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Update vilbert.py #1065

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Update vilbert.py #1065

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6043e09
Update vilbert.py
dk25021999 Aug 31, 2021
2180572
Update vilbert.py
dk25021999 Sep 1, 2021
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12 changes: 12 additions & 0 deletions mmf/models/vilbert.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@
import torch.nn.functional as F
from mmf.common.registry import registry
from mmf.models import BaseModel
from mmf.models.transformers.heads.itm import ITM
from mmf.modules.hf_layers import replace_with_jit
from mmf.utils.configuration import get_mmf_cache_dir
from mmf.utils.modeling import get_optimizer_parameters_for_bert
Expand Down Expand Up @@ -1061,6 +1062,7 @@ def __init__(self, config):
self.visual_target = config.visual_target
self.num_negative = config.num_negative
self.loss_fct = CrossEntropyLoss(ignore_index=-1)


if self.visual_target == 0:
self.vis_criterion = nn.KLDivLoss(reduction="none")
Expand Down Expand Up @@ -1099,6 +1101,8 @@ def forward(
image_label: Optional[Tensor] = None,
image_target: Optional[Tensor] = None,
output_all_attention_masks: bool = False,
itm_loss: bool = False,
next_sentence_label: Optional[Dict[str, Dict[str, torch.Tensor]]] = None,
) -> Dict[str, Tensor]:
masked_img_loss: Optional[Tensor] = None
(
Expand Down Expand Up @@ -1226,6 +1230,14 @@ def forward(
prediction_scores_t.view(-1, self.vocab_size), masked_lm_labels.view(-1)
)
output["masked_lm_loss"] = masked_lm_loss.unsqueeze(0)

if itm_loss is not False:
itm_head = ITM({"type": "itm", "hidden_size": self.vocab_size})
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This should be initialized in the init not in the forward pass.

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Hi! corrected the initialization thing. I checked the snippet I added separately in similar manner to "ITM head test" but was not able to test the ViLBERTForPretraining (As after loading yaml file its throwing 'dict' object has no attribute 'bert_model_name') even without making changes in original code.

seq_output = torch.cat(sequence_output_t, sequence_output_v)
multimodal_alignment_loss = itm_head(seq_output, processed_sample_list = next_sentence_label)
if multimodal_alignment_loss is not None:
output["itm_loss"] = multimodal_alignment_loss["losses"]["itm_loss"]

# next_sentence_loss = self.loss_fct(
# seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)
# )
Expand Down