单目3D初始代码

ultralytics/models/sam/sam3/vl_combiner.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+"""Provides utility to combine a vision backbone with a language backbone."""
+
+from __future__ import annotations
+
+from copy import copy
+
+import torch
+import torch.nn as nn
+from torch.nn.attention import SDPBackend, sdpa_kernel
+
+from .necks import Sam3DualViTDetNeck
+
+
+class SAM3VLBackbone(nn.Module):
+    """This backbone combines a vision backbone and a language backbone without fusion. As such it is more of a
+    convenience wrapper to handle the two backbones together.
+
+    It adds support for activation checkpointing and compilation.
+    """
+
+    def __init__(
+        self,
+        visual: Sam3DualViTDetNeck,
+        text,
+        compile_visual: bool = False,
+        act_ckpt_whole_vision_backbone: bool = False,
+        act_ckpt_whole_language_backbone: bool = False,
+        scalp=0,
+    ):
+        """Initialize the backbone combiner.
+
+        :param visual: The vision backbone to use
+        :param text: The text encoder to use
+        """
+        super().__init__()
+        self.vision_backbone: Sam3DualViTDetNeck = torch.compile(visual) if compile_visual else visual
+        self.language_backbone = text
+        self.scalp = scalp
+        # allow running activation checkpointing on the entire vision and language backbones
+        self.act_ckpt_whole_vision_backbone = act_ckpt_whole_vision_backbone
+        self.act_ckpt_whole_language_backbone = act_ckpt_whole_language_backbone
+
+    def forward(
+        self,
+        samples: torch.Tensor,
+        captions: list[str],
+        input_boxes: torch.Tensor = None,
+        additional_text: list[str] | None = None,
+    ):
+        """Forward pass of the backbone combiner.
+
+        :param samples: The input images
+        :param captions: The input captions
+        :param input_boxes: If the text contains place-holders for boxes, this
+            parameter contains the tensor containing their spatial features
+        :param additional_text: This can be used to encode some additional text
+            (different from the captions) in the same forward of the backbone
+        :return: Output dictionary with the following keys:
+            - vision_features: The output of the vision backbone
+            - language_features: The output of the language backbone
+            - language_mask: The attention mask of the language backbone
+            - vision_pos_enc: The positional encoding of the vision backbone
+            - (optional) additional_text_features: The output of the language
+                backbone for the additional text
+            - (optional) additional_text_mask: The attention mask of the
+                language backbone for the additional text
+        """
+        output = self.forward_image(samples)
+        output.update(self.forward_text(captions, input_boxes, additional_text))
+        return output
+
+    def forward_image(self, samples: torch.Tensor):
+        """Forward pass of the vision backbone and get both SAM3 and SAM2 features."""
+        # Forward through backbone
+        sam3_features, sam3_pos, sam2_features, sam2_pos = self.vision_backbone.forward(samples)
+        if self.scalp > 0:
+            # Discard the lowest resolution features
+            sam3_features, sam3_pos = (
+                sam3_features[: -self.scalp],
+                sam3_pos[: -self.scalp],
+            )
+            if sam2_features is not None and sam2_pos is not None:
+                sam2_features, sam2_pos = (
+                    sam2_features[: -self.scalp],
+                    sam2_pos[: -self.scalp],
+                )
+
+        sam2_output = None
+
+        if sam2_features is not None and sam2_pos is not None:
+            sam2_src = sam2_features[-1]
+            sam2_output = {
+                "vision_features": sam2_src,
+                "vision_pos_enc": sam2_pos,
+                "backbone_fpn": sam2_features,
+            }
+
+        sam3_src = sam3_features[-1]
+        return {
+            "vision_features": sam3_src,
+            "vision_pos_enc": sam3_pos,
+            "backbone_fpn": sam3_features,
+            "sam2_backbone_out": sam2_output,
+        }
+
+    def forward_image_sam2(self, samples: torch.Tensor):
+        """Forward pass of the vision backbone to get SAM2 features only."""
+        xs = self.vision_backbone.trunk(samples)
+        x = xs[-1]  # simpleFPN
+
+        assert self.vision_backbone.sam2_convs is not None, "SAM2 neck is not available."
+        sam2_features, sam2_pos = self.vision_backbone.sam_forward_feature_levels(x, self.vision_backbone.sam2_convs)
+
+        if self.scalp > 0:
+            # Discard the lowest resolution features
+            sam2_features, sam2_pos = (
+                sam2_features[: -self.scalp],
+                sam2_pos[: -self.scalp],
+            )
+
+        return {
+            "vision_features": sam2_features[-1],
+            "vision_pos_enc": sam2_pos,
+            "backbone_fpn": sam2_features,
+        }
+
+    def forward_text(self, captions, input_boxes=None, additional_text=None):
+        """Forward pass of the text encoder."""
+        output = {}
+
+        # Forward through text_encoder
+        text_to_encode = copy(captions)
+        if additional_text is not None:
+            # if there are additional_text, we piggy-back them into this forward.
+            # They'll be used later for output alignment
+            text_to_encode += additional_text
+
+        with sdpa_kernel([SDPBackend.MATH, SDPBackend.EFFICIENT_ATTENTION, SDPBackend.FLASH_ATTENTION]):
+            text_attention_mask, text_memory, text_embeds = self.language_backbone(text_to_encode, input_boxes)
+
+        if additional_text is not None:
+            output["additional_text_features"] = text_memory[:, -len(additional_text) :]
+            output["additional_text_mask"] = text_attention_mask[-len(additional_text) :]
+
+        text_memory = text_memory[:, : len(captions)]
+        text_attention_mask = text_attention_mask[: len(captions)]
+        text_embeds = text_embeds[:, : len(captions)]
+        output["language_features"] = text_memory
+        output["language_mask"] = text_attention_mask
+        output["language_embeds"] = text_embeds  # Text embeddings before forward to the encoder
+
+        return output
+
+    def set_imgsz(self, imgsz: list[int] = [1008, 1008]):
+        """Set the image size for the vision backbone."""
+        self.vision_backbone.set_imgsz(imgsz)