深度学习——SAM(Segment-Anything)代码详解

引言

从去年年初至今，SAM(Segment Anything )已经问世快一年了，SAM凭借其强大而突出的泛化性能在各项任务上取得了优异的表现，广大的研究者竞相跟进，对SAM以及其应用做了广泛而深入的研究，产生了许许多多的研究成果。写下这篇文章的时间是2024年的3月13日，写作这篇文章一方面是让自己对SAM有一个更清晰透彻的了解，另一方面是为后来者提供一下学习上的方面。对于论文，网上有很多很多的讲解，我在此就不加赘述了，本文主要关注代码的部分，对代码进行逐层的剖析。

代码目录

论文链接地址：https://ai.facebook.com/research/publications/segment-anything/
github仓库：https://github.com/facebookresearch/segment-anything
我下载代码的时间是2024年的3月13日，代码的完整目录结构是这样的

其中：
assets：存放的是图片
demo:存放的是前端部署的代码
notebooks:存的是使用的教程，包含三部分，第一部分是onnx跨平台实例，第二部分automatic_mask_generator_example是全景分割，第三部分predictor_example是prompt（使用point或bbox）分割
script：存放的是一些导出的脚本
segment_anything：这个是项目的核心代码
其余的目录和文件可以忽略不计
因此作为一个初学者，你可以对这个目录进行化简，方便学习和理解代码的全貌。（注：项目的代码可以不安装，从github下载下来后，配置完权重后可以直接运行，这种方式比较适合学习和后续研究）

上图是目录化简后的全貌，多出的checkpoints 目录存放的是网络的权重：vit_h,vit_l,vit_b ，在显存不是很充足的情况下(GPU 显存小于12G）请选用vit_b。

segment-anything 代码详解

build_sam.py

这个文件包含三层的封装,最外层是sam_model_registry，它提供了统一的接口，用来选择vit_h,vit_l,vit_b,默认使用vit_h

sam_model_registry = {
    "default": build_sam_vit_h,
    "vit_h": build_sam_vit_h,
    "vit_l": build_sam_vit_l,
    "vit_b": build_sam_vit_b,
}
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然后是三种模型的构建,也就是第二层build_sam_vit_x,这三个sam模型的差别主要体现维度，深度，注意力机制头的个数，在哪几层做注意力机制

def build_sam_vit_h(checkpoint=None):
    return _build_sam(
        encoder_embed_dim=1280,
        encoder_depth=32,
        encoder_num_heads=16,
        encoder_global_attn_indexes=[7, 15, 23, 31],
        checkpoint=checkpoint,
    )


build_sam = build_sam_vit_h


def build_sam_vit_l(checkpoint=None):
    return _build_sam(
        encoder_embed_dim=1024,
        encoder_depth=24,
        encoder_num_heads=16,
        encoder_global_attn_indexes=[5, 11, 17, 23],
        checkpoint=checkpoint,
    )


def build_sam_vit_b(checkpoint=None):
    return _build_sam(
        encoder_embed_dim=768,
        encoder_depth=12,
        encoder_num_heads=12,
        encoder_global_attn_indexes=[2, 5, 8, 11],
        checkpoint=checkpoint,
    )
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这段代码是sam 模型构建的统一代码，主要构建一个image_encoder,prompt_encoder,mask_decoder,以及在有权重的情况下加载sam的权重

def _build_sam(
    encoder_embed_dim,
    encoder_depth,
    encoder_num_heads,
    encoder_global_attn_indexes,
    checkpoint=None,
):
    prompt_embed_dim = 256
    image_size = 1024
    vit_patch_size = 16
    image_embedding_size = image_size // vit_patch_size
    sam = Sam(
        image_encoder=ImageEncoderViT(
            depth=encoder_depth,
            embed_dim=encoder_embed_dim,
            img_size=image_size,
            mlp_ratio=4,
            norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
            num_heads=encoder_num_heads,
            patch_size=vit_patch_size,
            qkv_bias=True,
            use_rel_pos=True,
            global_attn_indexes=encoder_global_attn_indexes,
            window_size=14,
            out_chans=prompt_embed_dim,
        ),
        prompt_encoder=PromptEncoder(
            embed_dim=prompt_embed_dim,
            image_embedding_size=(image_embedding_size, image_embedding_size),
            input_image_size=(image_size, image_size),
            mask_in_chans=16,
        ),
        mask_decoder=MaskDecoder(
            num_multimask_outputs=3,
            transformer=TwoWayTransformer(
                depth=2,
                embedding_dim=prompt_embed_dim,
                mlp_dim=2048,
                num_heads=8,
            ),
            transformer_dim=prompt_embed_dim,
            iou_head_depth=3,
            iou_head_hidden_dim=256,
        ),
        pixel_mean=[123.675, 116.28, 103.53],
        pixel_std=[58.395, 57.12, 57.375],
    )
    sam.eval()
    if checkpoint is not None:
        with open(checkpoint, "rb") as f:
            state_dict = torch.load(f)
        sam.load_state_dict(state_dict)
    return sam
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predictor.py

predictor.py文件实现了SamPredictor类，该类中包含两个重要的函数，一个是set_image函数,一个是predict函数,通过这两个函数可以反复高效地预测图片。

首先来看set_image这个函数

1. 对输入的图像按照长边和目标尺寸的比例缩放
2. 转换成tensor
3. 转换成[1,3,h,w]的形式
4. 调用set_torch_image函数获得image在经过了image_encoder之后的特征或者说是image_embedding

  def set_image(
        self,
        image: np.ndarray,     # 需要是[h,w,c]的形式，uint8类型
        image_format: str = "RGB",  #RGB ，BGR
    ) -> None:
   
        assert image_format in [
            "RGB",
            "BGR",
        ], f"image_format must be in ['RGB', 'BGR'], is {image_format}."    #对类型进行断言判断
        if image_format != self.model.image_format:
            image = image[..., ::-1]

        # Transform the image to the form expected by the model
        input_image = self.transform.apply_image(image)  #对按长边和目标尺寸的比例缩放
        input_image_torch = torch.as_tensor(input_image, device=self.device)  #转换成tensor
        input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]   #转换成[1,3,h,w]

        self.set_torch_image(input_image_torch, image.shape[:2])
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对于set_torch_image这个函数，主要有两个功能

1. 对transformed_image进行预处理减去imagenet均值，除以imagenet标准差
2. 对输入图像进行image_encoder编码

 def set_torch_image(
        self,
        transformed_image: torch.Tensor,
        original_image_size: Tuple[int, ...],   #原始的未经转换过的图像的大小
    ) -> None:
       
        assert (
            len(transformed_image.shape) == 4
            and transformed_image.shape[1] == 3
            and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size
        ), f"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}."
        self.reset_image()

        self.original_size = original_image_size
        self.input_size = tuple(transformed_image.shape[-2:])
        input_image = self.model.preprocess(transformed_image)  #图像预处理，减去均值，除以方差
        self.features = self.model.image_encoder(input_image)  #对图像进行进行image_encoder编码
        self.is_image_set = True
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set_image只需要做一次，反复使用，predict函数可以做多次，predict函数有以下几个参数
point_coords: 是一个nx2的数组，以[x,y]的形式传入
point_labels: 长度为n的数组，前景点为1，背景点为0
bbox :长度为4的数组，形式为xyxy
mask_input:低分辨率的mask，来源于前一个迭代，形状为1xhxw, 其中h=w=256
multimask_output ：当为true的时候会返回3个mask，对于模棱两可的prompt比如一个点，多输出可以比单单输出产生更高质量的Mask,如果只有一个mask是被需要的，可以通过quality score 来筛选mask,对于非模棱两可的输入，比如多个prompt，将multmask_output设置为false可以得到更好的结果
return_logits:如果设置为true，返回非抑制后的值，否则返回二值化的mask

   def predict(
        self,
        point_coords: Optional[np.ndarray] = None,
        point_labels: Optional[np.ndarray] = None,
        box: Optional[np.ndarray] = None,
        mask_input: Optional[np.ndarray] = None,
        multimask_output: bool = True,
        return_logits: bool = False,
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        """
        Predict masks for the given input prompts, using the currently set image.
        Returns:
          (np.ndarray): The output masks in CxHxW format, where C is the
            number of masks, and (H, W) is the original image size.
          (np.ndarray): An array of length C containing the model's
            predictions for the quality of each mask.
          (np.ndarray): An array of shape CxHxW, where C is the number
            of masks and H=W=256. These low resolution logits can be passed to
            a subsequent iteration as mask input.
        """
        if not self.is_image_set:
            raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")

        # Transform input prompts
        coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None
        if point_coords is not None:
            assert (
                point_labels is not None
            ), "point_labels must be supplied if point_coords is supplied."
            point_coords = self.transform.apply_coords(point_coords, self.original_size)  #和图像尺寸一致
            coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device)
            labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)
            coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :] 
             #在原有的基础上扩充一个维度[1,n,2]  ,[1,n]
        if box is not None:
            box = self.transform.apply_boxes(box, self.original_size)
            box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device)
            box_torch = box_torch[None, :] 
             #在原有的基础上扩充一个维度[1,n,4]
        if mask_input is not None:
            mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device)
            mask_input_torch = mask_input_torch[None, :, :, :]

        masks, iou_predictions, low_res_masks = self.predict_torch(
            coords_torch,
            labels_torch,
            box_torch,
            mask_input_torch,
            multimask_output,
            return_logits=return_logits,
        )
	
        masks_np = masks[0].detach().cpu().numpy()
        iou_predictions_np = iou_predictions[0].detach().cpu().numpy()
        low_res_masks_np = low_res_masks[0].detach().cpu().numpy()
        return masks_np, iou_predictions_np, low_res_masks_np
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在predict函数中调用了 predict_torch这个函数来完成mask的预测，首先是调用了prompt_encoder，然后调用mask_decoder进行解码，最后对mask进行后处理

  def predict_torch(
        self,
        point_coords: Optional[torch.Tensor],
        point_labels: Optional[torch.Tensor],
        boxes: Optional[torch.Tensor] = None,
        mask_input: Optional[torch.Tensor] = None,
        multimask_output: bool = True,
        return_logits: bool = False,
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
  
        if not self.is_image_set:
            raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")

        if point_coords is not None:
            points = (point_coords, point_labels)
        else:
            points = None

        # Embed prompts
        sparse_embeddings, dense_embeddings = self.model.prompt_encoder(
            points=points,
            boxes=boxes,
            masks=mask_input,
        )

        # Predict masks
        low_res_masks, iou_predictions = self.model.mask_decoder(
            image_embeddings=self.features,
            image_pe=self.model.prompt_encoder.get_dense_pe(),
            sparse_prompt_embeddings=sparse_embeddings,
            dense_prompt_embeddings=dense_embeddings,
            multimask_output=multimask_output,
        )

        # Upscale the masks to the original image resolution
        masks = self.model.postprocess_masks(low_res_masks, self.input_size, self.original_size)

        if not return_logits:
            masks = masks > self.model.mask_threshold

        return masks, iou_predictions, low_res_masks
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图像处理流程

automatic_mask_generator.py

automatic_mask_generator.py中实现了自动全景分割的类SamAutomaticMaskGenerator,通过产生一些列的网格点prompt，调用SamPredictor生成mask，然后去除低质量的点
model ：SAM 模型
points_per_side：每条边的采样点个数，总点数是points_per_side的平方，如果该参数没有指定，需要显示指定point_grids
points_per_batch：每批次运行的点的个数，数字越大越快，但是会消耗更多的显存
pred_iou_thresh： iou阈值
stability_score_thresh ：score阈值
stability_score_offset：没看懂
box_nms_thresh：非极大值抑制
crop_n_layers ：层数，大于n>0时，在这张图片上进行n次全图分割
crop_nms_thresh:非极大值抑制
crop_overlap_ratio：crop的重合比例
crop_n_points_downscale_factor ：每层每条边的点数降多少倍，就比如如果为2，每条边的点数就变成16，总点数256
point_grids ：一系列的点
min_mask_region_area ：最小区域面积
output_mode ：输出模式

    def __init__(
        self,
        model: Sam,
        points_per_side: Optional[int] = 32,
        points_per_batch: int = 64,
        pred_iou_thresh: float = 0.88,
        stability_score_thresh: float = 0.95,
        stability_score_offset: float = 1.0,
        box_nms_thresh: float = 0.7,
        crop_n_layers: int = 0,
        crop_nms_thresh: float = 0.7,
        crop_overlap_ratio: float = 512 / 1500,
        crop_n_points_downscale_factor: int = 1,
        point_grids: Optional[List[np.ndarray]] = None,
        min_mask_region_area: int = 0,
        output_mode: str = "binary_mask",
    ) -> None:
        """
        Using a SAM model, generates masks for the entire image.
        Generates a grid of point prompts over the image, then filters
        low quality and duplicate masks. The default settings are chosen
        for SAM with a ViT-H backbone.

        assert (points_per_side is None) != (
            point_grids is None
        ), "Exactly one of points_per_side or point_grid must be provided."
        #生成网格点，或者批量指定
        if points_per_side is not None:
            self.point_grids = build_all_layer_point_grids(
                points_per_side,
                crop_n_layers,
                crop_n_points_downscale_factor,
            )
        elif point_grids is not None:
            self.point_grids = point_grids
        else:
            raise ValueError("Can't have both points_per_side and point_grid be None.")

        assert output_mode in [
            "binary_mask",
            "uncompressed_rle",
            "coco_rle",
        ], f"Unknown output_mode {output_mode}."
        if output_mode == "coco_rle":
            from pycocotools import mask as mask_utils  # type: ignore # noqa: F401

        if min_mask_region_area > 0:
            import cv2  # type: ignore # noqa: F401

        self.predictor = SamPredictor(model)
        self.points_per_batch = points_per_batch
        self.pred_iou_thresh = pred_iou_thresh
        self.stability_score_thresh = stability_score_thresh
        self.stability_score_offset = stability_score_offset
        self.box_nms_thresh = box_nms_thresh
        self.crop_n_layers = crop_n_layers
        self.crop_nms_thresh = crop_nms_thresh
        self.crop_overlap_ratio = crop_overlap_ratio
        self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
        self.min_mask_region_area = min_mask_region_area
        self.output_mode = output_mode
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在__init__()函数中最终要的是生成网格点，默认每条边生成32个点，总共生成32的平方个点，这些点是归一化的点

generate函数用来生成mask，它是一系列操作的一个封装，返回的是一个list，列表里包含每个mask_region的相关信息

def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
     
        # Generate masks
        mask_data = self._generate_masks(image)    #核心函数

        # Filter small disconnected regions and holes in masks
        if self.min_mask_region_area > 0:
            mask_data = self.postprocess_small_regions(
                mask_data,
                self.min_mask_region_area,
                max(self.box_nms_thresh, self.crop_nms_thresh),
            )

        # Encode masks
        if self.output_mode == "coco_rle":
            mask_data["segmentations"] = [coco_encode_rle(rle) for rle in mask_data["rles"]]
        elif self.output_mode == "binary_mask":
            mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
        else:
            mask_data["segmentations"] = mask_data["rles"]

        # Write mask records
        curr_anns = []
        for idx in range(len(mask_data["segmentations"])):
            ann = {
                "segmentation": mask_data["segmentations"][idx],
                "area": area_from_rle(mask_data["rles"][idx]),
                "bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
                "predicted_iou": mask_data["iou_preds"][idx].item(),
                "point_coords": [mask_data["points"][idx].tolist()],
                "stability_score": mask_data["stability_score"][idx].item(),
                "crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
            }
            curr_anns.append(ann)

        return curr_anns
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在generate函数中会调用 _generate_masks函数

 def _generate_masks(self, image: np.ndarray) -> MaskData:
        orig_size = image.shape[:2]
        crop_boxes, layer_idxs = generate_crop_boxes(
            orig_size, self.crop_n_layers, self.crop_overlap_ratio
        )

        # Iterate over image crops
        data = MaskData()
        for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
            crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
            data.cat(crop_data)

        # Remove duplicate masks between crops
        if len(crop_boxes) > 1:
            # Prefer masks from smaller crops
            scores = 1 / box_area(data["crop_boxes"])
            scores = scores.to(data["boxes"].device)
            keep_by_nms = batched_nms(
                data["boxes"].float(),
                scores,
                torch.zeros_like(data["boxes"][:, 0]),  # categories
                iou_threshold=self.crop_nms_thresh,
            )
            data.filter(keep_by_nms)

        data.to_numpy()
        return data
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对crop出来的图片进行进行预测

 def _process_crop(
        self,
        image: np.ndarray,
        crop_box: List[int],
        crop_layer_idx: int,
        orig_size: Tuple[int, ...],
    ) -> MaskData:
        # Crop the image and calculate embeddings
        x0, y0, x1, y1 = crop_box
        cropped_im = image[y0:y1, x0:x1, :]
        cropped_im_size = cropped_im.shape[:2]
        self.predictor.set_image(cropped_im)

        # Get points for this crop
        points_scale = np.array(cropped_im_size)[None, ::-1]
        points_for_image = self.point_grids[crop_layer_idx] * points_scale

        # Generate masks for this crop in batches
        data = MaskData()
        for (points,) in batch_iterator(self.points_per_batch, points_for_image):
            batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size)
            data.cat(batch_data)
            del batch_data
        self.predictor.reset_image()

        # Remove duplicates within this crop.
        keep_by_nms = batched_nms(
            data["boxes"].float(),
            data["iou_preds"],
            torch.zeros_like(data["boxes"][:, 0]),  # categories
            iou_threshold=self.box_nms_thresh,
        )
        data.filter(keep_by_nms)

        # Return to the original image frame
        data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box)
        data["points"] = uncrop_points(data["points"], crop_box)
        data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))])

        return data
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输入批量的点批量预测

  def _process_batch(
        self,
        points: np.ndarray,
        im_size: Tuple[int, ...],
        crop_box: List[int],
        orig_size: Tuple[int, ...],
    ) -> MaskData:
        orig_h, orig_w = orig_size

        # Run model on this batch
        transformed_points = self.predictor.transform.apply_coords(points, im_size)
        in_points = torch.as_tensor(transformed_points, device=self.predictor.device)
        in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)
        masks, iou_preds, _ = self.predictor.predict_torch(
            in_points[:, None, :],   #[b,n,2]
            in_labels[:, None],      #[b,n]
            multimask_output=True,
            return_logits=True,
        )

        # Serialize predictions and store in MaskData
        data = MaskData(
            masks=masks.flatten(0, 1),
            iou_preds=iou_preds.flatten(0, 1),
            points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
        )
        del masks

        # Filter by predicted IoU
        if self.pred_iou_thresh > 0.0:
            keep_mask = data["iou_preds"] > self.pred_iou_thresh
            data.filter(keep_mask)

        # Calculate stability score
        data["stability_score"] = calculate_stability_score(
            data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset
        )
        if self.stability_score_thresh > 0.0:
            keep_mask = data["stability_score"] >= self.stability_score_thresh
            data.filter(keep_mask)

        # Threshold masks and calculate boxes
        data["masks"] = data["masks"] > self.predictor.model.mask_threshold
        data["boxes"] = batched_mask_to_box(data["masks"])

        # Filter boxes that touch crop boundaries
        keep_mask = ~is_box_near_crop_edge(data["boxes"], crop_box, [0, 0, orig_w, orig_h])
        if not torch.all(keep_mask):
            data.filter(keep_mask)

        # Compress to RLE
        data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w)
        data["rles"] = mask_to_rle_pytorch(data["masks"])
        del data["masks"]

        return data
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