Release GLINT-CT

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+   Copyright [yyyy] [name of copyright owner]
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+   Licensed under the Apache License, Version 2.0 (the "License");
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+
+   Unless required by applicable law or agreed to in writing, software
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+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

LICENSES/MIT.txt

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) Meta Platforms, Inc. and affiliates.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -0,0 +1,135 @@
+---
+library_name: transformers
+license: cc-by-nc-4.0
+tags:
+- medical
+- radiology
+- vision-language
+- chest-ct
+- 3d-medical-imaging
+- glint
+- custom_code
+- arxiv:2606.03180
+datasets:
+- ibrahimhamamci/CT-RATE
+---
+
+# GLINT-CT
+
+<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-CXR">GLINT-CXR</a>
+</p>
+
+GLINT-CT is the chest CT model of **GLINT** (*GLINT: Sparsely Gated Vision-Language Alignment for Fine-Grained
+Radiology Representations*, NeurIPS 2026).
+
+- **Vision encoder:** V-JEPA 2.1 ViT-L/16, 384×384, 80 frames / 240 slices
+- **Text encoder:** `sentence-transformers/all-mpnet-base-v2` (fine-tuned)
+- **Alignment:** linear projection (1024→768) + LayerNorm, mean-pooled text + LayerNorm, SGA similarity head
+- **Training:** CT-RATE, with Sparsely Gated Alignment (SGA) and Dense Feature Regularization (DFR) from a frozen
+  V-JEPA 2.1 ViT-G 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-CT --local-dir glint-ct && cd glint-ct
+python inference.py --volume volume.nii.gz --text "There is a lung nodule."
+# prints the sentence probability and the 18 CT-RATE findings,
+# saves similarity_map.nii.gz and ct_preprocessed.nii.gz
+```
+
+Both files share one affine, so the map overlays the CT in ITK-SNAP / 3D Slicer.
+
+### AutoModel
+
+```python
+import torch
+from transformers import AutoModel, AutoTokenizer
+from utils import load_ct_volume, model_inference, save_similarity_map, similarity_scores_to_volume   # bundled helpers
+
+repo = "Deepnoid/GLINT-CT"
+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)
+
+pixel_values, affine = load_ct_volume("volume.nii.gz", return_affine=True)   # [1, 80, 3, 384, 384]
+prob, scores = model_inference(pixel_values, "There is a lung nodule.", model=model, tokenizer=tokenizer)
+volume = similarity_scores_to_volume(scores, model)                          # (240, 384, 384)
+save_similarity_map(volume, affine, "similarity_map.nii.gz", pixel_values, "ct_preprocessed.nii.gz")
+```
+
+- **Outputs:** `prob` is the sentence probability (sigmoid); `scores` is the per-token similarity in [0, 1]
+  (40 × 24 × 24 tokens).
+- **Inputs:** `utils.load_ct_volume` applies the training preprocessing (IPL, HU [-1000, 1000] → [-1, 1],
+  1.5 × 0.75 × 0.75 mm, crop/pad to 240×480×480, resize to 240×384×384) and returns `[1, 80, 3, 384, 384]`.
+- **Unreliable NIfTI affine:** pass `raw_spacing=(sx, sy, sz)` to `load_ct_volume` (`--raw_spacing` in the script).
+- **Underlying call:** `model.compute_logits(pixel_values, [encoded_text])` returns `logits` and `similarity_scores`.
+
+Vision encoder only (`vision_only=True`):
+
+```python
+import torch
+from transformers import AutoModel
+from utils import load_ct_volume   # bundled helper
+
+repo = "Deepnoid/GLINT-CT"
+vision = AutoModel.from_pretrained(repo, trust_remote_code=True, vision_only=True, dtype=torch.float32).eval()
+
+pixel_values = load_ct_volume("volume.nii.gz")   # [1, 80, 3, 384, 384]
+with torch.no_grad():
+    out = vision(pixel_values)
+print(out.last_hidden_state.shape)               # (1, 23040, 1024)
+```
+
+## Requirements
+
+`transformers==4.56.1`, `torch`, `entmax`, `monai`, `nibabel`.
+
+> ⚠️ Keep `transformers==4.56.1`, the version the bundled code was validated with.
+
+## 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 V-JEPA 2.1 ViT-G 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
+
+- **Weights and code:** [CC BY-NC 4.0](LICENSE).
+- **Vision encoder:** fine-tuned from V-JEPA 2.1 ViT-L (© Meta Platforms, Inc., MIT); `vjepa21_encoder.py` adapts
+  [facebookresearch/vjepa2](https://github.com/facebookresearch/vjepa2) (MIT, `LICENSES/MIT.txt`).
+- **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 +1,408 @@
+{
+  "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": [
+        44
+      ],
+      "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": 2048,
+      "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": 1664,
+      "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"
+    ],
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+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
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+    "id2label": {
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+    "intermediate_size": 3072,
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+    "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,
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+    "num_hidden_layers": 12,
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+    "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": "",
+    "add_cross_attention": false,
+    "arch": "vjepa2_1_vit_large_384",
+    "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": 1024,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "img_size": 384,
+    "input_modality": 3,
+    "input_slices": 240,
+    "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": "vjepa2.1",
+    "no_repeat_ngram_size": 0,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_channels": 3,
+    "num_frames": 80,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "prefix": null,
+    "pretrained_checkpoint": 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,
+    "use_hierarchical": false
+  },
+  "vision_only": false
+}

configuration_glint.py

@@ -0,0 +1,154 @@
+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 == "vjepa2.1":
+            from .vjepa21_encoder import ARCH_MAP, VIT_EMBED_DIMS
+
+            arch = vision_config.get("arch", "vjepa2_1_vit_large_384")
+            factory_name = ARCH_MAP[arch][0].__name__
+            embed_dim = VIT_EMBED_DIMS.get(factory_name, 1024)
+            use_hierarchical = vision_config.get("use_hierarchical", False)
+            n_levels = ARCH_MAP[arch][1]
+            vision_config["hidden_size"] = embed_dim * n_levels if use_hierarchical else embed_dim
+        elif 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 +1,16 @@
+# Data splits (chest CT)
+
+- Split ID lists; download each dataset from its source under its own terms.
+- `val` is for model selection; `test` is official unless noted.
+
+| Dataset | Files | ID format | Train / Val / Test | Notes |
+|---|---|---|---|---|
+| LIDC-IDRI | `splits/lidc_idri/` | `PatientID/StudyUID/SeriesUID` | 705 / 148 / 157 | Patient-disjoint |
+| RSNA-PE | `splits/rsna_pe/` | `StudyUID/SeriesUID` | 4,983 / 1,062 / 1,060 | Study-level (Ke et al., MIDL 2024) |
+| MSD-Lung | `splits/msd_lung/` | MSD case id | 44 / 6 / 13 | Holdout of 63 cases |
+| NSCLC-Radiomics | `splits/nsclc_radiomics/` | `LUNG1-XXX` | 294 / 42 / 84 | LUNG1-035/128 excluded (corrupt) |
+| CT-RATE | `splits/ct_rate/` | volume name | 43,400 / 2,994 / 3,002 | `test` = official validation (1,304 patients); `val` = 1,304 patients from the official train |
+
+CT-RATE notes (the lists already exclude CT-RATE's non-chest volumes and 3 unreadable ones):
+- **Pretraining:** also excludes `pretrain_exclusions.txt` (ReXGroundingCT overlap) and volumes with an empty report decomposition → 43,337 / 2,990 / 3,002.
+- **Fine-tuning:** also excludes 11 unusable volumes (`ft/chest_ct/*/dataset/ct_rate.py`) → 43,390 / 2,993 / 3,002.

data/splits/ct_rate/pretrain_exclusions.txt

@@ -0,0 +1,53 @@
+train_1267_a_4
+train_11755_a_3
+train_11755_a_4
+train_13082_a_1
+train_13591_a_1
+train_13035_a_1
+train_13177_a_2
+train_13301_a_2
+train_13583_d_2
+train_13113_a_1
+train_13256_b_2
+train_13119_c_1
+train_13492_b_2
+train_13155_a_2
+train_13417_d_1
+train_13076_a_1
+train_13102_a_1
+train_13111_a_2
+train_13075_a_2
+train_13440_a_1
+train_13624_a_2
+train_13426_a_2
+train_13013_a_1
+train_13471_a_1
+train_13141_a_1
+train_13479_j_1
+train_12991_a_1
+train_13288_a_2
+train_13447_a_1
+train_13577_a_2
+train_13430_a_2
+train_13497_a_1
+train_13292_a_2
+train_13431_a_1
+train_13020_a_2
+train_13278_b_2
+train_13252_b_2
+train_13098_a_2
+train_12994_a_1
+train_13572_a_2
+train_13166_a_2
+train_13479_e_1
+train_13481_a_2
+train_13092_a_2
+train_13631_a_1
+train_13398_a_1
+train_13189_a_1
+train_13284_a_2
+train_13148_a_2
+train_13249_b_2
+train_13444_a_2
+train_13316_a_2
+train_13339_a_1

data/splits/ct_rate/test.txt

@@ -0,0 +1,3002 @@
+valid_1_a_1
+valid_1_a_2
+valid_2_a_1
+valid_2_a_2
+valid_3_a_1
+valid_3_a_2
+valid_4_a_1
+valid_4_a_2
+valid_4_b_1
+valid_4_b_2
+valid_5_a_1
+valid_5_a_2
+valid_6_a_1
+valid_6_a_2
+valid_7_a_1
+valid_7_a_2
+valid_7_b_1
+valid_7_b_2
+valid_8_a_1
+valid_8_a_2
+valid_9_a_1
+valid_9_a_2
+valid_10_a_1
+valid_10_a_2
+valid_11_a_1
+valid_11_a_2
+valid_12_a_1
+valid_12_a_2
+valid_13_a_1
+valid_13_a_2
+valid_14_a_1
+valid_14_a_2
+valid_15_a_1
+valid_15_a_2
+valid_16_a_1
+valid_16_a_2
+valid_17_a_1
+valid_17_a_2
+valid_18_a_1
+valid_18_a_2
+valid_19_a_1
+valid_20_a_1
+valid_20_a_2
+valid_20_b_1
+valid_20_b_2
+valid_21_a_1
+valid_21_a_2
+valid_22_a_1
+valid_22_a_2
+valid_23_a_1
+valid_23_a_2
+valid_24_a_1
+valid_24_a_2
+valid_25_a_1
+valid_25_a_2
+valid_26_a_1
+valid_26_a_2
+valid_27_a_1
+valid_27_a_2
+valid_27_b_1
+valid_27_b_2
+valid_28_a_1
+valid_28_a_2
+valid_29_a_1
+valid_29_a_2
+valid_30_a_1
+valid_31_a_1
+valid_31_a_2
+valid_32_a_1
+valid_33_a_1
+valid_33_a_2
+valid_34_a_1
+valid_34_a_2
+valid_35_a_1
+valid_35_a_2
+valid_36_a_1
+valid_37_a_1
+valid_38_a_1
+valid_38_a_2
+valid_39_a_1
+valid_39_a_2
+valid_40_a_1
+valid_40_a_2
+valid_41_a_1
+valid_41_a_2
+valid_41_b_1
+valid_41_b_2
+valid_42_a_1
+valid_43_a_1
+valid_43_a_2
+valid_44_a_1
+valid_44_a_2
+valid_45_a_1
+valid_45_a_2
+valid_46_a_1
+valid_46_a_2
+valid_47_a_1
+valid_47_a_2
+valid_48_a_1
+valid_48_a_2
+valid_49_a_1
+valid_49_a_2
+valid_50_a_1
+valid_50_a_2
+valid_51_a_1
+valid_51_a_2
+valid_52_a_1
+valid_52_a_2
+valid_53_a_1
+valid_53_a_2
+valid_53_b_1
+valid_53_b_2
+valid_54_a_1
+valid_54_a_2
+valid_55_a_1
+valid_55_a_2
+valid_56_a_1
+valid_56_a_2
+valid_57_a_1
+valid_57_a_2
+valid_58_a_1
+valid_58_a_2
+valid_59_a_1
+valid_59_a_2
+valid_60_a_1
+valid_60_a_2
+valid_60_b_1
+valid_60_b_2
+valid_60_c_1
+valid_60_c_2
+valid_61_a_1
+valid_61_a_2
+valid_62_a_1
+valid_62_a_2
+valid_62_b_1
+valid_62_b_2
+valid_62_c_1
+valid_62_c_2
+valid_63_a_1
+valid_63_a_2
+valid_63_b_1
+valid_63_b_2
+valid_64_a_1
+valid_64_a_2
+valid_64_a_3
+valid_64_b_1
+valid_64_b_2
+valid_64_c_1
+valid_64_c_2
+valid_65_a_1
+valid_66_a_1
+valid_67_a_1
+valid_68_a_1
+valid_69_a_1
+valid_69_a_2
+valid_70_a_1
+valid_70_a_2
+valid_71_a_1
+valid_71_a_2
+valid_71_b_1
+valid_71_b_2
+valid_72_a_1
+valid_72_a_2
+valid_73_a_1
+valid_73_a_2
+valid_74_a_1
+valid_74_a_2
+valid_75_a_1
+valid_75_a_2
+valid_76_a_1
+valid_77_a_1
+valid_77_a_2
+valid_78_a_1
+valid_78_a_2
+valid_79_a_1
+valid_79_a_2
+valid_80_a_1
+valid_80_a_2
+valid_81_a_1
+valid_81_a_2
+valid_82_a_1
+valid_82_a_2
+valid_83_a_1
+valid_83_a_2
+valid_84_a_1
+valid_84_a_2
+valid_85_a_1
+valid_85_a_2
+valid_86_a_1
+valid_86_a_2
+valid_87_a_1
+valid_87_a_2
+valid_88_a_1
+valid_88_a_2
+valid_89_a_1
+valid_89_a_2
+valid_90_a_1
+valid_90_a_2
+valid_90_b_1
+valid_90_b_2
+valid_90_c_1
+valid_90_c_2
+valid_91_a_1
+valid_91_a_2
+valid_92_a_1
+valid_92_a_2
+valid_93_a_1
+valid_93_a_2
+valid_94_a_1
+valid_94_a_2
+valid_95_a_1
+valid_95_a_2
+valid_96_a_1
+valid_96_a_2
+valid_96_b_1
+valid_96_b_2
+valid_97_a_1
+valid_97_a_2
+valid_98_a_1
+valid_98_a_2
+valid_99_a_1
+valid_99_a_2
+valid_100_a_1
+valid_100_a_2
+valid_101_a_1
+valid_101_b_1
+valid_101_b_2
+valid_102_a_1
+valid_102_a_2
+valid_103_a_1
+valid_103_a_2
+valid_104_a_1
+valid_104_a_2
+valid_105_a_1
+valid_105_a_2
+valid_106_a_1
+valid_106_a_2
+valid_106_a_3
+valid_106_a_4
+valid_107_a_1
+valid_107_a_2
+valid_107_b_1
+valid_107_b_2
+valid_108_a_1
+valid_108_a_2
+valid_109_a_3
+valid_109_a_5
+valid_110_a_1
+valid_111_a_1
+valid_111_a_2
+valid_112_a_1
+valid_112_a_2
+valid_113_a_1
+valid_113_a_2
+valid_114_a_1
+valid_114_a_2
+valid_114_b_1
+valid_114_b_2
+valid_115_a_1
+valid_115_a_2
+valid_116_a_1
+valid_116_a_2
+valid_117_a_1
+valid_117_a_2
+valid_117_b_1
+valid_117_b_2
+valid_118_a_1
+valid_118_a_2
+valid_118_b_1
+valid_118_b_2
+valid_118_c_1
+valid_118_c_2
+valid_118_d_1
+valid_118_d_2
+valid_119_a_1
+valid_119_a_2
+valid_120_a_1
+valid_120_a_2
+valid_121_a_1
+valid_121_a_2
+valid_122_a_1
+valid_122_a_2
+valid_123_a_1
+valid_123_a_2
+valid_124_a_1
+valid_124_a_2
+valid_125_a_1
+valid_125_a_2
+valid_126_a_1
+valid_126_a_2
+valid_127_a_1
+valid_127_a_2
+valid_128_a_1
+valid_128_a_2
+valid_129_a_1
+valid_130_a_1
+valid_130_a_2
+valid_131_a_1
+valid_131_a_2
+valid_132_a_1
+valid_132_a_2
+valid_133_a_1
+valid_133_a_2
+valid_134_a_1
+valid_134_a_2
+valid_135_a_1
+valid_135_a_2
+valid_136_a_1
+valid_136_a_2
+valid_137_a_1
+valid_137_a_2
+valid_138_a_1
+valid_138_a_2
+valid_139_a_1
+valid_139_a_2
+valid_140_a_1
+valid_140_a_2
+valid_141_a_1
+valid_141_a_2
+valid_142_a_1
+valid_143_a_1
+valid_143_a_2
+valid_144_a_1
+valid_145_a_1
+valid_145_a_2
+valid_146_a_1
+valid_146_a_2
+valid_147_a_1
+valid_147_a_2
+valid_148_a_1
+valid_148_a_2
+valid_149_a_1
+valid_149_a_2
+valid_150_a_1
+valid_150_a_2
+valid_151_a_1
+valid_151_a_2
+valid_151_b_1
+valid_151_b_2
+valid_151_c_1
+valid_151_c_2
+valid_152_a_1
+valid_153_a_1
+valid_153_a_2
+valid_154_a_1
+valid_154_a_2
+valid_155_a_1
+valid_155_a_2
+valid_156_a_1
+valid_156_a_2
+valid_157_a_1
+valid_157_a_2
+valid_158_a_1
+valid_158_a_2
+valid_159_a_1
+valid_159_a_2
+valid_160_a_1
+valid_160_a_2
+valid_161_a_1
+valid_161_a_2
+valid_162_a_1
+valid_162_a_2
+valid_162_b_1
+valid_162_b_2
+valid_163_a_1
+valid_163_a_2
+valid_163_b_1
+valid_163_b_2
+valid_163_c_1
+valid_163_c_2
+valid_163_d_1
+valid_163_d_2
+valid_164_a_1
+valid_164_a_2
+valid_165_a_1
+valid_165_a_2
+valid_166_a_1
+valid_166_a_2
+valid_167_a_1
+valid_167_a_2
+valid_168_a_1
+valid_168_a_2
+valid_168_b_1
+valid_168_b_2
+valid_169_a_1
+valid_169_a_2
+valid_170_a_1
+valid_170_a_2
+valid_170_b_1
+valid_170_b_2
+valid_171_a_1
+valid_171_a_2
+valid_172_a_1
+valid_172_a_2
+valid_173_a_1
+valid_173_a_2
+valid_174_a_1
+valid_174_a_2
+valid_175_a_1
+valid_175_a_2
+valid_176_a_1
+valid_176_a_2
+valid_177_a_1
+valid_177_a_2
+valid_178_a_1
+valid_178_a_2
+valid_179_a_1
+valid_179_a_2
+valid_180_a_1
+valid_180_a_2
+valid_180_b_1
+valid_180_b_2
+valid_181_a_1
+valid_181_a_2
+valid_182_a_1
+valid_182_a_2
+valid_183_a_1
+valid_183_a_2
+valid_184_a_1
+valid_184_a_2
+valid_185_a_1
+valid_185_a_2
+valid_186_a_1
+valid_186_a_2
+valid_187_a_1
+valid_187_a_2
+valid_188_a_1
+valid_188_a_2
+valid_188_b_1
+valid_188_b_2
+valid_189_a_1
+valid_189_a_2
+valid_190_a_1
+valid_190_a_2
+valid_191_a_1
+valid_191_a_2
+valid_191_b_1
+valid_191_b_2
+valid_192_a_1
+valid_192_a_2
+valid_193_a_1
+valid_193_a_2
+valid_194_a_1
+valid_194_a_2
+valid_194_b_1
+valid_194_b_2
+valid_195_a_1
+valid_195_a_2
+valid_196_a_1
+valid_196_a_2
+valid_197_a_1
+valid_197_a_2
+valid_198_a_1
+valid_198_a_2
+valid_199_a_1
+valid_199_a_2
+valid_200_a_1
+valid_200_a_2
+valid_201_a_1
+valid_201_a_2
+valid_202_a_1
+valid_202_a_2
+valid_202_b_1
+valid_202_b_2
+valid_203_a_1
+valid_203_a_2
+valid_204_a_1
+valid_204_a_2
+valid_205_a_1
+valid_205_a_2
+valid_206_a_1
+valid_206_a_2
+valid_207_a_1
+valid_207_a_2
+valid_208_a_3
+valid_208_a_5
+valid_209_a_1
+valid_209_a_2
+valid_210_a_1
+valid_210_a_2
+valid_210_b_1
+valid_211_a_1
+valid_211_a_2
+valid_212_a_1
+valid_212_a_2
+valid_212_b_1
+valid_212_b_2
+valid_213_a_1
+valid_213_a_2
+valid_214_a_1
+valid_214_a_2
+valid_215_a_1
+valid_215_a_2
+valid_215_b_1
+valid_215_b_2
+valid_215_c_1
+valid_215_c_2
+valid_216_a_1
+valid_216_a_2
+valid_217_a_1
+valid_217_a_2
+valid_218_a_1
+valid_218_a_2
+valid_219_a_1
+valid_219_a_2
+valid_220_a_1
+valid_220_a_2
+valid_221_a_1
+valid_221_a_2
+valid_222_a_1
+valid_222_a_2
+valid_223_a_1
+valid_223_a_2
+valid_224_a_1
+valid_224_a_2
+valid_225_a_1
+valid_225_a_2
+valid_225_b_1
+valid_225_b_2
+valid_226_a_1
+valid_226_a_2
+valid_227_a_1
+valid_227_a_2
+valid_228_a_1
+valid_229_a_1
+valid_229_a_2
+valid_229_b_1
+valid_229_b_2
+valid_230_a_1
+valid_230_a_2
+valid_231_a_1
+valid_231_a_2
+valid_232_a_1
+valid_232_a_2
+valid_233_a_1
+valid_234_a_1
+valid_234_a_2
+valid_235_a_1
+valid_236_a_1
+valid_236_a_2
+valid_237_a_1
+valid_237_a_2
+valid_238_a_1
+valid_239_a_1
+valid_239_a_2
+valid_240_a_1
+valid_240_a_2
+valid_241_a_1
+valid_241_a_2
+valid_242_a_1
+valid_242_a_2
+valid_243_a_1
+valid_243_a_2
+valid_244_a_1
+valid_244_a_2
+valid_245_a_1
+valid_245_a_2
+valid_246_a_1
+valid_246_a_2
+valid_247_a_1
+valid_247_a_2
+valid_248_a_1
+valid_248_a_2
+valid_249_a_1
+valid_249_a_2
+valid_250_a_1
+valid_250_a_2
+valid_250_b_1
+valid_250_b_2
+valid_250_c_1
+valid_250_c_2
+valid_250_d_1
+valid_250_d_2
+valid_250_e_1
+valid_250_e_2
+valid_250_f_1
+valid_250_f_2
+valid_251_a_1
+valid_251_a_2
+valid_252_a_1
+valid_252_a_2
+valid_253_a_1
+valid_254_a_1
+valid_254_a_2
+valid_255_a_1
+valid_255_a_2
+valid_256_a_1
+valid_256_a_2
+valid_257_a_3
+valid_257_a_5
+valid_258_a_1
+valid_258_a_2
+valid_258_b_1
+valid_258_b_2
+valid_258_c_1
+valid_258_d_1
+valid_258_d_2
+valid_259_a_1
+valid_259_a_2
+valid_260_a_1
+valid_260_a_2
+valid_261_a_1
+valid_261_a_2
+valid_262_a_1
+valid_262_a_2
+valid_263_a_1
+valid_263_a_2
+valid_263_b_1
+valid_263_b_2
+valid_264_a_1
+valid_264_a_2
+valid_265_a_1
+valid_265_a_2
+valid_266_a_1
+valid_266_a_2
+valid_267_a_1
+valid_267_a_2
+valid_268_a_1
+valid_268_a_2
+valid_269_a_1
+valid_270_a_1
+valid_270_a_2
+valid_271_a_1
+valid_271_a_2
+valid_272_a_1
+valid_273_a_1
+valid_274_a_1
+valid_274_b_1
+valid_274_b_2
+valid_274_c_1
+valid_274_c_2
+valid_275_a_1
+valid_275_a_2
+valid_276_a_1
+valid_276_a_2
+valid_277_a_1
+valid_277_a_2
+valid_277_b_1
+valid_277_b_2
+valid_277_c_1
+valid_277_c_2
+valid_277_d_1
+valid_277_d_2
+valid_277_e_1
+valid_277_e_2
+valid_277_f_1
+valid_277_f_2
+valid_277_g_1
+valid_277_g_2
+valid_278_a_1
+valid_278_a_2
+valid_278_b_1
+valid_278_b_2
+valid_279_a_1
+valid_279_a_2
+valid_280_a_1
+valid_280_a_2
+valid_281_a_1
+valid_281_a_2
+valid_282_a_1
+valid_282_a_2
+valid_283_a_1
+valid_284_a_1
+valid_284_a_2
+valid_285_a_1
+valid_285_a_2
+valid_286_a_1
+valid_286_a_2
+valid_287_a_1
+valid_287_a_2
+valid_288_a_1
+valid_288_a_2
+valid_289_a_1
+valid_289_a_2
+valid_290_a_1
+valid_290_a_2
+valid_291_a_1
+valid_291_a_2
+valid_292_a_1
+valid_293_a_1
+valid_293_a_2
+valid_294_a_1
+valid_294_a_2
+valid_295_a_1
+valid_295_a_2
+valid_296_a_1
+valid_297_a_1
+valid_298_a_1
+valid_298_a_2
+valid_299_a_1
+valid_299_a_2
+valid_300_a_1
+valid_300_a_2
+valid_301_a_1
+valid_301_a_2
+valid_302_a_1
+valid_302_a_2
+valid_303_a_1
+valid_303_a_2
+valid_304_a_1
+valid_304_a_2
+valid_305_a_1
+valid_305_a_2
+valid_306_a_1
+valid_306_a_2
+valid_307_a_1
+valid_307_a_2
+valid_308_a_1
+valid_308_a_2
+valid_309_a_1
+valid_309_a_2
+valid_310_a_1
+valid_310_a_2
+valid_311_a_1
+valid_312_a_1
+valid_312_a_2
+valid_313_a_1
+valid_313_a_2
+valid_314_a_1
+valid_314_a_2
+valid_315_a_1
+valid_315_a_2
+valid_316_a_1
+valid_316_a_2
+valid_317_a_1
+valid_317_a_2
+valid_318_a_1
+valid_318_a_2
+valid_319_a_1
+valid_319_a_2
+valid_320_a_1
+valid_320_a_2
+valid_321_a_1
+valid_321_a_2
+valid_322_a_1
+valid_322_a_2
+valid_323_a_1
+valid_323_a_2
+valid_324_a_1
+valid_324_a_2
+valid_325_a_1
+valid_325_a_2
+valid_326_a_1
+valid_326_a_2
+valid_327_a_1
+valid_327_a_2
+valid_328_a_1
+valid_328_a_2
+valid_329_a_1
+valid_329_a_2
+valid_330_a_1
+valid_330_a_2
+valid_331_a_1
+valid_331_a_2
+valid_331_b_1
+valid_331_b_2
+valid_331_c_1
+valid_331_c_2
+valid_331_d_1
+valid_331_d_2
+valid_331_e_1
+valid_331_e_2
+valid_332_a_1
+valid_332_a_2
+valid_332_b_1
+valid_332_b_2
+valid_333_a_1
+valid_333_a_2
+valid_333_b_1
+valid_333_b_2
+valid_334_a_1
+valid_334_a_2
+valid_334_a_3
+valid_335_a_1
+valid_335_a_2
+valid_336_a_1
+valid_336_a_2
+valid_337_a_1
+valid_337_a_2
+valid_338_a_1
+valid_338_a_2
+valid_339_a_1
+valid_339_a_2
+valid_340_a_3
+valid_340_a_5
+valid_341_a_1
+valid_341_a_2
+valid_342_a_1
+valid_342_a_2
+valid_343_a_1
+valid_343_a_2
+valid_344_a_1
+valid_344_a_2
+valid_345_a_1
+valid_345_a_2
+valid_346_a_1
+valid_346_a_2
+valid_347_a_1
+valid_347_a_2
+valid_348_a_1
+valid_348_a_2
+valid_349_a_1
+valid_349_a_2
+valid_350_a_1
+valid_350_a_2
+valid_351_a_1
+valid_351_a_2
+valid_351_b_1
+valid_351_b_2
+valid_352_a_1
+valid_352_a_2
+valid_353_a_1
+valid_353_a_2
+valid_354_a_1
+valid_354_a_2
+valid_355_a_1
+valid_355_a_2
+valid_355_b_1
+valid_355_b_2
+valid_356_a_1
+valid_356_a_2
+valid_357_a_1
+valid_357_a_2
+valid_357_b_1
+valid_357_b_2
+valid_357_c_1
+valid_357_c_2
+valid_357_d_1
+valid_357_d_2
+valid_357_e_1
+valid_357_e_2
+valid_358_a_1
+valid_359_a_1
+valid_359_a_2
+valid_360_a_1
+valid_360_a_2
+valid_361_a_1
+valid_361_a_2
+valid_362_a_1
+valid_362_a_2
+valid_362_b_1
+valid_362_b_2
+valid_363_a_1
+valid_363_a_2
+valid_364_a_1
+valid_364_a_2
+valid_365_a_3
+valid_365_a_5
+valid_366_a_1
+valid_366_a_2
+valid_367_a_1
+valid_367_a_2
+valid_368_a_1
+valid_368_a_2
+valid_369_a_1
+valid_369_a_2
+valid_370_a_1
+valid_370_a_2
+valid_371_a_1
+valid_371_a_2
+valid_372_a_1
+valid_373_a_1
+valid_373_a_2
+valid_373_b_1
+valid_373_b_2
+valid_374_a_1
+valid_374_a_2
+valid_375_a_1
+valid_376_a_1
+valid_376_a_2
+valid_377_a_1
+valid_377_a_2
+valid_378_a_1
+valid_378_a_2
+valid_379_a_1
+valid_379_a_2
+valid_380_a_1
+valid_380_a_2
+valid_381_a_1
+valid_381_a_2
+valid_382_a_1
+valid_382_a_2
+valid_382_b_1
+valid_382_b_2
+valid_382_c_1
+valid_382_c_2
+valid_383_a_1
+valid_384_a_1
+valid_385_a_1
+valid_386_a_1
+valid_386_a_2
+valid_387_a_1
+valid_387_a_2
+valid_387_b_1
+valid_387_b_2
+valid_388_a_1
+valid_388_a_2
+valid_388_b_1
+valid_388_b_2
+valid_389_a_1
+valid_389_a_2
+valid_389_b_1
+valid_389_b_2
+valid_390_a_1
+valid_390_a_2
+valid_391_a_1
+valid_391_a_2
+valid_392_a_1
+valid_392_a_2
+valid_393_a_1
+valid_393_a_2
+valid_394_a_1
+valid_394_a_2
+valid_395_a_1
+valid_396_a_1
+valid_397_a_1
+valid_397_a_2
+valid_398_a_1
+valid_399_a_1
+valid_399_a_2
+valid_400_a_1
+valid_400_a_2
+valid_401_a_1
+valid_401_a_2
+valid_402_a_1
+valid_403_a_1
+valid_403_b_1
+valid_403_b_2
+valid_404_a_1
+valid_404_a_2
+valid_405_a_1
+valid_406_a_1
+valid_406_a_2
+valid_407_a_1
+valid_407_a_2
+valid_408_a_1
+valid_408_a_2
+valid_409_a_1
+valid_409_a_2
+valid_410_a_1
+valid_410_a_2
+valid_410_b_1
+valid_410_b_2
+valid_410_c_1
+valid_410_c_2
+valid_411_a_1
+valid_412_a_1
+valid_412_a_2
+valid_413_a_1
+valid_413_a_2
+valid_414_a_1
+valid_414_a_2
+valid_414_b_1
+valid_414_b_2
+valid_415_a_1
+valid_415_a_2
+valid_416_a_1
+valid_416_a_2
+valid_416_b_1
+valid_416_b_2
+valid_417_a_3
+valid_417_a_5
+valid_418_a_1
+valid_418_a_2
+valid_419_a_1
+valid_419_a_2
+valid_420_a_1
+valid_420_a_2
+valid_421_a_1
+valid_421_a_2
+valid_422_a_1
+valid_422_a_2
+valid_423_a_1
+valid_423_a_2
+valid_423_b_1
+valid_423_b_2
+valid_423_c_1
+valid_423_c_2
+valid_424_a_1
+valid_424_a_2
+valid_425_a_1
+valid_425_a_2
+valid_426_a_1
+valid_426_a_2
+valid_427_a_1
+valid_427_a_2
+valid_428_a_1
+valid_428_a_2
+valid_429_a_1
+valid_429_a_2
+valid_429_b_1
+valid_429_b_2
+valid_430_a_1
+valid_430_a_2
+valid_431_a_1
+valid_432_a_1
+valid_432_a_2
+valid_433_a_1
+valid_433_a_2
+valid_434_a_1
+valid_435_a_1
+valid_435_a_2
+valid_436_a_1
+valid_436_a_2
+valid_437_a_1
+valid_437_a_2
+valid_437_b_1
+valid_437_b_2
+valid_438_a_1
+valid_438_a_2
+valid_439_a_1
+valid_439_a_2
+valid_440_a_1
+valid_441_a_1
+valid_441_a_2
+valid_442_a_1
+valid_442_a_2
+valid_443_a_1
+valid_443_a_2
+valid_444_a_1
+valid_444_a_2
+valid_445_a_1
+valid_445_a_2
+valid_446_a_1
+valid_446_a_2
+valid_446_b_1
+valid_446_b_2
+valid_447_a_1
+valid_447_a_2
+valid_448_a_1
+valid_448_a_2
+valid_449_a_1
+valid_449_a_2
+valid_449_b_1
+valid_449_b_2
+valid_450_a_1
+valid_451_a_1
+valid_451_a_2
+valid_452_a_1
+valid_452_a_2
+valid_453_a_1
+valid_453_a_2
+valid_454_a_1
+valid_454_b_1
+valid_454_c_1
+valid_454_c_2
+valid_455_a_1
+valid_455_a_2
+valid_456_a_1
+valid_456_a_2
+valid_456_b_1
+valid_456_b_2
+valid_457_a_1
+valid_457_a_2
+valid_457_b_1
+valid_457_b_2
+valid_457_c_1
+valid_457_c_2
+valid_457_d_1
+valid_457_d_2
+valid_458_a_1
+valid_458_a_2
+valid_459_a_1
+valid_459_a_2
+valid_460_a_1
+valid_460_a_2
+valid_461_a_1
+valid_461_a_2
+valid_462_a_1
+valid_462_a_2
+valid_463_a_1
+valid_463_a_2
+valid_464_a_1
+valid_464_a_2
+valid_465_a_1
+valid_465_a_2
+valid_466_a_1
+valid_466_a_2
+valid_467_a_1
+valid_467_a_2
+valid_468_a_1
+valid_468_a_2
+valid_469_a_1
+valid_469_a_2
+valid_470_a_1
+valid_470_a_2
+valid_471_a_1
+valid_471_a_2
+valid_472_a_1
+valid_472_a_2
+valid_473_a_1
+valid_473_a_2
+valid_474_a_1
+valid_474_a_2
+valid_475_a_1
+valid_475_a_2
+valid_475_b_1
+valid_475_b_2
+valid_475_c_1
+valid_475_c_2
+valid_475_d_1
+valid_475_d_2
+valid_475_e_1
+valid_475_e_2
+valid_476_a_1
+valid_476_a_2
+valid_477_a_1
+valid_477_a_2
+valid_478_a_1
+valid_478_a_2
+valid_479_a_1
+valid_479_a_2
+valid_480_a_1
+valid_480_a_2
+valid_481_a_1
+valid_481_a_2
+valid_482_a_1
+valid_482_a_2
+valid_482_b_1
+valid_482_b_2
+valid_482_c_1
+valid_482_c_2
+valid_482_d_1
+valid_482_d_2
+valid_482_e_1
+valid_482_e_2
+valid_482_f_1
+valid_482_f_2
+valid_482_g_1
+valid_482_g_2
+valid_482_h_1
+valid_482_h_2
+valid_483_a_1
+valid_483_a_2
+valid_484_a_1
+valid_484_a_2
+valid_484_b_1
+valid_484_b_2
+valid_485_a_1
+valid_485_a_2
+valid_486_a_1
+valid_486_a_2
+valid_487_a_1
+valid_487_a_2
+valid_487_b_1
+valid_487_b_2
+valid_488_a_1
+valid_488_a_2
+valid_489_a_1
+valid_489_a_2
+valid_490_a_1
+valid_490_a_2
+valid_491_a_1
+valid_491_a_2
+valid_492_a_1
+valid_492_a_2
+valid_492_b_1
+valid_492_b_2
+valid_493_a_1
+valid_493_a_2
+valid_493_b_1
+valid_493_b_2
+valid_493_c_1
+valid_493_c_2
+valid_494_a_1
+valid_494_a_2
+valid_495_a_1
+valid_495_a_2
+valid_496_a_1
+valid_496_a_2
+valid_497_a_1
+valid_497_a_2
+valid_498_a_1
+valid_498_a_2
+valid_499_a_1
+valid_499_a_2
+valid_500_a_1
+valid_500_a_2
+valid_500_b_1
+valid_500_b_2
+valid_500_c_1
+valid_500_c_2
+valid_500_d_1
+valid_500_d_2
+valid_501_a_1
+valid_501_a_2
+valid_502_a_1
+valid_502_a_2
+valid_503_a_1
+valid_503_a_2
+valid_503_b_1
+valid_503_b_2
+valid_504_a_1
+valid_504_a_2
+valid_505_a_1
+valid_505_a_2
+valid_506_a_1
+valid_506_a_2
+valid_507_a_1
+valid_507_a_2
+valid_508_a_1
+valid_509_a_1
+valid_509_a_2
+valid_509_b_1
+valid_509_b_2
+valid_510_a_1
+valid_510_a_2
+valid_511_a_1
+valid_512_a_1
+valid_512_a_2
+valid_512_b_1
+valid_512_b_2
+valid_513_a_1
+valid_513_a_2
+valid_514_a_1
+valid_514_a_2
+valid_515_a_1
+valid_515_a_2
+valid_516_a_1
+valid_517_a_1
+valid_517_a_2
+valid_518_a_1
+valid_518_a_2
+valid_519_a_1
+valid_519_a_2
+valid_520_a_1
+valid_520_a_3
+valid_521_a_1
+valid_521_a_2
+valid_522_a_1
+valid_523_a_1
+valid_523_a_2
+valid_524_a_1
+valid_524_a_2
+valid_525_a_1
+valid_526_a_1
+valid_526_a_2
+valid_527_a_1
+valid_527_a_2
+valid_528_a_1
+valid_528_a_2
+valid_529_a_1
+valid_529_a_2
+valid_530_a_1
+valid_530_a_2
+valid_531_a_1
+valid_531_a_2
+valid_531_b_1
+valid_531_b_2
+valid_532_a_1
+valid_532_a_2
+valid_533_a_1
+valid_533_a_2
+valid_534_a_1
+valid_534_b_1
+valid_534_b_2
+valid_535_a_1
+valid_535_a_2
+valid_535_b_1
+valid_535_c_1
+valid_535_c_2
+valid_536_a_1
+valid_536_a_2
+valid_536_b_1
+valid_536_b_2
+valid_537_a_1
+valid_537_a_2
+valid_537_b_1
+valid_537_b_2
+valid_538_a_1
+valid_538_a_2
+valid_538_b_1
+valid_538_b_2
+valid_538_c_1
+valid_538_c_2
+valid_539_a_1
+valid_539_a_2
+valid_540_a_1
+valid_540_a_2
+valid_541_a_1
+valid_541_a_2
+valid_542_a_1
+valid_542_a_2
+valid_543_a_1
+valid_543_a_2
+valid_544_a_1
+valid_544_a_2
+valid_545_a_1
+valid_545_a_2
+valid_546_a_1
+valid_546_a_2
+valid_547_a_1
+valid_547_a_2
+valid_548_a_1
+valid_548_a_2
+valid_549_a_1
+valid_549_a_2
+valid_550_a_1
+valid_550_a_2
+valid_551_a_1
+valid_551_a_2
+valid_552_a_1
+valid_552_a_2
+valid_553_a_1
+valid_553_a_2
+valid_554_a_1
+valid_554_a_2
+valid_555_a_1
+valid_555_a_2
+valid_556_a_1
+valid_556_a_2
+valid_557_a_1
+valid_557_a_2
+valid_558_a_1
+valid_558_a_2
+valid_559_a_1
+valid_559_a_2
+valid_560_a_1
+valid_560_a_2
+valid_561_a_1
+valid_561_a_2
+valid_561_b_1
+valid_562_a_1
+valid_562_a_2
+valid_563_a_1
+valid_563_a_2
+valid_564_a_1
+valid_564_a_2
+valid_564_b_1
+valid_564_b_2
+valid_565_a_1
+valid_565_a_2
+valid_565_b_1
+valid_565_b_2
+valid_566_a_1
+valid_566_a_2
+valid_566_b_1
+valid_566_b_2
+valid_567_a_1
+valid_567_a_2
+valid_568_a_1
+valid_568_a_2
+valid_569_a_1
+valid_569_a_2
+valid_569_b_3
+valid_569_b_5
+valid_569_c_1
+valid_569_c_2
+valid_570_a_1
+valid_571_a_1
+valid_571_a_2
+valid_572_a_1
+valid_572_a_2
+valid_573_a_1
+valid_573_a_2
+valid_574_a_1
+valid_575_a_1
+valid_575_a_2
+valid_575_b_1
+valid_575_b_2
+valid_576_a_1
+valid_577_a_1
+valid_577_a_2
+valid_578_a_1
+valid_578_a_2
+valid_579_a_1
+valid_579_a_2
+valid_580_a_1
+valid_580_a_2
+valid_581_a_1
+valid_581_a_2
+valid_582_a_1
+valid_582_a_2
+valid_583_a_1
+valid_583_a_2
+valid_584_a_1
+valid_584_a_2
+valid_585_a_1
+valid_585_a_2
+valid_586_a_1
+valid_586_a_2
+valid_587_a_1
+valid_587_a_2
+valid_588_a_1
+valid_588_a_2
+valid_589_a_1
+valid_589_a_2
+valid_589_b_1
+valid_589_b_2
+valid_590_a_1
+valid_590_a_2
+valid_591_a_1
+valid_591_a_2
+valid_592_a_1
+valid_593_a_1
+valid_593_a_2
+valid_594_a_1
+valid_595_a_1
+valid_595_a_2
+valid_596_a_1
+valid_596_a_2
+valid_597_a_1
+valid_597_a_2
+valid_598_a_1
+valid_598_a_2
+valid_599_a_1
+valid_600_a_1
+valid_600_a_2
+valid_601_a_1
+valid_601_a_2
+valid_602_a_1
+valid_602_a_2
+valid_603_a_1
+valid_603_a_2
+valid_604_a_1
+valid_604_a_2
+valid_605_a_1
+valid_605_a_2
+valid_606_a_1
+valid_606_a_2
+valid_606_b_1
+valid_606_b_2
+valid_607_a_1
+valid_607_a_2
+valid_608_a_1
+valid_608_a_2
+valid_609_a_1
+valid_609_a_2
+valid_610_a_1
+valid_610_a_2
+valid_611_a_1
+valid_611_a_2
+valid_611_b_1
+valid_611_b_2
+valid_612_a_1
+valid_612_a_2
+valid_613_a_1
+valid_613_a_2
+valid_614_a_1
+valid_614_a_2
+valid_615_a_1
+valid_615_a_2
+valid_616_a_1
+valid_617_a_1
+valid_617_a_2
+valid_618_a_1
+valid_618_a_2
+valid_619_a_1
+valid_619_a_2
+valid_620_a_1
+valid_620_a_2
+valid_621_a_1
+valid_621_a_2
+valid_622_a_1
+valid_622_a_2
+valid_623_a_1
+valid_623_a_2
+valid_624_a_1
+valid_624_a_2
+valid_624_b_1
+valid_624_b_2
+valid_625_a_1
+valid_625_a_2
+valid_626_a_1
+valid_626_a_2
+valid_627_a_1
+valid_627_a_2
+valid_628_a_1
+valid_628_a_2
+valid_629_a_1
+valid_629_a_2
+valid_630_a_1
+valid_630_a_2
+valid_631_a_1
+valid_631_a_2
+valid_632_a_1
+valid_633_a_1
+valid_633_a_2
+valid_634_a_1
+valid_634_a_2
+valid_635_a_1
+valid_635_a_2
+valid_636_a_1
+valid_636_a_2
+valid_636_b_1
+valid_636_b_2
+valid_636_c_1
+valid_636_c_2
+valid_637_a_1
+valid_637_a_2
+valid_638_a_1
+valid_638_a_2
+valid_639_a_1
+valid_639_a_2
+valid_640_a_1
+valid_640_a_2
+valid_641_a_1
+valid_642_a_1
+valid_643_a_1
+valid_643_a_2
+valid_643_b_1
+valid_643_b_2
+valid_644_a_1
+valid_644_a_2
+valid_645_a_1
+valid_645_a_2
+valid_645_b_1
+valid_645_b_2
+valid_645_c_1
+valid_645_c_2
+valid_646_a_1
+valid_646_a_2
+valid_647_a_1
+valid_647_a_2
+valid_648_a_1
+valid_648_a_2
+valid_649_a_1
+valid_649_a_2
+valid_650_a_1
+valid_650_a_2
+valid_651_a_1
+valid_651_b_1
+valid_651_b_2
+valid_652_a_1
+valid_652_a_2
+valid_653_a_1
+valid_653_a_2
+valid_654_a_1
+valid_654_a_2
+valid_655_a_1
+valid_655_a_2
+valid_655_b_1
+valid_655_b_2
+valid_656_a_1
+valid_656_a_2
+valid_657_a_1
+valid_657_a_2
+valid_658_a_1
+valid_658_a_2
+valid_659_a_1
+valid_660_a_1
+valid_660_a_2
+valid_661_a_1
+valid_661_a_2
+valid_662_a_1
+valid_662_a_2
+valid_663_a_1
+valid_663_a_2
+valid_664_a_1
+valid_664_a_2
+valid_665_a_1
+valid_665_a_2
+valid_666_a_1
+valid_666_a_2
+valid_667_a_1
+valid_667_a_2
+valid_667_b_1
+valid_667_b_2
+valid_667_c_1
+valid_667_c_2
+valid_668_a_1
+valid_668_a_2
+valid_668_b_1
+valid_668_b_2
+valid_669_a_1
+valid_669_a_2
+valid_670_a_1
+valid_670_a_2
+valid_671_a_1
+valid_671_a_2
+valid_672_a_1
+valid_672_a_2
+valid_673_a_1
+valid_673_a_2
+valid_674_a_1
+valid_674_a_2
+valid_675_a_1
+valid_675_a_2
+valid_675_b_1
+valid_675_b_2
+valid_675_c_1
+valid_675_c_2
+valid_676_a_1
+valid_676_a_2
+valid_677_a_1
+valid_677_a_2
+valid_678_a_1
+valid_678_a_2
+valid_679_a_1
+valid_679_a_2
+valid_680_a_1
+valid_680_a_2
+valid_681_a_1
+valid_681_a_2
+valid_682_a_1
+valid_682_a_2
+valid_683_a_1
+valid_683_a_2
+valid_684_a_1
+valid_684_a_2
+valid_685_a_1
+valid_685_a_2
+valid_686_a_1
+valid_686_a_2
+valid_687_a_1
+valid_687_a_2
+valid_688_a_1
+valid_688_a_2
+valid_689_a_1
+valid_689_a_2
+valid_690_a_1
+valid_690_a_2
+valid_691_a_1
+valid_691_a_2
+valid_692_a_1
+valid_693_a_1
+valid_693_a_2
+valid_694_a_1
+valid_694_a_2
+valid_694_b_1
+valid_694_b_2
+valid_695_a_1
+valid_695_a_2
+valid_696_a_1
+valid_696_a_2
+valid_697_a_1
+valid_697_a_2
+valid_698_a_1
+valid_699_a_1
+valid_699_a_2
+valid_700_a_1
+valid_700_a_2
+valid_701_a_1
+valid_701_a_2
+valid_702_a_1
+valid_702_a_2
+valid_703_a_1
+valid_703_a_2
+valid_704_a_1
+valid_704_a_2
+valid_705_a_1
+valid_705_a_2
+valid_706_a_1
+valid_706_a_2
+valid_707_a_1
+valid_707_a_2
+valid_708_a_1
+valid_708_a_2
+valid_709_a_1
+valid_709_a_2
+valid_710_a_1
+valid_710_a_2
+valid_711_a_1
+valid_711_a_2
+valid_712_a_1
+valid_712_a_2
+valid_713_a_1
+valid_713_a_2
+valid_713_b_1
+valid_713_b_2
+valid_713_c_1
+valid_713_c_2
+valid_714_a_1
+valid_715_a_1
+valid_715_a_2
+valid_715_b_1
+valid_715_b_2
+valid_716_a_1
+valid_716_a_2
+valid_717_a_1
+valid_717_a_2
+valid_717_b_1
+valid_717_b_2
+valid_717_c_1
+valid_717_c_2
+valid_718_a_1
+valid_718_a_2
+valid_719_a_1
+valid_719_a_2
+valid_720_a_1
+valid_720_a_2
+valid_721_a_1
+valid_721_a_2
+valid_722_a_1
+valid_722_a_2
+valid_722_b_1
+valid_722_b_2
+valid_723_a_1
+valid_723_a_2
+valid_724_a_1
+valid_724_a_2
+valid_725_a_1
+valid_725_a_2
+valid_726_a_3
+valid_726_a_5
+valid_727_a_1
+valid_727_a_2
+valid_728_a_1
+valid_728_a_2
+valid_729_a_1
+valid_729_a_2
+valid_730_a_1
+valid_730_a_2
+valid_731_a_1
+valid_731_a_2
+valid_732_a_1
+valid_732_a_2
+valid_733_a_1
+valid_733_a_2
+valid_734_a_1
+valid_734_a_2
+valid_735_a_1
+valid_735_a_2
+valid_736_a_1
+valid_736_a_2
+valid_737_a_1
+valid_737_a_2
+valid_738_a_1
+valid_738_a_2
+valid_739_a_1
+valid_739_a_2
+valid_740_a_1
+valid_740_a_2
+valid_741_a_1
+valid_741_a_2
+valid_741_b_1
+valid_741_b_2
+valid_742_a_1
+valid_743_a_1
+valid_743_a_2
+valid_744_a_1
+valid_744_a_2
+valid_745_a_1
+valid_746_a_1
+valid_746_a_2
+valid_747_a_1
+valid_747_a_2
+valid_748_a_1
+valid_748_a_2
+valid_749_a_1
+valid_749_a_2
+valid_750_a_1
+valid_750_a_2
+valid_751_a_1
+valid_751_a_2
+valid_752_a_1
+valid_752_a_2
+valid_752_b_1
+valid_752_b_2
+valid_753_a_1
+valid_753_a_2
+valid_754_a_1
+valid_754_a_2
+valid_755_a_1
+valid_755_a_2
+valid_756_a_1
+valid_756_a_2
+valid_757_a_1
+valid_757_a_2
+valid_758_a_1
+valid_758_a_2
+valid_759_a_1
+valid_759_a_2
+valid_760_a_1
+valid_760_a_2
+valid_761_a_1
+valid_761_a_2
+valid_762_a_1
+valid_762_a_2
+valid_763_a_1
+valid_763_a_2
+valid_764_a_1
+valid_764_a_2
+valid_765_a_1
+valid_765_a_2
+valid_766_a_1
+valid_766_a_2
+valid_767_a_1
+valid_767_a_2
+valid_768_a_1
+valid_768_a_2
+valid_769_a_1
+valid_769_a_2
+valid_770_a_1
+valid_770_a_2
+valid_770_b_1
+valid_770_c_1
+valid_771_a_1
+valid_771_a_2
+valid_771_b_1
+valid_771_b_2
+valid_772_a_1
+valid_772_a_2
+valid_773_a_1
+valid_773_a_2
+valid_774_a_1
+valid_774_a_2
+valid_774_b_1
+valid_774_b_2
+valid_775_a_1
+valid_775_a_2
+valid_776_a_1
+valid_776_a_2
+valid_777_a_1
+valid_777_a_2
+valid_778_a_1
+valid_778_a_2
+valid_778_b_1
+valid_778_b_2
+valid_779_a_1
+valid_779_a_2
+valid_780_a_1
+valid_780_a_2
+valid_781_a_1
+valid_781_a_2
+valid_782_a_1
+valid_782_a_2
+valid_783_a_1
+valid_783_a_2
+valid_784_a_1
+valid_784_a_2
+valid_785_a_1
+valid_785_a_2
+valid_786_a_1
+valid_786_a_2
+valid_787_a_1
+valid_787_a_2
+valid_787_b_1
+valid_787_b_2
+valid_787_c_1
+valid_787_c_2
+valid_787_d_1
+valid_787_d_2
+valid_788_a_1
+valid_788_a_2
+valid_789_a_1
+valid_789_a_2
+valid_790_a_1
+valid_790_a_2
+valid_791_a_1
+valid_791_a_2
+valid_792_a_1
+valid_792_a_2
+valid_793_a_1
+valid_794_a_1
+valid_794_a_2
+valid_795_a_1
+valid_795_a_2
+valid_796_a_1
+valid_796_a_2
+valid_797_a_1
+valid_797_a_2
+valid_798_a_1
+valid_798_a_2
+valid_799_a_1
+valid_799_a_2
+valid_800_a_1
+valid_801_a_1
+valid_801_a_2
+valid_802_a_1
+valid_802_a_2
+valid_803_a_1
+valid_803_a_2
+valid_804_a_1
+valid_804_a_2
+valid_804_b_1
+valid_804_b_2
+valid_805_a_1
+valid_805_a_2
+valid_806_a_1
+valid_806_a_2
+valid_807_a_1
+valid_807_a_2
+valid_808_a_1
+valid_808_a_2
+valid_809_a_1
+valid_809_a_2
+valid_810_a_1
+valid_810_a_2
+valid_810_b_1
+valid_810_b_2
+valid_811_a_1
+valid_811_a_2
+valid_812_a_1
+valid_812_a_2
+valid_813_a_1
+valid_813_a_2
+valid_814_a_1
+valid_814_a_2
+valid_815_a_1
+valid_815_a_2
+valid_815_b_1
+valid_815_b_2
+valid_815_c_1
+valid_815_c_2
+valid_816_a_1
+valid_816_a_2
+valid_817_a_1
+valid_817_a_2
+valid_818_a_1
+valid_818_a_2
+valid_819_a_1
+valid_819_a_2
+valid_820_a_1
+valid_820_a_2
+valid_821_a_1
+valid_821_a_2
+valid_822_a_1
+valid_822_a_2
+valid_823_a_1
+valid_823_a_2
+valid_824_a_1
+valid_824_a_2
+valid_824_b_1
+valid_824_b_2
+valid_825_a_1
+valid_825_a_2
+valid_825_b_1
+valid_825_b_2
+valid_826_a_1
+valid_826_a_2
+valid_827_a_1
+valid_827_a_2
+valid_828_a_1
+valid_828_a_2
+valid_829_a_1
+valid_829_a_2
+valid_829_b_1
+valid_829_b_2
+valid_830_a_1
+valid_830_a_2
+valid_830_b_1
+valid_830_b_2
+valid_830_c_1
+valid_830_c_2
+valid_831_a_1
+valid_831_a_2
+valid_832_a_1
+valid_832_a_2
+valid_832_b_1
+valid_832_b_2
+valid_833_a_1
+valid_833_a_2
+valid_834_a_1
+valid_834_a_2
+valid_835_a_1
+valid_835_a_2
+valid_836_a_1
+valid_836_a_2
+valid_837_a_1
+valid_837_a_2
+valid_838_a_1
+valid_838_a_2
+valid_839_a_1
+valid_839_a_2
+valid_840_a_1
+valid_840_a_2
+valid_841_a_1
+valid_841_a_2
+valid_842_a_1
+valid_842_a_2
+valid_843_a_1
+valid_843_a_2
+valid_844_a_1
+valid_844_a_2
+valid_844_b_1
+valid_844_b_2
+valid_845_a_1
+valid_845_a_2
+valid_846_a_1
+valid_846_a_2
+valid_847_a_1
+valid_847_a_2
+valid_848_a_1
+valid_848_a_2
+valid_849_a_1
+valid_849_a_2
+valid_850_a_1
+valid_850_a_2
+valid_850_b_1
+valid_850_b_2
+valid_850_c_1
+valid_850_c_2
+valid_850_d_1
+valid_850_d_2
+valid_850_e_1
+valid_850_e_2
+valid_850_f_1
+valid_850_f_2
+valid_851_a_1
+valid_851_a_2
+valid_852_a_1
+valid_852_a_2
+valid_853_a_1
+valid_853_a_2
+valid_854_a_1
+valid_854_a_2
+valid_855_a_1
+valid_855_a_2
+valid_856_a_1
+valid_857_a_1
+valid_857_a_2
+valid_858_a_1
+valid_858_a_2
+valid_859_a_1
+valid_859_a_2
+valid_860_a_1
+valid_861_a_1
+valid_861_a_2
+valid_862_a_1
+valid_862_a_2
+valid_863_a_1
+valid_863_a_2
+valid_863_a_3
+valid_864_a_1
+valid_864_a_2
+valid_865_a_1
+valid_866_a_1
+valid_866_a_2
+valid_867_a_1
+valid_867_a_2
+valid_868_a_1
+valid_868_a_2
+valid_869_a_1
+valid_869_a_2
+valid_869_b_1
+valid_869_b_2
+valid_870_a_1
+valid_870_a_2
+valid_871_a_1
+valid_871_a_2
+valid_872_a_1
+valid_872_a_2
+valid_873_a_1
+valid_873_a_2
+valid_874_a_1
+valid_874_a_2
+valid_875_a_1
+valid_875_a_2
+valid_876_a_1
+valid_876_a_2
+valid_876_b_1
+valid_876_b_2
+valid_877_a_1
+valid_877_a_2
+valid_878_a_1
+valid_878_a_2
+valid_879_a_1
+valid_879_a_2
+valid_880_a_1
+valid_880_a_2
+valid_881_a_1
+valid_881_a_2
+valid_882_a_1
+valid_882_a_2
+valid_883_a_1
+valid_883_a_2
+valid_884_a_3
+valid_884_a_5
+valid_884_b_1
+valid_884_b_2
+valid_885_a_1
+valid_885_a_2
+valid_886_a_1
+valid_886_a_2
+valid_887_a_1
+valid_887_a_2
+valid_888_a_1
+valid_888_a_2
+valid_889_a_1
+valid_889_a_2
+valid_890_a_1
+valid_890_a_2
+valid_891_a_1
+valid_892_a_1
+valid_893_a_1
+valid_893_a_2
+valid_894_a_1
+valid_894_a_2
+valid_895_a_1
+valid_895_a_2
+valid_896_a_1
+valid_896_a_2
+valid_897_a_1
+valid_897_a_2
+valid_898_a_1
+valid_898_a_2
+valid_898_b_1
+valid_898_b_2
+valid_899_a_1
+valid_899_a_2
+valid_900_a_1
+valid_900_a_2
+valid_901_a_1
+valid_901_a_2
+valid_902_a_1
+valid_902_a_2
+valid_903_a_1
+valid_904_a_1
+valid_904_a_2
+valid_905_a_1
+valid_905_a_2
+valid_906_a_1
+valid_906_a_2
+valid_906_b_1
+valid_907_a_1
+valid_908_a_1
+valid_908_a_2
+valid_909_a_1
+valid_909_a_2
+valid_910_a_1
+valid_910_a_2
+valid_911_a_1
+valid_911_a_2
+valid_912_a_1
+valid_912_a_2
+valid_913_a_1
+valid_913_a_2
+valid_914_a_1
+valid_914_a_2
+valid_915_a_1
+valid_915_a_2
+valid_915_b_1
+valid_915_b_2
+valid_915_c_1
+valid_915_c_2
+valid_916_a_1
+valid_916_a_2
+valid_916_b_1
+valid_916_b_2
+valid_916_c_1
+valid_916_c_2
+valid_917_a_1
+valid_917_a_2
+valid_918_a_1
+valid_918_a_2
+valid_919_a_1
+valid_919_a_2
+valid_920_a_1
+valid_920_a_2
+valid_921_a_1
+valid_921_a_2
+valid_922_a_1
+valid_922_a_2
+valid_923_a_1
+valid_923_a_2
+valid_924_a_1
+valid_924_a_2
+valid_925_a_1
+valid_925_a_2
+valid_926_a_1
+valid_926_a_2
+valid_927_a_1
+valid_927_a_2
+valid_928_a_1
+valid_928_a_2
+valid_928_b_1
+valid_928_b_2
+valid_929_a_1
+valid_929_a_2
+valid_930_a_1
+valid_930_a_2
+valid_931_a_1
+valid_931_a_2
+valid_931_b_1
+valid_931_b_2
+valid_932_a_1
+valid_932_a_2
+valid_933_a_1
+valid_933_b_1
+valid_933_b_2
+valid_934_a_1
+valid_934_a_2
+valid_935_a_1
+valid_935_a_2
+valid_936_a_1
+valid_936_a_2
+valid_936_b_1
+valid_937_a_1
+valid_937_a_2
+valid_937_b_1
+valid_937_b_2
+valid_937_c_1
+valid_937_c_2
+valid_937_d_1
+valid_937_d_2
+valid_937_e_1
+valid_937_e_2
+valid_937_f_1
+valid_937_f_2
+valid_937_g_1
+valid_937_g_2
+valid_937_h_1
+valid_937_h_2
+valid_938_a_1
+valid_939_a_1
+valid_939_a_2
+valid_940_a_1
+valid_940_a_2
+valid_941_a_1
+valid_941_a_2
+valid_942_a_1
+valid_942_a_2
+valid_943_a_1
+valid_943_a_2
+valid_944_a_1
+valid_944_a_2
+valid_945_a_1
+valid_945_a_2
+valid_945_b_1
+valid_945_b_2
+valid_946_a_1
+valid_947_a_1
+valid_947_a_2
+valid_948_a_1
+valid_948_a_2
+valid_949_a_1
+valid_949_a_2
+valid_950_a_1
+valid_950_a_2
+valid_951_a_1
+valid_951_a_2
+valid_952_a_1
+valid_952_a_2
+valid_953_a_1
+valid_953_a_2
+valid_954_a_1
+valid_954_a_2
+valid_955_a_1
+valid_956_a_1
+valid_956_a_2
+valid_957_a_1
+valid_957_a_2
+valid_958_a_1
+valid_958_a_2
+valid_959_a_1
+valid_959_a_2
+valid_960_a_1
+valid_960_a_2
+valid_961_a_1
+valid_961_a_2
+valid_962_a_1
+valid_962_a_2
+valid_963_a_1
+valid_963_a_2
+valid_964_a_1
+valid_964_a_2
+valid_965_a_1
+valid_965_a_2
+valid_966_a_1
+valid_966_a_2
+valid_967_a_1
+valid_967_a_2
+valid_968_a_1
+valid_968_a_2
+valid_968_b_1
+valid_968_b_2
+valid_968_c_1
+valid_968_c_2
+valid_968_d_1
+valid_968_d_2
+valid_969_a_1
+valid_969_a_2
+valid_969_b_1
+valid_969_b_2
+valid_970_a_1
+valid_970_a_2
+valid_971_a_1
+valid_971_a_2
+valid_971_b_1
+valid_971_b_2
+valid_972_a_1
+valid_972_a_2
+valid_973_a_1
+valid_973_a_2
+valid_974_a_1
+valid_974_a_2
+valid_975_a_1
+valid_975_a_2
+valid_976_a_1
+valid_976_a_2
+valid_977_a_1
+valid_977_a_2
+valid_978_a_1
+valid_978_a_2
+valid_979_a_1
+valid_979_a_2
+valid_979_b_1
+valid_979_b_2
+valid_980_a_1
+valid_980_a_2
+valid_981_a_1
+valid_982_a_1
+valid_982_a_2
+valid_983_a_1
+valid_983_a_2
+valid_984_a_1
+valid_984_a_2
+valid_985_a_1
+valid_985_a_2
+valid_986_a_1
+valid_986_a_2
+valid_987_a_1
+valid_987_a_2
+valid_988_a_1
+valid_988_a_2
+valid_989_a_1
+valid_989_a_2
+valid_990_a_1
+valid_990_a_2
+valid_991_a_1
+valid_991_a_2
+valid_991_b_1
+valid_991_b_2
+valid_992_a_1
+valid_993_a_1
+valid_993_a_2
+valid_994_a_3
+valid_994_a_5
+valid_995_a_1
+valid_995_a_2
+valid_996_a_1
+valid_996_a_2
+valid_997_a_1
+valid_997_a_2
+valid_998_a_1
+valid_998_a_2
+valid_999_a_1
+valid_999_a_2
+valid_1000_a_1
+valid_1000_a_2
+valid_1001_a_1
+valid_1001_a_2
+valid_1002_a_1
+valid_1002_a_2
+valid_1003_a_1
+valid_1003_a_2
+valid_1004_a_1
+valid_1004_a_2
+valid_1005_a_1
+valid_1005_a_2
+valid_1005_b_1
+valid_1005_b_2
+valid_1006_a_1
+valid_1006_a_2
+valid_1006_b_1
+valid_1006_b_2
+valid_1006_c_1
+valid_1006_c_2
+valid_1007_a_1
+valid_1007_a_2
+valid_1008_a_1
+valid_1008_a_2
+valid_1009_a_1
+valid_1010_a_1
+valid_1010_a_2
+valid_1011_a_1
+valid_1011_a_2
+valid_1012_a_1
+valid_1012_a_2
+valid_1013_a_1
+valid_1013_a_2
+valid_1014_a_1
+valid_1014_a_2
+valid_1015_a_1
+valid_1015_a_2
+valid_1016_a_1
+valid_1016_a_2
+valid_1016_b_1
+valid_1016_b_2
+valid_1016_c_1
+valid_1016_c_2
+valid_1016_d_1
+valid_1016_d_2
+valid_1016_e_1
+valid_1016_e_2
+valid_1016_f_1
+valid_1016_f_2
+valid_1017_a_1
+valid_1018_a_1
+valid_1018_a_2
+valid_1019_a_1
+valid_1019_b_1
+valid_1019_b_2
+valid_1019_c_1
+valid_1019_c_2
+valid_1019_d_1
+valid_1020_a_1
+valid_1020_a_2
+valid_1021_a_1
+valid_1021_a_2
+valid_1022_a_1
+valid_1022_a_2
+valid_1023_a_1
+valid_1023_a_2
+valid_1024_a_1
+valid_1025_a_1
+valid_1025_a_2
+valid_1025_b_1
+valid_1025_b_2
+valid_1025_c_1
+valid_1025_c_2
+valid_1026_a_1
+valid_1026_a_2
+valid_1027_a_1
+valid_1028_a_1
+valid_1028_a_2
+valid_1029_a_1
+valid_1030_a_1
+valid_1030_a_2
+valid_1031_a_1
+valid_1031_a_2
+valid_1032_a_1
+valid_1032_a_2
+valid_1033_a_1
+valid_1033_a_2
+valid_1034_a_1
+valid_1034_a_2
+valid_1035_a_1
+valid_1035_a_2
+valid_1036_a_1
+valid_1036_a_2
+valid_1037_a_1
+valid_1037_a_2
+valid_1038_a_1
+valid_1038_a_2
+valid_1039_a_1
+valid_1040_a_1
+valid_1040_a_2
+valid_1041_a_1
+valid_1041_a_2
+valid_1041_b_1
+valid_1041_b_2
+valid_1041_c_1
+valid_1041_c_2
+valid_1041_d_1
+valid_1041_d_2
+valid_1042_a_1
+valid_1042_a_2
+valid_1043_a_1
+valid_1043_a_2
+valid_1044_a_1
+valid_1044_a_2
+valid_1045_a_1
+valid_1045_a_2
+valid_1046_a_1
+valid_1046_a_2
+valid_1047_a_1
+valid_1047_a_2
+valid_1048_a_1
+valid_1048_a_2
+valid_1049_a_1
+valid_1049_a_2
+valid_1050_a_1
+valid_1050_a_2
+valid_1051_a_1
+valid_1051_a_2
+valid_1052_a_1
+valid_1053_a_1
+valid_1053_a_2
+valid_1054_a_1
+valid_1054_a_2
+valid_1055_a_1
+valid_1055_a_2
+valid_1056_a_1
+valid_1056_a_2
+valid_1057_a_1
+valid_1057_a_2
+valid_1057_b_1
+valid_1057_b_2
+valid_1058_a_1
+valid_1058_a_2
+valid_1059_a_1
+valid_1059_a_2
+valid_1060_a_1
+valid_1060_a_2
+valid_1061_a_1
+valid_1061_a_2
+valid_1062_a_1
+valid_1062_a_2
+valid_1063_a_1
+valid_1063_a_2
+valid_1064_a_1
+valid_1064_a_2
+valid_1065_a_1
+valid_1065_a_2
+valid_1066_a_1
+valid_1066_a_2
+valid_1067_a_1
+valid_1067_a_2
+valid_1067_b_1
+valid_1067_b_2
+valid_1067_c_1
+valid_1067_c_2
+valid_1067_d_1
+valid_1067_d_2
+valid_1068_a_1
+valid_1068_a_2
+valid_1069_a_1
+valid_1069_a_2
+valid_1069_b_1
+valid_1069_b_2
+valid_1070_a_1
+valid_1070_a_2
+valid_1071_a_1
+valid_1071_a_2
+valid_1072_a_1
+valid_1073_a_1
+valid_1074_a_1
+valid_1074_a_2
+valid_1075_a_1
+valid_1075_a_2
+valid_1076_a_1
+valid_1076_a_2
+valid_1077_a_1
+valid_1077_a_2
+valid_1078_a_1
+valid_1078_a_2
+valid_1079_a_1
+valid_1079_a_2
+valid_1080_a_1
+valid_1081_a_1
+valid_1081_a_2
+valid_1081_b_1
+valid_1081_b_2
+valid_1082_a_1
+valid_1082_a_2
+valid_1082_b_1
+valid_1082_b_2
+valid_1083_a_1
+valid_1083_a_2
+valid_1084_a_1
+valid_1084_a_2
+valid_1085_a_1
+valid_1085_a_2
+valid_1086_a_1
+valid_1086_a_2
+valid_1087_a_1
+valid_1087_a_2
+valid_1088_a_1
+valid_1088_a_2
+valid_1089_a_1
+valid_1089_a_2
+valid_1090_a_1
+valid_1090_a_2
+valid_1091_a_1
+valid_1091_a_2
+valid_1092_a_1
+valid_1092_a_2
+valid_1093_a_1
+valid_1093_a_2
+valid_1094_a_1
+valid_1094_a_2
+valid_1095_a_1
+valid_1096_a_1
+valid_1096_a_2
+valid_1097_a_1
+valid_1097_a_2
+valid_1097_b_1
+valid_1097_b_2
+valid_1097_c_1
+valid_1097_c_2
+valid_1098_a_1
+valid_1098_a_2
+valid_1099_a_1
+valid_1099_a_2
+valid_1100_a_1
+valid_1100_a_2
+valid_1101_a_1
+valid_1101_a_2
+valid_1102_a_1
+valid_1102_a_2
+valid_1103_a_1
+valid_1103_a_2
+valid_1103_b_1
+valid_1103_b_2
+valid_1104_a_1
+valid_1104_a_2
+valid_1105_a_1
+valid_1105_a_2
+valid_1106_a_1
+valid_1106_a_2
+valid_1107_a_1
+valid_1107_a_2
+valid_1108_a_1
+valid_1109_a_1
+valid_1109_a_2
+valid_1110_a_1
+valid_1110_a_2
+valid_1111_a_1
+valid_1112_a_3
+valid_1112_a_5
+valid_1113_a_1
+valid_1113_a_2
+valid_1114_a_1
+valid_1114_a_2
+valid_1115_a_1
+valid_1116_a_1
+valid_1116_a_2
+valid_1117_a_1
+valid_1117_a_2
+valid_1118_a_1
+valid_1118_a_2
+valid_1119_a_1
+valid_1119_a_2
+valid_1120_a_1
+valid_1120_a_2
+valid_1121_a_1
+valid_1121_a_2
+valid_1122_a_1
+valid_1122_a_2
+valid_1123_a_1
+valid_1123_a_2
+valid_1124_a_1
+valid_1124_a_2
+valid_1125_a_1
+valid_1125_a_2
+valid_1126_a_1
+valid_1126_a_2
+valid_1126_b_1
+valid_1126_b_2
+valid_1126_c_1
+valid_1126_c_2
+valid_1127_a_1
+valid_1127_a_2
+valid_1128_a_1
+valid_1128_a_2
+valid_1129_a_1
+valid_1129_a_2
+valid_1130_a_1
+valid_1130_a_2
+valid_1131_a_1
+valid_1131_a_2
+valid_1131_b_1
+valid_1131_b_2
+valid_1132_a_1
+valid_1132_a_2
+valid_1133_a_1
+valid_1133_a_2
+valid_1134_a_1
+valid_1134_a_2
+valid_1135_a_1
+valid_1135_a_2
+valid_1136_a_1
+valid_1136_a_2
+valid_1137_a_1
+valid_1138_a_1
+valid_1138_a_2
+valid_1139_a_1
+valid_1139_a_2
+valid_1140_a_1
+valid_1140_a_2
+valid_1141_a_1
+valid_1141_a_2
+valid_1142_a_1
+valid_1142_a_2
+valid_1143_a_1
+valid_1143_a_2
+valid_1144_a_1
+valid_1144_a_2
+valid_1145_a_1
+valid_1145_a_2
+valid_1146_a_1
+valid_1147_a_1
+valid_1147_a_2
+valid_1147_b_1
+valid_1147_b_2
+valid_1148_a_1
+valid_1148_a_2
+valid_1148_b_1
+valid_1148_b_2
+valid_1149_a_1
+valid_1150_a_1
+valid_1150_a_2
+valid_1151_a_1
+valid_1151_a_2
+valid_1152_a_1
+valid_1152_a_2
+valid_1153_a_1
+valid_1153_a_2
+valid_1154_a_1
+valid_1154_a_2
+valid_1155_a_1
+valid_1155_a_2
+valid_1156_a_1
+valid_1156_a_2
+valid_1156_b_1
+valid_1156_b_2
+valid_1157_a_1
+valid_1157_a_2
+valid_1158_a_1
+valid_1158_a_2
+valid_1159_a_1
+valid_1160_a_1
+valid_1160_a_2
+valid_1161_a_1
+valid_1161_a_2
+valid_1162_a_1
+valid_1162_a_2
+valid_1163_a_1
+valid_1163_a_2
+valid_1164_a_1
+valid_1165_a_1
+valid_1165_a_2
+valid_1166_a_1
+valid_1167_a_1
+valid_1167_a_2
+valid_1168_a_1
+valid_1168_a_2
+valid_1169_a_1
+valid_1169_a_2
+valid_1170_a_3
+valid_1170_a_4
+valid_1170_a_6
+valid_1170_b_1
+valid_1170_b_2
+valid_1170_c_1
+valid_1170_c_2
+valid_1171_a_1
+valid_1171_a_2
+valid_1171_b_1
+valid_1171_b_2
+valid_1172_a_1
+valid_1172_a_2
+valid_1173_a_1
+valid_1173_a_2
+valid_1174_a_1
+valid_1174_a_2
+valid_1175_a_1
+valid_1175_a_2
+valid_1176_a_1
+valid_1176_a_2
+valid_1177_a_1
+valid_1177_a_2
+valid_1178_a_1
+valid_1178_a_2
+valid_1179_a_1
+valid_1179_a_2
+valid_1180_a_1
+valid_1181_a_1
+valid_1181_a_2
+valid_1182_a_1
+valid_1182_a_2
+valid_1183_a_1
+valid_1184_a_1
+valid_1184_a_2
+valid_1184_b_1
+valid_1184_b_2
+valid_1184_c_1
+valid_1184_c_2
+valid_1185_a_1
+valid_1185_a_2
+valid_1186_a_1
+valid_1186_a_2
+valid_1187_a_1
+valid_1187_a_2
+valid_1188_a_1
+valid_1188_a_2
+valid_1189_a_1
+valid_1189_a_2
+valid_1190_a_1
+valid_1190_a_2
+valid_1191_a_1
+valid_1191_a_2
+valid_1192_a_1
+valid_1192_a_2
+valid_1192_b_1
+valid_1192_b_2
+valid_1193_a_1
+valid_1193_a_2
+valid_1194_a_1
+valid_1194_a_2
+valid_1195_a_1
+valid_1196_a_1
+valid_1196_a_2
+valid_1197_a_1
+valid_1197_a_2
+valid_1198_a_1
+valid_1198_a_2
+valid_1199_a_1
+valid_1199_a_2
+valid_1200_a_1
+valid_1200_a_2
+valid_1201_a_1
+valid_1201_a_2
+valid_1202_a_1
+valid_1202_a_2
+valid_1203_a_1
+valid_1203_a_2
+valid_1204_a_1
+valid_1204_a_2
+valid_1205_a_1
+valid_1205_a_2
+valid_1206_a_1
+valid_1206_a_2
+valid_1207_a_1
+valid_1207_a_2
+valid_1208_a_1
+valid_1208_a_2
+valid_1209_a_1
+valid_1209_a_2
+valid_1209_b_1
+valid_1209_b_2
+valid_1210_a_1
+valid_1210_a_2
+valid_1211_a_1
+valid_1211_a_2
+valid_1212_a_1
+valid_1212_a_2
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data/splits/ct_rate/val.txt

@@ -0,0 +1,2994 @@
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data/splits/msd_lung/val.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 +1,83 @@
+"""Score a sentence against a chest CT volume, save its similarity map as NIfTI and score the 18 CT-RATE findings.
+
+python inference.py --volume volume.nii.gz --text "There is a lung nodule."
+"""
+
+import argparse
+import os
+import warnings
+
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+from utils import (
+    load_ct_volume,
+    model_inference,
+    model_inference_multiple_text,
+    save_similarity_map,
+    similarity_scores_to_volume,
+)
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+REPO_DIR = os.path.dirname(os.path.abspath(__file__))
+
+CT_RATE_LABELS = [
+    "Medical material",
+    "Arterial wall calcification",
+    "Cardiomegaly",
+    "Pericardial effusion",
+    "Coronary artery wall calcification",
+    "Hiatal hernia",
+    "Lymphadenopathy",
+    "Emphysema",
+    "Atelectasis",
+    "Lung nodule",
+    "Lung opacity",
+    "Pulmonary fibrotic sequela",
+    "Pleural effusion",
+    "Mosaic attenuation pattern",
+    "Peribronchial thickening",
+    "Consolidation",
+    "Bronchiectasis",
+    "Interlobular septal thickening",
+]
+
+
+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)
+    model = AutoModel.from_pretrained(REPO_DIR, trust_remote_code=True, dtype=dtype).to(device).eval()
+    return {"tokenizer": tokenizer, "model": model}
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    parser.add_argument("--volume", default="volume.nii.gz", help="NIfTI CT volume")
+    parser.add_argument("--text", default="There is a lung nodule.")
+    parser.add_argument("--output", default="similarity_map.nii.gz", help="similarity map (NIfTI)")
+    parser.add_argument(
+        "--ct_output", default="ct_preprocessed.nii.gz", help="preprocessed CT on the same grid (NIfTI)"
+    )
+    parser.add_argument(
+        "--raw_spacing", type=float, nargs=3, default=None, help="voxel spacing if the NIfTI affine is unreliable"
+    )
+    args = parser.parse_args()
+
+    models = load_model()
+    pixel_values, affine = load_ct_volume(args.volume, raw_spacing=args.raw_spacing, return_affine=True)
+
+    prob, scores = model_inference(pixel_values, args.text, **models)
+    print(f"{args.text!r}: probability {prob.item():.3f}")
+    volume = similarity_scores_to_volume(scores, models["model"])
+    save_similarity_map(volume, affine, args.output, pixel_values, args.ct_output)
+    print(f"similarity map saved to {args.output} (CT: {args.ct_output})")
+
+    prompts = [f"There is {label}" for label in CT_RATE_LABELS]  # zero-shot evaluation prompts
+    probs, _ = model_inference_multiple_text(pixel_values, prompts, **models)
+    for label, p in zip(CT_RATE_LABELS, probs.flatten().tolist()):
+        print(f"  {label:42s} {p:.3f}")
+
+
+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:b1e2e8c6afe5b38e39b066f2bad1c17e95e33027defad8ae2436b14573368ba5
+size 1708162556

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

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 +1,185 @@
+"""Inference helpers for the GLINT-CT vision-language model.
+
+CT volumes are preprocessed with the same MONAI pipeline used during GLINT
+training/zero-shot evaluation (orientation IPL, resample to 1.5/0.75/0.75 mm,
+HU window [-1000, 1000] -> [-1, 1], 240x384x384), then regrouped into
+``[num_frames=80, 3, 384, 384]`` and ImageNet-normalised.
+"""
+
+import numpy as np
+import torch
+
+# GLINT-CT pretraining preprocessing (glint/configs/chest_ct_monai.yaml)
+CT_MONAI_CONFIG = {
+    "orientation": "IPL",
+    "target_spacing": (1.5, 0.75, 0.75),
+    "roi_size": (240, 480, 480),
+    "final_size": (240, 384, 384),
+    "hu_range": (-1000.0, 1000.0),
+    "normalize_range": (-1.0, 1.0),
+}
+
+
+def build_ct_transform(orientd=True, raw_spacing=None):
+    from monai.transforms import (
+        CenterSpatialCropd,
+        Compose,
+        EnsureChannelFirstd,
+        EnsureTyped,
+        Lambdad,
+        LoadImaged,
+        Orientationd,
+        Resized,
+        ScaleIntensityRanged,
+        Spacingd,
+        SpatialPadd,
+    )
+
+    cfg = CT_MONAI_CONFIG
+    b_min, b_max = cfg["normalize_range"]
+
+    def _patch_affine(img):
+        if raw_spacing is not None and hasattr(img, "affine"):
+            sx, sy, sz = (float(x) for x in raw_spacing)
+            a = torch.eye(4, dtype=img.affine.dtype, device=img.affine.device)
+            a[0, 0], a[1, 1], a[2, 2] = sx, sy, sz
+            img.affine = a
+        return img
+
+    steps = [
+        LoadImaged(keys=["image"], image_only=False),
+        EnsureChannelFirstd(keys=["image"]),
+        *([Orientationd(keys=["image"], axcodes=cfg["orientation"])] if orientd else []),
+        *([Lambdad(keys=["image"], func=_patch_affine)] if raw_spacing is not None else []),
+        ScaleIntensityRanged(
+            keys=["image"],
+            a_min=cfg["hu_range"][0],
+            a_max=cfg["hu_range"][1],
+            b_min=b_min,
+            b_max=b_max,
+            clip=True,
+        ),
+        Spacingd(keys=["image"], pixdim=cfg["target_spacing"], mode="bilinear"),
+        CenterSpatialCropd(keys=["image"], roi_size=cfg["roi_size"]),
+        SpatialPadd(keys=["image"], spatial_size=cfg["roi_size"], mode="constant", constant_values=b_min),
+        Resized(keys=["image"], spatial_size=cfg["final_size"], mode="trilinear"),
+        EnsureTyped(keys=["image"], dtype=torch.float32),
+    ]
+    return Compose(steps)
+
+
+def normalize_pixel_values_videos(pixel_values_videos):
+    """(D, H, W) in [-1, 1] -> (num_frames, 3, H, W) ImageNet-normalised."""
+    s = pixel_values_videos.shape
+    num_channels = 3
+    pixel_values_videos = pixel_values_videos.reshape(-1, num_channels, s[-2], s[-1])
+
+    pixel_values_videos = (pixel_values_videos + 1) / 2
+
+    image_mean = torch.tensor([0.485, 0.456, 0.406], device=pixel_values_videos.device).view(1, 3, 1, 1)
+    image_std = torch.tensor([0.229, 0.224, 0.225], device=pixel_values_videos.device).view(1, 3, 1, 1)
+    pixel_values_videos = (pixel_values_videos - image_mean) / image_std
+
+    return pixel_values_videos
+
+
+def load_ct_volume(volume, orientd=True, raw_spacing=None, return_affine=False):
+    """Load and preprocess a CT volume.
+
+    volume : path to a NIfTI (.nii/.nii.gz) file, or a (240, 384, 384) tensor/ndarray in HU that is
+             already on the preprocessed grid (IPL, 1.5/0.75/0.75 mm resampled, cropped/padded, resized);
+             only the HU window is applied to arrays. Returns pixel_values of shape
+             (1, num_frames, 3, H, W), plus the 4x4 world affine of the preprocessed grid if
+             ``return_affine`` (None for array input).
+    """
+    affine = None
+    if isinstance(volume, str):
+        transform = build_ct_transform(orientd=orientd, raw_spacing=raw_spacing)
+        out = transform({"image": volume})
+        image = out["image"].squeeze(0)  # (1, D, H, W) -> (D, H, W)
+        affine = np.asarray(out["image"].affine, dtype=np.float64)
+    else:
+        image = torch.as_tensor(np.asarray(volume), dtype=torch.float32)
+        if tuple(image.shape) != tuple(CT_MONAI_CONFIG["final_size"]):
+            raise ValueError(
+                f"Array input must be on the preprocessed grid {CT_MONAI_CONFIG['final_size']}, got {tuple(image.shape)}"
+            )
+        (a_min, a_max), (b_min, b_max) = CT_MONAI_CONFIG["hu_range"], CT_MONAI_CONFIG["normalize_range"]
+        image = (image.clamp(a_min, a_max) - a_min) / (a_max - a_min) * (b_max - b_min) + b_min
+
+    pixel_values_videos = normalize_pixel_values_videos(image).unsqueeze(0)  # (1, num_frames, 3, H, W)
+    return (pixel_values_videos, affine) if return_affine else pixel_values_videos
+
+
+def similarity_scores_to_volume(similarity_scores, model, target_shape=CT_MONAI_CONFIG["final_size"]):
+    """Per-token scores ``[1, 1, num_tokens]`` -> (D, H, W) map on the preprocessed grid (trilinear)."""
+    vc = model.config.vision_config
+    patch_size = getattr(vc, "patch_size", None) or 16
+    frame_length = vc.num_channels * (getattr(vc, "tubelet_size", None) or 2)
+    grid = (vc.input_slices // frame_length, vc.img_size // patch_size, vc.img_size // patch_size)
+
+    volume = similarity_scores.float().reshape(grid)
+    if target_shape is not None:
+        volume = torch.nn.functional.interpolate(
+            volume[None, None], size=tuple(target_shape), mode="trilinear", align_corners=False
+        )[0, 0]
+    return volume
+
+
+def save_similarity_map(similarity_volume, affine, save_path, pixel_values=None, ct_save_path=None):
+    """Save the similarity map (and optionally the preprocessed CT) as NIfTI in world space.
+
+    With the affine from ``load_ct_volume(..., return_affine=True)`` the map overlays the
+    original CT in ITK-SNAP / 3D Slicer. ``pixel_values`` + ``ct_save_path`` also save the
+    preprocessed volume (HU window [-1000, 1000]) on the same grid.
+    """
+    import nibabel as nib
+
+    sim = similarity_volume.detach().float().cpu().numpy() if torch.is_tensor(similarity_volume) else similarity_volume
+    nib.save(nib.Nifti1Image(sim.astype(np.float32), affine), save_path)
+
+    if pixel_values is not None and ct_save_path is not None:
+        mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
+        std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)
+        frames = pixel_values[0].detach().float().cpu() * std + mean  # (num_frames, 3, H, W) in [0, 1]
+        hu = (frames.reshape(-1, *frames.shape[-2:]) * 2 - 1) * 1000.0
+        nib.save(nib.Nifti1Image(hu.numpy().astype(np.float32), affine), ct_save_path)
+
+
+def _as_pixel_values(volume, raw_spacing):
+    if torch.is_tensor(volume) and volume.dim() == 5:  # already preprocessed by load_ct_volume
+        return volume
+    return load_ct_volume(volume, raw_spacing=raw_spacing)
+
+
+@torch.no_grad()
+def model_inference(volume, text, model, tokenizer, raw_spacing=None):
+    """Return (similarity_prob, similarity_scores) for one CT volume and one phrase.
+
+    similarity_prob   : scalar sigmoid probability that the phrase applies.
+    similarity_scores : per-token similarity in [0, 1] over the V-JEPA video tokens
+                        (shape [1, 1, num_tokens]); ``similarity_scores_to_volume``
+                        maps it onto the preprocessed CT grid.
+
+    volume may also be the ``pixel_values`` returned by ``load_ct_volume``.
+    """
+    pixel_values = _as_pixel_values(volume, raw_spacing).to(model.device, dtype=model.dtype)
+
+    tokenized_text = tokenizer(
+        text,
+        padding=True,
+        truncation=True,
+        return_tensors="pt",
+    ).to(model.device)
+
+    output = model.compute_logits(pixel_values, [tokenized_text])
+    similarity_prob = output["logits"].sigmoid()
+    return similarity_prob, output.get("similarity_scores")
+
+
+@torch.no_grad()
+def model_inference_multiple_text(volume, text_list, model, tokenizer, raw_spacing=None):
+    pixel_values = _as_pixel_values(volume, raw_spacing).to(model.device, dtype=model.dtype)
+    enc = tokenizer(text_list, padding=True, truncation=True, return_tensors="pt").to(model.device)
+    output = model.compute_logits(pixel_values, [enc])
+    return output["logits"].sigmoid(), output.get("similarity_scores")

vision_encoders.py

@@ -0,0 +1,31 @@
+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))
+
+    elif config.model_type == "vjepa2.1":
+        from .vjepa21_encoder import VJEPA21Encoder
+
+        model = VJEPA21Encoder(config)
+
+    else:
+        raise NotImplementedError()
+
+    return model

vjepa21_encoder.py

@@ -0,0 +1,1106 @@
+"""V-JEPA 2.1 encoder.
+
+Adapted from https://github.com/facebookresearch/vjepa2
+Copyright (c) Meta Platforms, Inc. and affiliates. MIT License (LICENSES/MIT.txt)
+drop_path from timm (https://github.com/huggingface/pytorch-image-models).
+Copyright 2020 Ross Wightman. Apache License 2.0 (LICENSES/Apache-2.0.txt)
+"""
+
+import logging
+import math
+
+import torch
+
+logger = logging.getLogger("glint")
+
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    def norm_cdf(x):
+        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
+
+    with torch.no_grad():
+        lower = norm_cdf((a - mean) / std)
+        upper = norm_cdf((b - mean) / std)
+
+        tensor.uniform_(2 * lower - 1, 2 * upper - 1)
+
+        tensor.erfinv_()
+
+        tensor.mul_(std * math.sqrt(2.0))
+        tensor.add_(mean)
+
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
+    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
+
+
+def repeat_interleave_batch(x, B, repeat):
+    N = len(x) // B
+    x = torch.cat([torch.cat([x[i * B : (i + 1) * B] for _ in range(repeat)], dim=0) for i in range(N)], dim=0)
+    return x
+
+
+def apply_masks(x, masks, concat=True):
+    all_x = []
+    for m in masks:
+        mask_keep = m.unsqueeze(-1).repeat(1, 1, x.size(-1))
+        all_x += [torch.gather(x, dim=1, index=mask_keep)]
+    if not concat:
+        return all_x
+
+    return torch.cat(all_x, dim=0)
+
+
+import torch.nn as nn  # noqa: E402
+
+
+class PatchEmbed3D(nn.Module):
+    def __init__(
+        self,
+        patch_size=16,
+        tubelet_size=2,
+        in_chans=3,
+        embed_dim=768,
+    ):
+        super().__init__()
+        self.patch_size = patch_size
+        self.tubelet_size = tubelet_size
+
+        self.proj = nn.Conv3d(
+            in_channels=in_chans,
+            out_channels=embed_dim,
+            kernel_size=(tubelet_size, patch_size, patch_size),
+            stride=(tubelet_size, patch_size, patch_size),
+        )
+
+    def forward(self, x, **kwargs):
+        B, C, T, H, W = x.shape
+        x = self.proj(x).flatten(2).transpose(1, 2)
+        return x
+
+
+import torch.nn.functional as F  # noqa: E402
+from torch.nn.attention import SDPBackend, sdpa_kernel  # noqa: E402
+
+
+def drop_path(x, drop_prob=0.0, training=False, scale_by_keep=True):
+    if drop_prob == 0.0 or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
+    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+    if keep_prob > 0.0 and scale_by_keep:
+        random_tensor.div_(keep_prob)
+    return x * random_tensor
+
+
+def rotate_queries_or_keys(x, pos, n_registers, has_cls_first):
+    B, num_heads, N, D = x.size()
+    assert D % 2 == 0, "Embedding dimension must be a multiple of 2 for block matrix rotation"
+
+    n_cls = 1 if has_cls_first else 0
+    start_ctx = n_cls
+    end_ctx = N - n_registers
+
+    x_cls = x[..., :n_cls, :] if n_cls else None
+    x_ctx = x[..., start_ctx:end_ctx, :]
+    x_reg = x[..., end_ctx:, :] if n_registers > 0 else None
+
+    omega = torch.arange(D // 2, dtype=x.dtype, device=x.device)
+    omega /= D / 2.0
+    omega = 1.0 / 10000**omega
+    freq = torch.einsum("..., f -> ... f", pos, omega)
+
+    emb_sin = freq.sin()
+    emb_cos = freq.cos()
+
+    emb_sin = emb_sin.repeat_interleave(2, dim=-1)
+    emb_cos = emb_cos.repeat_interleave(2, dim=-1)
+
+    y = x_ctx.unflatten(-1, (-1, 2))
+    y1, y2 = y.unbind(
+        dim=-1,
+    )
+    y = torch.stack((-y2, y1), dim=-1)
+    y = y.flatten(-2)
+    out_ctx = (x_ctx * emb_cos) + (y * emb_sin)
+
+    parts = []
+    if n_cls:
+        parts.append(x_cls)
+    parts.append(out_ctx)
+    if n_registers:
+        parts.append(x_reg)
+    out = torch.cat(parts, dim=-2)
+
+    return out
+
+
+class DropPath(nn.Module):
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
+
+    def extra_repr(self) -> str:
+        return "p={}".format(self.drop_prob)
+
+
+class MLP(nn.Module):
+    def __init__(
+        self,
+        in_features,
+        hidden_features=None,
+        out_features=None,
+        act_layer=nn.GELU,
+        drop=0.0,
+    ):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class SwiGLUFFN(nn.Module):
+    def __init__(
+        self,
+        in_features,
+        hidden_features=None,
+        out_features=None,
+        act_layer=nn.SiLU,
+        drop=0.0,
+        wide_silu=True,
+    ):
+        super().__init__()
+        out_features = out_features or in_features
+        swiglu_hidden_features = hidden_features = hidden_features or in_features
+        if wide_silu:
+            swiglu_hidden_features = int(2 * hidden_features / 3)
+            align_as = 8
+            swiglu_hidden_features = (swiglu_hidden_features + align_as - 1) // align_as * align_as
+        self.fc1 = nn.Linear(in_features, swiglu_hidden_features)
+        self.fc2 = nn.Linear(in_features, swiglu_hidden_features)
+        self.act = act_layer()
+        self.fc3 = nn.Linear(swiglu_hidden_features, out_features)
+
+    def forward(self, x):
+        x1 = self.fc1(x)
+        x2 = self.fc2(x)
+        hidden = F.silu(x1) * x2
+        return self.fc3(hidden)
+
+
+class RoPEAttention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_heads=8,
+        qkv_bias=False,
+        qk_scale=None,
+        attn_drop=0.0,
+        proj_drop=0.0,
+        use_sdpa=True,
+        grid_size=14,
+        is_causal=False,
+        n_registers=0,
+        has_cls_first=False,
+        interpolate_rope=False,
+        patch_size=16,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop_prob = proj_drop
+        self.proj_drop = nn.Dropout(proj_drop)
+        self.use_sdpa = use_sdpa
+        self.d_dim = int(2 * ((head_dim // 3) // 2))
+        self.h_dim = int(2 * ((head_dim // 3) // 2))
+        self.w_dim = int(2 * ((head_dim // 3) // 2))
+        self.grid_size = grid_size
+        self.is_causal = is_causal
+        self.n_registers = n_registers
+        self.has_cls_first = has_cls_first
+        self.interpolate_rope = interpolate_rope
+        self.pretrained_patch_size = patch_size
+        if patch_size == 14:
+            self.pretrained_grid_size = int(252 / patch_size)
+        elif patch_size == 16:
+            self.pretrained_grid_size = int(256 / patch_size)
+
+    def _get_frame_pos(self, ids, H_patches=None, W_patches=None):
+        if H_patches is None or W_patches is None:
+            tokens_per_frame = int(self.grid_size * self.grid_size)
+        else:
+            tokens_per_frame = int(H_patches * W_patches)
+        return ids // tokens_per_frame
+
+    def _get_height_pos(self, ids, H_patches=None, W_patches=None):
+        if H_patches is None or W_patches is None:
+            tokens_per_frame = int(self.grid_size * self.grid_size)
+            tokens_per_row = self.grid_size
+        else:
+            tokens_per_frame = int(H_patches * W_patches)
+            tokens_per_row = W_patches
+        frame_ids = self._get_frame_pos(ids, H_patches, W_patches)
+        ids = ids - tokens_per_frame * frame_ids
+        return ids // tokens_per_row
+
+    def separate_positions(self, ids, H_patches=None, W_patches=None):
+        if H_patches is None or W_patches is None:
+            tokens_per_frame = int(self.grid_size * self.grid_size)
+            tokens_per_row = self.grid_size
+        else:
+            tokens_per_frame = int(H_patches * W_patches)
+            tokens_per_row = W_patches
+        frame_ids = self._get_frame_pos(ids, H_patches, W_patches)
+        height_ids = self._get_height_pos(ids, H_patches, W_patches)
+        width_ids = (ids - tokens_per_frame * frame_ids) - tokens_per_row * height_ids
+        return 1.0 * frame_ids, 1.0 * height_ids, 1.0 * width_ids
+
+    def forward(
+        self,
+        x,
+        mask=None,
+        T=None,
+        H_patches=None,
+        W_patches=None,
+        return_attn=False,
+    ):
+        B, N, C = x.size()
+        N_ctx = N - self.n_registers
+        grid_depth = int(N_ctx // (self.grid_size * self.grid_size))
+
+        qkv = self.qkv(x).unflatten(-1, (3, self.num_heads, -1)).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        if mask is not None:
+            mask = mask.unsqueeze(1).repeat(1, self.num_heads, 1)
+            d_mask, h_mask, w_mask = self.separate_positions(mask, H_patches, W_patches)
+        else:
+            if T is None or H_patches is None or W_patches is None:
+                mask = torch.arange(int(grid_depth * self.grid_size * self.grid_size), device=x.device)
+            else:
+                mask = torch.arange(int(T * H_patches * W_patches), device=x.device)
+            d_mask, h_mask, w_mask = self.separate_positions(mask, H_patches, W_patches)
+
+        if self.interpolate_rope:
+            if H_patches is None:
+                H_patches = int(self.grid_size)
+            if W_patches is None:
+                W_patches = int(self.grid_size)
+            h_mask = h_mask * (self.pretrained_grid_size - 1) / (H_patches - 1)
+            w_mask = w_mask * (self.pretrained_grid_size - 1) / (W_patches - 1)
+
+        s = 0
+        qd = rotate_queries_or_keys(
+            q[..., s : s + self.d_dim],
+            pos=d_mask,
+            n_registers=self.n_registers,
+            has_cls_first=self.has_cls_first,
+        )
+        kd = rotate_queries_or_keys(
+            k[..., s : s + self.d_dim],
+            pos=d_mask,
+            n_registers=self.n_registers,
+            has_cls_first=self.has_cls_first,
+        )
+        s += self.d_dim
+        qh = rotate_queries_or_keys(
+            q[..., s : s + self.h_dim],
+            pos=h_mask,
+            n_registers=self.n_registers,
+            has_cls_first=self.has_cls_first,
+        )
+        kh = rotate_queries_or_keys(
+            k[..., s : s + self.h_dim],
+            pos=h_mask,
+            n_registers=self.n_registers,
+            has_cls_first=self.has_cls_first,
+        )
+        s += self.h_dim
+        qw = rotate_queries_or_keys(
+            q[..., s : s + self.w_dim],
+            pos=w_mask,
+            n_registers=self.n_registers,
+            has_cls_first=self.has_cls_first,
+        )
+        kw = rotate_queries_or_keys(
+            k[..., s : s + self.w_dim],
+            pos=w_mask,
+            n_registers=self.n_registers,
+            has_cls_first=self.has_cls_first,
+        )
+        s += self.w_dim
+
+        if s < self.head_dim:
+            qr = q[..., s:]
+            kr = k[..., s:]
+            q = torch.cat([qd, qh, qw, qr], dim=-1)
+            k = torch.cat([kd, kh, kw, kr], dim=-1)
+        else:
+            q = torch.cat([qd, qh, qw], dim=-1)
+            k = torch.cat([kd, kh, kw], dim=-1)
+
+        q = q.to(v.dtype)
+        k = k.to(v.dtype)
+
+        if self.use_sdpa:
+            with sdpa_kernel([SDPBackend.FLASH_ATTENTION, SDPBackend.EFFICIENT_ATTENTION, SDPBackend.MATH]):
+                x = F.scaled_dot_product_attention(q, k, v, dropout_p=self.proj_drop_prob, is_causal=self.is_causal)
+                attn = None
+        else:
+            attn = (q @ k.transpose(-2, -1)) * self.scale
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            x = attn @ v
+
+        x = x.transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        if return_attn:
+            return x, attn
+        else:
+            return x, None
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_heads=8,
+        qkv_bias=False,
+        qk_scale=None,
+        attn_drop=0.0,
+        proj_drop=0.0,
+        use_sdpa=True,
+        is_causal=False,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop_prob = proj_drop
+        self.proj_drop = nn.Dropout(proj_drop)
+        self.use_sdpa = use_sdpa
+        self.is_causal = is_causal
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        if self.use_sdpa:
+            with sdpa_kernel([SDPBackend.FLASH_ATTENTION, SDPBackend.EFFICIENT_ATTENTION, SDPBackend.MATH]):
+                x = F.scaled_dot_product_attention(q, k, v, dropout_p=self.proj_drop_prob, is_causal=self.is_causal)
+                attn = None
+        else:
+            attn = (q @ k.transpose(-2, -1)) * self.scale
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            x = attn @ v
+
+        x = x.transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class Block(nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_heads,
+        mlp_ratio=4.0,
+        qkv_bias=False,
+        qk_scale=None,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        act_layer=nn.GELU,
+        wide_silu=True,
+        norm_layer=nn.LayerNorm,
+        use_sdpa=True,
+        is_causal=False,
+        grid_size=16,
+        use_rope=False,
+        n_registers=0,
+        has_cls_first=False,
+        interpolate_rope=False,
+        patch_size=16,
+        **kwargs,
+    ):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.use_rope = use_rope
+        if use_rope:
+            self.attn = RoPEAttention(
+                dim,
+                num_heads=num_heads,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                attn_drop=attn_drop,
+                use_sdpa=use_sdpa,
+                is_causal=is_causal,
+                grid_size=grid_size,
+                proj_drop=drop,
+                n_registers=n_registers,
+                has_cls_first=has_cls_first,
+                interpolate_rope=interpolate_rope,
+                patch_size=patch_size,
+            )
+        else:
+            self.attn = Attention(
+                dim,
+                num_heads=num_heads,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                attn_drop=attn_drop,
+                use_sdpa=use_sdpa,
+                is_causal=is_causal,
+                proj_drop=drop,
+            )
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        if act_layer is nn.SiLU:
+            self.mlp = SwiGLUFFN(
+                in_features=dim,
+                hidden_features=mlp_hidden_dim,
+                act_layer=act_layer,
+                wide_silu=wide_silu,
+                drop=drop,
+            )
+        else:
+            self.mlp = MLP(
+                in_features=dim,
+                hidden_features=mlp_hidden_dim,
+                act_layer=act_layer,
+                drop=drop,
+            )
+
+    def forward(
+        self,
+        x,
+        mask=None,
+        T=None,
+        H_patches=None,
+        W_patches=None,
+        return_attn=False,
+        mode="video",
+    ):
+        if self.use_rope:
+            y, attn = self.attn(
+                self.norm1(x),
+                mask=mask,
+                T=T,
+                H_patches=H_patches,
+                W_patches=W_patches,
+                return_attn=return_attn,
+            )
+        else:
+            y = self.attn(self.norm1(x))
+            attn = None
+        x = x + self.drop_path(y)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        if return_attn:
+            return x, attn
+        else:
+            return x, None
+
+
+class Lambda_LinearWarmupHold:
+    def __init__(self, lambda_value: float, start_iter: int = 15_000, end_iter: int = 30_000):
+        assert end_iter > start_iter, "end_iter must be > start_iter"
+        self.lambda_value = float(lambda_value)
+        self.start = int(start_iter)
+        self.end = int(end_iter)
+        self.span = self.end - self.start
+
+    def value(self, global_iter: int) -> float:
+        if global_iter < self.start:
+            return 0.0
+        if global_iter >= self.end:
+            return self.lambda_value
+        alpha = (global_iter - self.start) / self.span
+        return self.lambda_value * alpha
+
+
+from functools import partial  # noqa: E402
+
+
+class VisionTransformer(nn.Module):
+    def __init__(
+        self,
+        img_size=(224, 224),
+        patch_size=16,
+        num_frames=1,
+        tubelet_size=2,
+        in_chans=3,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.0,
+        norm_layer=nn.LayerNorm,
+        init_std=0.02,
+        out_layers=None,
+        uniform_power=False,
+        use_silu=False,
+        wide_silu=True,
+        use_sdpa=True,
+        use_activation_checkpointing=False,
+        is_causal=False,
+        use_rope=False,
+        init_type: str = "default",
+        handle_nonsquare_inputs=True,
+        img_temporal_dim_size=None,
+        n_registers=0,
+        has_cls_first=False,
+        interpolate_rope=False,
+        modality_embedding=True,
+        n_output_distillation=4,
+        **kwargs,
+    ):
+        super().__init__()
+        self.num_features = self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.out_layers = out_layers
+        self.init_type = init_type
+        self.handle_nonsquare_inputs = handle_nonsquare_inputs
+        self.img_temporal_dim_size = img_temporal_dim_size
+
+        if type(img_size) is int:
+            img_size = (img_size, img_size)
+        self.img_height, self.img_width = img_size
+        self.patch_size = patch_size
+        self.num_frames = num_frames
+        self.tubelet_size = tubelet_size
+        self.is_video = num_frames > 1
+
+        self.use_activation_checkpointing = use_activation_checkpointing
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
+
+        if self.is_video:
+            self.patch_embed = PatchEmbed3D(
+                patch_size=patch_size,
+                tubelet_size=tubelet_size,
+                in_chans=in_chans,
+                embed_dim=embed_dim,
+            )
+            self.num_patches = (num_frames // tubelet_size) * (img_size[0] // patch_size) * (img_size[1] // patch_size)
+        else:
+            self.patch_embed = PatchEmbed3D(
+                patch_size=patch_size,
+                tubelet_size=1,
+                in_chans=in_chans,
+                embed_dim=embed_dim,
+            )
+            self.num_patches = (img_size[0] // patch_size) * (img_size[1] // patch_size)
+
+        if self.img_temporal_dim_size is not None:
+            if not isinstance(self.img_temporal_dim_size, int):
+                raise ValueError(f"img_temporal_dim_size must be an int, got {self.img_temporal_dim_size}")
+            self.patch_embed_img = PatchEmbed3D(
+                patch_size=patch_size,
+                tubelet_size=1,
+                in_chans=in_chans,
+                embed_dim=embed_dim,
+            )
+        else:
+            self.patch_embed_img = None
+
+        self.uniform_power = uniform_power
+
+        self.use_rope = use_rope
+        self.blocks = nn.ModuleList(
+            [
+                Block(
+                    use_rope=use_rope,
+                    grid_size=img_size[0] // patch_size,
+                    grid_depth=num_frames // tubelet_size,
+                    dim=embed_dim,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    use_sdpa=use_sdpa,
+                    is_causal=is_causal,
+                    qkv_bias=qkv_bias,
+                    qk_scale=qk_scale,
+                    drop=drop_rate,
+                    act_layer=nn.SiLU if use_silu else nn.GELU,
+                    wide_silu=wide_silu,
+                    attn_drop=attn_drop_rate,
+                    drop_path=dpr[i],
+                    norm_layer=norm_layer,
+                    n_registers=n_registers,
+                    has_cls_first=has_cls_first,
+                    interpolate_rope=interpolate_rope,
+                    patch_size=patch_size,
+                )
+                for i in range(depth)
+            ]
+        )
+
+        self.attn_out = False
+        self.init_std = init_std
+        self.apply(self._init_weights)
+        self._rescale_blocks()
+
+        if depth == 12:
+            self.hierarchical_layers = [2, 5, 8, 11]
+            if n_output_distillation == 4:
+                self.out_layers_distillation = [2, 5, 8, 11]
+            elif n_output_distillation == 1:
+                self.out_layers_distillation = [11]
+
+        elif depth == 24:
+            self.hierarchical_layers = [5, 11, 17, 23]
+            if n_output_distillation == 4:
+                self.out_layers_distillation = [5, 11, 17, 23]
+            elif n_output_distillation == 1:
+                self.out_layers_distillation = [23]
+
+        elif depth == 40:
+            self.hierarchical_layers = [9, 19, 29, 39]
+            if n_output_distillation == 4:
+                self.out_layers_distillation = [9, 19, 29, 39]
+            elif n_output_distillation == 1:
+                self.out_layers_distillation = [39]
+
+        elif depth == 48:
+            self.hierarchical_layers = [11, 23, 37, 47]
+            if n_output_distillation == 4:
+                self.out_layers_distillation = [11, 23, 37, 47]
+            elif n_output_distillation == 1:
+                self.out_layers_distillation = [47]
+        else:
+            print("Check the code! ;)")
+        self.norms_block = nn.ModuleList([norm_layer(embed_dim) for _ in range(len(self.hierarchical_layers))])
+
+        self.cls_token = None
+        self.return_hierarchical = False
+
+        self.modality_embedding = False
+        if modality_embedding:
+            self.img_mod_embed = nn.Parameter(torch.zeros(1, 1, embed_dim))
+            self.video_mod_embed = nn.Parameter(torch.zeros(1, 1, embed_dim))
+            nn.init.normal_(self.img_mod_embed, std=1e-6)
+            nn.init.normal_(self.video_mod_embed, std=1e-6)
+            self.modality_embedding = True
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+            return
+        if self.init_type == "default":
+            if isinstance(m, nn.Linear):
+                trunc_normal_(m.weight, std=self.init_std)
+                if isinstance(m, nn.Linear) and m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Conv2d):
+                trunc_normal_(m.weight, std=self.init_std)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Conv3d):
+                trunc_normal_(m.weight, std=self.init_std)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+        elif self.init_type == "xavier_uniform":
+            if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv3d):
+                nn.init.xavier_uniform_(m.weight)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+        elif self.init_type == "xavier_normal":
+            if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv3d):
+                nn.init.xavier_normal_(m.weight)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+        else:
+            raise ValueError(f"Unknown init type {self.init_type}")
+
+    def _rescale_blocks(self):
+        def rescale(param, layer_id):
+            param.div_(math.sqrt(2.0 * layer_id))
+
+        for layer_id, layer in enumerate(self.blocks):
+            rescale(layer.attn.proj.weight.data, layer_id + 1)
+            rescale(layer.mlp.fc2.weight.data, layer_id + 1)
+
+    def get_num_layers(self):
+        return len(self.blocks)
+
+    def no_weight_decay(self):
+        return {}
+
+    def check_temporal_dim(self, shape) -> bool:
+        if self.img_temporal_dim_size is not None:
+            if shape[2] == self.img_temporal_dim_size:
+                return True
+
+        return False
+
+    def forward(self, x, masks=None, training=False):
+        if masks is not None and not isinstance(masks, list):
+            masks = [masks]
+
+        if x.ndim == 4:
+            _, _, H, W = x.shape
+            T = 1
+        elif x.ndim == 5:
+            _, _, T, H, W = x.shape
+            if self.check_temporal_dim(x.shape):
+                T = T // 1
+            else:
+                T = T // self.tubelet_size
+
+        H_patches = H // self.patch_size
+        W_patches = W // self.patch_size
+        if not self.handle_nonsquare_inputs:
+            T = H_patches = W_patches = None
+
+        if self.check_temporal_dim(x.shape):
+            assert self.patch_embed_img is not None
+            x = self.patch_embed_img(x)
+            mode = "img"
+            if self.modality_embedding:
+                x += self.img_mod_embed.repeat(x.shape[0], 1, 1)
+        else:
+            x = self.patch_embed(x)
+            mode = "video"
+            if self.modality_embedding:
+                x += self.video_mod_embed.repeat(x.shape[0], 1, 1)
+
+        if masks is not None:
+            x = apply_masks(x, masks)
+            masks = torch.cat(masks, dim=0)
+
+        outs = []
+        hier = []
+        for i, blk in enumerate(self.blocks):
+            if self.use_activation_checkpointing:
+                x, attn = torch.utils.checkpoint.checkpoint(
+                    blk,
+                    x,
+                    masks,
+                    T=T,
+                    H_patches=H_patches,
+                    W_patches=W_patches,
+                    use_reentrant=False,
+                    return_attn=self.attn_out,
+                    mode=mode,
+                )
+            else:
+                x, attn = blk(
+                    x,
+                    mask=masks,
+                    T=T,
+                    H_patches=H_patches,
+                    W_patches=W_patches,
+                    return_attn=self.attn_out,
+                    mode=mode,
+                )
+
+            if self.out_layers is not None and i in self.out_layers:
+                out_idx = self.hierarchical_layers.index(i)
+                out_norm = self.norms_block[out_idx](x)
+                outs.append(out_norm)
+
+            if i in self.out_layers_distillation:
+                out_idx = self.hierarchical_layers.index(i)
+                hier.append(self.norms_block[out_idx](x))
+
+        if self.out_layers is not None:
+            return outs
+
+        if training or self.return_hierarchical:
+            hier = torch.cat(hier, dim=2)
+            return hier
+        else:
+            x = self.norms_block[-1](x)
+            return x
+
+
+def vit_base(patch_size=16, **kwargs):
+    model = VisionTransformer(
+        patch_size=patch_size,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4,
+        qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6),
+        **kwargs,
+    )
+    return model
+
+
+def vit_large(patch_size=16, **kwargs):
+    model = VisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1024,
+        depth=24,
+        num_heads=16,
+        mlp_ratio=4,
+        qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6),
+        **kwargs,
+    )
+    return model
+
+
+def vit_huge(patch_size=16, **kwargs):
+    model = VisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1280,
+        depth=32,
+        num_heads=16,
+        mlp_ratio=4,
+        qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6),
+        **kwargs,
+    )
+    return model
+
+
+def vit_giant_xformers(patch_size=16, **kwargs):
+    model = VisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1408,
+        depth=40,
+        num_heads=22,
+        mlp_ratio=48 / 11,
+        qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6),
+        **kwargs,
+    )
+    return model
+
+
+def vit_gigantic_xformers(patch_size=16, **kwargs):
+    model = VisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1664,
+        depth=48,
+        num_heads=26,
+        mlp_ratio=64 / 13,
+        qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6),
+        **kwargs,
+    )
+    return model
+
+
+VIT_EMBED_DIMS = {
+    "vit_base": 768,
+    "vit_large": 1024,
+    "vit_huge": 1280,
+    "vit_giant_xformers": 1408,
+    "vit_gigantic_xformers": 1664,
+}
+
+ARCH_MAP = {
+    "vjepa2_1_vit_base_384": (vit_base, 1),
+    "vjepa2_1_vit_large_384": (vit_large, 1),
+    "vjepa2_1_vit_giant_384": (vit_giant_xformers, 4),
+    "vjepa2_1_vit_gigantic_384": (vit_gigantic_xformers, 4),
+}
+
+
+class VisionEncoderOutput:
+    def __init__(self, last_hidden_state, hidden_states=None):
+        self.last_hidden_state = last_hidden_state
+        self.hidden_states = hidden_states
+
+    def __getitem__(self, key):
+        return getattr(self, key)
+
+    def get(self, key, default=None):
+        return getattr(self, key, default)
+
+
+class VJEPA21Encoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        arch = getattr(config, "arch", "vjepa2_1_vit_large_384")
+        img_size = getattr(config, "img_size", 384)
+        num_frames = getattr(config, "num_frames", 80)
+        n_out = getattr(config, "n_output_distillation", None)
+        use_ckpt = getattr(config, "use_activation_checkpointing", False)
+
+        if arch not in ARCH_MAP:
+            raise ValueError(f"Unknown arch '{arch}'. Choose from {list(ARCH_MAP.keys())}")
+        factory_fn, default_n_out = ARCH_MAP[arch]
+        if n_out is None:
+            n_out = default_n_out
+
+        self.encoder = factory_fn(
+            img_size=(img_size, img_size),
+            num_frames=num_frames,
+            tubelet_size=2,
+            use_sdpa=True,
+            use_silu=False,
+            wide_silu=True,
+            uniform_power=False,
+            use_rope=True,
+            img_temporal_dim_size=1,
+            interpolate_rope=True,
+            n_output_distillation=n_out,
+            use_activation_checkpointing=use_ckpt,
+        )
+
+        self.use_hierarchical = getattr(config, "use_hierarchical", False)
+        self.encoder.return_hierarchical = self.use_hierarchical
+
+        n_levels = len(self.encoder.out_layers_distillation)
+        self._hidden_size = self.encoder.embed_dim * n_levels if self.use_hierarchical else self.encoder.embed_dim
+
+        ckpt_path = getattr(config, "pretrained_checkpoint", None)
+        if ckpt_path:
+            import os
+
+            if not os.path.isabs(ckpt_path):
+                pretrained_dir = getattr(config, "pretrained_dir", "")
+                ckpt_path = os.path.join(pretrained_dir, ckpt_path)
+            load_vjepa21_checkpoint(self, ckpt_path)
+
+    @property
+    def hidden_size(self):
+        return self._hidden_size
+
+    def gradient_checkpointing_enable(self, gradient_checkpointing_kwargs=None):
+        self.encoder.use_activation_checkpointing = True
+
+    def gradient_checkpointing_disable(self):
+        self.encoder.use_activation_checkpointing = False
+
+    def save_pretrained(self, save_directory, **kwargs):
+        import os
+
+        from safetensors.torch import save_file
+
+        os.makedirs(save_directory, exist_ok=True)
+
+        self.config.save_pretrained(save_directory)
+
+        save_file(self.state_dict(), os.path.join(save_directory, "model.safetensors"))
+        logger.info(f"VJEPA21Encoder saved to {save_directory}")
+
+    def forward(self, pixel_values, output_hidden_states=False, **kwargs):
+        x = pixel_values.permute(0, 2, 1, 3, 4).contiguous()
+
+        if output_hidden_states:
+            if self.encoder.check_temporal_dim(x.shape):
+                x_tok = self.encoder.patch_embed_img(x)
+                if self.encoder.modality_embedding:
+                    x_tok = x_tok + self.encoder.img_mod_embed
+            else:
+                x_tok = self.encoder.patch_embed(x)
+                if self.encoder.modality_embedding:
+                    x_tok = x_tok + self.encoder.video_mod_embed
+
+            _, _, T, H, W = x.shape
+            T_p = T // (1 if self.encoder.check_temporal_dim(x.shape) else self.encoder.tubelet_size)
+            H_p, W_p = H // self.encoder.patch_size, W // self.encoder.patch_size
+
+            hidden_states = [x_tok]
+            h = x_tok
+            for blk in self.encoder.blocks:
+                h, _ = blk(h, mask=None, T=T_p, H_patches=H_p, W_patches=W_p)
+                hidden_states.append(h)
+
+            if self.use_hierarchical:
+                hier = []
+                for idx in self.encoder.out_layers_distillation:
+                    out_idx = self.encoder.hierarchical_layers.index(idx)
+                    hier.append(self.encoder.norms_block[out_idx](hidden_states[idx]))
+                features = torch.cat(hier, dim=2)
+            else:
+                features = self.encoder.norms_block[-1](hidden_states[-1])
+
+            return VisionEncoderOutput(last_hidden_state=features, hidden_states=tuple(hidden_states))
+
+        features = self.encoder(x, masks=None, training=self.use_hierarchical and self.training)
+        return VisionEncoderOutput(last_hidden_state=features, hidden_states=None)
+
+
+def load_vjepa21_checkpoint(model, checkpoint_path, strict=False):
+    import os
+
+    if not os.path.exists(checkpoint_path):
+        raise FileNotFoundError(f"V-JEPA 2.1 checkpoint not found: {checkpoint_path}")
+
+    if os.path.isdir(checkpoint_path):
+        st_file = os.path.join(checkpoint_path, "model.safetensors")
+        if not os.path.exists(st_file):
+            raise FileNotFoundError(f"Directory {checkpoint_path} is missing model.safetensors")
+        from safetensors.torch import load_file
+
+        logger.info(f"Detected VJEPA21Encoder safetensors dir: {checkpoint_path}")
+        state_dict = load_file(st_file)
+        missing, unexpected = model.load_state_dict(state_dict, strict=strict)
+        n_loaded = len(state_dict) - len(unexpected)
+        logger.info(
+            f"V-JEPA 2.1 safetensors loaded: {n_loaded} keys, {len(missing)} missing, {len(unexpected)} unexpected"
+        )
+        if missing:
+            logger.info(f"  Missing (first 10): {missing[:10]}")
+        if unexpected:
+            logger.info(f"  Unexpected (first 10): {unexpected[:10]}")
+        return model
+
+    state_dict = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
+
+    if "ema_encoder" in state_dict:
+        logger.info("Detected V-JEPA 2.1 format (ema_encoder)")
+        state_dict = state_dict["ema_encoder"]
+    elif "target_encoder" in state_dict:
+        logger.info("Target Encoder only detected")
+        state_dict = state_dict["target_encoder"]
+    elif "model" in state_dict:
+        state_dict = state_dict["model"]
+
+    cleaned = {}
+    for k, v in state_dict.items():
+        cleaned[k.replace("module.", "").replace("backbone.", "")] = v
+
+    mapped = {f"encoder.{k}": v for k, v in cleaned.items()}
+
+    missing, unexpected = model.load_state_dict(mapped, strict=strict)
+    n_loaded = len(mapped) - len(unexpected)
+    logger.info(f"V-JEPA 2.1 loaded: {n_loaded} keys, {len(missing)} missing, {len(unexpected)} unexpected")
+    if missing:
+        logger.info(f"  Missing (first 10): {missing[:10]}")
+    if unexpected:
+        logger.info(f"  Unexpected (first 10): {unexpected[:10]}")
+    return model