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"""
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
import re
from lavis.common.registry import registry
from lavis.processors.base_processor import BaseProcessor
from lavis.processors.randaugment import RandomAugment
from omegaconf import OmegaConf
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
import os
from itertools import chain
import numpy as np
import torch
from transformers import GPT2Tokenizer
SPECIAL_TOKENS_DICT = {
"bos_token": "<bos>",
"eos_token": "<eos>",
"additional_special_tokens": ["<speaker1>", "<speaker2>", "<video>", "<cap>"],
"pad_token": "<pad>",
}
SPECIAL_TOKENS = [
"<bos>",
"<eos>",
"<speaker1>",
"<speaker2>",
"<cap>",
"<video>",
"<pad>",
]
class GPTVideoFeatureBaseProcessor(BaseProcessor):
def __init__(self, visual_ft=["i3d_rgb"], audio_ft=["vggish"]):
self.visual_ft = visual_ft
self.audio_ft = audio_ft
@registry.register_processor("gpt_dialogue")
class GPTDialogueProcessor(BaseProcessor):
def __init__(self, max_turns=3, use_caption=True):
self.max_turns = max_turns
self.use_caption = use_caption
self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
self.tokenizer.add_special_tokens(SPECIAL_TOKENS_DICT)
def sample_sequence(self, caption, history, answer):
bos, eos, speaker1, speaker2, cap = self.tokenizer.convert_tokens_to_ids(
SPECIAL_TOKENS[:-2]
)
instance = {}
sequence = [caption] + history + [answer]
sequence = [s + [eos] for s in sequence]
instance["input_ids"] = list(chain(*sequence))
instance["token_type_ids"] = [cap] * len(sequence[0]) + [
speaker2 if i % 2 else speaker1
for i, s in enumerate(sequence[1:])
for _ in s
]
instance["labels"] = ([-1] * sum(len(s) for s in sequence[:-1])) + sequence[-1]
assert len(instance["input_ids"]) == len(instance["token_type_ids"])
assert len(instance["token_type_ids"]) == len(instance["labels"])
for k, v in instance.items():
instance[k] = torch.Tensor(v).long()
return instance
def padding(self, seq, pad_token=-1):
if pad_token == -1:
pad_token = self.tokenizer.pad_token_id
padded_seq = torch.nn.utils.rnn.pad_sequence(
seq, batch_first=True, padding_value=pad_token
)
return padded_seq
def get_attention_mask(self, seq, pad_token=-1):
if pad_token == -1:
pad_token = self.tokenizer.pad_token_id
return seq != pad_token
def __call__(self, ann):
if self.use_caption:
caption = " ".join([ann["caption"], ann["summary"]])
caption = self.tokenizer.encode(caption)
else:
caption = []
dial_history = []
for turn in ann["dialog"][-self.max_turns :]:
dial_history.append(turn["question"])
dial_history.append(turn["answer"])
dial_history.append(ann["question"])
dial_history = [self.tokenizer.encode(t) for t in dial_history]
answer = self.tokenizer.encode(ann["answer"])
item = self.sample_sequence(caption, dial_history, answer)
return item
@classmethod
def from_config(cls, cfg=None):
if cfg is None:
cfg = OmegaConf.create()
use_caption = cfg.get("use_caption", True)
max_turns = cfg.get("max_turns", 3)
return cls(max_turns=max_turns, use_caption=use_caption)
@registry.register_processor("gpt_video_ft")
class GPTVideoFeatureProcessor(GPTVideoFeatureBaseProcessor):
def __init__(self, visual_ft, audio_ft):
super().__init__(visual_ft, audio_ft)
self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
self.tokenizer.add_special_tokens(SPECIAL_TOKENS_DICT)
def padding(self, seq):
padded_seq = torch.nn.utils.rnn.pad_sequence(
seq, batch_first=True, padding_value=1.0
)
return padded_seq
def get_attention_mask(self, seq):
return torch.sum(seq != 1, dim=2) != 0
def __call__(self, ft_root, vname):
all_ft = []
for ft_name in self.visual_ft:
ft_path = os.path.join(ft_root, ft_name, vname)
all_ft.append(np.load(ft_path + ".npy"))
for ft_name in self.audio_ft:
ft_path = os.path.join(ft_root, ft_name, vname)
all_ft.append(np.load(ft_path + ".npy"))
min_len = min([len(ft) for ft in all_ft])
# TODO: use other sampling method (e.g. uniform sampling)
sampled_ft = [ft[:min_len] for ft in all_ft]
sampled_ft = np.concatenate(sampled_ft, axis=1)
item = {}
item["video_fts"] = torch.Tensor(sampled_ft)
video_type_token = self.tokenizer.convert_tokens_to_ids("<video>")
item["token_type_ids"] = torch.Tensor(
[video_type_token] * len(sampled_ft)
).long()
return item
@classmethod
def from_config(cls, cfg=None):
if cfg is None:
cfg = OmegaConf.create()
visual_ft = cfg.get("visual_ft", ["i3d_rgb"])
audio_ft = cfg.get("audio_ft", ["vggish"])
return cls(visual_ft=visual_ft, audio_ft=audio_ft)
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