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# ------------------------------------------------------------------------
# Grounding DINO
# url: https://github.com/IDEA-Research/GroundingDINO
# Copyright (c) 2023 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
import torch
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from torch import Tensor, nn
from torchvision.ops.boxes import nms
from transformers import BertConfig, BertModel, BertPreTrainedModel
from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions
class BertModelWarper(nn.Module):
def __init__(self, bert_model):
super().__init__()
# self.bert = bert_modelc
self.config = bert_model.config
self.embeddings = bert_model.embeddings
self.encoder = bert_model.encoder
self.pooler = bert_model.pooler
self.get_extended_attention_mask = bert_model.get_extended_attention_mask
self.invert_attention_mask = bert_model.invert_attention_mask
self.get_head_mask = bert_model.get_head_mask
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
use_cache (:obj:`bool`, `optional`):
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
"""
output_attentions = (
output_attentions if output_attentions is not None else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
batch_size, seq_length = input_shape
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size, seq_length = input_shape
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
# past_key_values_length
past_key_values_length = (
past_key_values[0][0].shape[2] if past_key_values is not None else 0
)
if attention_mask is None:
attention_mask = torch.ones(
((batch_size, seq_length + past_key_values_length)), device=device
)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
attention_mask, input_shape, device
)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
# import ipdb; ipdb.set_trace()
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
class TextEncoderShell(nn.Module):
def __init__(self, text_encoder):
super().__init__()
self.text_encoder = text_encoder
self.config = self.text_encoder.config
def forward(self, **kw):
# feed into text encoder
return self.text_encoder(**kw)
def generate_masks_with_special_tokens(tokenized, special_tokens_list, tokenizer):
"""Generate attention mask between each pair of special tokens
Args:
input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
special_tokens_mask (list): special tokens mask.
Returns:
torch.Tensor: attention mask between each special tokens.
"""
input_ids = tokenized["input_ids"]
bs, num_token = input_ids.shape
# special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
for special_token in special_tokens_list:
special_tokens_mask |= input_ids == special_token
# idxs: each row is a list of indices of special tokens
idxs = torch.nonzero(special_tokens_mask)
# generate attention mask and positional ids
attention_mask = (
torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
)
position_ids = torch.zeros((bs, num_token), device=input_ids.device)
previous_col = 0
for i in range(idxs.shape[0]):
row, col = idxs[i]
if (col == 0) or (col == num_token - 1):
attention_mask[row, col, col] = True
position_ids[row, col] = 0
else:
attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
position_ids[row, previous_col + 1 : col + 1] = torch.arange(
0, col - previous_col, device=input_ids.device
)
previous_col = col
# # padding mask
# padding_mask = tokenized['attention_mask']
# attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
return attention_mask, position_ids.to(torch.long)
def generate_masks_with_special_tokens_and_transfer_map(tokenized, special_tokens_list, tokenizer):
"""Generate attention mask between each pair of special tokens
Args:
input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
special_tokens_mask (list): special tokens mask.
Returns:
torch.Tensor: attention mask between each special tokens.
"""
input_ids = tokenized["input_ids"]
bs, num_token = input_ids.shape
# special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
for special_token in special_tokens_list:
special_tokens_mask |= input_ids == special_token
# idxs: each row is a list of indices of special tokens
idxs = torch.nonzero(special_tokens_mask)
# generate attention mask and positional ids
attention_mask = (
torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
)
position_ids = torch.zeros((bs, num_token), device=input_ids.device)
cate_to_token_mask_list = [[] for _ in range(bs)]
previous_col = 0
for i in range(idxs.shape[0]):
row, col = idxs[i]
if (col == 0) or (col == num_token - 1):
attention_mask[row, col, col] = True
position_ids[row, col] = 0
else:
attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
position_ids[row, previous_col + 1 : col + 1] = torch.arange(
0, col - previous_col, device=input_ids.device
)
c2t_maski = torch.zeros((num_token), device=input_ids.device).bool()
c2t_maski[previous_col + 1 : col] = True
cate_to_token_mask_list[row].append(c2t_maski)
previous_col = col
cate_to_token_mask_list = [
torch.stack(cate_to_token_mask_listi, dim=0)
for cate_to_token_mask_listi in cate_to_token_mask_list
]
# # padding mask
# padding_mask = tokenized['attention_mask']
# attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
return attention_mask, position_ids.to(torch.long), cate_to_token_mask_list
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