Add @dataclass decorator to CXRBertOutput

modelling_cxrbert.py

@@ -0,0 +1,146 @@
+#  ------------------------------------------------------------------------------------------
+#  Copyright (c) Microsoft Corporation. All rights reserved.
+#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
+#  ------------------------------------------------------------------------------------------
+
+from dataclasses import dataclass
+from typing import Any, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch import Tensor as T
+from transformers import BertForMaskedLM
+from transformers.modeling_outputs import ModelOutput
+
+from health_multimodal.text.model.configuration_cxrbert import CXRBertConfig
+
+BERTTupleOutput = Tuple[T, T, T, T, T]
+
+
+@dataclass
+class CXRBertOutput(ModelOutput):
+    last_hidden_state: torch.FloatTensor
+    logits: Optional[torch.FloatTensor] = None
+    cls_projected_embedding: Optional[torch.FloatTensor] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
+class BertProjectionHead(nn.Module):
+    """Projection head to be used with BERT CLS token.
+
+    This is similar to ``BertPredictionHeadTransform`` in HuggingFace.
+
+    :param config: Configuration for BERT.
+    """
+
+    def __init__(self, config: CXRBertConfig) -> None:
+        super().__init__()
+        self.dense_to_hidden = nn.Linear(config.hidden_size, config.projection_size)
+        self.transform_act_fn = nn.functional.gelu
+        self.LayerNorm = nn.LayerNorm(config.projection_size, eps=1e-12)
+        self.dense_to_output = nn.Linear(config.projection_size, config.projection_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense_to_hidden(hidden_states)
+        hidden_states = self.transform_act_fn(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states)
+        hidden_states = self.dense_to_output(hidden_states)
+
+        return hidden_states
+
+
+class CXRBertModel(BertForMaskedLM):
+    """
+    Implements the CXR-BERT model outlined in the manuscript:
+    Boecking et al. "Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing", 2022
+    https://link.springer.com/chapter/10.1007/978-3-031-20059-5_1
+
+    Extends the HuggingFace BertForMaskedLM model by adding a separate projection head. The projection "[CLS]" token is
+    used to align the latent vectors of image and text modalities.
+    """
+
+    config_class = CXRBertConfig  # type: ignore
+
+    def __init__(self, config: CXRBertConfig):
+        super().__init__(config)
+
+        self.cls_projection_head = BertProjectionHead(config)
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_cls_projected_embedding: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs: Any
+    ) -> Union[BERTTupleOutput, CXRBertOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        bert_for_masked_lm_output = super().forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=True,
+            return_dict=True,
+        )
+
+        last_hidden_state = bert_for_masked_lm_output.hidden_states[-1]
+        cls_projected_embedding = (
+            self.cls_projection_head(last_hidden_state[:, 0, :]) if output_cls_projected_embedding else None
+        )
+
+        if return_dict:
+            return CXRBertOutput(
+                last_hidden_state=last_hidden_state,
+                logits=bert_for_masked_lm_output.logits,
+                cls_projected_embedding=cls_projected_embedding,
+                hidden_states=bert_for_masked_lm_output.hidden_states if output_hidden_states else None,
+                attentions=bert_for_masked_lm_output.attentions,
+            )
+        else:
+            return (
+                last_hidden_state,
+                bert_for_masked_lm_output.logits,
+                cls_projected_embedding,
+                bert_for_masked_lm_output.hidden_states,
+                bert_for_masked_lm_output.attentions,
+            )
+
+    def get_projected_text_embeddings(
+        self, input_ids: torch.Tensor, attention_mask: torch.Tensor, normalize_embeddings: bool = True
+    ) -> torch.Tensor:
+        """
+        Returns l2-normalised projected cls token embeddings for the given input token ids and attention mask.
+        The joint latent space is trained using a contrastive objective between image and text data modalities.
+
+        :param input_ids: (batch_size, sequence_length)
+        :param attention_mask: (batch_size, sequence_length)
+        :param normalize_embeddings: Whether to l2-normalise the embeddings.
+        :return: (batch_size, projection_size)
+        """
+
+        outputs = self.forward(
+            input_ids=input_ids, attention_mask=attention_mask, output_cls_projected_embedding=True, return_dict=True
+        )
+        assert isinstance(outputs, CXRBertOutput)
+
+        cls_projected_embedding = outputs.cls_projected_embedding
+        assert cls_projected_embedding is not None
+
+        if normalize_embeddings:
+            return F.normalize(cls_projected_embedding, dim=1)
+
+        return cls_projected_embedding