Add DINOV3 with assistance from the Gemini CLI.

keras_hub/api/layers/__init__.py

@@ -93,6 +93,9 @@
 from keras_hub.src.models.dinov2.dinov2_image_converter import (
     DINOV2ImageConverter as DINOV2ImageConverter,
 )
+from keras_hub.src.models.dinov3.dinov3_image_converter import (
+    DINOV3ImageConverter as DINOV3ImageConverter,
+)
 from keras_hub.src.models.efficientnet.efficientnet_image_converter import (
     EfficientNetImageConverter as EfficientNetImageConverter,
 )

keras_hub/api/models/__init__.py

@@ -184,6 +184,9 @@
 from keras_hub.src.models.dinov2.dinov2_backbone import (
     DINOV2Backbone as DINOV2Backbone,
 )
+from keras_hub.src.models.dinov3.dinov3_backbone import (
+    DINOV3Backbone as DINOV3Backbone,
+)
 from keras_hub.src.models.distil_bert.distil_bert_backbone import (
     DistilBertBackbone as DistilBertBackbone,
 )

keras_hub/src/models/dinov2/dinov2_layers.py

@@ -502,7 +502,9 @@ def call(self, inputs, training=None):
 
     def get_config(self):
         config = super().get_config()
-        config.update({"hidden_dim": self.hidden_dim})
+        config.update(
+            {"hidden_dim": self.hidden_dim, "init_values": self.init_values}
+        )
         return config
 
     def compute_output_shape(self, input_shape):

keras_hub/src/models/dinov3/__init__.py

@@ -0,0 +1,5 @@
+from keras_hub.src.models.dinov3.dinov3_backbone import DINOV3Backbone
+from keras_hub.src.models.dinov3.dinov3_presets import backbone_presets
+from keras_hub.src.utils.preset_utils import register_presets
+
+register_presets(backbone_presets, DINOV3Backbone)

keras_hub/src/models/dinov3/dinov3_backbone.py

@@ -0,0 +1,263 @@
+from keras import layers
+
+from keras_hub.src.api_export import keras_hub_export
+from keras_hub.src.models.dinov3.dinov3_layers import DINOV3Embedding
+from keras_hub.src.models.dinov3.dinov3_layers import DINOV3Encoder
+from keras_hub.src.models.dinov3.dinov3_layers import (
+    DINOV3RopePositionEmbedding,
+)
+from keras_hub.src.models.feature_pyramid_backbone import FeaturePyramidBackbone
+from keras_hub.src.utils.keras_utils import standardize_data_format
+
+
+@keras_hub_export("keras_hub.models.DINOV3Backbone")
+class DINOV3Backbone(FeaturePyramidBackbone):
+    """DINOV3 core network with hyperparameters.
+
+    Args:
+        patch_size: int. The size of each square patch in the input image.
+        num_layers: int. The number of transformer layers.
+        hidden_dim: int. The size of the transformer hidden state at the end
+            of each transformer layer.
+        num_heads: int. The number of attention heads for each transformer.
+        intermediate_dim: int. The output dimension of the first Dense layer in
+            a two-layer feedforward network for each transformer.
+        layer_scale_init_value: float. The initial value for the layer scale in
+            the transformer layers. Defaults to `1.0`.
+        num_register_tokens: int. The number of register tokens to use in the
+            embedding layer. Defaults to `0`.
+        use_mask_token: bool. Whether to use a mask token in the embedding
+            layer. Defaults to `True`.
+        hidden_activation: str or callable. Activation to use in the MLP.
+            Defaults to `"gelu"`.
+        use_gated_mlp: bool. Whether to use Gated MLP layers. Defaults to
+            `False`.
+        use_query_bias: bool. Whether to use a bias for the query projection.
+            Defaults to `True`.
+        use_key_bias: bool. Whether to use a bias for the key projection.
+            Defaults to `True`.
+        use_value_bias: bool. Whether to use a bias for the value projection.
+            Defaults to `True`.
+        use_proj_bias: bool. Whether to use a bias for the output projection.
+            Defaults to `True`.
+        use_mlp_bias: bool. Whether to use a bias for the dense layers in MLP.
+            Defaults to `True`.
+        attention_dropout: float. The dropout rate for the attention
+            probabilities. Defaults to `0.0`.
+        drop_path_rate: float. The drop path rate to use. Defaults to `0.0`.
+        image_shape: tuple. The input shape without the batch size. Defaults to
+            `(518, 518, 3)`.
+        rope_theta: float. The base period of the rotary position embeddings.
+            Defaults to `100.0`.
+        apply_layernorm: bool. Whether to apply layer normalization to the
+            outputs of each stage in the feature pyramid. Defaults to `False`.
+        data_format: `None` or str. If specified, either `"channels_last"` or
+            `"channels_first"`. The ordering of the dimensions in the
+            inputs. `"channels_last"` corresponds to inputs with shape
+            `(batch_size, height, width, channels)`
+            while `"channels_first"` corresponds to inputs with shape
+            `(batch_size, channels, height, width)`. It defaults to the
+            `image_data_format` value found in your Keras config file at
+            `~/.keras/keras.json`. If you never set it, then it will be
+            `"channels_last"`.
+        dtype: string or `keras.mixed_precision.DTypePolicy`. The dtype to use
+            for the models computations and weights. Note that some
+            computations, such as softmax and layer normalization will always
+            be done a float32 precision regardless of dtype.
+
+    Example:
+    ```python
+    # Pretrained DINOV3 model.
+    input_data = {
+        "images": np.ones(shape=(1, 518, 518, 3), dtype="float32"),
+    }
+    model = keras_hub.models.DINOV3Backbone.from_preset(
+        "dinov3_vit_small_lvd1689m"
+    )
+    model(input_data)
+
+    # Pretrained DINOV3 model with custom image shape.
+    input_data = {
+        "images": np.ones(shape=(1, 224, 224, 3), dtype="float32"),
+    }
+    model = keras_hub.models.DINOV3Backbone.from_preset(
+        "dinov3_vit_small_lvd1689m", image_shape=(224, 224, 3)
+    )
+    model(input_data)
+
+    # Randomly initialized DINOV3 model with custom config.
+    model = keras_hub.models.DINOV3Backbone(
+        patch_size=14,
+        num_layers=2,
+        hidden_dim=32,
+        num_heads=2,
+        intermediate_dim=128,
+        image_shape=(224, 224, 3),
+    )
+    model(input_data)
+
+    # Accessing feature pyramid outputs.
+    backbone = keras_hub.models.DINOV3Backbone.from_preset(
+        "dinov3_vit_small_lvd1689m", image_shape=(224, 224, 3)
+    )
+    model = keras.Model(
+        inputs=backbone.inputs,
+        outputs=backbone.pyramid_outputs,
+    )
+    features = model(input_data)
+    ```
+    """
+
+    def __init__(
+        self,
+        patch_size,
+        num_layers,
+        hidden_dim,
+        num_heads,
+        intermediate_dim,
+        layer_scale_init_value=1.0,
+        num_register_tokens=4,
+        use_mask_token=True,
+        hidden_activation="gelu",
+        use_gated_mlp=False,
+        use_query_bias=True,
+        use_key_bias=True,
+        use_value_bias=True,
+        use_proj_bias=True,
+        use_mlp_bias=True,
+        attention_dropout=0.0,
+        drop_path_rate=0.0,
+        layer_norm_eps=1e-5,
+        image_shape=(518, 518, 3),
+        rope_theta=100.0,
+        apply_layernorm=False,
+        data_format=None,
+        dtype=None,
+        name=None,
+        **kwargs,
+    ):
+        data_format = standardize_data_format(data_format)
+
+        prefix = str(name) + "_" if name is not None else ""
+
+        # === Layers ===
+        self.embeddings = DINOV3Embedding(
+            hidden_dim=hidden_dim,
+            patch_size=patch_size,
+            num_register_tokens=num_register_tokens,
+            use_mask_token=use_mask_token,
+            data_format=data_format,
+            dtype=dtype,
+            name=f"{prefix}embeddings",
+        )
+        self.rope_embedding = DINOV3RopePositionEmbedding(
+            hidden_dim=hidden_dim,
+            num_heads=num_heads,
+            rope_theta=rope_theta,
+            patch_size=patch_size,
+            dtype=dtype,
+            name=f"{prefix}rope_embedding",
+        )
+        self.encoder = DINOV3Encoder(
+            num_layers=num_layers,
+            hidden_dim=hidden_dim,
+            num_heads=num_heads,
+            intermediate_dim=intermediate_dim,
+            layer_scale_init_value=layer_scale_init_value,
+            hidden_activation=hidden_activation,
+            use_gated_mlp=use_gated_mlp,
+            use_query_bias=use_query_bias,
+            use_key_bias=use_key_bias,
+            use_value_bias=use_value_bias,
+            use_proj_bias=use_proj_bias,
+            use_mlp_bias=use_mlp_bias,
+            attention_dropout=attention_dropout,
+            drop_path_rate=drop_path_rate,
+            layer_norm_eps=layer_norm_eps,
+            dtype=dtype,
+            name=f"{prefix}encoder",
+        )
+        self.layernorm = layers.LayerNormalization(
+            epsilon=layer_norm_eps, dtype=dtype, name=f"{prefix}layernorm"
+        )
+
+        # === Functional Model ===
+        pyramid_outputs = {}
+        image_input = layers.Input(shape=image_shape, name="pixel_values")
+        x = self.embeddings(image_input)
+        pyramid_outputs["stem"] = x
+
+        position_embeddings = self.rope_embedding(image_input)
+        num_prefix_tokens = 1 + num_register_tokens
+
+        x, encoder_pyramid_outputs = self.encoder(
+            x,
+            position_embeddings=position_embeddings,
+            num_prefix_tokens=num_prefix_tokens,
+        )
+        pyramid_outputs.update(encoder_pyramid_outputs)
+        x = self.layernorm(x)
+        if apply_layernorm:
+            for key in pyramid_outputs:
+                pyramid_outputs[key] = self.layernorm(pyramid_outputs[key])
+        outputs = x
+        super().__init__(
+            inputs={"pixel_values": image_input},
+            outputs=outputs,
+            dtype=dtype,
+            name=name,
+            **kwargs,
+        )
+
+        # === Config ===
+        self.patch_size = int(patch_size)
+        self.num_layers = int(num_layers)
+        self.hidden_dim = int(hidden_dim)
+        self.num_heads = int(num_heads)
+        self.intermediate_dim = int(intermediate_dim)
+        self.layer_scale_init_value = float(layer_scale_init_value)
+        self.num_register_tokens = int(num_register_tokens)
+        self.use_mask_token = bool(use_mask_token)
+        self.hidden_activation = hidden_activation
+        self.use_gated_mlp = bool(use_gated_mlp)
+        self.use_query_bias = bool(use_query_bias)
+        self.use_key_bias = bool(use_key_bias)
+        self.use_value_bias = bool(use_value_bias)
+        self.use_proj_bias = bool(use_proj_bias)
+        self.use_mlp_bias = bool(use_mlp_bias)
+        self.attention_dropout = float(attention_dropout)
+        self.drop_path_rate = float(drop_path_rate)
+        self.layer_norm_eps = float(layer_norm_eps)
+        self.image_shape = image_shape
+        self.rope_theta = rope_theta
+        self.apply_layernorm = apply_layernorm
+        self.pyramid_outputs = pyramid_outputs
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "patch_size": self.patch_size,
+                "num_layers": self.num_layers,
+                "hidden_dim": self.hidden_dim,
+                "num_heads": self.num_heads,
+                "intermediate_dim": self.intermediate_dim,
+                "num_register_tokens": self.num_register_tokens,
+                "use_mask_token": self.use_mask_token,
+                "layer_scale_init_value": self.layer_scale_init_value,
+                "hidden_activation": self.hidden_activation,
+                "use_gated_mlp": self.use_gated_mlp,
+                "use_query_bias": self.use_query_bias,
+                "use_key_bias": self.use_key_bias,
+                "use_value_bias": self.use_value_bias,
+                "use_proj_bias": self.use_proj_bias,
+                "use_mlp_bias": self.use_mlp_bias,
+                "attention_dropout": self.attention_dropout,
+                "drop_path_rate": self.drop_path_rate,
+                "layer_norm_eps": self.layer_norm_eps,
+                "image_shape": self.image_shape,
+                "rope_theta": self.rope_theta,
+                "apply_layernorm": self.apply_layernorm,
+            }
+        )
+        return config