add model

config.json

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+{
+  "_name_or_path": "/tmp/hf_demo_may_11th",
+  "architectures": [
+    "CXRBertModel"
+  ],
+  "attention_probs_dropout_prob": 0.25,
+  "auto_map": {
+    "AutoConfig": "configuration_cxrbert.CXRBertConfig",
+    "AutoModel": "modeling_cxrbert.CXRBertModel"
+  },
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.25,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "cxr-bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "projection_size": 128,
+  "torch_dtype": "float32",
+  "transformers_version": "4.18.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

configuration_cxrbert.py

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+#  ------------------------------------------------------------------------------------------
+#  Copyright (c) Microsoft Corporation. All rights reserved.
+#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
+#  ------------------------------------------------------------------------------------------
+
+from typing import Any
+
+from transformers import BertConfig
+
+
+class CXRBertConfig(BertConfig):
+    model_type = "cxr-bert"
+
+    def __init__(self, projection_size: int = 128, **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+        self.projection_size = projection_size

modeling_cxrbert.py

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+#  ------------------------------------------------------------------------------------------
+#  Copyright (c) Microsoft Corporation. All rights reserved.
+#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
+#  ------------------------------------------------------------------------------------------
+
+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 .configuration_cxrbert import CXRBertConfig
+
+BERTTupleOutput = Tuple[T, T, T, T, T]
+
+class CXRBertOutput(ModelOutput):
+    last_hidden_state: torch.FloatTensor
+    prediction_logits: torch.FloatTensor
+    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, it's similar to `BertPredictionHeadTransform` in HuggingFace library.
+    :param config: CXRBertConfig
+    :return: (batch_size, output_size)
+    '''
+    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://arxiv.org/abs/2204.09817
+
+    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
+
+    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,
+                prediction_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) -> 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)
+        :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)
+
+        normalized_cls_embedding = F.normalize(outputs.cls_projected_embedding, dim=1)
+        return normalized_cls_embedding

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a41d2f4b33e5bdbacabecb8d34c4420163e2165b9fdc6915014e8263a0d0782b
+size 438588639