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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import torch
import torch.nn.functional as F
from mmengine.model import BaseModule
from mmpretrain.models.utils.box_utils import (box_cxcywh_to_xyxy,
generalized_box_iou)
from mmpretrain.registry import MODELS, TOKENIZER
@MODELS.register_module()
class GroundingHead(BaseModule):
"""bbox Coordination generation head for multi-modal pre-trained task,
adapted by BLIP. Normally used for visual grounding.
Args:
loss: dict,
decoder: dict,
init_cfg (dict, optional): the config to control the initialization.
Defaults to None.
"""
def __init__(
self,
decoder: dict = None,
tokenizer: dict = None,
box_l1_loss_coeff=4.0,
box_giou_loss_coeff=2.0,
init_cfg: Optional[dict] = None,
) -> None:
super(GroundingHead, self).__init__(init_cfg=init_cfg)
''' init the decoder from med_config'''
self.decoder = None
if decoder:
self.decoder = MODELS.build(decoder)
self.loss_fn = torch.nn.CrossEntropyLoss(
reduction='none', ignore_index=-100)
self.box_l1_loss_coeff = box_l1_loss_coeff
self.box_giou_loss_coeff = box_giou_loss_coeff
if isinstance(tokenizer, dict):
self.tokenizer = TOKENIZER.build(tokenizer)
else:
self.tokenizer = tokenizer
self.image_res = 640
prefix_ids = torch.tensor(
self.tokenizer.convert_tokens_to_ids(['[unused339]']))
target_ids = torch.tensor(
self.tokenizer.convert_tokens_to_ids(
[f'[unused{340+_}]' for _ in range(self.image_res + 1)]))
self.register_buffer('prefix_ids', prefix_ids)
self.register_buffer('target_ids', target_ids)
bbox_prob_mask = torch.zeros(len(self.tokenizer))
bbox_prob_mask[self.target_ids[0]:self.target_ids[-1] + 1] = 1
bbox_prob_mask = (1.0 - bbox_prob_mask) * -10000.0
self.register_buffer('bbox_prob_mask', bbox_prob_mask)
self.bin_start_idx = self.target_ids[0]
def forward(self, text_embedding, text_embedding_mask,
encoder_hidden_states, encoder_attention_mask):
# localize prompt token, text embedding
merged_encode_hs = torch.cat([encoder_hidden_states, text_embedding],
1)
merge_att_mask = torch.cat(
[encoder_attention_mask, text_embedding_mask], 1)
loc_prompt = self.prompt.weight.T
loc_prompt = torch.repeat_interleave(loc_prompt,
merge_att_mask.shape[0],
0).unsqueeze(1)
loc_prompt_mask = torch.ones(loc_prompt.shape[:-1]).long().to(
loc_prompt.device)
decoder_out = self.decoder(
inputs_embeds=loc_prompt,
attention_mask=loc_prompt_mask,
encoder_hidden_states=merged_encode_hs,
encoder_attention_mask=merge_att_mask,
output_hidden_states=True,
labels=None,
)
decoder_hs = decoder_out.hidden_states[-1][:, 0, :]
box_pred = self.box_head(decoder_hs)
return decoder_out, decoder_hs, box_pred
def loss(self,
text_embedding,
text_embedding_mask,
encoder_hidden_states,
encoder_attention_mask,
decoder_targets,
return_scores=False):
"""Calculate losses from the extracted features.
Args:
feats (dict): The features extracted from the backbone.
data_samples (List[BaseDataElement]): The annotation data of
every samples.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
merged_encode_hs = torch.cat([encoder_hidden_states, text_embedding],
1)
merge_att_mask = torch.cat(
[encoder_attention_mask, text_embedding_mask], 1)
answer_targets = (decoder_targets *
self.image_res).long() + self.bin_start_idx
prefix_ids = torch.repeat_interleave(self.prefix_ids,
merge_att_mask.shape[0],
0).unsqueeze(-1)
prefix_ids = torch.cat([prefix_ids, answer_targets], dim=1)
answer_output = self.decoder(
prefix_ids,
encoder_hidden_states=merged_encode_hs,
encoder_attention_mask=merge_att_mask,
labels=None,
return_dict=True,
)
prob_mask = self.bbox_prob_mask.view(1, 1,
self.bbox_prob_mask.shape[-1])
prediction_scores = answer_output.logits + prob_mask
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
labels = prefix_ids[:, 1:].contiguous()
vocab_size = len(self.tokenizer)
loss_seq_init = self.loss_fn(
shifted_prediction_scores.view(-1, vocab_size), labels.view(-1))
with torch.no_grad():
pred_box = (torch.argmax(
prediction_scores[:, :-1, :].contiguous(), dim=-1) -
self.bin_start_idx) / self.image_res
weight_bbox = F.l1_loss(
pred_box, decoder_targets, reduction='none').clamp(
0, 5) * self.box_l1_loss_coeff
weight_giou = (1 - torch.diag(
generalized_box_iou(
box_cxcywh_to_xyxy(pred_box),
box_cxcywh_to_xyxy(decoder_targets)))
) * self.box_giou_loss_coeff
bs = text_embedding.shape[0]
loss_seq = loss_seq_init[:].view(bs, -1, 4)
loss_seq = loss_seq * weight_bbox
loss_seq = loss_seq * weight_giou.unsqueeze(1)
loss_seq = loss_seq.mean()
losses = {
'loss_seq': loss_seq,
'loss_seq_init': loss_seq_init.mean(),
'loss': loss_seq,
'box_l1': weight_bbox.mean(-1).mean().detach(),
'box_giou': weight_giou.mean().detach()
}
return losses
def predict(
self,
text_embedding,
text_embedding_mask,
encoder_hidden_states,
encoder_attention_mask,
):
"""Generates the bbox coordinates at inference time."""
merged_encode_hs = torch.cat([encoder_hidden_states, text_embedding],
1)
merge_att_mask = torch.cat(
[encoder_attention_mask, text_embedding_mask], 1)
prefix_ids = torch.repeat_interleave(self.prefix_ids,
merge_att_mask.shape[0],
0).unsqueeze(-1)
for _ in range(4):
decoder_output = self.decoder(
prefix_ids,
encoder_hidden_states=merged_encode_hs,
encoder_attention_mask=merge_att_mask,
labels=None,
return_dict=True,
)
prob_mask = self.bbox_prob_mask.view(1, 1,
self.bbox_prob_mask.shape[-1])
prediction_scores = decoder_output.logits + prob_mask
prefix_ids = torch.cat([
prefix_ids,
torch.argmax(prediction_scores[:, -1, :], dim=-1).unsqueeze(1)
],
dim=1)
pred_box = self.process_bbox(prefix_ids[:, 1:]) # xywh 0-1 to xyxy 0-1
return pred_box
@torch.no_grad()
def process_bbox(self, bbox):
bbox = bbox - self.bin_start_idx
bbox = torch.true_divide(bbox, self.image_res)
bbox = box_cxcywh_to_xyxy(bbox)
bbox = torch.clip(bbox, 0, 1)
assert torch.all(bbox <= 1)
return bbox
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