Release GLINT-CXR

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LICENSE.md

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+# DINOv3 License
+
+*Last Updated: August 19, 2025*
+
+**“Agreement”** means the terms and conditions for use, reproduction, distribution and modification of the DINO Materials set forth herein.
+
+**“DINO Materials”** means, collectively, Documentation and the models, software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code, and other elements of the foregoing distributed by Meta and made available under this Agreement.
+
+**“Documentation”** means the specifications, manuals and documentation accompanying
+DINO Materials distributed by Meta.
+
+**“Licensee”** or **“you”** means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.
+
+**“Meta”** or **“we”** means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) or Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).
+
+**“Sanctions”** means any economic or trade sanctions or restrictions administered or enforced by the United States (including the Office of Foreign Assets Control of the U.S. Department of the Treasury (“OFAC”), the U.S. Department of State and the U.S. Department of Commerce), the United Nations, the European Union, or the United Kingdom.
+
+**“Trade Controls”** means any of the following: Sanctions and applicable export and import controls.
+
+By clicking “I Accept” below or by using or distributing any portion or element of the DINO Materials, you agree to be bound by this Agreement.
+
+## 1. License Rights and Redistribution.
+
+a. <ins>Grant of Rights</ins>. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the DINO Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the DINO Materials.
+
+b. <ins>Redistribution and Use</ins>.
+
+i. Distribution of DINO Materials, and any derivative works thereof, are subject to the terms of this Agreement. If you distribute or make the DINO Materials, or any derivative works thereof, available to a third party, you may only do so under the terms of this Agreement and you shall provide a copy of this Agreement with any such DINO Materials.
+
+ii.  If you submit for publication the results of research you perform on, using, or otherwise in connection with DINO Materials, you must acknowledge the use of DINO Materials in your publication.
+
+iii. Your use of the DINO Materials must comply with applicable laws and regulations, including Trade Control Laws and applicable privacy and data protection laws.
+
+iv. Your use of the DINO Materials will not involve or encourage others to reverse engineer, decompile or discover the underlying components of the DINO Materials.
+
+v. You are not the target of Trade Controls and your use of DINO Materials must comply with Trade Controls. You agree not to use, or permit others to use, DINO Materials for any activities subject to the International Traffic in Arms Regulations (ITAR) or end uses prohibited by Trade Controls, including those related to military or warfare purposes, nuclear industries or applications, espionage, or the development or use of guns or illegal weapons.
+
+## 2. User Support.
+
+Your use of the DINO Materials is done at your own discretion; Meta does not process any information nor provide any service in relation to such use.  Meta is under no obligation to provide any support services for the DINO Materials. Any support provided is “as is”, “with all faults”, and without warranty of any kind.
+
+## 3. Disclaimer of Warranty.
+
+UNLESS REQUIRED BY APPLICABLE LAW, THE DINO MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE DINO MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE DINO MATERIALS AND ANY OUTPUT AND RESULTS.
+
+## 4. Limitation of Liability.
+
+IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY DIRECT OR INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.
+
+## 5. Intellectual Property.
+
+a. Subject to Meta’s ownership of DINO Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the DINO Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.
+
+b. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the DINO Materials, outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the DINO Materials.
+
+## 6. Term and Termination.
+
+The term of this Agreement will commence upon your acceptance of this Agreement or access to the DINO Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the DINO Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.
+
+## 7. Governing Law and Jurisdiction.
+
+This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.
+
+## 8. Modifications and Amendments.
+
+Meta may modify this Agreement from time to time; provided that they are similar in spirit to the current version of the Agreement, but may differ in detail to address new problems or concerns. All such changes will be effective immediately. Your continued use of the DINO Materials after any modification to this Agreement constitutes your agreement to such modification. Except as provided in this Agreement, no modification or addition to any provision of this Agreement will be binding unless it is in writing and signed by an authorized representative of both you and Meta.

LICENSES/Apache-2.0.txt

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LICENSES/CC-BY-NC-4.0.txt

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+Creative Commons may be contacted at creativecommons.org.

README.md

@@ -0,0 +1,139 @@
+---
+library_name: transformers
+license: other
+license_name: dinov3-license
+license_link: LICENSE.md
+tags:
+- medical
+- radiology
+- vision-language
+- chest-x-ray
+- glint
+- custom_code
+- arxiv:2606.03180
+base_model: facebook/dinov3-vitl16-pretrain-lvd1689m
+---
+
+# GLINT-CXR
+
+<p>
+📝 <a href="https://arxiv.org/abs/2606.03180">Paper</a> •
+💻 <a href="https://github.com/deepnoid-ai/GLINT">Code</a> •
+🌐 <a href="https://deepnoid-ai.github.io/GLINT/">Project page</a> •
+🤗 <a href="https://huggingface.co/Deepnoid/GLINT-CT">GLINT-CT</a>
+</p>
+
+GLINT-CXR is the chest X-ray model of **GLINT** (*GLINT: Sparsely Gated Vision-Language Alignment for Fine-Grained
+Radiology Representations*, NeurIPS 2026).
+
+**Built with DINOv3.**
+
+- **Vision encoder:** DINOv3 ViT-L/16, input 1024×1024 (4096 patches)
+- **Text encoder:** `sentence-transformers/all-mpnet-base-v2` (fine-tuned)
+- **Alignment:** linear projection (1024→768) + LayerNorm, mean-pooled text + LayerNorm, SGA similarity head
+- **Training:** MIMIC-CXR, with Sparsely Gated Alignment (SGA) and Dense Feature Regularization (DFR) from a frozen
+  DINOv3 ViT-7B teacher
+
+> ⚠️ GLINT is released for research purposes only and is not intended for clinical use, including the diagnosis or treatment of any medical condition.
+
+## Usage
+
+Both examples use the bundled `inference.py` / `utils.py`; run them from the downloaded repo.
+
+### Full pipeline
+
+```bash
+hf download Deepnoid/GLINT-CXR --local-dir glint-cxr && cd glint-cxr
+python inference.py --image cxr.png --text "There is a right pleural effusion."
+# prints the sentence probability and saves the heat-map overlay to similarity_map.png
+```
+
+### AutoModel
+
+```python
+import torch
+from transformers import AutoModel, AutoTokenizer, AutoImageProcessor
+from utils import model_inference, save_similarity_map   # bundled helpers
+
+repo = "Deepnoid/GLINT-CXR"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model = AutoModel.from_pretrained(repo, trust_remote_code=True, dtype=torch.float32).to(device).eval()
+tokenizer = AutoTokenizer.from_pretrained(repo)
+image_processor = AutoImageProcessor.from_pretrained(repo)
+
+prob, similarity_map = model_inference(
+    "cxr.png", "There is a right pleural effusion.",
+    model=model, tokenizer=tokenizer, image_processor=image_processor,
+)
+save_similarity_map("cxr.png", similarity_map, "similarity_map.png")
+```
+
+- **Outputs:** `prob` is the sentence probability (sigmoid); `similarity_map` is the per-pixel map in [0, 1].
+- **Inputs:** PNG / JPG (8- or 16-bit) or DICOM, min-max stretched to 8 bit by `utils.load_image` as in evaluation.
+- **Underlying call:** `model.compute_logits(pixel_values, [encoded_text])` returns `logits` and `similarity_scores`
+  (64×64 patches).
+
+Vision encoder only (`vision_only=True`):
+
+```python
+import torch
+from transformers import AutoModel, AutoImageProcessor
+from utils import load_image   # bundled helper
+
+repo = "Deepnoid/GLINT-CXR"
+vision = AutoModel.from_pretrained(repo, trust_remote_code=True, vision_only=True, dtype=torch.float32).eval()
+image_processor = AutoImageProcessor.from_pretrained(repo)
+
+pixel_values = image_processor(load_image("cxr.png"), return_tensors="pt")["pixel_values"]
+with torch.no_grad():
+    out = vision(pixel_values)
+print(out.last_hidden_state.shape)    # (1, 4101, 1024): CLS + 4 registers + 4096 patches
+```
+
+No gated DINOv3 download is needed; the model is built from this repo alone.
+
+## Requirements
+
+`transformers==4.56.1`, `torch`, `torchvision`, `entmax`; `utils.py` also uses `opencv-python` (overlay) and `pydicom`
+(DICOM).
+
+> ⚠️ Keep `transformers==4.56.1`: 5.x silently skips the DINOv3 vision weights, leaving a random encoder.
+
+## Notes
+
+- Training and fine-tuning code: [GitHub](https://github.com/deepnoid-ai/GLINT); split ID lists: [`data/`](./tree/main/data).
+- `dfr.py` and its projector weights are used only in training; the DINOv3 ViT-7B teacher is not included.
+- `vision_only=True` still downloads the full checkpoint; `save_pretrained` on the loaded model keeps a vision-only copy.
+
+## Acknowledgments
+
+This work was supported by the Technology Innovation Program (RS-2025-02221011,
+Development of Medical-Specialized Multimodal Hyperscale Generative AI Technology
+for Global Integration) funded by the Ministry of Trade, Industry & Energy (MOTIE,
+South Korea), and by the Advanced GPU Utilization Support Program funded by the
+Korean government (Ministry of Science and ICT) (Project No. 02-26-91-0330).
+
+## License
+
+- **Model weights:** derivative of Meta's [DINOv3](https://github.com/facebookresearch/dinov3) ViT-L/16 (LVD-1689M)
+  weights, © Meta Platforms, Inc., under the [DINOv3 License](LICENSE.md).
+  - Using or distributing them means accepting its terms; redistributions must include the license.
+- **Code (`*.py`):** [CC BY-NC 4.0](LICENSES/CC-BY-NC-4.0.txt), as in the [GitHub repository](https://github.com/deepnoid-ai/GLINT).
+- **Text encoder:** fine-tuned from
+  [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
+  (Apache-2.0, modified; `LICENSES/Apache-2.0.txt`).
+
+## Citation
+
+```bibtex
+@inproceedings{park2026glint,
+  title     = {{GLINT}: Sparsely Gated Vision-Language Alignment for Fine-Grained Radiology Representations},
+  author    = {Park, Jonggwon and Lee, Seongeun and Park, Junhyun and
+               Yun, Hannah and Kim, Hyunwoong and Jeong, Sohyun and
+               Kang, Hyewon and Yoon, Byungmu and Choi, Kyoyun},
+  booktitle = {The Fortieth Annual Conference on Neural Information Processing Systems},
+  year      = {2026},
+  url       = {https://arxiv.org/abs/2606.03180}
+}
+```

align_layers.py

@@ -0,0 +1,83 @@
+from torch import nn
+from transformers import PreTrainedModel
+from transformers.models.dinov2.modeling_dinov2 import Dinov2Encoder
+
+from .configuration_glint import AlignLayerConfig
+
+
+def build_align_layer(config):
+    if config.model_type == "align_transformer":
+        model = AlignTransformer(config)
+    elif config.model_type == "identity":
+        model = Identity()
+    elif config.model_type == "mlp":
+        model = MLP(config)
+    elif config.model_type == "linear":
+        model = Linear(config)
+    else:
+        raise NotImplementedError()
+
+    return model
+
+
+class AlignTransformer(PreTrainedModel):
+    def __init__(self, config: AlignLayerConfig):
+        super().__init__(config)
+
+        self.linear = nn.Linear(config.input_dim, config.hidden_size)
+
+        if config.num_hidden_layers:
+            self.transformer_layers = Dinov2Encoder(config)
+        else:
+            self.transformer_layers = None
+
+        if config.use_layer_norm:
+            self.layer_norm = nn.LayerNorm(config.hidden_size)
+        else:
+            self.layer_norm = None
+
+    def forward(self, vision_tokens):
+        if self.transformer_layers is not None:
+            vision_tokens = self.linear(vision_tokens)
+            vision_tokens = self.transformer_layers(vision_tokens)["last_hidden_state"]
+
+        if self.layer_norm is not None:
+            vision_tokens = self.layer_norm(vision_tokens)
+
+        return vision_tokens
+
+
+class Identity(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        return x
+
+
+class Linear(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.linear = nn.Linear(config.input_dim, config.hidden_size)
+
+    def forward(self, x):
+        return self.linear(x)
+
+
+class MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.mlp_layer = nn.Sequential(
+            nn.Linear(config.input_dim, config.input_dim),
+            nn.ReLU(inplace=True),
+            nn.Dropout(config.dropout),
+            nn.Linear(config.input_dim, config.input_dim),
+            nn.ReLU(inplace=True),
+            nn.Dropout(config.dropout),
+            nn.Linear(config.input_dim, config.hidden_size),
+        )
+
+    def forward(self, x):
+        return self.mlp_layer(x)

common_layers.py

@@ -0,0 +1,22 @@
+from torch import nn
+from transformers.modeling_utils import PreTrainedModel
+
+
+class BasePreTrainedModel(PreTrainedModel):
+    supports_gradient_checkpointing = True
+
+    def _init_weights(self, module):
+        if (
+            isinstance(module, nn.Conv2d)  # noqa: SIM101
+            or isinstance(module, nn.Embedding)
+            or isinstance(module, nn.Linear)
+        ):
+            module.weight.data.normal_(mean=0.0, std=0.02)
+            if hasattr(module, "bias") and module.bias is not None:
+                module.bias.data.zero_()
+
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        elif isinstance(module, nn.Parameter):
+            raise ValueError()

config.json

@@ -0,0 +1,427 @@
+{
+  "align_layer_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": null,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dtype": null,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": null,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_size": 768,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "input_dim": 1024,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "min_length": 0,
+    "model_type": "linear",
+    "no_repeat_ngram_size": 0,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "prefix": null,
+    "problem_type": null,
+    "projector_config": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torchscript": false,
+    "typical_p": 1.0,
+    "use_bfloat16": false
+  },
+  "architectures": [
+    "GLINTModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_glint.GLINTConfig",
+    "AutoModel": "modeling_glint.GLINTModel"
+  },
+  "can_return_loss": true,
+  "compute_logits_type": "vl_alignment",
+  "dfr_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": null,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dtype": null,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": null,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_mapping": {
+      "student_layers": [
+        22
+      ],
+      "teacher_layers": [
+        36
+      ],
+      "use_multiple_projectors": true
+    },
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "min_length": 0,
+    "model_type": "",
+    "no_repeat_ngram_size": 0,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "prefix": null,
+    "problem_type": null,
+    "projector_config": {
+      "_name_or_path": "",
+      "activation": "silu",
+      "add_cross_attention": false,
+      "architectures": null,
+      "bad_words_ids": null,
+      "begin_suppress_tokens": null,
+      "bos_token_id": null,
+      "chunk_size_feed_forward": 0,
+      "cross_attention_hidden_size": null,
+      "decoder_start_token_id": null,
+      "diversity_penalty": 0.0,
+      "do_sample": false,
+      "dropout": 0.0,
+      "dtype": null,
+      "early_stopping": false,
+      "encoder_no_repeat_ngram_size": 0,
+      "eos_token_id": null,
+      "exponential_decay_length_penalty": null,
+      "finetuning_task": null,
+      "forced_bos_token_id": null,
+      "forced_eos_token_id": null,
+      "hidden_dim": 4096,
+      "id2label": {
+        "0": "LABEL_0",
+        "1": "LABEL_1"
+      },
+      "input_dim": 1024,
+      "is_decoder": false,
+      "is_encoder_decoder": false,
+      "label2id": {
+        "LABEL_0": 0,
+        "LABEL_1": 1
+      },
+      "length_penalty": 1.0,
+      "max_length": 20,
+      "min_length": 0,
+      "model_type": "mlp",
+      "no_repeat_ngram_size": 0,
+      "num_beam_groups": 1,
+      "num_beams": 1,
+      "num_layers": 3,
+      "num_return_sequences": 1,
+      "out_dim": 4096,
+      "output_attentions": false,
+      "output_hidden_states": false,
+      "output_scores": false,
+      "pad_token_id": null,
+      "prefix": null,
+      "problem_type": null,
+      "pruned_heads": {},
+      "remove_invalid_values": false,
+      "repetition_penalty": 1.0,
+      "return_dict": true,
+      "return_dict_in_generate": false,
+      "sep_token_id": null,
+      "suppress_tokens": null,
+      "task_specific_params": null,
+      "temperature": 1.0,
+      "tf_legacy_loss": false,
+      "tie_encoder_decoder": false,
+      "tie_word_embeddings": true,
+      "tokenizer_class": null,
+      "top_k": 50,
+      "top_p": 1.0,
+      "torchscript": false,
+      "typical_p": 1.0,
+      "use_bfloat16": false
+    },
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torchscript": false,
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": true,
+    "vfm_teacher_config": null
+  },
+  "dtype": "float32",
+  "loss": {
+    "AlignmentLoss": {
+      "local_loss": true,
+      "loss_kwargs": {
+        "temperature": 0.07
+      },
+      "loss_type": "mpnce",
+      "rank": 0,
+      "vl_logit_kwargs": {
+        "embed_dim": 768,
+        "gate_hidden_dim": 768,
+        "gate_log_temperature": 2.302585,
+        "gate_proj_mode": "mlp",
+        "sem_hidden_dim": 768,
+        "sem_proj_mode": "identity"
+      },
+      "vl_logit_type": "gate_confidence_shift",
+      "world_size": 1
+    },
+    "DFRLoss": {
+      "rank": 0,
+      "use_token_wise": true,
+      "world_size": 1
+    },
+    "apply": [
+      "AlignmentLoss",
+      "DFRLoss"
+    ],
+    "ratio": [
+      1.0,
+      8.0
+    ]
+  },
+  "text_config": {
+    "_name_or_path": "sentence-transformers/all-mpnet-base-v2",
+    "add_cross_attention": false,
+    "architectures": [
+      "MPNetForMaskedLM"
+    ],
+    "attention_probs_dropout_prob": 0.1,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": 0,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dtype": null,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 2,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 768,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 514,
+    "min_length": 0,
+    "model_type": "mpnet",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 12,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 12,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 1,
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "relative_attention_num_buckets": 32,
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torchscript": false,
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cls_token": false,
+    "vocab_size": 30527
+  },
+  "transformers_version": "4.56.1",
+  "use_text_layer_norm": true,
+  "use_vision_layer_norm": true,
+  "vision_config": {
+    "_name_or_path": "facebook/dinov3-vitl16-pretrain-lvd1689m",
+    "add_cross_attention": false,
+    "architectures": [
+      "DINOv3ViTModel"
+    ],
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": null,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "drop_path_rate": 0.0,
+    "dtype": "float32",
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": null,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "gelu",
+    "hidden_size": 1024,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "image_size": 224,
+    "img_size": 1024,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "key_bias": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "layerscale_value": 1.0,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "min_length": 0,
+    "mlp_bias": true,
+    "model_type": "dinov3_vit",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 16,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_channels": 3,
+    "num_hidden_layers": 24,
+    "num_register_tokens": 4,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "patch_size": 16,
+    "pos_embed_jitter": null,
+    "pos_embed_rescale": 2.0,
+    "pos_embed_shift": null,
+    "prefix": null,
+    "problem_type": null,
+    "proj_bias": true,
+    "pruned_heads": {},
+    "query_bias": true,
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "rope_theta": 100.0,
+    "sep_token_id": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torchscript": false,
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cls_token": false,
+    "use_gated_mlp": false,
+    "value_bias": true
+  },
+  "vision_only": false
+}

configuration_glint.py

@@ -0,0 +1,145 @@
+from transformers.configuration_utils import PretrainedConfig
+
+
+class SubConfig(PretrainedConfig):
+    # PretrainedConfig.to_dict() emits the class-level model_type (""), breaking save/load round-trips
+    def to_dict(self):
+        output = super().to_dict()
+        output["model_type"] = self.model_type
+        return output
+
+
+class VisionConfig(SubConfig):
+    def __init__(
+        self,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+    @staticmethod
+    def from_exp_config(vision_config: dict):
+        model_type = vision_config["model_type"]
+
+        if model_type not in ["vjepa2", "dinov2", "dinov3_vit"]:
+            raise NotImplementedError()
+
+        vision_config = VisionConfig(**vision_config)
+
+        return vision_config
+
+
+class TextConfig(SubConfig):
+    def __init__(
+        self,
+        model_type,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.model_type = model_type
+
+    @staticmethod
+    def from_exp_config(
+        text_config: dict,
+    ):
+        if text_config["model_type"] not in ["mpnet", "modernbert", "cxr-bert"]:
+            raise NotImplementedError()
+
+        return TextConfig(**text_config)
+
+
+class AlignLayerConfig(SubConfig):
+    def __init__(
+        self,
+        model_type: str = "align_transformer",
+        projector_config=None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.model_type = model_type
+        self.projector_config = projector_config
+
+    @staticmethod
+    def from_exp_config(
+        align_layer_config: dict,
+    ):
+        if align_layer_config["model_type"] == "align_transformer":
+            from transformers.models.dinov2.configuration_dinov2 import Dinov2Config
+
+            config = Dinov2Config(**align_layer_config)
+            config = config.to_dict()
+            align_layer_config.update(config)
+            align_layer_config["model_type"] = "align_transformer"
+
+        align_layer_config = AlignLayerConfig(
+            **align_layer_config,
+        )
+
+        return align_layer_config
+
+
+class ProjectorConfig(SubConfig):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+
+class DFRConfig(SubConfig):
+    def __init__(
+        self,
+        vfm_teacher_config: dict = None,
+        projector_config: dict = None,
+        layer_mapping: dict = None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.vfm_teacher_config = vfm_teacher_config
+        self.projector_config = projector_config
+        self.layer_mapping = layer_mapping
+
+    @staticmethod
+    def from_exp_config(dfr_config: dict):
+        if "vfm_teacher_config" in dfr_config and dfr_config["vfm_teacher_config"] is not None:
+            dfr_config["vfm_teacher_config"] = VisionConfig.from_exp_config(dfr_config["vfm_teacher_config"])
+
+        if "projector_config" in dfr_config and dfr_config["projector_config"] is not None:
+            dfr_config["projector_config"] = ProjectorConfig(**dfr_config["projector_config"])
+
+        return DFRConfig(**dfr_config)
+
+
+class GLINTConfig(PretrainedConfig):
+    is_composition = True
+
+    def __init__(
+        self,
+        vision_config: dict = None,
+        text_config: dict = None,
+        align_layer_config: dict = None,
+        predictor_config: dict = None,
+        prototype_head_config: dict = None,
+        dfr_config: dict = None,
+        vision_only: bool = False,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.vision_only = vision_only
+
+        if vision_config is not None:
+            pretrained_dir = kwargs.get("pretrained_dir")
+            if pretrained_dir and "pretrained_dir" not in vision_config:
+                vision_config["pretrained_dir"] = pretrained_dir
+            self.vision_config = VisionConfig.from_exp_config(vision_config)
+
+        if text_config is not None:
+            self.text_config = TextConfig.from_exp_config(text_config)
+
+        if align_layer_config is not None:
+            self.align_layer_config = AlignLayerConfig.from_exp_config(align_layer_config)
+
+        if dfr_config is not None:
+            pretrained_dir = kwargs.get("pretrained_dir")
+            teacher = dfr_config.get("vfm_teacher_config")
+            if pretrained_dir and teacher is not None:
+                teacher.setdefault("pretrained_dir", pretrained_dir)
+            self.dfr_config = DFRConfig.from_exp_config(dfr_config)
+
+        self.kwargs = kwargs

data/README.md

@@ -0,0 +1,13 @@
+# Data splits (chest X-ray)
+
+- Fine-tuning split ID lists; download each dataset from its source under its own terms.
+- `val` is for model selection.
+
+| Dataset | Files | ID format | Train / Val / Test | Notes |
+|---|---|---|---|---|
+| ChestXray14 | `splits/chestxray14/` | image file name | 77,871 / 8,653 / 25,596 | Official test (patient-disjoint) |
+| VinDr-CXR | `splits/vindr_cxr/` | VinDr image id | 13,494 / 1,506 / 3,000 | Official test |
+| RSNA-Pneumonia | `splits/rsna_pneumonia/` | `patientId` | 21,346 / 2,669 / 2,669 | Patient-level split of the official train |
+| SIIM-ACR | `splits/siim_acr/` | `SOPInstanceUID` | 9,303 / 1,372 / 1,372 | BenchX test split |
+| ChestX-Det | `splits/chestx_det/` | image file name | 2,722 / 303 / 553 | Official test |
+| MIMIC-CXR (report generation) | — | — | 27,079 / 2,130 / 3,858 | Official test, non-empty findings |

data/splits/chestx_det/test.txt

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dfr.py

@@ -0,0 +1,199 @@
+import logging
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .vision_encoders import build_vision_encoder
+
+logger = logging.getLogger("glint")
+
+
+def build_projector(config):
+    if config.model_type == "mlp":
+        model = MLP(config)
+    else:
+        raise NotImplementedError()
+
+    return model
+
+
+def get_activation(activation_name):
+    activation_map = {"silu": nn.SiLU(), "relu": nn.ReLU(), "gelu": nn.GELU(), "tanh": nn.Tanh()}
+    activation_name = activation_name.lower()
+    if activation_name not in activation_map:
+        raise ValueError(f"Unknown activation: {activation_name}. Available: {list(activation_map.keys())}")
+    return activation_map[activation_name]
+
+
+class MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+
+        activation = get_activation(getattr(config, "activation", "silu"))
+        dropout = getattr(config, "dropout", 0.0)
+        num_layers = getattr(config, "num_layers", 3)
+
+        layers = []
+        layers.append(nn.Linear(config.input_dim, config.hidden_dim))
+        layers.append(activation)
+        if dropout > 0:
+            layers.append(nn.Dropout(dropout))
+
+        for _ in range(num_layers - 2):
+            layers.append(nn.Linear(config.hidden_dim, config.hidden_dim))
+            layers.append(activation)
+            if dropout > 0:
+                layers.append(nn.Dropout(dropout))
+
+        layers.append(nn.Linear(config.hidden_dim, config.out_dim))
+
+        self.mlp = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.mlp(x)
+
+
+class DFRLoss(nn.Module):
+    def __init__(
+        self,
+        use_token_wise=True,
+        **kwargs,
+    ):
+        super().__init__()
+        self.use_token_wise = use_token_wise
+
+    def forward(
+        self,
+        proj_student_features,
+        teacher_features,
+    ):
+        outputs = dict()
+
+        if isinstance(proj_student_features, list):
+            total_loss = 0.0
+            n = 0
+            for ps, ts in zip(proj_student_features, teacher_features):
+                if isinstance(ps, dict):
+                    for k in ps.keys():
+                        total_loss += self._compute_loss(ps[k], ts[k])
+                        n += 1
+                else:
+                    total_loss += self._compute_loss(ps, ts)
+                    n += 1
+            loss = total_loss / max(n, 1)
+
+        elif isinstance(proj_student_features, dict):
+            total_loss = 0
+            num_pairs = len(proj_student_features)
+            for key in proj_student_features.keys():
+                proj_student = proj_student_features[key]
+                teacher = teacher_features[key]
+                total_loss += self._compute_loss(proj_student, teacher)
+            loss = total_loss / num_pairs
+        else:
+            loss = self._compute_loss(proj_student_features, teacher_features)
+
+        losses = {
+            "dfr_loss": loss,
+            "loss": loss,
+        }
+        outputs["losses"] = losses
+
+        return outputs
+
+    def _compute_loss(self, proj_student, teacher):
+        proj_student = F.normalize(proj_student, p=2, dim=-1)
+        teacher = F.normalize(teacher, p=2, dim=-1)
+
+        if self.use_token_wise:
+            similarity = torch.sum(proj_student * teacher, dim=-1)
+            loss = (1.0 - similarity).mean()
+        else:
+            proj_student_global = proj_student.mean(dim=1)
+            teacher_global = teacher.mean(dim=1)
+            similarity = torch.sum(proj_student_global * teacher_global, dim=-1)
+            loss = (1.0 - similarity).mean()
+
+        loss = loss.mean()
+        return loss
+
+
+class DFRModule(nn.Module):
+    def __init__(self, dfr_config):
+        super().__init__()
+        self.config = dfr_config
+        self.use_cache = getattr(dfr_config, "use_cache", False)
+
+        if not self.use_cache:
+            self.vision_teacher_model = build_vision_encoder(dfr_config.vfm_teacher_config)
+        else:
+            logger.info("DFRModule: use_cache=True, skipping teacher model loading")
+
+        self.layer_mapping = dfr_config.layer_mapping
+        student_layers = self.layer_mapping["student_layers"]
+        teacher_layers = self.layer_mapping["teacher_layers"]
+
+        self._validate_layer_mapping(student_layers, teacher_layers)
+        self.num_layer_pairs = len(student_layers)
+
+        self._build_projectors(dfr_config.projector_config, student_layers, teacher_layers)
+
+    def _validate_layer_mapping(self, student_layers, teacher_layers):
+        if len(student_layers) != len(teacher_layers):
+            raise ValueError(
+                f"student_layer and teacher_layer must have same length. "
+                f"Got {len(student_layers)} and {len(teacher_layers)}"
+            )
+
+    def _build_projectors(self, projector_config, student_layers, teacher_layers):
+        use_multiple_projectors = self.layer_mapping.get("use_multiple_projectors", False)
+
+        if use_multiple_projectors and self.num_layer_pairs > 1:
+            self.projector = nn.ModuleDict()
+            for s_layer, t_layer in zip(student_layers, teacher_layers):
+                projector_key = f"projector_{s_layer}_{t_layer}"
+                self.projector[projector_key] = build_projector(projector_config)
+            self.use_multiple_projectors = True
+        else:
+            self.projector = build_projector(projector_config)
+            self.use_multiple_projectors = False
+
+    def forward(self, pixel_values, student_hidden_states, cached_teacher_features=None):
+        student_layers = self.layer_mapping["student_layers"]
+        teacher_layers = self.layer_mapping["teacher_layers"]
+
+        if cached_teacher_features is not None:
+            teacher_hidden_states = cached_teacher_features
+        else:
+            with torch.no_grad():
+                teacher_outputs = self.vision_teacher_model(pixel_values, output_hidden_states=True)
+
+        results = {
+            "teacher_features": {},
+            "proj_student_features": {},
+        }
+
+        for s_layer, t_layer in zip(student_layers, teacher_layers):
+            student_feat = student_hidden_states[s_layer]
+
+            if cached_teacher_features is not None:
+                teacher_feat = teacher_hidden_states[t_layer]
+                teacher_feat = teacher_feat.to(dtype=student_feat.dtype)
+            else:
+                teacher_feat = teacher_outputs.hidden_states[t_layer]
+
+            proj_student_feat = self._project_student_features(student_feat, s_layer, t_layer)
+
+            results["teacher_features"][f"layer_{s_layer}_{t_layer}"] = teacher_feat
+            results["proj_student_features"][f"layer_{s_layer}_{t_layer}"] = proj_student_feat
+
+        return results
+
+    def _project_student_features(self, student_feat, s_layer, t_layer):
+        if self.use_multiple_projectors:
+            projector_key = f"projector_{s_layer}_{t_layer}"
+            return self.projector[projector_key](student_feat)
+        else:
+            return self.projector(student_feat)

inference.py

@@ -0,0 +1,43 @@
+"""Score a sentence against a chest X-ray and save its similarity map.
+
+python inference.py --image cxr.png --text "There is a right pleural effusion."
+"""
+
+import argparse
+import os
+import warnings
+
+import torch
+from transformers import AutoImageProcessor, AutoModel, AutoTokenizer
+
+from utils import model_inference, save_similarity_map
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+REPO_DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+def load_model(device=None, dtype=torch.float32):
+    device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+    tokenizer = AutoTokenizer.from_pretrained(REPO_DIR)
+    image_processor = AutoImageProcessor.from_pretrained(REPO_DIR)
+    model = AutoModel.from_pretrained(REPO_DIR, trust_remote_code=True, dtype=dtype).to(device).eval()
+    return {"tokenizer": tokenizer, "image_processor": image_processor, "model": model}
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Score a sentence against a chest X-ray and save its similarity map.")
+    parser.add_argument("--image", default="cxr.png", help="PNG / JPG or DICOM file")
+    parser.add_argument("--text", default="There is effusion.")
+    parser.add_argument("--output", default="similarity_map.png", help="heat-map overlay")
+    args = parser.parse_args()
+
+    models = load_model()
+    prob, similarity_map = model_inference(args.image, args.text, **models)
+    print(f"{args.text!r}: probability {prob.item():.3f}")
+    save_similarity_map(args.image, similarity_map, args.output)
+    print(f"similarity map saved to {args.output}")
+
+
+if __name__ == "__main__":
+    main()

losses.py

@@ -0,0 +1,607 @@
+import logging
+
+import numpy as np
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+import torch.nn.functional as F
+from entmax import entmax_bisect
+
+from .dfr import DFRLoss  # noqa: F401
+
+logger = logging.getLogger("glint")
+
+
+def pad_and_gather(tensor):
+    local_size = torch.tensor(tensor.size(), device=tensor.device)
+
+    all_sizes = [torch.zeros_like(local_size) for _ in range(dist.get_world_size())]
+    dist.all_gather(all_sizes, local_size)
+
+    max_size = torch.stack(all_sizes).max(dim=0)[0]
+
+    padded_tensor = torch.zeros(max_size.tolist(), device=tensor.device)
+    padded_tensor[: local_size[0]] = tensor
+
+    gathered_tensors = dist.nn.all_gather(padded_tensor)
+
+    gathered_tensors = [g[: s[0]] for g, s in zip(gathered_tensors, all_sizes)]
+
+    gathered_tensors = torch.cat(gathered_tensors, dim=0)
+
+    return gathered_tensors
+
+
+def pad_and_gather_2nd_dim(tensor, mask):
+    local_size = torch.tensor(tensor.size(), device=tensor.device)
+
+    if local_size[1] == 0:
+        local_size = torch.tensor([local_size[0], 1, local_size[2]], device=tensor.device)
+        tensor = torch.zeros(
+            (local_size[0], 1, local_size[2]),
+            device=tensor.device,
+            requires_grad=True,
+        )
+        mask = torch.zeros((local_size[0], 1), device=mask.device, dtype=torch.bool)
+
+    all_sizes = [torch.zeros_like(local_size) for _ in range(dist.get_world_size())]
+    dist.all_gather(all_sizes, local_size)
+
+    max_len = max([i[1].item() for i in all_sizes])
+
+    padded_tensor = torch.zeros((local_size[0], max_len, local_size[2]), device=tensor.device)
+    padded_tensor[:, : local_size[1]] = tensor
+
+    padded_mask = torch.zeros((local_size[0], max_len), device=mask.device)
+    padded_mask[:, : local_size[1]] = mask
+
+    gathered_tensors = dist.nn.all_gather(padded_tensor)
+    gathered_masks = dist.nn.all_gather(padded_mask)
+
+    gathered_tensors = torch.cat(gathered_tensors, dim=0)
+    gathered_masks = torch.cat(gathered_masks, dim=0)
+
+    return gathered_tensors, gathered_masks
+
+
+def _build_projection(embed_dim, hidden_dim, mode="mlp"):
+    if mode == "identity":
+        return nn.Identity()
+    if mode == "linear":
+        return nn.Linear(embed_dim, hidden_dim)
+    if mode == "mlp":
+        return nn.Sequential(
+            nn.Linear(embed_dim, hidden_dim),
+            nn.GELU(),
+            nn.Linear(hidden_dim, embed_dim),
+        )
+    raise ValueError(f"Unknown projection mode: {mode}")
+
+
+class GatedConfidenceShift(nn.Module):
+    def __init__(
+        self,
+        embed_dim=768,
+        gate_hidden_dim=768,
+        sem_hidden_dim=768,
+        gate_proj_mode="mlp",
+        sem_proj_mode="identity",
+        gate_log_temperature=2.302585,
+        shift_w_init=0.0,
+        shift_w_no_weight_decay=True,
+    ):
+        super().__init__()
+
+        self.text_gate_proj = _build_projection(embed_dim, gate_hidden_dim, gate_proj_mode)
+        self.vision_gate_proj = _build_projection(embed_dim, gate_hidden_dim, gate_proj_mode)
+        self.gate_log_temperature = nn.Parameter(torch.FloatTensor([gate_log_temperature]))
+
+        self.text_sem_proj = _build_projection(embed_dim, sem_hidden_dim, sem_proj_mode)
+        self.vision_sem_proj = _build_projection(embed_dim, sem_hidden_dim, sem_proj_mode)
+
+        self.shift_w = nn.Parameter(torch.tensor(float(shift_w_init)))
+        if shift_w_no_weight_decay:
+            self.shift_w._no_weight_decay = True
+
+    def _compute_gate_evidence(self, gate):
+        w = entmax_bisect(gate, alpha=2.0, n_iter=50, dim=-1)
+        return (w * gate).sum(dim=-1)
+
+    def forward(self, text_features, vision_tokens, need_attn_weights=False, repeat=True, **kwargs):
+        if repeat:
+            query = text_features.unsqueeze(0).expand(vision_tokens.shape[0], text_features.shape[0], -1)
+        else:
+            assert text_features.dim() == 3
+            query = text_features
+
+        text_gate = F.normalize(self.text_gate_proj(query), p=2, dim=-1)
+        vision_gate = F.normalize(self.vision_gate_proj(vision_tokens), p=2, dim=-1)
+        gate_scores = torch.bmm(text_gate, vision_gate.permute(0, 2, 1))
+
+        gate = torch.sigmoid(gate_scores * self.gate_log_temperature.exp())
+        weight = gate
+
+        gate_evidence = self._compute_gate_evidence(gate)
+        shift = self.shift_w * gate_evidence
+
+        sem_text_norm = F.normalize(self.text_sem_proj(query), p=2, dim=-1)
+        sem_patches_norm = F.normalize(self.vision_sem_proj(vision_tokens), p=2, dim=-1)
+
+        aggregated = torch.bmm(weight, sem_patches_norm)
+        aggregated_norm = F.normalize(aggregated, p=2, dim=-1)
+        cos_sim = (sem_text_norm * aggregated_norm).sum(dim=-1)
+        logits = (cos_sim + shift).T
+
+        if need_attn_weights:
+            sem_scores = torch.bmm(sem_text_norm, sem_patches_norm.permute(0, 2, 1))
+            if "loss_temperature" in kwargs:
+                scaled_sem = (sem_scores / kwargs["loss_temperature"]).sigmoid()
+            elif "logit_scale" in kwargs:
+                scaled_sem = (sem_scores * kwargs["logit_scale"] + kwargs.get("logit_bias", 0.0)).sigmoid()
+            else:
+                scaled_sem = sem_scores.sigmoid()
+
+            similarity_scores = [scaled_sem * weight]
+            _comp = {"gate": gate, "semantic": scaled_sem, "weight": weight}
+        else:
+            similarity_scores = None
+            _comp = None
+
+        return logits, similarity_scores, _comp
+
+
+def multi_positive_nce_loss(
+    logits,
+    group_map,
+    temperature,
+    additional_neg_mask=None,
+):
+    scaled_logits = torch.exp(logits / temperature)
+    np_total = group_map.size(0)
+    pos_logits = scaled_logits[torch.arange(np_total), group_map]
+
+    row_loss = get_row_loss(
+        scaled_logits[:np_total],
+        pos_logits,
+    )
+
+    neg_mask = torch.ones_like(scaled_logits)
+
+    if additional_neg_mask is not None:
+        neg_mask[-len(additional_neg_mask) :, :] = additional_neg_mask
+
+    neg_mask[torch.arange(np_total), group_map] = 0
+
+    column_loss = get_col_loss(
+        scaled_logits,
+        pos_logits,
+        neg_mask,
+        group_map,
+    )
+
+    losses = {}
+    losses["per_text_loss"] = row_loss.mean()
+    losses["per_image_loss"] = column_loss.mean()
+
+    losses["mpnce_loss"] = (losses["per_text_loss"] + losses["per_image_loss"]) / 2
+
+    losses["loss"] = losses["mpnce_loss"]
+
+    return losses
+
+
+def multi_positive_nce_local_loss(
+    logits_per_text,
+    logits_per_image,
+    group_map,
+    local_group_map,
+    local_group_map_per_image,
+    temperature,
+    local_rank,
+    B_local,
+    additional_neg_mask=None,
+):
+    logits_per_text = torch.exp(logits_per_text / temperature)
+    pos_logits_per_text = logits_per_text[torch.arange(logits_per_text.size(0)), local_group_map]
+
+    row_loss = get_row_loss(
+        logits_per_text,
+        pos_logits_per_text,
+    )
+
+    logits_per_image = torch.exp(logits_per_image / temperature)
+
+    local_start_idx = torch.where(group_map == local_rank * B_local)[0][0]
+    local_end_idx = local_start_idx + len(local_group_map_per_image)
+
+    pos_text_indices = torch.LongTensor(range(local_start_idx, local_end_idx))
+
+    pos_logits_per_image = logits_per_image[pos_text_indices, local_group_map_per_image]
+
+    neg_mask = torch.ones_like(logits_per_image)
+
+    if additional_neg_mask is not None:
+        neg_mask[-len(additional_neg_mask) :, :] = additional_neg_mask
+
+    neg_mask[pos_text_indices, local_group_map_per_image] = 0
+
+    column_loss = get_col_loss(
+        logits_per_image,
+        pos_logits_per_image,
+        neg_mask,
+        local_group_map_per_image,
+    )
+
+    losses = {}
+    losses["per_text_loss"] = row_loss.mean()
+    losses["per_image_loss"] = column_loss.mean()
+
+    losses["mpnce_loss"] = (losses["per_text_loss"] + losses["per_image_loss"]) / 2
+
+    losses["loss"] = losses["mpnce_loss"]
+    return losses
+
+
+def get_row_loss(
+    logits: torch.Tensor,
+    pos_logits: torch.Tensor,
+    eps: float = 1e-8,
+):
+    row_sum_logits = logits.sum(dim=1)
+    p_row = pos_logits / (row_sum_logits + eps)
+
+    return -torch.log(p_row + eps)
+
+
+def get_col_loss(
+    logits: torch.Tensor,
+    pos_logits: torch.Tensor,
+    neg_mask: torch.Tensor,
+    group_map: torch.Tensor,
+    eps: float = 1e-8,
+):
+    neg_logits = logits * neg_mask
+    sum_neg_logits = neg_logits.sum(dim=0)
+    sum_neg_logits = sum_neg_logits[group_map]
+    p_column = pos_logits / (pos_logits + sum_neg_logits + eps)
+
+    return -torch.log(p_column + eps)
+
+
+class LossFunctions(nn.Module):
+    def __init__(self, loss_cfg, **kwargs):
+        super().__init__()
+        self.loss_ratio = dict()
+        self.loss_dict = nn.ModuleDict()
+        for loss_type, ratio in zip(loss_cfg["apply"], loss_cfg["ratio"]):
+            logger.info(f"Build {loss_type} loss function ...")
+            if loss_cfg[loss_type] is None:
+                loss_cfg[loss_type] = dict()
+            if torch.distributed.is_available() and torch.distributed.is_initialized():
+                loss_cfg[loss_type]["rank"] = torch.distributed.get_rank()
+                loss_cfg[loss_type]["world_size"] = torch.distributed.get_world_size()
+            self.loss_dict[loss_type] = LOSS_REGISTRY[loss_type](**loss_cfg[loss_type])
+            self.loss_ratio[loss_type] = ratio
+
+
+class AlignmentLoss(nn.Module):
+    def __init__(
+        self,
+        vl_logit_type="gate_confidence_shift",
+        vl_logit_kwargs={},
+        loss_type="mpnce",
+        loss_kwargs={},
+        local_loss=False,
+        **kwargs,
+    ):
+        super().__init__()
+
+        self.vl_logit_type = vl_logit_type
+        self.init_vl_logit_kwargs(vl_logit_kwargs)
+
+        self.loss_type = loss_type
+        self.init_loss_kwargs(loss_kwargs)
+
+        self.local_loss = local_loss
+
+    def forward(
+        self,
+        enc_sents,
+        vision_tokens,
+        forward_text_model=None,
+        ddp_gather=True,
+        need_attn_weights=False,
+        compute_loss=True,
+        text_feats_list=None,
+        neg_enc_sents=None,
+        neg_text_feats_list=None,
+        **kwargs,
+    ):
+        outputs = {}
+
+        B_local = len(vision_tokens)
+        local_rank = dist.get_rank() if (ddp_gather and dist.is_initialized()) else 0
+
+        text_features, group_map, local_text_features, local_group_map = self.get_text_features(
+            enc_sents, forward_text_model, ddp_gather, text_feats_list
+        )
+
+        local_vision_tokens = vision_tokens
+        if ddp_gather and dist.is_initialized():
+            vision_tokens = torch.cat(dist.nn.all_gather(vision_tokens), dim=0)
+
+        if not compute_loss:
+            logits_outputs = self.compute_logits(text_features, vision_tokens, need_attn_weights)
+
+            logits = logits_outputs["logits"]
+
+            if self.loss_type == "mpnce":
+                logits = logits / self.loss_temperature.exp()
+
+            logits_outputs["logits"] = logits
+            outputs.update(logits_outputs)
+            return outputs
+
+        if neg_enc_sents or neg_text_feats_list:
+            hidden_dim = vision_tokens.shape[-1]
+
+            (
+                neg_text_features,
+                neg_text_mask,
+                local_neg_text_features,
+                local_neg_text_mask,
+            ) = self.get_neg_text_features(
+                neg_enc_sents,
+                forward_text_model,
+                hidden_dim,
+                ddp_gather,
+                neg_text_feats_list,
+                device=vision_tokens.device,
+            )
+        else:
+            (
+                neg_text_features,
+                neg_text_mask,
+                local_neg_text_features,
+                local_neg_text_mask,
+            ) = (None, None, None, None)
+
+        if not (self.local_loss and self.training):
+            logits_outputs = self.compute_logits(text_features, vision_tokens, need_attn_weights)
+            logits = logits_outputs["logits"]
+
+            if neg_text_features is not None:
+                neg_logits_outputs = self.compute_logits(
+                    neg_text_features, vision_tokens, need_attn_weights, repeat=False
+                )
+
+                logits = torch.cat([logits, neg_logits_outputs["logits"]], dim=0)
+
+            outputs["logits"] = logits
+
+            losses = self.compute_loss(
+                logits,
+                group_map,
+                additional_neg_mask=neg_text_mask,
+                text_features=text_features,
+            )
+
+        else:
+            logits_outputs_per_text = self.compute_logits(local_text_features, vision_tokens, need_attn_weights)
+            logits_per_text = logits_outputs_per_text["logits"]
+            outputs["logits_per_text"] = logits_per_text
+
+            logits_outputs_per_image = self.compute_logits(text_features, local_vision_tokens, need_attn_weights)
+            logits_per_image = logits_outputs_per_image["logits"]
+
+            if local_neg_text_features is not None:
+                neg_logits_outputs = self.compute_logits(
+                    local_neg_text_features,
+                    local_vision_tokens,
+                    need_attn_weights,
+                    repeat=False,
+                )
+
+                logits_per_image = torch.cat([logits_per_image, neg_logits_outputs["logits"]], dim=0)
+
+            outputs["logits_per_image"] = logits_per_image
+            local_group_map_per_image = local_group_map - local_rank * B_local
+
+            losses = self.compute_local_loss(
+                logits_per_text,
+                logits_per_image,
+                group_map,
+                local_group_map,
+                local_group_map_per_image,
+                local_rank,
+                B_local,
+                additional_neg_mask=local_neg_text_mask,
+                text_features=text_features,
+            )
+
+        outputs["losses"] = losses
+        return outputs
+
+    def get_feats_list(self, text_feats_list, ddp_gather=True):
+        text_features = list()
+        group_list = list()
+
+        B_local = sum([len(i) for i in text_feats_list])
+        local_rank = dist.get_rank() if (ddp_gather and dist.is_initialized()) else 0
+
+        local_index = 0
+        for i, features in enumerate(text_feats_list):
+            for j, feats in enumerate(features):
+                text_features.append(feats)
+                group_list.extend([local_index + local_rank * B_local] * feats.size(0))
+                local_index += 1
+
+        return text_features, group_list
+
+    def get_text_features(
+        self,
+        enc_sents,
+        forward_text_model,
+        ddp_gather=True,
+        text_feats_list=None,
+    ):
+        if text_feats_list is not None:
+            text_features, group_list = self.get_feats_list(text_feats_list, ddp_gather)
+        else:
+            text_features = list()
+            group_list = list()
+
+            B_local = len(enc_sents)
+            local_rank = dist.get_rank() if (ddp_gather and dist.is_initialized()) else 0
+
+            for i, kp in enumerate(enc_sents):
+                feats = forward_text_model(kp)
+
+                feat = feats["text_features"]
+                text_features.append(feat)
+
+                global_index = i + local_rank * B_local
+                group_list.extend([global_index] * feat.size(0))
+
+        text_features = torch.cat(text_features, dim=0)
+        group_map = torch.tensor(group_list, device=text_features.device)
+
+        local_text_features = text_features
+        local_group_map = group_map
+
+        if ddp_gather and dist.is_initialized():
+            text_features = pad_and_gather(text_features)
+
+            group_map = pad_and_gather(group_map)
+            group_map = group_map.long()
+
+        return text_features, group_map, local_text_features, local_group_map
+
+    def get_neg_feats_list(self, neg_text_feats_list, hidden_dim=None, device=None):
+        text_features = list()
+        for i, features in enumerate(neg_text_feats_list):
+            for j, feats in enumerate(features):
+                text_features.append(feats)
+
+        return text_features
+
+    def get_neg_text_features(
+        self,
+        neg_enc_sents,
+        forward_text_model,
+        hidden_dim,
+        ddp_gather=True,
+        neg_text_feats_list=None,
+        device=None,
+    ):
+        if neg_text_feats_list is not None:
+            neg_feats_list = self.get_neg_feats_list(neg_text_feats_list, hidden_dim, device)
+        else:
+            neg_feats_list = list()
+
+            for i, kp in enumerate(neg_enc_sents):
+                if isinstance(kp["input_ids"], torch.Tensor):
+                    feats = forward_text_model(kp)
+                    feat = feats["text_features"]
+                else:
+                    feat = torch.zeros((0, hidden_dim)).to(device)
+
+                neg_feats_list.append(feat)
+
+        neg_text_features = torch.nn.utils.rnn.pad_sequence(neg_feats_list, batch_first=True, padding_value=0)
+
+        neg_text_mask = torch.zeros(
+            len(neg_feats_list),
+            neg_text_features.size(1),
+            dtype=torch.bool,
+            device=neg_text_features.device,
+        )
+        for i, feat in enumerate(neg_feats_list):
+            neg_text_mask[i, : feat.size(0)] = True
+
+        local_neg_text_features = neg_text_features
+        local_neg_text_mask = neg_text_mask
+
+        if ddp_gather and dist.is_initialized():
+            neg_text_features, neg_text_mask = pad_and_gather_2nd_dim(neg_text_features, neg_text_mask)
+
+        local_neg_text_mask = local_neg_text_mask.T
+        neg_text_mask = neg_text_mask.T
+
+        return (
+            neg_text_features,
+            neg_text_mask,
+            local_neg_text_features,
+            local_neg_text_mask,
+        )
+
+    def init_vl_logit_kwargs(self, vl_logit_kwargs):
+        self.vl_logit_kwargs = {}
+        if self.vl_logit_type != "gate_confidence_shift":
+            raise NotImplementedError(f"Unsupported vl_logit_type: {self.vl_logit_type}")
+        vl_logit_kwargs.pop("attn_temperature", None)
+        self.sga = GatedConfidenceShift(**vl_logit_kwargs)
+
+    def compute_logits(self, text_features, vision_tokens, need_attn_weights, repeat=True):
+        outputs = {}
+
+        sim_scale_kwargs = {}
+        if hasattr(self, "loss_temperature"):
+            sim_scale_kwargs["loss_temperature"] = self.loss_temperature.exp()
+
+        logits, attn_weights_list, sim_components = self.sga(
+            text_features,
+            vision_tokens,
+            need_attn_weights,
+            repeat=repeat,
+            **sim_scale_kwargs,
+            **self.vl_logit_kwargs,
+        )
+        outputs["logits"] = logits
+        outputs["attn_weights"] = attn_weights_list
+        if sim_components is not None:
+            outputs["sim_components"] = sim_components
+        return outputs
+
+    def init_loss_kwargs(self, loss_kwargs):
+        self.loss_kwargs = {}
+        if self.loss_type != "mpnce":
+            raise NotImplementedError(f"Unsupported loss_type: {self.loss_type}")
+        self.loss_temperature = nn.Parameter(torch.FloatTensor([np.log(loss_kwargs["temperature"])]))
+
+    def compute_loss(self, logits, group_map, additional_neg_mask=None, **kwargs):
+        return multi_positive_nce_loss(
+            logits,
+            group_map,
+            additional_neg_mask=additional_neg_mask,
+            temperature=self.loss_temperature.exp(),
+            **self.loss_kwargs,
+        )
+
+    def compute_local_loss(
+        self,
+        logits_per_text,
+        logits_per_image,
+        group_map,
+        local_group_map,
+        local_group_map_per_image,
+        local_rank,
+        B_local,
+        additional_neg_mask=None,
+        **kwargs,
+    ):
+        return multi_positive_nce_local_loss(
+            logits_per_text,
+            logits_per_image,
+            group_map,
+            local_group_map,
+            local_group_map_per_image,
+            temperature=self.loss_temperature.exp(),
+            local_rank=local_rank,
+            B_local=B_local,
+            additional_neg_mask=additional_neg_mask,
+            **self.loss_kwargs,
+        )
+
+
+LOSS_REGISTRY = {"AlignmentLoss": AlignmentLoss, "DFRLoss": DFRLoss}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7accd30f33da4e7880dbf39876e076c04355c579bec0af07776f5ac344cef296
+size 1814192788

modeling_glint.py

@@ -0,0 +1,386 @@
+import logging
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .align_layers import build_align_layer
+from .common_layers import BasePreTrainedModel
+from .configuration_glint import GLINTConfig
+from .losses import LossFunctions
+from .text_encoders import build_text_encoder
+from .vision_encoders import build_vision_encoder
+
+logger = logging.getLogger("glint")
+
+
+class GLINTModel(BasePreTrainedModel):
+    config_class = GLINTConfig
+    # only relevant when vision_only=True drops these submodules; in VLM mode every key loads
+    _keys_to_ignore_on_load_unexpected = [
+        r"^text_model\.",
+        r"^align_layer\.",
+        r"^loss_fns\.",
+        r"^dfr_module\.",
+        r"^vision_layer_norm\.",
+        r"^text_layer_norm\.",
+    ]
+
+    def build_vision_model(self, config):
+        vision_config = config.vision_config
+        vision_model = build_vision_encoder(vision_config)
+
+        return vision_model
+
+    def build_text_model(self, config):
+        text_config = config.text_config
+        text_model = build_text_encoder(text_config)
+
+        return text_model
+
+    def build_align_layer_model(self, config):
+        align_layer_config = config.align_layer_config
+        align_layer = build_align_layer(align_layer_config)
+
+        return align_layer
+
+    def build_dfr_module(self, config):
+        from .dfr import DFRModule
+
+        dfr_module = DFRModule(config.dfr_config)
+        return dfr_module
+
+    def __init__(self, config: GLINTConfig):
+        super().__init__(config)
+
+        logger.info("Build vision model ...")
+        self.vision_model = self.build_vision_model(config)
+
+        if getattr(config, "vision_only", False):
+            logger.info("vision_only=True: skipping text model, align layer, losses and DFR module.")
+            return
+
+        logger.info("Build text model ...")
+        self.text_model = self.build_text_model(config)
+
+        logger.info("Build align layer model ...")
+        self.align_layer = self.build_align_layer_model(config)
+
+        logger.info("Build loss functions ...")
+        loss_cfg = config.kwargs["loss"]
+
+        self.loss_fns = LossFunctions(loss_cfg)
+
+        if hasattr(config, "dfr_config") and config.dfr_config is not None:
+            logger.info("Build dfr module ...")
+            self.dfr_module = self.build_dfr_module(config)
+
+        self.vision_layer_norm = (
+            nn.LayerNorm(config.align_layer_config.hidden_size) if config.use_vision_layer_norm else None
+        )
+        self.text_layer_norm = nn.LayerNorm(config.text_config.hidden_size) if config.use_text_layer_norm else None
+
+        self.compute_logits_type = config.kwargs.get("compute_logits_type")
+        self.module_to_update = config.kwargs.get("module_to_update")
+
+    def forward_vision_model(self, pixel_values, output_hidden_states=False):
+        vision_model_type = self.config.vision_config.model_type
+
+        vision_hidden_states = None
+
+        use_cls_token = getattr(self.config.vision_config, "use_cls_token", False)
+
+        if vision_model_type == "dinov2":
+            vision_outputs = self.vision_model(pixel_values, output_hidden_states=output_hidden_states)
+            vision_tokens = vision_outputs["last_hidden_state"]
+            if not use_cls_token:
+                vision_tokens = vision_tokens[:, 1:, :]
+            vision_hidden_states = vision_outputs.get("hidden_states", None)
+
+        elif vision_model_type == "dinov3_vit":
+            vision_outputs = self.vision_model(pixel_values, output_hidden_states=output_hidden_states)
+            original_output = vision_outputs["last_hidden_state"]
+            patch_tokens = original_output[:, 1 + self.config.vision_config.num_register_tokens :, :]
+            if use_cls_token:
+                cls_token = original_output[:, :1, :]
+                vision_tokens = torch.cat([cls_token, patch_tokens], dim=1)
+            else:
+                vision_tokens = patch_tokens
+
+            vision_hidden_states = vision_outputs.get("hidden_states", None)
+
+        elif vision_model_type == "vjepa2":
+            vision_outputs = self.vision_model(
+                pixel_values, skip_predictor=True, output_hidden_states=output_hidden_states
+            )
+            vision_tokens = vision_outputs["last_hidden_state"]
+            vision_hidden_states = vision_outputs.get("hidden_states", None)
+        elif vision_model_type == "vjepa2.1":
+            vision_outputs = self.vision_model(pixel_values, output_hidden_states=output_hidden_states)
+            vision_tokens = vision_outputs["last_hidden_state"]
+            vision_hidden_states = vision_outputs.get("hidden_states", None)
+        else:
+            raise NotImplementedError
+
+        outputs = {}
+
+        vision_tokens = self.align_layer(vision_tokens)
+
+        if self.vision_layer_norm is not None:
+            vision_tokens = self.vision_layer_norm(vision_tokens)
+
+        outputs["vision_tokens"] = vision_tokens
+        outputs["vision_hidden_states"] = vision_hidden_states
+
+        return outputs
+
+    def forward_text_model(self, encoded_input):
+        text_outputs = {}
+
+        text_model_type = self.config.text_config.model_type
+        if text_model_type == "mpnet":
+            model_output = self.text_model(encoded_input["input_ids"], attention_mask=encoded_input["attention_mask"])
+            token_embeddings = model_output[0]
+
+        else:
+            raise NotImplementedError
+
+        if self.config.text_config.use_cls_token:
+            text_features = token_embeddings[:, 0, :]
+        else:
+            input_mask_expanded = (
+                encoded_input["attention_mask"].unsqueeze(-1).expand(token_embeddings.size()).to(token_embeddings.dtype)
+            )
+            text_features = torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
+                input_mask_expanded.sum(1), min=1e-9
+            )
+
+        if self.text_layer_norm is not None:
+            text_features = self.text_layer_norm(text_features)
+
+        text_outputs["text_features"] = text_features
+        text_outputs["text_features_norm"] = F.normalize(text_features, p=2, dim=1)
+
+        return text_outputs
+
+    def forward(
+        self,
+        pixel_values=None,
+        enc_sents=None,
+        neg_enc_sents=None,
+        return_loss=True,
+        forward_accum_outputs=False,
+        forward_accum_loss=False,
+        **kwargs,
+    ):
+        if forward_accum_outputs:
+            return self.forward_accum_outputs(
+                pixel_values=pixel_values,
+                enc_sents=enc_sents,
+                neg_enc_sents=neg_enc_sents,
+                **kwargs,
+            )
+
+        if forward_accum_loss:
+            return self.forward_accum_loss(**kwargs)
+
+        if getattr(self.config, "vision_only", False):
+            return self.vision_model(pixel_values)
+
+        outputs = {}
+        use_dfr_module = hasattr(self, "dfr_module") and self.dfr_module is not None
+        vision_outputs = self.forward_vision_model(pixel_values, output_hidden_states=use_dfr_module)
+
+        if use_dfr_module:
+            dfr_outputs = self.dfr_module(
+                pixel_values=pixel_values,
+                student_hidden_states=vision_outputs["vision_hidden_states"],
+                cached_teacher_features=kwargs.get("cached_teacher_features"),
+            )
+            outputs.update(dfr_outputs)
+
+        outputs.update(vision_outputs)
+
+        if return_loss:
+            loss = 0
+            losses = {}
+
+            for loss_type, loss_fn in self.loss_fns.loss_dict.items():
+                loss_ratio = self.loss_fns.loss_ratio[loss_type]
+
+                if loss_fn.__class__.__name__ == "AlignmentLoss":
+                    loss_outputs = loss_fn(
+                        enc_sents=enc_sents,
+                        neg_enc_sents=neg_enc_sents,
+                        vision_tokens=outputs["vision_tokens"],
+                        forward_text_model=self.forward_text_model,
+                    )
+                    _losses = loss_outputs["losses"]
+                    loop_loss = _losses.pop("loss")
+                    losses.update(_losses)
+
+                elif loss_fn.__class__.__name__ == "DFRLoss":
+                    loss_outputs = loss_fn(
+                        proj_student_features=outputs["proj_student_features"],
+                        teacher_features=outputs["teacher_features"],
+                    )
+                    _losses = loss_outputs["losses"]
+                    loop_loss = _losses.pop("loss")
+                    losses.update(_losses)
+
+                else:
+                    raise NotImplementedError
+
+                loss += loop_loss * loss_ratio
+
+            losses["loss"] = loss
+
+            outputs["loss"] = loss
+            outputs["losses"] = losses
+
+        return outputs
+
+    def compute_logits(
+        self,
+        pixel_values,
+        enc_sents,
+        **kwargs,
+    ):
+        vision_outputs = self.forward_vision_model(pixel_values)
+
+        outputs = {}
+
+        if self.compute_logits_type == "vl_alignment":
+            loss_fn = self.loss_fns.loss_dict["AlignmentLoss"]
+
+            splited_sentences = [
+                {
+                    "input_ids": enc_sents[0]["input_ids"][i : i + 1],
+                    "attention_mask": enc_sents[0]["attention_mask"][i : i + 1],
+                }
+                for i in range(enc_sents[0]["input_ids"].size(0))
+            ]
+
+            loss_outputs = loss_fn(
+                splited_sentences,
+                vision_outputs["vision_tokens"],
+                self.forward_text_model,
+                ddp_gather=False,
+                need_attn_weights=True,
+                compute_loss=False,
+            )
+            outputs.update(loss_outputs)
+
+            similarity_scores = loss_outputs["attn_weights"][0]
+            if getattr(self.config.vision_config, "use_cls_token", False):
+                similarity_scores = similarity_scores[:, :, 1:]
+            outputs["similarity_scores"] = similarity_scores
+
+            if "sim_components" in loss_outputs:
+                comp = loss_outputs["sim_components"]
+                if getattr(self.config.vision_config, "use_cls_token", False):
+                    comp = {k: v[:, :, 1:] for k, v in comp.items()}
+                outputs["sim_components"] = comp
+
+            logits = loss_outputs["logits"]
+            logits = logits.T
+
+        else:
+            raise NotImplementedError
+
+        outputs["logits"] = logits
+        return outputs
+
+    def forward_accum_outputs(
+        self,
+        pixel_values,
+        enc_sents=None,
+        neg_enc_sents=None,
+        **kwargs,
+    ):
+        use_dfr_module = hasattr(self, "dfr_module") and self.dfr_module is not None
+
+        outputs = {}
+
+        vision_outputs = self.forward_vision_model(pixel_values, output_hidden_states=use_dfr_module)
+        outputs.update(vision_outputs)
+
+        if use_dfr_module:
+            dfr_outputs = self.dfr_module(
+                pixel_values=pixel_values,
+                student_hidden_states=vision_outputs["vision_hidden_states"],
+                cached_teacher_features=kwargs.get("cached_teacher_features"),
+            )
+            outputs.update(dfr_outputs)
+
+        text_outputs = []
+        for kp in enc_sents:
+            text_outputs.append(self.forward_text_model(kp)["text_features"])
+
+        text_outputs = {"text_features": text_outputs}
+        outputs.update(text_outputs)
+
+        if neg_enc_sents is not None:
+            negative_text_outputs = []
+            for kp in neg_enc_sents:
+                if isinstance(kp["input_ids"], torch.Tensor):
+                    negative_text_outputs.append(self.forward_text_model(kp)["text_features"])
+                else:
+                    negative_text_outputs.append(
+                        torch.zeros((0, self.config.text_config.hidden_size)).to(pixel_values.device)
+                    )
+
+            negative_text_outputs = {"negative_text_features": negative_text_outputs}
+            outputs.update(negative_text_outputs)
+
+        if "ids_mask" in kwargs:
+            outputs["ids_mask"] = kwargs["ids_mask"]
+            outputs["mask"] = kwargs["mask"]
+
+        return outputs
+
+    def forward_accum_loss(
+        self,
+        vision_tokens,
+        text_features,
+        negative_text_features=None,
+        **kwargs,
+    ):
+        outputs = {}
+        losses = {}
+        loss = 0
+
+        for loss_type, loss_fn in self.loss_fns.loss_dict.items():
+            loss_ratio = self.loss_fns.loss_ratio[loss_type]
+
+            if loss_fn.__class__.__name__ == "AlignmentLoss":
+                loss_outputs = loss_fn(
+                    enc_sents=None,
+                    neg_enc_sents=None,
+                    vision_tokens=vision_tokens,
+                    forward_text_model=None,
+                    text_feats_list=text_features,
+                    neg_text_feats_list=negative_text_features,
+                )
+                _losses = loss_outputs["losses"]
+                loop_loss = _losses.pop("loss")
+                losses.update(_losses)
+            elif loss_fn.__class__.__name__ == "DFRLoss":
+                loss_outputs = loss_fn(
+                    proj_student_features=kwargs.get("proj_student_features"),
+                    teacher_features=kwargs.get("teacher_features"),
+                )
+                _losses = loss_outputs["losses"]
+                loop_loss = _losses.pop("loss")
+                losses.update(_losses)
+            else:
+                raise NotImplementedError
+
+            loss += loop_loss * loss_ratio
+
+        losses["loss"] = loss
+
+        outputs["loss"] = loss
+        outputs["losses"] = losses
+
+        return outputs

preprocessor_config.json

@@ -0,0 +1,31 @@
+{
+  "crop_size": null,
+  "data_format": "channels_first",
+  "default_to_square": true,
+  "device": null,
+  "disable_grouping": null,
+  "do_center_crop": null,
+  "do_convert_rgb": null,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "DINOv3ViTImageProcessorFast",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "input_data_format": null,
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "return_tensors": null,
+  "size": {
+    "height": 1024,
+    "width": 1024
+  }
+}

text_encoders.py

@@ -0,0 +1,10 @@
+from transformers import MPNetConfig, MPNetModel
+
+
+def build_text_encoder(config):
+    if config.model_type == "mpnet":
+        fields = config.to_dict()
+        fields.pop("model_type", None)
+        return MPNetModel(MPNetConfig(**fields))
+    else:
+        raise NotImplementedError(f"Unsupported text model_type: {config.model_type}")

tokenizer_config.json

@@ -0,0 +1,72 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}

utils.py

@@ -0,0 +1,128 @@
+"""Inference helpers for the GLINT-CXR vision-language model.
+
+These utilities mirror the zero-shot grounding / phrase-similarity pipeline used
+to evaluate GLINT on chest X-rays.  ``model.compute_logits`` already removes the
+CLS token from the returned similarity map (the model was trained with
+``use_cls_token=False``), so the helpers here only reshape and interpolate.
+"""
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from PIL import Image
+
+
+@torch.no_grad()
+def model_inference(image, text, model, image_processor, tokenizer):
+    """Return (similarity_prob, similarity_map) for one image and one phrase.
+
+    similarity_prob : scalar sigmoid probability that the phrase applies to the image.
+    similarity_map  : per-pixel similarity map in [0, 1] (H x W), upsampled to the
+                      original image resolution.
+    """
+    image = load_image(image)
+    (width, height) = image.size
+    image_size = (height, width)
+
+    image_processor_outputs = image_processor(image)
+    processed_image = torch.FloatTensor(np.array(image_processor_outputs["pixel_values"])).to(model.device)
+
+    tokenized_text = tokenizer(
+        text,
+        padding=True,
+        truncation=True,
+        return_tensors="pt",
+    ).to(model.device)
+
+    output = model.compute_logits(processed_image, [tokenized_text])
+
+    logits = output["logits"]
+    similarity_prob = logits.sigmoid()
+
+    similarity_scores = output["similarity_scores"]
+    similarity_scores = similarity_scores.view(-1)
+    similarity_scores = interpolate_similarity_scores(similarity_scores, image_size, image_processor)
+    similarity_map = similarity_scores[0]
+
+    return similarity_prob, similarity_map
+
+
+def save_similarity_map(image, similarity_map, save_path, threshold=0.0, beta=0.5):
+    """Save a JET heat-map of ``similarity_map`` overlaid on ``image`` (path or PIL.Image) as a PNG."""
+    import cv2
+
+    img_rgb = np.array(load_image(image))
+    sim = similarity_map.detach().float().cpu().numpy() if torch.is_tensor(similarity_map) else similarity_map
+    sim = cv2.resize(sim.astype(np.float32), (img_rgb.shape[1], img_rgb.shape[0]), interpolation=cv2.INTER_LINEAR)
+    sim = (np.clip(sim, threshold, 1.0) - threshold) / (1.0 - threshold + 1e-8)
+    sim_u8 = (sim * 255).astype(np.uint8)
+
+    heatmap = cv2.cvtColor(cv2.applyColorMap(sim_u8, cv2.COLORMAP_JET), cv2.COLOR_BGR2RGB)
+    Image.fromarray(cv2.addWeighted(img_rgb, 1 - beta, heatmap, beta, 0)).save(save_path)
+
+
+@torch.no_grad()
+def model_inference_multiple_text(image, text_list, model, image_processor, tokenizer):
+    probs, similarity_maps = [], []
+    for text in text_list:
+        prob, similarity_map = model_inference(image, text, model, image_processor, tokenizer)
+        probs.append(prob)
+        similarity_maps.append(similarity_map)
+
+    return torch.stack(probs), torch.stack(similarity_maps)
+
+
+def interpolate_similarity_scores(similarity_scores, origin_size, image_processor):
+    (height, width) = origin_size
+    patch_size = int(similarity_scores.shape[-1] ** 0.5)
+    scores = similarity_scores.view(1, 1, patch_size, patch_size)
+
+    interpolated_scores = F.interpolate(
+        scores,
+        size=(height, width),
+        mode="bilinear",
+        align_corners=False,
+    )
+    interpolated_scores = interpolated_scores.squeeze(1)
+    return interpolated_scores
+
+
+def dicom_to_pil_image(input_file_path):
+    """Extract a grayscale image from a DICOM file and return it as a PIL.Image."""
+    import cv2
+    import pydicom
+
+    dcm_file = pydicom.dcmread(input_file_path)
+    raw_image = dcm_file.pixel_array
+
+    if raw_image.ndim != 2 or raw_image.size == 0:
+        raise ValueError(f"Expecting a non-empty single-channel DICOM image, got shape {raw_image.shape}")
+
+    raw_image = raw_image - raw_image.min()
+    rescaled_image = (raw_image / raw_image.max() * 255).astype(np.uint8)
+
+    if dcm_file.PhotometricInterpretation == "MONOCHROME1":
+        rescaled_image = cv2.bitwise_not(rescaled_image)
+
+    return Image.fromarray(cv2.equalizeHist(rescaled_image))
+
+
+def load_image(image):
+    """Load an image from a path (.png/.jpg/.jpeg/.dcm) or a PIL.Image object."""
+    if isinstance(image, str):
+        if image.lower().endswith(".dcm"):
+            image = dicom_to_pil_image(image)
+        elif image.lower().endswith((".png", ".jpg", ".jpeg")):
+            image = Image.open(image)
+        else:
+            raise ValueError(f"Invalid image type: {image}")
+    elif not isinstance(image, Image.Image):
+        raise ValueError(f"Invalid image type: {type(image)}")
+
+    if image.size[0] <= 0 or image.size[1] <= 0:
+        raise ValueError(f"Image has invalid size (W: {image.size[0]}, H: {image.size[1]}).")
+
+    # per-image min-max stretch to 8 bit, as in the zero-shot evaluation (also handles 16-bit inputs)
+    array = np.asarray(image, dtype=np.float32)
+    array = (array - array.min()) / max(float(array.max() - array.min()), 1e-8) * 255.0
+    return Image.fromarray(np.rint(array).astype(np.uint8)).convert("RGB")

vision_encoders.py

@@ -0,0 +1,26 @@
+import torch.nn as nn
+from transformers import Dinov2Config, Dinov2Model, DINOv3ViTConfig, DINOv3ViTModel, VJEPA2Config, VJEPA2Model
+
+
+def _hf_config(config_class, config):
+    fields = config.to_dict()
+    fields.pop("model_type", None)
+    return config_class(**fields)
+
+
+def build_vision_encoder(config):
+    if config.model_type == "dinov2":
+        model = Dinov2Model(_hf_config(Dinov2Config, config))
+        nn.init.trunc_normal_(model.embeddings.mask_token, std=0.02)
+
+    elif config.model_type == "dinov3_vit":
+        model = DINOv3ViTModel(_hf_config(DINOv3ViTConfig, config))
+        nn.init.trunc_normal_(model.embeddings.mask_token, std=0.02)
+
+    elif config.model_type == "vjepa2":
+        model = VJEPA2Model(_hf_config(VJEPA2Config, config))
+
+    else:
+        raise NotImplementedError()
+
+    return model