# Copyright (c) Facebook, Inc. All Rights Reserved
import torch
import os
import numpy as np
import pickle
from . import retri
from ..utils import get_local_rank
class VectorPool(object):
"""
Base class of retrieval space.
"""
def __init__(self, config):
from transformers import AutoConfig
self.hidden_size = AutoConfig.from_pretrained(
config.dataset.bert_name).hidden_size
self.retriever_cls = getattr(retri, config.retriever_cls)
def __call__(self, sample, **kwargs):
raise NotImplementedError
def build_retriver(
self,
retriever_cls=None,
hidden_size=None,
centroids=512,
db_type="flatl2",
examples_per_cent_to_train=48
):
"""merge results from multiple gpus and return a retriver.."""
self.retriver = retriever_cls(
hidden_size, centroids, db_type, examples_per_cent_to_train)
return self.retriver
def __repr__(self):
if hasattr(self, "retriver"):
retriver_name = str(len(self.retriver))
else:
retriver_name = "no retriver field yet"
return self.__class__.__name__ \
+ "(" + retriver_name + ")"
class VideoVectorPool(VectorPool):
"""
average clips of a video as video representation.
"""
def __init__(self, config):
super().__init__(config)
self.build_retriver(self.retriever_cls, self.hidden_size)
def __call__(self, sample, subsampling, **kwargs):
hidden_states = (
sample["pooled_video"] + sample["pooled_text"]) / 2.
hidden_states = hidden_states.view(
-1, subsampling,
hidden_states.size(-1))
hidden_states = torch.mean(hidden_states, dim=1)
hidden_states = hidden_states.cpu().detach().numpy()
video_ids = []
for offset_idx, video_id in enumerate(sample["video_id"]):
if isinstance(video_id, tuple) and len(video_id) == 3:
# a sharded video_id.
video_id = video_id[0]
video_ids.append(video_id)
assert len(video_ids) == len(hidden_states)
self.retriver.add(
hidden_states.astype("float32"),
video_ids
)
class DistributedVectorPool(VectorPool):
"""
support sync of multiple gpus/nodes.
"""
def __init__(self, config):
super().__init__(config)
self.out_dir = os.path.join(
config.fairseq.checkpoint.save_dir,
"retri")
os.makedirs(self.out_dir, exist_ok=True)
self.hidden_states = []
self.video_ids = []
def build_retriver(
self,
retriever_cls=None,
hidden_size=None,
centroids=4096,
db_type="flatl2",
examples_per_cent_to_train=48
):
if retriever_cls is None:
retriever_cls = self.retriever_cls
if hidden_size is None:
hidden_size = self.hidden_size
"""merge results from multiple gpus and return a retriver.."""
if torch.distributed.is_initialized():
self.save()
# sync saving.
torch.distributed.barrier()
world_size = torch.distributed.get_world_size()
else:
world_size = 1
self.retriver = retriever_cls(
hidden_size, centroids, db_type, examples_per_cent_to_train)
# each gpu process has its own retriever.
for local_rank in range(world_size):
if get_local_rank() == 0:
print("load local_rank", local_rank)
hidden_states, video_ids = self.load(local_rank)
hidden_states = hidden_states.astype("float32")
self.retriver.add(hidden_states, video_ids)
return self.retriver
def load(self, local_rank):
hidden_states = np.load(
os.path.join(
self.out_dir,
"hidden_state" + str(local_rank) + ".npy"
)
)
with open(
os.path.join(
self.out_dir, "video_id" + str(local_rank) + ".pkl"),
"rb") as fr:
video_ids = pickle.load(fr)
return hidden_states, video_ids
def save(self):
hidden_states = np.vstack(self.hidden_states)
assert len(hidden_states) == len(self.video_ids), "{}, {}".format(
len(hidden_states),
len(self.video_ids)
)
local_rank = torch.distributed.get_rank() \
if torch.distributed.is_initialized() else 0
np.save(
os.path.join(
self.out_dir,
"hidden_state" + str(local_rank) + ".npy"),
hidden_states)
with open(
os.path.join(
self.out_dir,
"video_id" + str(local_rank) + ".pkl"),
"wb") as fw:
pickle.dump(
self.video_ids,
fw,
protocol=pickle.HIGHEST_PROTOCOL
)
class DistributedVideoVectorPool(DistributedVectorPool):
"""
average clips of a video as video representation.
"""
def __call__(self, sample, subsampling, **kwargs):
hidden_states = (
sample["pooled_video"] + sample["pooled_text"]) / 2.
hidden_states = hidden_states.view(
-1, subsampling,
hidden_states.size(-1))
hidden_states = torch.mean(hidden_states, dim=1)
hidden_states = hidden_states.cpu().detach().numpy()
video_ids = []
for offset_idx, video_id in enumerate(sample["video_id"]):
if isinstance(video_id, tuple) and len(video_id) == 3:
# a sharded video_id.
video_id = video_id[0]
video_ids.append(video_id)
assert len(video_ids) == len(hidden_states)
self.hidden_states.append(hidden_states)
self.video_ids.extend(video_ids)
# ------------ the following are deprecated --------------
class TextClipVectorPool(VectorPool):
def __init__(self, config):
from transformers import AutoConfig
hidden_size = AutoConfig.from_pretrained(
config.dataset.bert_name).hidden_size
retriever_cls = getattr(retri, config.retriever_cls)
self.build_retriver(retriever_cls, hidden_size)
def __call__(self, sample, **kwargs):
clip_meta = sample["clip_meta"].cpu()
assert torch.all(torch.le(clip_meta[:, 4], clip_meta[:, 5]))
text_meta = [tuple(item.tolist()) for item in clip_meta[:, 3:]]
if hasattr(self, "retriver"):
# build_retriver is called.
self.retriver.add(
sample["pooled_text"].cpu().numpy().astype("float32"),
text_meta
)
else:
raise NotImplementedError
class MMClipVectorPool(VectorPool):
"""
Multimodal Clip-level vector pool.
"""
def __init__(self, out_dir):
"""use hidden_states to store `(video, text)`."""
"""use video_ids to store `(video_id, start, end)`."""
super().__init__(out_dir)
def __call__(self, sample, **kwargs):
pooled_video = sample["pooled_video"].cpu().unsqueeze(1).numpy()
pooled_text = sample["pooled_text"].cpu().unsqueeze(1).numpy()
self.hidden_states.append(
np.concatenate([pooled_video, pooled_text], axis=1)
)
video_starts = sample["video_start"].cpu()
video_ends = sample["video_end"].cpu()
assert torch.all(torch.le(video_starts, video_ends))
text_starts = sample["text_start"].cpu()
text_ends = sample["text_end"].cpu()
assert torch.all(torch.le(text_starts, text_ends))
subsample_size = sample["pooled_video"].size(0) // len(sample["video_id"])
video_ids = [video_id for video_id in sample["video_id"]
for _ in range(subsample_size)
]
for video_id, video_start, video_end, text_start, text_end in zip(
video_ids, video_starts, video_ends, text_starts, text_ends):
self.video_ids.append((
video_id,
(int(video_start), int(video_end)),
(int(text_start), int(text_end))
))
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