Rename DA3 directory and document system status

Depth-Anything-V3-main/src/depth_anything_3/model/utils/block.py

@@ -0,0 +1,81 @@
+# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+
+from typing import Callable
+from torch import Tensor, nn
+
+from .attention import Attention, LayerScale, Mlp
+
+
+class Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        proj_bias: bool = True,
+        ffn_bias: bool = True,
+        drop: float = 0.0,
+        attn_drop: float = 0.0,
+        init_values=None,
+        drop_path: float = 0.0,
+        act_layer: Callable[..., nn.Module] = nn.GELU,
+        norm_layer: Callable[..., nn.Module] = nn.LayerNorm,
+        attn_class: Callable[..., nn.Module] = Attention,
+        ffn_layer: Callable[..., nn.Module] = Mlp,
+        qk_norm: bool = False,
+        rope=None,
+    ) -> None:
+        super().__init__()
+
+        self.norm1 = norm_layer(dim)
+
+        self.attn = attn_class(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            proj_bias=proj_bias,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+            qk_norm=qk_norm,
+            rope=rope,
+        )
+
+        self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = ffn_layer(
+            in_features=dim,
+            hidden_features=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+            bias=ffn_bias,
+        )
+        self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+
+        self.sample_drop_ratio = 0.0  # Equivalent to always having drop_path=0
+
+    def forward(self, x: Tensor, pos=None, attn_mask=None) -> Tensor:
+        def attn_residual_func(x: Tensor, pos=None, attn_mask=None) -> Tensor:
+            return self.ls1(self.attn(self.norm1(x), pos=pos, attn_mask=attn_mask))
+
+        def ffn_residual_func(x: Tensor) -> Tensor:
+            return self.ls2(self.mlp(self.norm2(x)))
+
+        # drop_path is always 0, so always take the else branch
+        x = x + attn_residual_func(x, pos=pos, attn_mask=attn_mask)
+        x = x + ffn_residual_func(x)
+        return x