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config.json

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+{
+  "architectures": [
+    "TimesformerModel"
+  ],
+  "depth": 8,
+  "dim": 512,
+  "n_heads": 6,
+  "num_classes": 16,
+  "num_frames": 26,
+  "patch_size": 16,
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.1",
+  "window_size": 64
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:49b7d35f53fdf7c8bc87dd227e002808f407567cba648c7d48d8681bae6ebb57
+size 151853128

vesuvius_timesformer/__init__.py

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+from .timesformer_config import TimesformerConfig
+from .timesformer_model import TimesformerModel
+
+__all__ = [
+    "TimesformerConfig",
+    "TimesformerModel",
+]

vesuvius_timesformer/timesformer_config.py

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+from transformers.configuration_utils import PretrainedConfig
+
+
+class TimesformerConfig(PretrainedConfig):
+    def __init__(
+        self,
+        window_size=64,
+        depth=8,
+        n_heads=6,
+        patch_size=16,
+        num_frames=26,
+        num_classes=16,
+        dim=512,
+        **kwargs,
+    ):
+        self.window_size = window_size
+        self.depth = depth
+        self.n_heads = n_heads
+        self.patch_size = patch_size
+        self.num_frames = num_frames
+        self.num_classes = num_classes
+        self.dim = dim
+        super().__init__(**kwargs)

vesuvius_timesformer/timesformer_model.py

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+from .timesformer_config import TimesformerConfig
+from transformers.modeling_utils import PreTrainedModel
+import torch
+from timesformer_pytorch import TimeSformer
+
+
+class TimesformerModel(PreTrainedModel):
+    config_class = TimesformerConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.backbone = TimeSformer(
+            dim=config.dim,
+            image_size=config.window_size,
+            patch_size=config.patch_size,
+            num_frames=config.num_frames,
+            num_classes=config.num_classes,
+            channels=1,
+            depth=config.depth,
+            heads=config.n_heads,
+            dim_head=64,
+            attn_dropout=0.1,
+            ff_dropout=0.1,
+        )
+        self.post_init()
+
+    def forward(self, tensor):
+        x = self.backbone(torch.permute(tensor, (0, 2, 1, 3, 4)))
+        x = x.view(-1, 1, 4, 4)
+        return x