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szsteven008 2025-01-01 16:23:11 +08:00 committed by GitHub
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commit bd929612c4
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1 changed files with 18 additions and 16 deletions
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groundingdino/models/GroundingDINO/groundingdino.py
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@ -230,6 +230,8 @@ class GroundingDINO(nn.Module):
input_ids: Tensor,
attention_mask: Tensor,
token_type_ids: Tensor,
position_ids: Tensor,
text_self_attention_masks: Tensor,
**kw):
"""The forward expects a NestedTensor, which consists of:
- samples.tensor: batched images, of shape [batch_size x 3 x H x W]
@ -262,22 +264,22 @@ class GroundingDINO(nn.Module):
"token_type_ids": token_type_ids,
}
(
text_self_attention_masks,
position_ids,
cate_to_token_mask_list,
) = generate_masks_with_special_tokens_and_transfer_map(
tokenized, self.specical_tokens, self.tokenizer
)
# (
# text_self_attention_masks,
# position_ids,
# cate_to_token_mask_list,
# ) = generate_masks_with_special_tokens_and_transfer_map(
# tokenized, self.specical_tokens, self.tokenizer
# )
if text_self_attention_masks.shape[1] > self.max_text_len:
text_self_attention_masks = text_self_attention_masks[
:, : self.max_text_len, : self.max_text_len
]
position_ids = position_ids[:, : self.max_text_len]
tokenized["input_ids"] = tokenized["input_ids"][:, : self.max_text_len]
tokenized["attention_mask"] = tokenized["attention_mask"][:, : self.max_text_len]
tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : self.max_text_len]
# if text_self_attention_masks.shape[1] > self.max_text_len:
# text_self_attention_masks = text_self_attention_masks[
# :, : self.max_text_len, : self.max_text_len
# ]
# position_ids = position_ids[:, : self.max_text_len]
# tokenized["input_ids"] = tokenized["input_ids"][:, : self.max_text_len]
# tokenized["attention_mask"] = tokenized["attention_mask"][:, : self.max_text_len]
# tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : self.max_text_len]
# extract text embeddings
if self.sub_sentence_present:
@ -292,7 +294,7 @@ class GroundingDINO(nn.Module):
encoded_text = self.feat_map(bert_output["last_hidden_state"]) # bs, 195, d_model
text_token_mask = tokenized["attention_mask"].bool() # bs, 195
# text_token_mask = tokenizedattention_mask.bool() # bs, 195
# text_token_mask = tokenized.attention_mask.bool() # bs, 195
# text_token_mask: True for nomask, False for mask
# text_self_attention_masks: True for nomask, False for mask