ultralytics 8.0.89 SAM predict and auto-annotate (#2298)

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ultralytics/__init__.py

@@ -1,9 +1,10 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.88'
+__version__ = '8.0.89'
 
 from ultralytics.hub import start
+from ultralytics.vit.sam import SAM
 from ultralytics.yolo.engine.model import YOLO
 from ultralytics.yolo.utils.checks import check_yolo as checks
 
-__all__ = '__version__', 'YOLO', 'checks', 'start'  # allow simpler import
+__all__ = '__version__', 'YOLO', 'SAM', 'checks', 'start'  # allow simpler import

ultralytics/nn/modules.py

@@ -495,6 +495,41 @@ class Detect(nn.Module):
             b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (.01 objects, 80 classes, 640 img)
 
 
+class MLPBlock(nn.Module):
+
+    def __init__(
+        self,
+        embedding_dim,
+        mlp_dim,
+        act=nn.GELU,
+    ):
+        super().__init__()
+        self.lin1 = nn.Linear(embedding_dim, mlp_dim)
+        self.lin2 = nn.Linear(mlp_dim, embedding_dim)
+        self.act = act()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.lin2(self.act(self.lin1(x)))
+
+
+# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
+# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119  # noqa
+class LayerNorm2d(nn.Module):
+
+    def __init__(self, num_channels, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(num_channels))
+        self.bias = nn.Parameter(torch.zeros(num_channels))
+        self.eps = eps
+
+    def forward(self, x):
+        u = x.mean(1, keepdim=True)
+        s = (x - u).pow(2).mean(1, keepdim=True)
+        x = (x - u) / torch.sqrt(s + self.eps)
+        x = self.weight[:, None, None] * x + self.bias[:, None, None]
+        return x
+
+
 class Segment(Detect):
     """YOLOv8 Segment head for segmentation models."""
 

ultralytics/vit/__init__.py

@@ -0,0 +1 @@
+from .sam import SAM  # noqa

ultralytics/vit/sam/__init__.py

@@ -0,0 +1,3 @@
+from .build import build_sam  # noqa
+from .model import SAM  # noqa
+from .modules.prompt_predictor import PromptPredictor  # noqa

ultralytics/vit/sam/amg.py

@@ -0,0 +1,311 @@
+import math
+from copy import deepcopy
+from itertools import product
+from typing import Any, Dict, Generator, ItemsView, List, Tuple
+
+import numpy as np
+import torch
+
+
+class MaskData:
+    """
+    A structure for storing masks and their related data in batched format.
+    Implements basic filtering and concatenation.
+    """
+
+    def __init__(self, **kwargs) -> None:
+        """Initialize a MaskData object, ensuring all values are supported types."""
+        for v in kwargs.values():
+            assert isinstance(
+                v, (list, np.ndarray, torch.Tensor)), 'MaskData only supports list, numpy arrays, and torch tensors.'
+        self._stats = dict(**kwargs)
+
+    def __setitem__(self, key: str, item: Any) -> None:
+        """Set an item in the MaskData object, ensuring it is a supported type."""
+        assert isinstance(
+            item, (list, np.ndarray, torch.Tensor)), 'MaskData only supports list, numpy arrays, and torch tensors.'
+        self._stats[key] = item
+
+    def __delitem__(self, key: str) -> None:
+        """Delete an item from the MaskData object."""
+        del self._stats[key]
+
+    def __getitem__(self, key: str) -> Any:
+        """Get an item from the MaskData object."""
+        return self._stats[key]
+
+    def items(self) -> ItemsView[str, Any]:
+        """Return an ItemsView of the MaskData object."""
+        return self._stats.items()
+
+    def filter(self, keep: torch.Tensor) -> None:
+        """Filter the MaskData object based on the given boolean tensor."""
+        for k, v in self._stats.items():
+            if v is None:
+                self._stats[k] = None
+            elif isinstance(v, torch.Tensor):
+                self._stats[k] = v[torch.as_tensor(keep, device=v.device)]
+            elif isinstance(v, np.ndarray):
+                self._stats[k] = v[keep.detach().cpu().numpy()]
+            elif isinstance(v, list) and keep.dtype == torch.bool:
+                self._stats[k] = [a for i, a in enumerate(v) if keep[i]]
+            elif isinstance(v, list):
+                self._stats[k] = [v[i] for i in keep]
+            else:
+                raise TypeError(f'MaskData key {k} has an unsupported type {type(v)}.')
+
+    def cat(self, new_stats: 'MaskData') -> None:
+        """Concatenate a new MaskData object to the current one."""
+        for k, v in new_stats.items():
+            if k not in self._stats or self._stats[k] is None:
+                self._stats[k] = deepcopy(v)
+            elif isinstance(v, torch.Tensor):
+                self._stats[k] = torch.cat([self._stats[k], v], dim=0)
+            elif isinstance(v, np.ndarray):
+                self._stats[k] = np.concatenate([self._stats[k], v], axis=0)
+            elif isinstance(v, list):
+                self._stats[k] = self._stats[k] + deepcopy(v)
+            else:
+                raise TypeError(f'MaskData key {k} has an unsupported type {type(v)}.')
+
+    def to_numpy(self) -> None:
+        """Convert all torch tensors in the MaskData object to numpy arrays."""
+        for k, v in self._stats.items():
+            if isinstance(v, torch.Tensor):
+                self._stats[k] = v.detach().cpu().numpy()
+
+
+def is_box_near_crop_edge(boxes: torch.Tensor,
+                          crop_box: List[int],
+                          orig_box: List[int],
+                          atol: float = 20.0) -> torch.Tensor:
+    """Return a boolean tensor indicating if boxes are near the crop edge."""
+    crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)
+    orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)
+    boxes = uncrop_boxes_xyxy(boxes, crop_box).float()
+    near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)
+    near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)
+    near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)
+    return torch.any(near_crop_edge, dim=1)
+
+
+def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:
+    """Convert bounding boxes from XYXY format to XYWH format."""
+    box_xywh = deepcopy(box_xyxy)
+    box_xywh[2] = box_xywh[2] - box_xywh[0]
+    box_xywh[3] = box_xywh[3] - box_xywh[1]
+    return box_xywh
+
+
+def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:
+    """Yield batches of data from the input arguments."""
+    assert args and all(len(a) == len(args[0]) for a in args), 'Batched iteration must have same-size inputs.'
+    n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)
+    for b in range(n_batches):
+        yield [arg[b * batch_size:(b + 1) * batch_size] for arg in args]
+
+
+def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
+    """Encode masks as uncompressed RLEs in the format expected by pycocotools."""
+    # Put in fortran order and flatten h,w
+    b, h, w = tensor.shape
+    tensor = tensor.permute(0, 2, 1).flatten(1)
+
+    # Compute change indices
+    diff = tensor[:, 1:] ^ tensor[:, :-1]
+    change_indices = diff.nonzero()
+
+    # Encode run length
+    out = []
+    for i in range(b):
+        cur_idxs = change_indices[change_indices[:, 0] == i, 1]
+        cur_idxs = torch.cat([
+            torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),
+            cur_idxs + 1,
+            torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device), ])
+        btw_idxs = cur_idxs[1:] - cur_idxs[:-1]
+        counts = [] if tensor[i, 0] == 0 else [0]
+        counts.extend(btw_idxs.detach().cpu().tolist())
+        out.append({'size': [h, w], 'counts': counts})
+    return out
+
+
+def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
+    """Compute a binary mask from an uncompressed RLE."""
+    h, w = rle['size']
+    mask = np.empty(h * w, dtype=bool)
+    idx = 0
+    parity = False
+    for count in rle['counts']:
+        mask[idx:idx + count] = parity
+        idx += count
+        parity ^= True
+    mask = mask.reshape(w, h)
+    return mask.transpose()  # Put in C order
+
+
+def area_from_rle(rle: Dict[str, Any]) -> int:
+    """Calculate the area of a mask from its uncompressed RLE."""
+    return sum(rle['counts'][1::2])
+
+
+def calculate_stability_score(masks: torch.Tensor, mask_threshold: float, threshold_offset: float) -> torch.Tensor:
+    """
+    Computes the stability score for a batch of masks. The stability
+    score is the IoU between the binary masks obtained by thresholding
+    the predicted mask logits at high and low values.
+    """
+    # One mask is always contained inside the other.
+    # Save memory by preventing unnecessary cast to torch.int64
+    intersections = ((masks > (mask_threshold + threshold_offset)).sum(-1, dtype=torch.int16).sum(-1,
+                                                                                                  dtype=torch.int32))
+    unions = ((masks > (mask_threshold - threshold_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32))
+    return intersections / unions
+
+
+def build_point_grid(n_per_side: int) -> np.ndarray:
+    """Generate a 2D grid of evenly spaced points in the range [0,1]x[0,1]."""
+    offset = 1 / (2 * n_per_side)
+    points_one_side = np.linspace(offset, 1 - offset, n_per_side)
+    points_x = np.tile(points_one_side[None, :], (n_per_side, 1))
+    points_y = np.tile(points_one_side[:, None], (1, n_per_side))
+    return np.stack([points_x, points_y], axis=-1).reshape(-1, 2)
+
+
+def build_all_layer_point_grids(n_per_side: int, n_layers: int, scale_per_layer: int) -> List[np.ndarray]:
+    """Generate point grids for all crop layers."""
+    return [build_point_grid(int(n_per_side / (scale_per_layer ** i))) for i in range(n_layers + 1)]
+
+
+def generate_crop_boxes(im_size: Tuple[int, ...], n_layers: int,
+                        overlap_ratio: float) -> Tuple[List[List[int]], List[int]]:
+    """Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer."""
+    crop_boxes, layer_idxs = [], []
+    im_h, im_w = im_size
+    short_side = min(im_h, im_w)
+
+    # Original image
+    crop_boxes.append([0, 0, im_w, im_h])
+    layer_idxs.append(0)
+
+    def crop_len(orig_len, n_crops, overlap):
+        """Crops bounding boxes to the size of the input image."""
+        return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))
+
+    for i_layer in range(n_layers):
+        n_crops_per_side = 2 ** (i_layer + 1)
+        overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))
+
+        crop_w = crop_len(im_w, n_crops_per_side, overlap)
+        crop_h = crop_len(im_h, n_crops_per_side, overlap)
+
+        crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]
+        crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]
+
+        # Crops in XYWH format
+        for x0, y0 in product(crop_box_x0, crop_box_y0):
+            box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]
+            crop_boxes.append(box)
+            layer_idxs.append(i_layer + 1)
+
+    return crop_boxes, layer_idxs
+
+
+def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
+    """Uncrop bounding boxes by adding the crop box offset."""
+    x0, y0, _, _ = crop_box
+    offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)
+    # Check if boxes has a channel dimension
+    if len(boxes.shape) == 3:
+        offset = offset.unsqueeze(1)
+    return boxes + offset
+
+
+def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
+    """Uncrop points by adding the crop box offset."""
+    x0, y0, _, _ = crop_box
+    offset = torch.tensor([[x0, y0]], device=points.device)
+    # Check if points has a channel dimension
+    if len(points.shape) == 3:
+        offset = offset.unsqueeze(1)
+    return points + offset
+
+
+def uncrop_masks(masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int) -> torch.Tensor:
+    """Uncrop masks by padding them to the original image size."""
+    x0, y0, x1, y1 = crop_box
+    if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:
+        return masks
+    # Coordinate transform masks
+    pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)
+    pad = (x0, pad_x - x0, y0, pad_y - y0)
+    return torch.nn.functional.pad(masks, pad, value=0)
+
+
+def remove_small_regions(mask: np.ndarray, area_thresh: float, mode: str) -> Tuple[np.ndarray, bool]:
+    """Remove small disconnected regions or holes in a mask, returning the mask and a modification indicator."""
+    import cv2  # type: ignore
+
+    assert mode in {'holes', 'islands'}
+    correct_holes = mode == 'holes'
+    working_mask = (correct_holes ^ mask).astype(np.uint8)
+    n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
+    sizes = stats[:, -1][1:]  # Row 0 is background label
+    small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
+    if not small_regions:
+        return mask, False
+    fill_labels = [0] + small_regions
+    if not correct_holes:
+        fill_labels = [i for i in range(n_labels) if i not in fill_labels]
+        # If every region is below threshold, keep largest
+        if not fill_labels:
+            fill_labels = [int(np.argmax(sizes)) + 1]
+    mask = np.isin(regions, fill_labels)
+    return mask, True
+
+
+def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:
+    """Encode uncompressed RLE (run-length encoding) to COCO RLE format."""
+    from pycocotools import mask as mask_utils  # type: ignore
+
+    h, w = uncompressed_rle['size']
+    rle = mask_utils.frPyObjects(uncompressed_rle, h, w)
+    rle['counts'] = rle['counts'].decode('utf-8')  # Necessary to serialize with json
+    return rle
+
+
+def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:
+    """
+    Calculates boxes in XYXY format around masks. Return [0,0,0,0] for
+    an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4.
+    """
+    # torch.max below raises an error on empty inputs, just skip in this case
+    if torch.numel(masks) == 0:
+        return torch.zeros(*masks.shape[:-2], 4, device=masks.device)
+
+    # Normalize shape to CxHxW
+    shape = masks.shape
+    h, w = shape[-2:]
+    masks = masks.flatten(0, -3) if len(shape) > 2 else masks.unsqueeze(0)
+    # Get top and bottom edges
+    in_height, _ = torch.max(masks, dim=-1)
+    in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]
+    bottom_edges, _ = torch.max(in_height_coords, dim=-1)
+    in_height_coords = in_height_coords + h * (~in_height)
+    top_edges, _ = torch.min(in_height_coords, dim=-1)
+
+    # Get left and right edges
+    in_width, _ = torch.max(masks, dim=-2)
+    in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]
+    right_edges, _ = torch.max(in_width_coords, dim=-1)
+    in_width_coords = in_width_coords + w * (~in_width)
+    left_edges, _ = torch.min(in_width_coords, dim=-1)
+
+    # If the mask is empty the right edge will be to the left of the left edge.
+    # Replace these boxes with [0, 0, 0, 0]
+    empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)
+    out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)
+    out = out * (~empty_filter).unsqueeze(-1)
+
+    # Return to original shape
+    return out.reshape(*shape[:-2], 4) if len(shape) > 2 else out[0]

ultralytics/vit/sam/autosize.py

@@ -0,0 +1,92 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from copy import deepcopy
+from typing import Tuple
+
+import numpy as np
+import torch
+from torch.nn import functional as F
+from torchvision.transforms.functional import resize, to_pil_image  # type: ignore
+
+
+class ResizeLongestSide:
+    """
+    Resizes images to the longest side 'target_length', as well as provides
+    methods for resizing coordinates and boxes. Provides methods for
+    transforming both numpy array and batched torch tensors.
+    """
+
+    def __init__(self, target_length: int) -> None:
+        self.target_length = target_length
+
+    def apply_image(self, image: np.ndarray) -> np.ndarray:
+        """
+        Expects a numpy array with shape HxWxC in uint8 format.
+        """
+        target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
+        return np.array(resize(to_pil_image(image), target_size))
+
+    def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
+        """
+        Expects a numpy array of length 2 in the final dimension. Requires the
+        original image size in (H, W) format.
+        """
+        old_h, old_w = original_size
+        new_h, new_w = self.get_preprocess_shape(original_size[0], original_size[1], self.target_length)
+        coords = deepcopy(coords).astype(float)
+        coords[..., 0] = coords[..., 0] * (new_w / old_w)
+        coords[..., 1] = coords[..., 1] * (new_h / old_h)
+        return coords
+
+    def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
+        """
+        Expects a numpy array shape Bx4. Requires the original image size
+        in (H, W) format.
+        """
+        boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)
+        return boxes.reshape(-1, 4)
+
+    def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:
+        """
+        Expects batched images with shape BxCxHxW and float format. This
+        transformation may not exactly match apply_image. apply_image is
+        the transformation expected by the model.
+        """
+        # Expects an image in BCHW format. May not exactly match apply_image.
+        target_size = self.get_preprocess_shape(image.shape[2], image.shape[3], self.target_length)
+        return F.interpolate(image, target_size, mode='bilinear', align_corners=False, antialias=True)
+
+    def apply_coords_torch(self, coords: torch.Tensor, original_size: Tuple[int, ...]) -> torch.Tensor:
+        """
+        Expects a torch tensor with length 2 in the last dimension. Requires the
+        original image size in (H, W) format.
+        """
+        old_h, old_w = original_size
+        new_h, new_w = self.get_preprocess_shape(original_size[0], original_size[1], self.target_length)
+        coords = deepcopy(coords).to(torch.float)
+        coords[..., 0] = coords[..., 0] * (new_w / old_w)
+        coords[..., 1] = coords[..., 1] * (new_h / old_h)
+        return coords
+
+    def apply_boxes_torch(self, boxes: torch.Tensor, original_size: Tuple[int, ...]) -> torch.Tensor:
+        """
+        Expects a torch tensor with shape Bx4. Requires the original image
+        size in (H, W) format.
+        """
+        boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)
+        return boxes.reshape(-1, 4)
+
+    @staticmethod
+    def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]:
+        """
+        Compute the output size given input size and target long side length.
+        """
+        scale = long_side_length * 1.0 / max(oldh, oldw)
+        newh, neww = oldh * scale, oldw * scale
+        neww = int(neww + 0.5)
+        newh = int(newh + 0.5)
+        return (newh, neww)

ultralytics/vit/sam/build.py

@@ -0,0 +1,121 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from functools import partial
+
+import torch
+
+from ...yolo.utils.downloads import attempt_download_asset
+from .modules.decoders import MaskDecoder
+from .modules.encoders import ImageEncoderViT, PromptEncoder
+from .modules.sam import Sam
+from .modules.transformer import TwoWayTransformer
+
+
+def build_sam_vit_h(checkpoint=None):
+    """Build and return a Segment Anything Model (SAM) h-size model."""
+    return _build_sam(
+        encoder_embed_dim=1280,
+        encoder_depth=32,
+        encoder_num_heads=16,
+        encoder_global_attn_indexes=[7, 15, 23, 31],
+        checkpoint=checkpoint,
+    )
+
+
+def build_sam_vit_l(checkpoint=None):
+    """Build and return a Segment Anything Model (SAM) l-size model."""
+    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):
+    """Build and return a Segment Anything Model (SAM) b-size model."""
+    return _build_sam(
+        encoder_embed_dim=768,
+        encoder_depth=12,
+        encoder_num_heads=12,
+        encoder_global_attn_indexes=[2, 5, 8, 11],
+        checkpoint=checkpoint,
+    )
+
+
+def _build_sam(
+    encoder_embed_dim,
+    encoder_depth,
+    encoder_num_heads,
+    encoder_global_attn_indexes,
+    checkpoint=None,
+):
+    """Builds the selected SAM model architecture."""
+    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:
+        attempt_download_asset(checkpoint)
+        with open(checkpoint, 'rb') as f:
+            state_dict = torch.load(f)
+        sam.load_state_dict(state_dict)
+    return sam
+
+
+sam_model_map = {
+    # "default": build_sam_vit_h,
+    'sam_h.pt': build_sam_vit_h,
+    'sam_l.pt': build_sam_vit_l,
+    'sam_b.pt': build_sam_vit_b, }
+
+
+def build_sam(ckpt='sam_b.pt'):
+    """Build a SAM model specified by ckpt."""
+    model_builder = sam_model_map.get(ckpt)
+    if not model_builder:
+        raise FileNotFoundError(f'{ckpt} is not a supported sam model. Available models are: \n {sam_model_map.keys()}')
+
+    return model_builder(ckpt)

ultralytics/vit/sam/model.py

@@ -0,0 +1,35 @@
+# SAM model interface
+
+from ultralytics.yolo.cfg import get_cfg
+
+from .build import build_sam
+from .predict import Predictor
+
+
+class SAM:
+
+    def __init__(self, model='sam_b.pt') -> None:
+        if model and not (model.endswith('.pt') or model.endswith('.pth')):
+            # Should raise AssertionError instead?
+            raise NotImplementedError('Segment anything prediction requires pre-trained checkpoint')
+        self.model = build_sam(model)
+        self.predictor = None  # reuse predictor
+
+    def predict(self, source, stream=False, **kwargs):
+        """Predicts and returns segmentation masks for given image or video source."""
+        overrides = dict(conf=0.25, task='segment', mode='predict')
+        overrides.update(kwargs)  # prefer kwargs
+        if not self.predictor:
+            self.predictor = Predictor(overrides=overrides)
+            self.predictor.setup_model(model=self.model)
+        else:  # only update args if predictor is already setup
+            self.predictor.args = get_cfg(self.predictor.args, overrides)
+        return self.predictor(source, stream=stream)
+
+    def train(self, **kwargs):
+        """Function trains models but raises an error as SAM models do not support training."""
+        raise NotImplementedError("SAM models don't support training")
+
+    def val(self, **kwargs):
+        """Run validation given dataset."""
+        raise NotImplementedError("SAM models don't support validation")

ultralytics/vit/sam/modules/decoders.py

@@ -0,0 +1,161 @@
+from typing import List, Tuple, Type
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from ultralytics.nn.modules import LayerNorm2d
+
+
+class MaskDecoder(nn.Module):
+
+    def __init__(
+        self,
+        *,
+        transformer_dim: int,
+        transformer: nn.Module,
+        num_multimask_outputs: int = 3,
+        activation: Type[nn.Module] = nn.GELU,
+        iou_head_depth: int = 3,
+        iou_head_hidden_dim: int = 256,
+    ) -> None:
+        """
+        Predicts masks given an image and prompt embeddings, using a
+        transformer architecture.
+
+        Arguments:
+          transformer_dim (int): the channel dimension of the transformer
+          transformer (nn.Module): the transformer used to predict masks
+          num_multimask_outputs (int): the number of masks to predict
+            when disambiguating masks
+          activation (nn.Module): the type of activation to use when
+            upscaling masks
+          iou_head_depth (int): the depth of the MLP used to predict
+            mask quality
+          iou_head_hidden_dim (int): the hidden dimension of the MLP
+            used to predict mask quality
+        """
+        super().__init__()
+        self.transformer_dim = transformer_dim
+        self.transformer = transformer
+
+        self.num_multimask_outputs = num_multimask_outputs
+
+        self.iou_token = nn.Embedding(1, transformer_dim)
+        self.num_mask_tokens = num_multimask_outputs + 1
+        self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
+
+        self.output_upscaling = nn.Sequential(
+            nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
+            LayerNorm2d(transformer_dim // 4),
+            activation(),
+            nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
+            activation(),
+        )
+        self.output_hypernetworks_mlps = nn.ModuleList([
+            MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3) for _ in range(self.num_mask_tokens)])
+
+        self.iou_prediction_head = MLP(transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth)
+
+    def forward(
+        self,
+        image_embeddings: torch.Tensor,
+        image_pe: torch.Tensor,
+        sparse_prompt_embeddings: torch.Tensor,
+        dense_prompt_embeddings: torch.Tensor,
+        multimask_output: bool,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Predict masks given image and prompt embeddings.
+
+        Arguments:
+          image_embeddings (torch.Tensor): the embeddings from the image encoder
+          image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
+          sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
+          dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
+          multimask_output (bool): Whether to return multiple masks or a single
+            mask.
+
+        Returns:
+          torch.Tensor: batched predicted masks
+          torch.Tensor: batched predictions of mask quality
+        """
+        masks, iou_pred = self.predict_masks(
+            image_embeddings=image_embeddings,
+            image_pe=image_pe,
+            sparse_prompt_embeddings=sparse_prompt_embeddings,
+            dense_prompt_embeddings=dense_prompt_embeddings,
+        )
+
+        # Select the correct mask or masks for output
+        mask_slice = slice(1, None) if multimask_output else slice(0, 1)
+        masks = masks[:, mask_slice, :, :]
+        iou_pred = iou_pred[:, mask_slice]
+
+        # Prepare output
+        return masks, iou_pred
+
+    def predict_masks(
+        self,
+        image_embeddings: torch.Tensor,
+        image_pe: torch.Tensor,
+        sparse_prompt_embeddings: torch.Tensor,
+        dense_prompt_embeddings: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Predicts masks. See 'forward' for more details."""
+        # Concatenate output tokens
+        output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
+        output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
+        tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
+
+        # Expand per-image data in batch direction to be per-mask
+        src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
+        src = src + dense_prompt_embeddings
+        pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
+        b, c, h, w = src.shape
+
+        # Run the transformer
+        hs, src = self.transformer(src, pos_src, tokens)
+        iou_token_out = hs[:, 0, :]
+        mask_tokens_out = hs[:, 1:(1 + self.num_mask_tokens), :]
+
+        # Upscale mask embeddings and predict masks using the mask tokens
+        src = src.transpose(1, 2).view(b, c, h, w)
+        upscaled_embedding = self.output_upscaling(src)
+        hyper_in_list: List[torch.Tensor] = [
+            self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]) for i in range(self.num_mask_tokens)]
+        hyper_in = torch.stack(hyper_in_list, dim=1)
+        b, c, h, w = upscaled_embedding.shape
+        masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
+
+        # Generate mask quality predictions
+        iou_pred = self.iou_prediction_head(iou_token_out)
+
+        return masks, iou_pred
+
+
+# Lightly adapted from
+# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
+class MLP(nn.Module):
+
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        output_dim: int,
+        num_layers: int,
+        sigmoid_output: bool = False,
+    ) -> None:
+        super().__init__()
+        self.num_layers = num_layers
+        h = [hidden_dim] * (num_layers - 1)
+        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
+        self.sigmoid_output = sigmoid_output
+
+    def forward(self, x):
+        """Executes feedforward within the neural network module and applies activation."""
+        for i, layer in enumerate(self.layers):
+            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+        if self.sigmoid_output:
+            x = F.sigmoid(x)
+        return x

ultralytics/vit/sam/modules/encoders.py

@@ -0,0 +1,582 @@
+from typing import Any, Optional, Tuple, Type
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ultralytics.nn.modules import LayerNorm2d, MLPBlock
+
+
+# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
+class ImageEncoderViT(nn.Module):
+
+    def __init__(
+            self,
+            img_size: int = 1024,
+            patch_size: int = 16,
+            in_chans: int = 3,
+            embed_dim: int = 768,
+            depth: int = 12,
+            num_heads: int = 12,
+            mlp_ratio: float = 4.0,
+            out_chans: int = 256,
+            qkv_bias: bool = True,
+            norm_layer: Type[nn.Module] = nn.LayerNorm,
+            act_layer: Type[nn.Module] = nn.GELU,
+            use_abs_pos: bool = True,
+            use_rel_pos: bool = False,
+            rel_pos_zero_init: bool = True,
+            window_size: int = 0,
+            global_attn_indexes: Tuple[int, ...] = (),
+    ) -> None:
+        """
+        Args:
+            img_size (int): Input image size.
+            patch_size (int): Patch size.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Patch embedding dimension.
+            depth (int): Depth of ViT.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_abs_pos (bool): If True, use absolute positional embeddings.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks.
+            global_attn_indexes (list): Indexes for blocks using global attention.
+        """
+        super().__init__()
+        self.img_size = img_size
+
+        self.patch_embed = PatchEmbed(
+            kernel_size=(patch_size, patch_size),
+            stride=(patch_size, patch_size),
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+        )
+
+        self.pos_embed: Optional[nn.Parameter] = None
+        if use_abs_pos:
+            # Initialize absolute positional embedding with pretrain image size.
+            self.pos_embed = nn.Parameter(torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim))
+
+        self.blocks = nn.ModuleList()
+        for i in range(depth):
+            block = Block(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                use_rel_pos=use_rel_pos,
+                rel_pos_zero_init=rel_pos_zero_init,
+                window_size=window_size if i not in global_attn_indexes else 0,
+                input_size=(img_size // patch_size, img_size // patch_size),
+            )
+            self.blocks.append(block)
+
+        self.neck = nn.Sequential(
+            nn.Conv2d(
+                embed_dim,
+                out_chans,
+                kernel_size=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+            nn.Conv2d(
+                out_chans,
+                out_chans,
+                kernel_size=3,
+                padding=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.patch_embed(x)
+        if self.pos_embed is not None:
+            x = x + self.pos_embed
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x = self.neck(x.permute(0, 3, 1, 2))
+
+        return x
+
+
+class PromptEncoder(nn.Module):
+
+    def __init__(
+        self,
+        embed_dim: int,
+        image_embedding_size: Tuple[int, int],
+        input_image_size: Tuple[int, int],
+        mask_in_chans: int,
+        activation: Type[nn.Module] = nn.GELU,
+    ) -> None:
+        """
+        Encodes prompts for input to SAM's mask decoder.
+
+        Arguments:
+          embed_dim (int): The prompts' embedding dimension
+          image_embedding_size (tuple(int, int)): The spatial size of the
+            image embedding, as (H, W).
+          input_image_size (int): The padded size of the image as input
+            to the image encoder, as (H, W).
+          mask_in_chans (int): The number of hidden channels used for
+            encoding input masks.
+          activation (nn.Module): The activation to use when encoding
+            input masks.
+        """
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.input_image_size = input_image_size
+        self.image_embedding_size = image_embedding_size
+        self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
+
+        self.num_point_embeddings: int = 4  # pos/neg point + 2 box corners
+        point_embeddings = [nn.Embedding(1, embed_dim) for _ in range(self.num_point_embeddings)]
+        self.point_embeddings = nn.ModuleList(point_embeddings)
+        self.not_a_point_embed = nn.Embedding(1, embed_dim)
+
+        self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])
+        self.mask_downscaling = nn.Sequential(
+            nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
+            LayerNorm2d(mask_in_chans // 4),
+            activation(),
+            nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
+            LayerNorm2d(mask_in_chans),
+            activation(),
+            nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
+        )
+        self.no_mask_embed = nn.Embedding(1, embed_dim)
+
+    def get_dense_pe(self) -> torch.Tensor:
+        """
+        Returns the positional encoding used to encode point prompts,
+        applied to a dense set of points the shape of the image encoding.
+
+        Returns:
+          torch.Tensor: Positional encoding with shape
+            1x(embed_dim)x(embedding_h)x(embedding_w)
+        """
+        return self.pe_layer(self.image_embedding_size).unsqueeze(0)
+
+    def _embed_points(
+        self,
+        points: torch.Tensor,
+        labels: torch.Tensor,
+        pad: bool,
+    ) -> torch.Tensor:
+        """Embeds point prompts."""
+        points = points + 0.5  # Shift to center of pixel
+        if pad:
+            padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)
+            padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
+            points = torch.cat([points, padding_point], dim=1)
+            labels = torch.cat([labels, padding_label], dim=1)
+        point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
+        point_embedding[labels == -1] = 0.0
+        point_embedding[labels == -1] += self.not_a_point_embed.weight
+        point_embedding[labels == 0] += self.point_embeddings[0].weight
+        point_embedding[labels == 1] += self.point_embeddings[1].weight
+        return point_embedding
+
+    def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
+        """Embeds box prompts."""
+        boxes = boxes + 0.5  # Shift to center of pixel
+        coords = boxes.reshape(-1, 2, 2)
+        corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
+        corner_embedding[:, 0, :] += self.point_embeddings[2].weight
+        corner_embedding[:, 1, :] += self.point_embeddings[3].weight
+        return corner_embedding
+
+    def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
+        """Embeds mask inputs."""
+        return self.mask_downscaling(masks)
+
+    def _get_batch_size(
+        self,
+        points: Optional[Tuple[torch.Tensor, torch.Tensor]],
+        boxes: Optional[torch.Tensor],
+        masks: Optional[torch.Tensor],
+    ) -> int:
+        """
+        Gets the batch size of the output given the batch size of the input prompts.
+        """
+        if points is not None:
+            return points[0].shape[0]
+        elif boxes is not None:
+            return boxes.shape[0]
+        elif masks is not None:
+            return masks.shape[0]
+        else:
+            return 1
+
+    def _get_device(self) -> torch.device:
+        return self.point_embeddings[0].weight.device
+
+    def forward(
+        self,
+        points: Optional[Tuple[torch.Tensor, torch.Tensor]],
+        boxes: Optional[torch.Tensor],
+        masks: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Embeds different types of prompts, returning both sparse and dense
+        embeddings.
+
+        Arguments:
+          points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
+            and labels to embed.
+          boxes (torch.Tensor or none): boxes to embed
+          masks (torch.Tensor or none): masks to embed
+
+        Returns:
+          torch.Tensor: sparse embeddings for the points and boxes, with shape
+            BxNx(embed_dim), where N is determined by the number of input points
+            and boxes.
+          torch.Tensor: dense embeddings for the masks, in the shape
+            Bx(embed_dim)x(embed_H)x(embed_W)
+        """
+        bs = self._get_batch_size(points, boxes, masks)
+        sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
+        if points is not None:
+            coords, labels = points
+            point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
+            sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
+        if boxes is not None:
+            box_embeddings = self._embed_boxes(boxes)
+            sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
+
+        if masks is not None:
+            dense_embeddings = self._embed_masks(masks)
+        else:
+            dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1,
+                                                                 1).expand(bs, -1, self.image_embedding_size[0],
+                                                                           self.image_embedding_size[1])
+
+        return sparse_embeddings, dense_embeddings
+
+
+class PositionEmbeddingRandom(nn.Module):
+    """
+    Positional encoding using random spatial frequencies.
+    """
+
+    def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
+        super().__init__()
+        if scale is None or scale <= 0.0:
+            scale = 1.0
+        self.register_buffer(
+            'positional_encoding_gaussian_matrix',
+            scale * torch.randn((2, num_pos_feats)),
+        )
+
+    def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
+        """Positionally encode points that are normalized to [0,1]."""
+        # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
+        coords = 2 * coords - 1
+        coords = coords @ self.positional_encoding_gaussian_matrix
+        coords = 2 * np.pi * coords
+        # outputs d_1 x ... x d_n x C shape
+        return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
+
+    def forward(self, size: Tuple[int, int]) -> torch.Tensor:
+        """Generate positional encoding for a grid of the specified size."""
+        h, w = size
+        device: Any = self.positional_encoding_gaussian_matrix.device
+        grid = torch.ones((h, w), device=device, dtype=torch.float32)
+        y_embed = grid.cumsum(dim=0) - 0.5
+        x_embed = grid.cumsum(dim=1) - 0.5
+        y_embed = y_embed / h
+        x_embed = x_embed / w
+
+        pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
+        return pe.permute(2, 0, 1)  # C x H x W
+
+    def forward_with_coords(self, coords_input: torch.Tensor, image_size: Tuple[int, int]) -> torch.Tensor:
+        """Positionally encode points that are not normalized to [0,1]."""
+        coords = coords_input.clone()
+        coords[:, :, 0] = coords[:, :, 0] / image_size[1]
+        coords[:, :, 1] = coords[:, :, 1] / image_size[0]
+        return self._pe_encoding(coords.to(torch.float))  # B x N x C
+
+
+class Block(nn.Module):
+    """Transformer blocks with support of window attention and residual propagation blocks"""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        norm_layer: Type[nn.Module] = nn.LayerNorm,
+        act_layer: Type[nn.Module] = nn.GELU,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks. If it equals 0, then
+                use global attention.
+            input_size (tuple(int, int) or None): Input resolution for calculating the relative
+                positional parameter size.
+        """
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            use_rel_pos=use_rel_pos,
+            rel_pos_zero_init=rel_pos_zero_init,
+            input_size=input_size if window_size == 0 else (window_size, window_size),
+        )
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
+
+        self.window_size = window_size
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        shortcut = x
+        x = self.norm1(x)
+        # Window partition
+        if self.window_size > 0:
+            H, W = x.shape[1], x.shape[2]
+            x, pad_hw = window_partition(x, self.window_size)
+
+        x = self.attn(x)
+        # Reverse window partition
+        if self.window_size > 0:
+            x = window_unpartition(x, self.window_size, pad_hw, (H, W))
+
+        x = shortcut + x
+        x = x + self.mlp(self.norm2(x))
+
+        return x
+
+
+class Attention(nn.Module):
+    """Multi-head Attention block with relative position embeddings."""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads.
+            qkv_bias (bool):  If True, add a learnable bias to query, key, value.
+            rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            input_size (tuple(int, int) or None): Input resolution for calculating the relative
+                positional parameter size.
+        """
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.proj = nn.Linear(dim, dim)
+
+        self.use_rel_pos = use_rel_pos
+        if self.use_rel_pos:
+            assert (input_size is not None), 'Input size must be provided if using relative positional encoding.'
+            # initialize relative positional embeddings
+            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
+            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, H, W, _ = x.shape
+        # qkv with shape (3, B, nHead, H * W, C)
+        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        # q, k, v with shape (B * nHead, H * W, C)
+        q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
+
+        attn = (q * self.scale) @ k.transpose(-2, -1)
+
+        if self.use_rel_pos:
+            attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
+
+        attn = attn.softmax(dim=-1)
+        x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
+        x = self.proj(x)
+
+        return x
+
+
+def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
+    """
+    Partition into non-overlapping windows with padding if needed.
+    Args:
+        x (tensor): input tokens with [B, H, W, C].
+        window_size (int): window size.
+
+    Returns:
+        windows: windows after partition with [B * num_windows, window_size, window_size, C].
+        (Hp, Wp): padded height and width before partition
+    """
+    B, H, W, C = x.shape
+
+    pad_h = (window_size - H % window_size) % window_size
+    pad_w = (window_size - W % window_size) % window_size
+    if pad_h > 0 or pad_w > 0:
+        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
+    Hp, Wp = H + pad_h, W + pad_w
+
+    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows, (Hp, Wp)
+
+
+def window_unpartition(windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int],
+                       hw: Tuple[int, int]) -> torch.Tensor:
+    """
+    Window unpartition into original sequences and removing padding.
+    Args:
+        windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
+        window_size (int): window size.
+        pad_hw (Tuple): padded height and width (Hp, Wp).
+        hw (Tuple): original height and width (H, W) before padding.
+
+    Returns:
+        x: unpartitioned sequences with [B, H, W, C].
+    """
+    Hp, Wp = pad_hw
+    H, W = hw
+    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
+    x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
+
+    if Hp > H or Wp > W:
+        x = x[:, :H, :W, :].contiguous()
+    return x
+
+
+def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
+    """
+    Get relative positional embeddings according to the relative positions of
+        query and key sizes.
+    Args:
+        q_size (int): size of query q.
+        k_size (int): size of key k.
+        rel_pos (Tensor): relative position embeddings (L, C).
+
+    Returns:
+        Extracted positional embeddings according to relative positions.
+    """
+    max_rel_dist = int(2 * max(q_size, k_size) - 1)
+    # Interpolate rel pos if needed.
+    if rel_pos.shape[0] != max_rel_dist:
+        # Interpolate rel pos.
+        rel_pos_resized = F.interpolate(
+            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
+            size=max_rel_dist,
+            mode='linear',
+        )
+        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
+    else:
+        rel_pos_resized = rel_pos
+
+    # Scale the coords with short length if shapes for q and k are different.
+    q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
+    k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
+    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
+
+    return rel_pos_resized[relative_coords.long()]
+
+
+def add_decomposed_rel_pos(
+    attn: torch.Tensor,
+    q: torch.Tensor,
+    rel_pos_h: torch.Tensor,
+    rel_pos_w: torch.Tensor,
+    q_size: Tuple[int, int],
+    k_size: Tuple[int, int],
+) -> torch.Tensor:
+    """
+    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
+    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py   # noqa B950
+    Args:
+        attn (Tensor): attention map.
+        q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
+        rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
+        rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
+        q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
+        k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
+
+    Returns:
+        attn (Tensor): attention map with added relative positional embeddings.
+    """
+    q_h, q_w = q_size
+    k_h, k_w = k_size
+    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
+    Rw = get_rel_pos(q_w, k_w, rel_pos_w)
+
+    B, _, dim = q.shape
+    r_q = q.reshape(B, q_h, q_w, dim)
+    rel_h = torch.einsum('bhwc,hkc->bhwk', r_q, Rh)
+    rel_w = torch.einsum('bhwc,wkc->bhwk', r_q, Rw)
+
+    attn = (attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]).view(
+        B, q_h * q_w, k_h * k_w)
+
+    return attn
+
+
+class PatchEmbed(nn.Module):
+    """
+    Image to Patch Embedding.
+    """
+
+    def __init__(
+            self,
+            kernel_size: Tuple[int, int] = (16, 16),
+            stride: Tuple[int, int] = (16, 16),
+            padding: Tuple[int, int] = (0, 0),
+            in_chans: int = 3,
+            embed_dim: int = 768,
+    ) -> None:
+        """
+        Args:
+            kernel_size (Tuple): kernel size of the projection layer.
+            stride (Tuple): stride of the projection layer.
+            padding (Tuple): padding size of the projection layer.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Patch embedding dimension.
+        """
+        super().__init__()
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x)
+        # B C H W -> B H W C
+        x = x.permute(0, 2, 3, 1)
+        return x

ultralytics/vit/sam/modules/mask_generator.py

@@ -0,0 +1,352 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+import torch
+from torchvision.ops.boxes import batched_nms, box_area  # type: ignore
+
+from ..amg import (MaskData, area_from_rle, batch_iterator, batched_mask_to_box, box_xyxy_to_xywh,
+                   build_all_layer_point_grids, calculate_stability_score, coco_encode_rle, generate_crop_boxes,
+                   is_box_near_crop_edge, mask_to_rle_pytorch, remove_small_regions, rle_to_mask, uncrop_boxes_xyxy,
+                   uncrop_masks, uncrop_points)
+from .prompt_predictor import PromptPredictor
+from .sam import Sam
+
+
+class SamAutomaticMaskGenerator:
+
+    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.
+
+        Arguments:
+          model (Sam): The SAM model to use for mask prediction.
+          points_per_side (int or None): The number of points to be sampled
+            along one side of the image. The total number of points is
+            points_per_side**2. If None, 'point_grids' must provide explicit
+            point sampling.
+          points_per_batch (int): Sets the number of points run simultaneously
+            by the model. Higher numbers may be faster but use more GPU memory.
+          pred_iou_thresh (float): A filtering threshold in [0,1], using the
+            model's predicted mask quality.
+          stability_score_thresh (float): A filtering threshold in [0,1], using
+            the stability of the mask under changes to the cutoff used to binarize
+            the model's mask predictions.
+          stability_score_offset (float): The amount to shift the cutoff when
+            calculated the stability score.
+          box_nms_thresh (float): The box IoU cutoff used by non-maximal
+            suppression to filter duplicate masks.
+          crop_n_layers (int): If >0, mask prediction will be run again on
+            crops of the image. Sets the number of layers to run, where each
+            layer has 2**i_layer number of image crops.
+          crop_nms_thresh (float): The box IoU cutoff used by non-maximal
+            suppression to filter duplicate masks between different crops.
+          crop_overlap_ratio (float): Sets the degree to which crops overlap.
+            In the first crop layer, crops will overlap by this fraction of
+            the image length. Later layers with more crops scale down this overlap.
+          crop_n_points_downscale_factor (int): The number of points-per-side
+            sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
+          point_grids (list(np.ndarray) or None): A list over explicit grids
+            of points used for sampling, normalized to [0,1]. The nth grid in the
+            list is used in the nth crop layer. Exclusive with points_per_side.
+          min_mask_region_area (int): If >0, postprocessing will be applied
+            to remove disconnected regions and holes in masks with area smaller
+            than min_mask_region_area. Requires opencv.
+          output_mode (str): The form masks are returned in. Can be 'binary_mask',
+            'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
+            For large resolutions, 'binary_mask' may consume large amounts of
+            memory.
+        """
+
+        assert (points_per_side is None) != (point_grids is
+                                             None), 'Exactly one of points_per_side or point_grid must be provided.'
+        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 = PromptPredictor(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
+
+    # TODO: Temporary implementation for compatibility
+    def __call__(self, image: np.ndarray, augment=False, visualize=False) -> List[Dict[str, Any]]:
+        return self.generate(image)
+
+    @torch.no_grad()
+    def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
+        """
+        Generates masks for the given image.
+
+        Arguments:
+          image (np.ndarray): The image to generate masks for, in HWC uint8 format.
+
+        Returns:
+           list(dict(str, any)): A list over records for masks. Each record is
+             a dict containing the following keys:
+               segmentation (dict(str, any) or np.ndarray): The mask. If
+                 output_mode='binary_mask', is an array of shape HW. Otherwise,
+                 is a dictionary containing the RLE.
+               bbox (list(float)): The box around the mask, in XYWH format.
+               area (int): The area in pixels of the mask.
+               predicted_iou (float): The model's own prediction of the mask's
+                 quality. This is filtered by the pred_iou_thresh parameter.
+               point_coords (list(list(float))): The point coordinates input
+                 to the model to generate this mask.
+               stability_score (float): A measure of the mask's quality. This
+                 is filtered on using the stability_score_thresh parameter.
+               crop_box (list(float)): The crop of the image used to generate
+                 the mask, given in XYWH format.
+        """
+
+        # Generate masks
+        mask_data = self._generate_masks(image)
+
+        # 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
+
+    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
+
+    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
+
+    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, :],
+            in_labels[:, None],
+            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
+
+    @staticmethod
+    def postprocess_small_regions(mask_data: MaskData, min_area: int, nms_thresh: float) -> MaskData:
+        """
+        Removes small disconnected regions and holes in masks, then reruns
+        box NMS to remove any new duplicates.
+
+        Edits mask_data in place.
+
+        Requires open-cv as a dependency.
+        """
+        if len(mask_data['rles']) == 0:
+            return mask_data
+
+        # Filter small disconnected regions and holes
+        new_masks = []
+        scores = []
+        for rle in mask_data['rles']:
+            mask = rle_to_mask(rle)
+
+            mask, changed = remove_small_regions(mask, min_area, mode='holes')
+            unchanged = not changed
+            mask, changed = remove_small_regions(mask, min_area, mode='islands')
+            unchanged = unchanged and not changed
+
+            new_masks.append(torch.as_tensor(mask).unsqueeze(0))
+            # Give score=0 to changed masks and score=1 to unchanged masks
+            # so NMS will prefer ones that didn't need postprocessing
+            scores.append(float(unchanged))
+
+        # Recalculate boxes and remove any new duplicates
+        masks = torch.cat(new_masks, dim=0)
+        boxes = batched_mask_to_box(masks)
+        keep_by_nms = batched_nms(
+            boxes.float(),
+            torch.as_tensor(scores),
+            torch.zeros_like(boxes[:, 0]),  # categories
+            iou_threshold=nms_thresh,
+        )
+
+        # Only recalculate RLEs for masks that have changed
+        for i_mask in keep_by_nms:
+            if scores[i_mask] == 0.0:
+                mask_torch = masks[i_mask].unsqueeze(0)
+                mask_data['rles'][i_mask] = mask_to_rle_pytorch(mask_torch)[0]
+                mask_data['boxes'][i_mask] = boxes[i_mask]  # update res directly
+        mask_data.filter(keep_by_nms)
+
+        return mask_data

ultralytics/vit/sam/modules/prompt_predictor.py

@@ -0,0 +1,240 @@
+from typing import Optional, Tuple
+
+import numpy as np
+import torch
+
+from ..autosize import ResizeLongestSide
+from .sam import Sam
+
+
+class PromptPredictor:
+
+    def __init__(self, sam_model: Sam) -> None:
+        """
+        Uses SAM to calculate the image embedding for an image, and then
+        allow repeated, efficient mask prediction given prompts.
+
+        Arguments:
+          sam_model (Sam): The model to use for mask prediction.
+        """
+        super().__init__()
+        self.model = sam_model
+        self.transform = ResizeLongestSide(sam_model.image_encoder.img_size)
+        self.reset_image()
+
+    def set_image(self, image: np.ndarray, image_format: str = 'RGB') -> None:
+        """
+        Calculates the image embeddings for the provided image, allowing
+        masks to be predicted with the 'predict' method.
+
+        Arguments:
+          image (np.ndarray): The image for calculating masks. Expects an
+            image in HWC uint8 format, with pixel values in [0, 255].
+          image_format (str): The color format of the image, in ['RGB', 'BGR'].
+        """
+        assert image_format in {'RGB', 'BGR'}, f"image_format must be in ['RGB', 'BGR'], is {image_format}."
+        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)
+        input_image_torch = torch.as_tensor(input_image, device=self.device)
+        input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]
+
+        self.set_torch_image(input_image_torch, image.shape[:2])
+
+    @torch.no_grad()
+    def set_torch_image(self, transformed_image: torch.Tensor, original_image_size: Tuple[int, ...]) -> None:
+        """
+        Calculates the image embeddings for the provided image, allowing
+        masks to be predicted with the 'predict' method. Expects the input
+        image to be already transformed to the format expected by the model.
+
+        Arguments:
+          transformed_image (torch.Tensor): The input image, with shape
+            1x3xHxW, which has been transformed with ResizeLongestSide.
+          original_image_size (tuple(int, int)): The size of the image
+            before transformation, in (H, W) format.
+        """
+        if len(transformed_image.shape) != 4 \
+                or transformed_image.shape[1] != 3 \
+                or max(*transformed_image.shape[2:]) != self.model.image_encoder.img_size:
+            raise ValueError('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)
+        self.features = self.model.image_encoder(input_image)
+        self.is_image_set = True
+
+    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.
+
+        Arguments:
+          point_coords (np.ndarray or None): A Nx2 array of point prompts to the
+            model. Each point is in (X,Y) in pixels.
+          point_labels (np.ndarray or None): A length N array of labels for the
+            point prompts. 1 indicates a foreground point and 0 indicates a
+            background point.
+          box (np.ndarray or None): A length 4 array given a box prompt to the
+            model, in XYXY format.
+          mask_input (np.ndarray): A low resolution mask input to the model, typically
+            coming from a previous prediction iteration. Has form 1xHxW, where
+            for SAM, H=W=256.
+          multimask_output (bool): If true, the model will return three masks.
+            For ambiguous input prompts (such as a single click), this will often
+            produce better masks than a single prediction. If only a single
+            mask is needed, the model's predicted quality score can be used
+            to select the best mask. For non-ambiguous prompts, such as multiple
+            input prompts, multimask_output=False can give better results.
+          return_logits (bool): If true, returns un-thresholded masks logits
+            instead of a binary mask.
+
+        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)
+            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, :]
+        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, :]
+        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
+
+    @torch.no_grad()
+    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]:
+        """
+        Predict masks for the given input prompts, using the currently set image.
+        Input prompts are batched torch tensors and are expected to already be
+        transformed to the input frame using ResizeLongestSide.
+
+        Arguments:
+          point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the
+            model. Each point is in (X,Y) in pixels.
+          point_labels (torch.Tensor or None): A BxN array of labels for the
+            point prompts. 1 indicates a foreground point and 0 indicates a
+            background point.
+          boxes (np.ndarray or None): A Bx4 array given a box prompt to the
+            model, in XYXY format.
+          mask_input (np.ndarray): A low resolution mask input to the model, typically
+            coming from a previous prediction iteration. Has form Bx1xHxW, where
+            for SAM, H=W=256. Masks returned by a previous iteration of the
+            predict method do not need further transformation.
+          multimask_output (bool): If true, the model will return three masks.
+            For ambiguous input prompts (such as a single click), this will often
+            produce better masks than a single prediction. If only a single
+            mask is needed, the model's predicted quality score can be used
+            to select the best mask. For non-ambiguous prompts, such as multiple
+            input prompts, multimask_output=False can give better results.
+          return_logits (bool): If true, returns un-thresholded masks logits
+            instead of a binary mask.
+
+        Returns:
+          (torch.Tensor): The output masks in BxCxHxW format, where C is the
+            number of masks, and (H, W) is the original image size.
+          (torch.Tensor): An array of shape BxC containing the model's
+            predictions for the quality of each mask.
+          (torch.Tensor): An array of shape BxCxHxW, where C is the number
+            of masks and H=W=256. These low res 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.')
+
+        points = (point_coords, point_labels) if point_coords is not None else 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
+
+    def get_image_embedding(self) -> torch.Tensor:
+        """
+        Returns the image embeddings for the currently set image, with
+        shape 1xCxHxW, where C is the embedding dimension and (H,W) are
+        the embedding spatial dimension of SAM (typically C=256, H=W=64).
+        """
+        if not self.is_image_set:
+            raise RuntimeError('An image must be set with .set_image(...) to generate an embedding.')
+        assert self.features is not None, 'Features must exist if an image has been set.'
+        return self.features
+
+    @property
+    def device(self) -> torch.device:
+        return self.model.device
+
+    def reset_image(self) -> None:
+        """Resets the currently set image."""
+        self.is_image_set = False
+        self.features = None
+        self.orig_h = None
+        self.orig_w = None
+        self.input_h = None
+        self.input_w = None

ultralytics/vit/sam/modules/sam.py

@@ -0,0 +1,169 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Any, Dict, List, Tuple
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from .decoders import MaskDecoder
+from .encoders import ImageEncoderViT, PromptEncoder
+
+
+class Sam(nn.Module):
+    mask_threshold: float = 0.0
+    image_format: str = 'RGB'
+
+    def __init__(
+        self,
+        image_encoder: ImageEncoderViT,
+        prompt_encoder: PromptEncoder,
+        mask_decoder: MaskDecoder,
+        pixel_mean: List[float] = [123.675, 116.28, 103.53],
+        pixel_std: List[float] = [58.395, 57.12, 57.375],
+    ) -> None:
+        """
+        SAM predicts object masks from an image and input prompts.
+
+        Arguments:
+          image_encoder (ImageEncoderViT): The backbone used to encode the
+            image into image embeddings that allow for efficient mask prediction.
+          prompt_encoder (PromptEncoder): Encodes various types of input prompts.
+          mask_decoder (MaskDecoder): Predicts masks from the image embeddings
+            and encoded prompts.
+          pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
+          pixel_std (list(float)): Std values for normalizing pixels in the input image.
+        """
+        super().__init__()
+        self.image_encoder = image_encoder
+        self.prompt_encoder = prompt_encoder
+        self.mask_decoder = mask_decoder
+        self.register_buffer('pixel_mean', torch.Tensor(pixel_mean).view(-1, 1, 1), False)
+        self.register_buffer('pixel_std', torch.Tensor(pixel_std).view(-1, 1, 1), False)
+
+    @property
+    def device(self) -> Any:
+        return self.pixel_mean.device
+
+    @torch.no_grad()
+    def forward(
+        self,
+        batched_input: List[Dict[str, Any]],
+        multimask_output: bool,
+    ) -> List[Dict[str, torch.Tensor]]:
+        """
+        Predicts masks end-to-end from provided images and prompts.
+        If prompts are not known in advance, using SamPredictor is
+        recommended over calling the model directly.
+
+        Arguments:
+          batched_input (list(dict)): A list over input images, each a
+            dictionary with the following keys. A prompt key can be
+            excluded if it is not present.
+              'image': The image as a torch tensor in 3xHxW format,
+                already transformed for input to the model.
+              'original_size': (tuple(int, int)) The original size of
+                the image before transformation, as (H, W).
+              'point_coords': (torch.Tensor) Batched point prompts for
+                this image, with shape BxNx2. Already transformed to the
+                input frame of the model.
+              'point_labels': (torch.Tensor) Batched labels for point prompts,
+                with shape BxN.
+              'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
+                Already transformed to the input frame of the model.
+              'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
+                in the form Bx1xHxW.
+          multimask_output (bool): Whether the model should predict multiple
+            disambiguating masks, or return a single mask.
+
+        Returns:
+          (list(dict)): A list over input images, where each element is
+            as dictionary with the following keys.
+              'masks': (torch.Tensor) Batched binary mask predictions,
+                with shape BxCxHxW, where B is the number of input prompts,
+                C is determined by multimask_output, and (H, W) is the
+                original size of the image.
+              'iou_predictions': (torch.Tensor) The model's predictions
+                of mask quality, in shape BxC.
+              'low_res_logits': (torch.Tensor) Low resolution logits with
+                shape BxCxHxW, where H=W=256. Can be passed as mask input
+                to subsequent iterations of prediction.
+        """
+        input_images = torch.stack([self.preprocess(x['image']) for x in batched_input], dim=0)
+        image_embeddings = self.image_encoder(input_images)
+
+        outputs = []
+        for image_record, curr_embedding in zip(batched_input, image_embeddings):
+            if 'point_coords' in image_record:
+                points = (image_record['point_coords'], image_record['point_labels'])
+            else:
+                points = None
+            sparse_embeddings, dense_embeddings = self.prompt_encoder(
+                points=points,
+                boxes=image_record.get('boxes', None),
+                masks=image_record.get('mask_inputs', None),
+            )
+            low_res_masks, iou_predictions = self.mask_decoder(
+                image_embeddings=curr_embedding.unsqueeze(0),
+                image_pe=self.prompt_encoder.get_dense_pe(),
+                sparse_prompt_embeddings=sparse_embeddings,
+                dense_prompt_embeddings=dense_embeddings,
+                multimask_output=multimask_output,
+            )
+            masks = self.postprocess_masks(
+                low_res_masks,
+                input_size=image_record['image'].shape[-2:],
+                original_size=image_record['original_size'],
+            )
+            masks = masks > self.mask_threshold
+            outputs.append({
+                'masks': masks,
+                'iou_predictions': iou_predictions,
+                'low_res_logits': low_res_masks, })
+        return outputs
+
+    def postprocess_masks(
+        self,
+        masks: torch.Tensor,
+        input_size: Tuple[int, ...],
+        original_size: Tuple[int, ...],
+    ) -> torch.Tensor:
+        """
+        Remove padding and upscale masks to the original image size.
+
+        Arguments:
+          masks (torch.Tensor): Batched masks from the mask_decoder,
+            in BxCxHxW format.
+          input_size (tuple(int, int)): The size of the image input to the
+            model, in (H, W) format. Used to remove padding.
+          original_size (tuple(int, int)): The original size of the image
+            before resizing for input to the model, in (H, W) format.
+
+        Returns:
+          (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
+            is given by original_size.
+        """
+        masks = F.interpolate(
+            masks,
+            (self.image_encoder.img_size, self.image_encoder.img_size),
+            mode='bilinear',
+            align_corners=False,
+        )
+        masks = masks[..., :input_size[0], :input_size[1]]
+        masks = F.interpolate(masks, original_size, mode='bilinear', align_corners=False)
+        return masks
+
+    def preprocess(self, x: torch.Tensor) -> torch.Tensor:
+        """Normalize pixel values and pad to a square input."""
+        # Normalize colors
+        x = (x - self.pixel_mean) / self.pixel_std
+
+        # Pad
+        h, w = x.shape[-2:]
+        padh = self.image_encoder.img_size - h
+        padw = self.image_encoder.img_size - w
+        return F.pad(x, (0, padw, 0, padh))

ultralytics/vit/sam/modules/transformer.py

@@ -0,0 +1,233 @@
+import math
+from typing import Tuple, Type
+
+import torch
+from torch import Tensor, nn
+
+from ultralytics.nn.modules import MLPBlock
+
+
+class TwoWayTransformer(nn.Module):
+
+    def __init__(
+        self,
+        depth: int,
+        embedding_dim: int,
+        num_heads: int,
+        mlp_dim: int,
+        activation: Type[nn.Module] = nn.ReLU,
+        attention_downsample_rate: int = 2,
+    ) -> None:
+        """
+        A transformer decoder that attends to an input image using
+        queries whose positional embedding is supplied.
+
+        Args:
+          depth (int): number of layers in the transformer
+          embedding_dim (int): the channel dimension for the input embeddings
+          num_heads (int): the number of heads for multihead attention. Must
+            divide embedding_dim
+          mlp_dim (int): the channel dimension internal to the MLP block
+          activation (nn.Module): the activation to use in the MLP block
+        """
+        super().__init__()
+        self.depth = depth
+        self.embedding_dim = embedding_dim
+        self.num_heads = num_heads
+        self.mlp_dim = mlp_dim
+        self.layers = nn.ModuleList()
+
+        for i in range(depth):
+            self.layers.append(
+                TwoWayAttentionBlock(
+                    embedding_dim=embedding_dim,
+                    num_heads=num_heads,
+                    mlp_dim=mlp_dim,
+                    activation=activation,
+                    attention_downsample_rate=attention_downsample_rate,
+                    skip_first_layer_pe=(i == 0),
+                ))
+
+        self.final_attn_token_to_image = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)
+        self.norm_final_attn = nn.LayerNorm(embedding_dim)
+
+    def forward(
+        self,
+        image_embedding: Tensor,
+        image_pe: Tensor,
+        point_embedding: Tensor,
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        Args:
+          image_embedding (torch.Tensor): image to attend to. Should be shape
+            B x embedding_dim x h x w for any h and w.
+          image_pe (torch.Tensor): the positional encoding to add to the image. Must
+            have the same shape as image_embedding.
+          point_embedding (torch.Tensor): the embedding to add to the query points.
+            Must have shape B x N_points x embedding_dim for any N_points.
+
+        Returns:
+          torch.Tensor: the processed point_embedding
+          torch.Tensor: the processed image_embedding
+        """
+        # BxCxHxW -> BxHWxC == B x N_image_tokens x C
+        bs, c, h, w = image_embedding.shape
+        image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
+        image_pe = image_pe.flatten(2).permute(0, 2, 1)
+
+        # Prepare queries
+        queries = point_embedding
+        keys = image_embedding
+
+        # Apply transformer blocks and final layernorm
+        for layer in self.layers:
+            queries, keys = layer(
+                queries=queries,
+                keys=keys,
+                query_pe=point_embedding,
+                key_pe=image_pe,
+            )
+
+        # Apply the final attention layer from the points to the image
+        q = queries + point_embedding
+        k = keys + image_pe
+        attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
+        queries = queries + attn_out
+        queries = self.norm_final_attn(queries)
+
+        return queries, keys
+
+
+class TwoWayAttentionBlock(nn.Module):
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_heads: int,
+        mlp_dim: int = 2048,
+        activation: Type[nn.Module] = nn.ReLU,
+        attention_downsample_rate: int = 2,
+        skip_first_layer_pe: bool = False,
+    ) -> None:
+        """
+        A transformer block with four layers: (1) self-attention of sparse
+        inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp
+        block on sparse inputs, and (4) cross attention of dense inputs to sparse
+        inputs.
+
+        Arguments:
+          embedding_dim (int): the channel dimension of the embeddings
+          num_heads (int): the number of heads in the attention layers
+          mlp_dim (int): the hidden dimension of the mlp block
+          activation (nn.Module): the activation of the mlp block
+          skip_first_layer_pe (bool): skip the PE on the first layer
+        """
+        super().__init__()
+        self.self_attn = Attention(embedding_dim, num_heads)
+        self.norm1 = nn.LayerNorm(embedding_dim)
+
+        self.cross_attn_token_to_image = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)
+        self.norm2 = nn.LayerNorm(embedding_dim)
+
+        self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
+        self.norm3 = nn.LayerNorm(embedding_dim)
+
+        self.norm4 = nn.LayerNorm(embedding_dim)
+        self.cross_attn_image_to_token = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)
+
+        self.skip_first_layer_pe = skip_first_layer_pe
+
+    def forward(self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor) -> Tuple[Tensor, Tensor]:
+        """Apply self-attention and cross-attention to queries and keys and return the processed embeddings."""
+
+        # Self attention block
+        if self.skip_first_layer_pe:
+            queries = self.self_attn(q=queries, k=queries, v=queries)
+        else:
+            q = queries + query_pe
+            attn_out = self.self_attn(q=q, k=q, v=queries)
+            queries = queries + attn_out
+        queries = self.norm1(queries)
+
+        # Cross attention block, tokens attending to image embedding
+        q = queries + query_pe
+        k = keys + key_pe
+        attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
+        queries = queries + attn_out
+        queries = self.norm2(queries)
+
+        # MLP block
+        mlp_out = self.mlp(queries)
+        queries = queries + mlp_out
+        queries = self.norm3(queries)
+
+        # Cross attention block, image embedding attending to tokens
+        q = queries + query_pe
+        k = keys + key_pe
+        attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
+        keys = keys + attn_out
+        keys = self.norm4(keys)
+
+        return queries, keys
+
+
+class Attention(nn.Module):
+    """
+    An attention layer that allows for downscaling the size of the embedding
+    after projection to queries, keys, and values.
+    """
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_heads: int,
+        downsample_rate: int = 1,
+    ) -> None:
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.internal_dim = embedding_dim // downsample_rate
+        self.num_heads = num_heads
+        assert self.internal_dim % num_heads == 0, 'num_heads must divide embedding_dim.'
+
+        self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
+        self.k_proj = nn.Linear(embedding_dim, self.internal_dim)
+        self.v_proj = nn.Linear(embedding_dim, self.internal_dim)
+        self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
+
+    def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
+        """Separate the input tensor into the specified number of attention heads."""
+        b, n, c = x.shape
+        x = x.reshape(b, n, num_heads, c // num_heads)
+        return x.transpose(1, 2)  # B x N_heads x N_tokens x C_per_head
+
+    def _recombine_heads(self, x: Tensor) -> Tensor:
+        """Recombine the separated attention heads into a single tensor."""
+        b, n_heads, n_tokens, c_per_head = x.shape
+        x = x.transpose(1, 2)
+        return x.reshape(b, n_tokens, n_heads * c_per_head)  # B x N_tokens x C
+
+    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
+        """Compute the attention output given the input query, key, and value tensors."""
+
+        # Input projections
+        q = self.q_proj(q)
+        k = self.k_proj(k)
+        v = self.v_proj(v)
+
+        # Separate into heads
+        q = self._separate_heads(q, self.num_heads)
+        k = self._separate_heads(k, self.num_heads)
+        v = self._separate_heads(v, self.num_heads)
+
+        # Attention
+        _, _, _, c_per_head = q.shape
+        attn = q @ k.permute(0, 1, 3, 2)  # B x N_heads x N_tokens x N_tokens
+        attn = attn / math.sqrt(c_per_head)
+        attn = torch.softmax(attn, dim=-1)
+
+        # Get output
+        out = attn @ v
+        out = self._recombine_heads(out)
+        out = self.out_proj(out)
+
+        return out

ultralytics/vit/sam/predict.py

@@ -0,0 +1,52 @@
+import numpy as np
+import torch
+
+from ultralytics.yolo.engine.predictor import BasePredictor
+from ultralytics.yolo.engine.results import Results
+from ultralytics.yolo.utils.torch_utils import select_device
+
+from .modules.mask_generator import SamAutomaticMaskGenerator
+
+
+class Predictor(BasePredictor):
+
+    def preprocess(self, im):
+        """Prepares input image for inference."""
+        # TODO: Only support bs=1 for now
+        # im = ResizeLongestSide(1024).apply_image(im[0])
+        # im = torch.as_tensor(im, device=self.device)
+        # im = im.permute(2, 0, 1).contiguous()[None, :, :, :]
+        return im[0]
+
+    def setup_model(self, model):
+        """Set up YOLO model with specified thresholds and device."""
+        device = select_device(self.args.device)
+        model.eval()
+        self.model = SamAutomaticMaskGenerator(model.to(device),
+                                               pred_iou_thresh=self.args.conf,
+                                               box_nms_thresh=self.args.iou)
+        self.device = device
+        # TODO: Temporary settings for compatibility
+        self.model.pt = False
+        self.model.triton = False
+        self.model.stride = 32
+        self.model.fp16 = False
+        self.done_warmup = True
+
+    def postprocess(self, preds, path, orig_imgs):
+        """Postprocesses inference output predictions to create detection masks for objects."""
+        names = dict(enumerate(list(range(len(preds)))))
+        results = []
+        # TODO
+        for i, pred in enumerate([preds]):
+            masks = torch.from_numpy(np.stack([p['segmentation'] for p in pred], axis=0))
+            orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
+            path = self.batch[0]
+            img_path = path[i] if isinstance(path, list) else path
+            results.append(Results(orig_img=orig_img, path=img_path, names=names, masks=masks))
+        return results
+
+    # def __call__(self, source=None, model=None, stream=False):
+    #     frame = cv2.imread(source)
+    #     preds = self.model.generate(frame)
+    #     return self.postprocess(preds, source, frame)

ultralytics/yolo/cfg/default.yaml

@@ -25,7 +25,6 @@ verbose: True  # whether to print verbose output
 seed: 0  # random seed for reproducibility
 deterministic: True  # whether to enable deterministic mode
 single_cls: False  # train multi-class data as single-class
-image_weights: False  # use weighted image selection for training
 rect: False  # rectangular training if mode='train' or rectangular validation if mode='val'
 cos_lr: False  # use cosine learning rate scheduler
 close_mosaic: 0  # (int) disable mosaic augmentation for final epochs

ultralytics/yolo/data/__init__.py

@@ -1,9 +1,9 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
 from .base import BaseDataset
-from .build import build_classification_dataloader, build_dataloader, load_inference_source
+from .build import build_dataloader, build_yolo_dataset, load_inference_source
 from .dataset import ClassificationDataset, SemanticDataset, YOLODataset
 from .dataset_wrappers import MixAndRectDataset
 
 __all__ = ('BaseDataset', 'ClassificationDataset', 'MixAndRectDataset', 'SemanticDataset', 'YOLODataset',
-           'build_classification_dataloader', 'build_dataloader', 'load_inference_source')
+           'build_yolo_dataset', 'build_dataloader', 'load_inference_source')

ultralytics/yolo/data/annotator.py

@@ -0,0 +1,42 @@
+from pathlib import Path
+
+from ultralytics import YOLO
+from ultralytics.vit.sam import PromptPredictor, build_sam
+from ultralytics.yolo.utils.torch_utils import select_device
+
+
+def auto_annotate(data, det_model='yolov8x.pt', sam_model='sam_b.pt', device='', output_dir=None):
+    device = select_device(device)
+    det_model = YOLO(det_model)
+    sam_model = build_sam(sam_model)
+    det_model.to(device)
+    sam_model.to(device)
+
+    if not output_dir:
+        output_dir = Path(str(data)).parent / 'labels'
+    Path(output_dir).mkdir(exist_ok=True, parents=True)
+
+    prompt_predictor = PromptPredictor(sam_model)
+    det_results = det_model(data, stream=True)
+
+    for result in det_results:
+        boxes = result.boxes.xyxy  # Boxes object for bbox outputs
+        class_ids = result.boxes.cls.int().tolist()  # noqa
+        prompt_predictor.set_image(result.orig_img)
+        masks, _, _ = prompt_predictor.predict_torch(
+            point_coords=None,
+            point_labels=None,
+            boxes=prompt_predictor.transform.apply_boxes_torch(boxes, result.orig_shape[:2]),
+            multimask_output=False,
+        )
+
+        result.update(masks=masks.squeeze(1))
+        segments = result.masks.xyn  # noqa
+
+        with open(str(Path(output_dir) / Path(result.path).stem) + '.txt', 'w') as f:
+            for i in range(len(segments)):
+                s = segments[i]
+                if len(s) == 0:
+                    continue
+                segment = map(str, segments[i].reshape(-1).tolist())
+                f.write(f'{class_ids[i]} ' + ' '.join(segment) + '\n')

ultralytics/yolo/data/base.py

@@ -24,17 +24,17 @@ class BaseDataset(Dataset):
     Base dataset class for loading and processing image data.
 
     Args:
-        img_path (str): Image path.
-        imgsz (int): Target image size for resizing. Default is 640.
-        cache (bool): Cache images in memory or on disk for faster loading. Default is False.
-        augment (bool): Apply data augmentation. Default is True.
-        hyp (dict): Dictionary of hyperparameters for data augmentation. Default is None.
-        prefix (str): Prefix for file paths. Default is an empty string.
-        rect (bool): Enable rectangular training. Default is False.
-        batch_size (int): Batch size for rectangular training. Default is None.
-        stride (int): Stride for rectangular training. Default is 32.
-        pad (float): Padding for rectangular training. Default is 0.5.
-        single_cls (bool): Use a single class for all labels. Default is False.
+        img_path (str): Path to the folder containing images.
+        imgsz (int, optional): Image size. Defaults to 640.
+        cache (bool, optional): Cache images to RAM or disk during training. Defaults to False.
+        augment (bool, optional): If True, data augmentation is applied. Defaults to True.
+        hyp (dict, optional): Hyperparameters to apply data augmentation. Defaults to None.
+        prefix (str, optional): Prefix to print in log messages. Defaults to ''.
+        rect (bool, optional): If True, rectangular training is used. Defaults to False.
+        batch_size (int, optional): Size of batches. Defaults to None.
+        stride (int, optional): Stride. Defaults to 32.
+        pad (float, optional): Padding. Defaults to 0.0.
+        single_cls (bool, optional): If True, single class training is used. Defaults to False.
         classes (list): List of included classes. Default is None.
 
     Attributes:

ultralytics/yolo/data/build.py

@@ -14,9 +14,8 @@ from ultralytics.yolo.data.dataloaders.stream_loaders import (LOADERS, LoadImage
 from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
 from ultralytics.yolo.utils.checks import check_file
 
-from ..utils import LOGGER, RANK, colorstr
-from ..utils.torch_utils import torch_distributed_zero_first
-from .dataset import ClassificationDataset, YOLODataset
+from ..utils import RANK, colorstr
+from .dataset import YOLODataset
 from .utils import PIN_MEMORY
 
 
@@ -70,34 +69,31 @@ def seed_worker(worker_id):  # noqa
     random.seed(worker_seed)
 
 
-def build_dataloader(cfg, batch, img_path, data_info, stride=32, rect=False, rank=-1, mode='train'):
-    """Return an InfiniteDataLoader or DataLoader for training or validation set."""
-    assert mode in ['train', 'val']
-    shuffle = mode == 'train'
-    if cfg.rect and shuffle:
-        LOGGER.warning("WARNING ⚠️ 'rect=True' is incompatible with DataLoader shuffle, setting shuffle=False")
-        shuffle = False
-    with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
-        dataset = YOLODataset(
-            img_path=img_path,
-            imgsz=cfg.imgsz,
-            batch_size=batch,
-            augment=mode == 'train',  # augmentation
-            hyp=cfg,  # TODO: probably add a get_hyps_from_cfg function
-            rect=cfg.rect or rect,  # rectangular batches
-            cache=cfg.cache or None,
-            single_cls=cfg.single_cls or False,
-            stride=int(stride),
-            pad=0.0 if mode == 'train' else 0.5,
-            prefix=colorstr(f'{mode}: '),
-            use_segments=cfg.task == 'segment',
-            use_keypoints=cfg.task == 'pose',
-            classes=cfg.classes,
-            data=data_info)
+def build_yolo_dataset(cfg, img_path, batch, data_info, mode='train', rect=False, stride=32):
+    """Build YOLO Dataset"""
+    dataset = YOLODataset(
+        img_path=img_path,
+        imgsz=cfg.imgsz,
+        batch_size=batch,
+        augment=mode == 'train',  # augmentation
+        hyp=cfg,  # TODO: probably add a get_hyps_from_cfg function
+        rect=cfg.rect or rect,  # rectangular batches
+        cache=cfg.cache or None,
+        single_cls=cfg.single_cls or False,
+        stride=int(stride),
+        pad=0.0 if mode == 'train' else 0.5,
+        prefix=colorstr(f'{mode}: '),
+        use_segments=cfg.task == 'segment',
+        use_keypoints=cfg.task == 'pose',
+        classes=cfg.classes,
+        data=data_info)
+    return dataset
 
+
+def build_dataloader(dataset, batch, workers, shuffle=True, rank=-1):
+    """Return an InfiniteDataLoader or DataLoader for training or validation set."""
     batch = min(batch, len(dataset))
     nd = torch.cuda.device_count()  # number of CUDA devices
-    workers = cfg.workers if mode == 'train' else cfg.workers * 2
     nw = min([os.cpu_count() // max(nd, 1), batch if batch > 1 else 0, workers])  # number of workers
     sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
     generator = torch.Generator()
@@ -110,36 +106,7 @@ def build_dataloader(cfg, batch, img_path, data_info, stride=32, rect=False, ran
                               pin_memory=PIN_MEMORY,
                               collate_fn=getattr(dataset, 'collate_fn', None),
                               worker_init_fn=seed_worker,
-                              generator=generator), dataset
-
-
-# Build classification
-# TODO: using cfg like `build_dataloader`
-def build_classification_dataloader(path,
-                                    imgsz=224,
-                                    batch_size=16,
-                                    augment=True,
-                                    cache=False,
-                                    rank=-1,
-                                    workers=8,
-                                    shuffle=True):
-    """Returns Dataloader object to be used with YOLOv5 Classifier."""
-    with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
-        dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache)
-    batch_size = min(batch_size, len(dataset))
-    nd = torch.cuda.device_count()
-    nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])
-    sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
-    generator = torch.Generator()
-    generator.manual_seed(6148914691236517205 + RANK)
-    return InfiniteDataLoader(dataset,
-                              batch_size=batch_size,
-                              shuffle=shuffle and sampler is None,
-                              num_workers=nw,
-                              sampler=sampler,
-                              pin_memory=PIN_MEMORY,
-                              worker_init_fn=seed_worker,
-                              generator=generator)  # or DataLoader(persistent_workers=True)
+                              generator=generator)
 
 
 def check_source(source):
@@ -168,7 +135,7 @@ def check_source(source):
     return source, webcam, screenshot, from_img, in_memory, tensor
 
 
-def load_inference_source(source=None, transforms=None, imgsz=640, vid_stride=1, stride=32, auto=True):
+def load_inference_source(source=None, imgsz=640, vid_stride=1):
     """
     Loads an inference source for object detection and applies necessary transformations.
 
@@ -192,23 +159,13 @@ def load_inference_source(source=None, transforms=None, imgsz=640, vid_stride=1,
     elif in_memory:
         dataset = source
     elif webcam:
-        dataset = LoadStreams(source,
-                              imgsz=imgsz,
-                              stride=stride,
-                              auto=auto,
-                              transforms=transforms,
-                              vid_stride=vid_stride)
+        dataset = LoadStreams(source, imgsz=imgsz, vid_stride=vid_stride)
     elif screenshot:
-        dataset = LoadScreenshots(source, imgsz=imgsz, stride=stride, auto=auto, transforms=transforms)
+        dataset = LoadScreenshots(source, imgsz=imgsz)
     elif from_img:
-        dataset = LoadPilAndNumpy(source, imgsz=imgsz, stride=stride, auto=auto, transforms=transforms)
+        dataset = LoadPilAndNumpy(source, imgsz=imgsz)
     else:
-        dataset = LoadImages(source,
-                             imgsz=imgsz,
-                             stride=stride,
-                             auto=auto,
-                             transforms=transforms,
-                             vid_stride=vid_stride)
+        dataset = LoadImages(source, imgsz=imgsz, vid_stride=vid_stride)
 
     # Attach source types to the dataset
     setattr(dataset, 'source_type', source_type)

ultralytics/yolo/data/dataloaders/stream_loaders.py

@@ -15,7 +15,6 @@ import requests
 import torch
 from PIL import Image
 
-from ultralytics.yolo.data.augment import LetterBox
 from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
 from ultralytics.yolo.utils import LOGGER, ROOT, is_colab, is_kaggle, ops
 from ultralytics.yolo.utils.checks import check_requirements
@@ -31,12 +30,11 @@ class SourceTypes:
 
 class LoadStreams:
     # YOLOv8 streamloader, i.e. `yolo predict source='rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP streams`
-    def __init__(self, sources='file.streams', imgsz=640, stride=32, auto=True, transforms=None, vid_stride=1):
+    def __init__(self, sources='file.streams', imgsz=640, vid_stride=1):
         """Initialize instance variables and check for consistent input stream shapes."""
         torch.backends.cudnn.benchmark = True  # faster for fixed-size inference
         self.mode = 'stream'
         self.imgsz = imgsz
-        self.stride = stride
         self.vid_stride = vid_stride  # video frame-rate stride
         sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources]
         n = len(sources)
@@ -72,10 +70,6 @@ class LoadStreams:
         LOGGER.info('')  # newline
 
         # Check for common shapes
-        s = np.stack([LetterBox(imgsz, auto, stride=stride)(image=x).shape for x in self.imgs])
-        self.rect = np.unique(s, axis=0).shape[0] == 1  # rect inference if all shapes equal
-        self.auto = auto and self.rect
-        self.transforms = transforms  # optional
         self.bs = self.__len__()
 
         if not self.rect:
@@ -110,14 +104,7 @@ class LoadStreams:
             raise StopIteration
 
         im0 = self.imgs.copy()
-        if self.transforms:
-            im = np.stack([self.transforms(x) for x in im0])  # transforms
-        else:
-            im = np.stack([LetterBox(self.imgsz, self.auto, stride=self.stride)(image=x) for x in im0])
-            im = im[..., ::-1].transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW
-            im = np.ascontiguousarray(im)  # contiguous
-
-        return self.sources, im, im0, None, ''
+        return self.sources, im0, None, ''
 
     def __len__(self):
         """Return the length of the sources object."""
@@ -126,7 +113,7 @@ class LoadStreams:
 
 class LoadScreenshots:
     # YOLOv8 screenshot dataloader, i.e. `yolo predict source=screen`
-    def __init__(self, source, imgsz=640, stride=32, auto=True, transforms=None):
+    def __init__(self, source, imgsz=640):
         """source = [screen_number left top width height] (pixels)."""
         check_requirements('mss')
         import mss  # noqa
@@ -140,9 +127,6 @@ class LoadScreenshots:
         elif len(params) == 5:
             self.screen, left, top, width, height = (int(x) for x in params)
         self.imgsz = imgsz
-        self.stride = stride
-        self.transforms = transforms
-        self.auto = auto
         self.mode = 'stream'
         self.frame = 0
         self.sct = mss.mss()
@@ -165,19 +149,13 @@ class LoadScreenshots:
         im0 = np.array(self.sct.grab(self.monitor))[:, :, :3]  # [:, :, :3] BGRA to BGR
         s = f'screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: '
 
-        if self.transforms:
-            im = self.transforms(im0)  # transforms
-        else:
-            im = LetterBox(self.imgsz, self.auto, stride=self.stride)(image=im0)
-            im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
-            im = np.ascontiguousarray(im)  # contiguous
         self.frame += 1
-        return str(self.screen), im, im0, None, s  # screen, img, original img, im0s, s
+        return str(self.screen), im0, None, s  # screen, img, original img, im0s, s
 
 
 class LoadImages:
     # YOLOv8 image/video dataloader, i.e. `yolo predict source=image.jpg/vid.mp4`
-    def __init__(self, path, imgsz=640, stride=32, auto=True, transforms=None, vid_stride=1):
+    def __init__(self, path, imgsz=640, vid_stride=1):
         """Initialize the Dataloader and raise FileNotFoundError if file not found."""
         if isinstance(path, str) and Path(path).suffix == '.txt':  # *.txt file with img/vid/dir on each line
             path = Path(path).read_text().rsplit()
@@ -198,13 +176,10 @@ class LoadImages:
         ni, nv = len(images), len(videos)
 
         self.imgsz = imgsz
-        self.stride = stride
         self.files = images + videos
         self.nf = ni + nv  # number of files
         self.video_flag = [False] * ni + [True] * nv
         self.mode = 'image'
-        self.auto = auto
-        self.transforms = transforms  # optional
         self.vid_stride = vid_stride  # video frame-rate stride
         self.bs = 1
         if any(videos):
@@ -254,14 +229,7 @@ class LoadImages:
                 raise FileNotFoundError(f'Image Not Found {path}')
             s = f'image {self.count}/{self.nf} {path}: '
 
-        if self.transforms:
-            im = self.transforms(im0)  # transforms
-        else:
-            im = LetterBox(self.imgsz, self.auto, stride=self.stride)(image=im0)
-            im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
-            im = np.ascontiguousarray(im)  # contiguous
-
-        return path, im, im0, self.cap, s
+        return [path], [im0], self.cap, s
 
     def _new_video(self, path):
         """Create a new video capture object."""
@@ -290,16 +258,13 @@ class LoadImages:
 
 class LoadPilAndNumpy:
 
-    def __init__(self, im0, imgsz=640, stride=32, auto=True, transforms=None):
+    def __init__(self, im0, imgsz=640):
         """Initialize PIL and Numpy Dataloader."""
         if not isinstance(im0, list):
             im0 = [im0]
         self.paths = [getattr(im, 'filename', f'image{i}.jpg') for i, im in enumerate(im0)]
         self.im0 = [self._single_check(im) for im in im0]
         self.imgsz = imgsz
-        self.stride = stride
-        self.auto = auto
-        self.transforms = transforms
         self.mode = 'image'
         # Generate fake paths
         self.bs = len(self.im0)
@@ -315,16 +280,6 @@ class LoadPilAndNumpy:
             im = np.ascontiguousarray(im)  # contiguous
         return im
 
-    def _single_preprocess(self, im, auto):
-        """Preprocesses a single image for inference."""
-        if self.transforms:
-            im = self.transforms(im)  # transforms
-        else:
-            im = LetterBox(self.imgsz, auto=auto, stride=self.stride)(image=im)
-            im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
-            im = np.ascontiguousarray(im)  # contiguous
-        return im
-
     def __len__(self):
         """Returns the length of the 'im0' attribute."""
         return len(self.im0)
@@ -333,11 +288,8 @@ class LoadPilAndNumpy:
         """Returns batch paths, images, processed images, None, ''."""
         if self.count == 1:  # loop only once as it's batch inference
             raise StopIteration
-        auto = all(x.shape == self.im0[0].shape for x in self.im0) and self.auto
-        im = [self._single_preprocess(im, auto) for im in self.im0]
-        im = np.stack(im, 0) if len(im) > 1 else im[0][None]
         self.count += 1
-        return self.paths, im, self.im0, None, ''
+        return self.paths, self.im0, None, ''
 
     def __iter__(self):
         """Enables iteration for class LoadPilAndNumpy."""
@@ -362,7 +314,7 @@ class LoadTensor:
         if self.count == 1:
             raise StopIteration
         self.count += 1
-        return None, self.im0, self.im0, None, ''  # self.paths, im, self.im0, None, ''
+        return None, self.im0, None, ''  # self.paths, im, self.im0, None, ''
 
     def __len__(self):
         """Returns the batch size."""

ultralytics/yolo/data/dataset.py

@@ -21,21 +21,9 @@ class YOLODataset(BaseDataset):
     Dataset class for loading object detection and/or segmentation labels in YOLO format.
 
     Args:
-        img_path (str): Path to the folder containing images.
-        imgsz (int, optional): Image size. Defaults to 640.
-        cache (bool, optional): Cache images to RAM or disk during training. Defaults to False.
-        augment (bool, optional): If True, data augmentation is applied. Defaults to True.
-        hyp (dict, optional): Hyperparameters to apply data augmentation. Defaults to None.
-        prefix (str, optional): Prefix to print in log messages. Defaults to ''.
-        rect (bool, optional): If True, rectangular training is used. Defaults to False.
-        batch_size (int, optional): Size of batches. Defaults to None.
-        stride (int, optional): Stride. Defaults to 32.
-        pad (float, optional): Padding. Defaults to 0.0.
-        single_cls (bool, optional): If True, single class training is used. Defaults to False.
+        data (dict, optional): A dataset YAML dictionary. Defaults to None.
         use_segments (bool, optional): If True, segmentation masks are used as labels. Defaults to False.
         use_keypoints (bool, optional): If True, keypoints are used as labels. Defaults to False.
-        data (dict, optional): A dataset YAML dictionary. Defaults to None.
-        classes (list): List of included classes. Default is None.
 
     Returns:
         (torch.utils.data.Dataset): A PyTorch dataset object that can be used for training an object detection model.
@@ -43,28 +31,12 @@ class YOLODataset(BaseDataset):
     cache_version = '1.0.2'  # dataset labels *.cache version, >= 1.0.0 for YOLOv8
     rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
 
-    def __init__(self,
-                 img_path,
-                 imgsz=640,
-                 cache=False,
-                 augment=True,
-                 hyp=None,
-                 prefix='',
-                 rect=False,
-                 batch_size=None,
-                 stride=32,
-                 pad=0.0,
-                 single_cls=False,
-                 use_segments=False,
-                 use_keypoints=False,
-                 data=None,
-                 classes=None):
+    def __init__(self, *args, data=None, use_segments=False, use_keypoints=False, **kwargs):
         self.use_segments = use_segments
         self.use_keypoints = use_keypoints
         self.data = data
         assert not (self.use_segments and self.use_keypoints), 'Can not use both segments and keypoints.'
-        super().__init__(img_path, imgsz, cache, augment, hyp, prefix, rect, batch_size, stride, pad, single_cls,
-                         classes)
+        super().__init__(*args, **kwargs)
 
     def cache_labels(self, path=Path('./labels.cache')):
         """Cache dataset labels, check images and read shapes.

ultralytics/yolo/engine/model.py

@@ -453,7 +453,7 @@ class YOLO:
                                        reduction_factor=3)
 
         # Define the callbacks for the hyperparameter search
-        tuner_callbacks = [WandbLoggerCallback(project='yolov8_tune') if wandb else None]
+        tuner_callbacks = [WandbLoggerCallback(project='yolov8_tune')] if wandb else []
 
         # Create the Ray Tune hyperparameter search tuner
         tuner = tune.Tuner(trainable_with_resources,

ultralytics/yolo/engine/predictor.py

@@ -31,11 +31,13 @@ import platform
 from pathlib import Path
 
 import cv2
+import numpy as np
+import torch
 
 from ultralytics.nn.autobackend import AutoBackend
 from ultralytics.yolo.cfg import get_cfg
 from ultralytics.yolo.data import load_inference_source
-from ultralytics.yolo.data.augment import classify_transforms
+from ultralytics.yolo.data.augment import LetterBox, classify_transforms
 from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, SETTINGS, callbacks, colorstr, ops
 from ultralytics.yolo.utils.checks import check_imgsz, check_imshow
 from ultralytics.yolo.utils.files import increment_path
@@ -106,9 +108,23 @@ class BasePredictor:
         self.callbacks = _callbacks or callbacks.get_default_callbacks()
         callbacks.add_integration_callbacks(self)
 
-    def preprocess(self, img):
-        """Prepares input image before inference."""
-        pass
+    def preprocess(self, im):
+        """Prepares input image before inference.
+
+        Args:
+            im (torch.Tensor | List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.
+        """
+        if not isinstance(im, torch.Tensor):
+            auto = all(x.shape == im[0].shape for x in im) and self.model.pt
+            im = np.stack([LetterBox(self.imgsz, auto=auto, stride=self.model.stride)(image=x) for x in im])
+            im = im[..., ::-1].transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW, (n, 3, h, w)
+            im = np.ascontiguousarray(im)  # contiguous
+            im = torch.from_numpy(im)
+        # NOTE: assuming im with (b, 3, h, w) if it's a tensor
+        img = im.to(self.device)
+        img = img.half() if self.model.fp16 else img.float()  # uint8 to fp16/32
+        img /= 255  # 0 - 255 to 0.0 - 1.0
+        return img
 
     def write_results(self, idx, results, batch):
         """Write inference results to a file or directory."""
@@ -165,16 +181,9 @@ class BasePredictor:
     def setup_source(self, source):
         """Sets up source and inference mode."""
         self.imgsz = check_imgsz(self.args.imgsz, stride=self.model.stride, min_dim=2)  # check image size
-        if self.args.task == 'classify':
-            transforms = getattr(self.model.model, 'transforms', classify_transforms(self.imgsz[0]))
-        else:  # predict, segment
-            transforms = None
-        self.dataset = load_inference_source(source=source,
-                                             transforms=transforms,
-                                             imgsz=self.imgsz,
-                                             vid_stride=self.args.vid_stride,
-                                             stride=self.model.stride,
-                                             auto=self.model.pt)
+        self.transforms = getattr(self.model.model, 'transforms', classify_transforms(
+            self.imgsz[0])) if self.args.task == 'classify' else None
+        self.dataset = load_inference_source(source=source, imgsz=self.imgsz, vid_stride=self.args.vid_stride)
         self.source_type = self.dataset.source_type
         if not getattr(self, 'stream', True) and (self.dataset.mode == 'stream' or  # streams
                                                   len(self.dataset) > 1000 or  # images
@@ -207,14 +216,12 @@ class BasePredictor:
         for batch in self.dataset:
             self.run_callbacks('on_predict_batch_start')
             self.batch = batch
-            path, im, im0s, vid_cap, s = batch
+            path, im0s, vid_cap, s = batch
             visualize = increment_path(self.save_dir / Path(path).stem, mkdir=True) if self.args.visualize else False
 
             # Preprocess
             with self.dt[0]:
-                im = self.preprocess(im)
-                if len(im.shape) == 3:
-                    im = im[None]  # expand for batch dim
+                im = self.preprocess(im0s)
 
             # Inference
             with self.dt[1]:
@@ -226,7 +233,7 @@ class BasePredictor:
             self.run_callbacks('on_predict_postprocess_end')
 
             # Visualize, save, write results
-            n = len(im)
+            n = len(im0s)
             for i in range(n):
                 self.results[i].speed = {
                     'preprocess': self.dt[0].dt * 1E3 / n,
@@ -234,8 +241,7 @@ class BasePredictor:
                     'postprocess': self.dt[2].dt * 1E3 / n}
                 if self.source_type.tensor:  # skip write, show and plot operations if input is raw tensor
                     continue
-                p, im0 = (path[i], im0s[i].copy()) if self.source_type.webcam or self.source_type.from_img \
-                    else (path, im0s.copy())
+                p, im0 = path[i], im0s[i].copy()
                 p = Path(p)
 
                 if self.args.verbose or self.args.save or self.args.save_txt or self.args.show:

ultralytics/yolo/engine/results.py

@@ -213,7 +213,8 @@ class Results(SimpleClass):
                 img = LetterBox(pred_masks.shape[1:])(image=annotator.result())
                 img_gpu = torch.as_tensor(img, dtype=torch.float16, device=pred_masks.data.device).permute(
                     2, 0, 1).flip(0).contiguous() / 255
-            annotator.masks(pred_masks.data, colors=[colors(x, True) for x in pred_boxes.cls], im_gpu=img_gpu)
+            idx = pred_boxes.cls if pred_boxes else range(len(pred_masks))
+            annotator.masks(pred_masks.data, colors=[colors(x, True) for x in idx], im_gpu=img_gpu)
 
         if pred_boxes and show_boxes:
             for d in reversed(pred_boxes):

ultralytics/yolo/engine/trainer.py

@@ -481,6 +481,10 @@ class BaseTrainer:
         """
         raise NotImplementedError('get_dataloader function not implemented in trainer')
 
+    def build_dataset(self, img_path, mode='train', batch=None):
+        """Build dataset"""
+        raise NotImplementedError('build_dataset function not implemented in trainer')
+
     def criterion(self, preds, batch):
         """
         Returns loss and individual loss items as Tensor.

ultralytics/yolo/engine/validator.py

@@ -207,6 +207,10 @@ class BaseValidator:
         """Get data loader from dataset path and batch size."""
         raise NotImplementedError('get_dataloader function not implemented for this validator')
 
+    def build_dataset(self, img_path):
+        """Build dataset"""
+        raise NotImplementedError('build_dataset function not implemented in validator')
+
     def preprocess(self, batch):
         """Preprocesses an input batch."""
         return batch

ultralytics/yolo/utils/callbacks/comet.py

@@ -13,20 +13,8 @@ try:
 except (ImportError, AssertionError):
     comet_ml = None
 
-COMET_MODE = os.getenv('COMET_MODE', 'online')
-COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'YOLOv8')
-# Determines how many batches of image predictions to log from the validation set
-COMET_EVAL_BATCH_LOGGING_INTERVAL = int(os.getenv('COMET_EVAL_BATCH_LOGGING_INTERVAL', 1))
-# Determines whether to log confusion matrix every evaluation epoch
-COMET_EVAL_LOG_CONFUSION_MATRIX = (os.getenv('COMET_EVAL_LOG_CONFUSION_MATRIX', 'true').lower() == 'true')
-# Determines whether to log image predictions every evaluation epoch
-COMET_EVAL_LOG_IMAGE_PREDICTIONS = (os.getenv('COMET_EVAL_LOG_IMAGE_PREDICTIONS', 'true').lower() == 'true')
-COMET_MAX_IMAGE_PREDICTIONS = int(os.getenv('COMET_MAX_IMAGE_PREDICTIONS', 100))
-
 # Ensures certain logging functions only run for supported tasks
 COMET_SUPPORTED_TASKS = ['detect']
-# Scales reported confidence scores (0.0-1.0) by this value
-COMET_MAX_CONFIDENCE_SCORE = int(os.getenv('COMET_MAX_CONFIDENCE_SCORE', 100))
 
 # Names of plots created by YOLOv8 that are logged to Comet
 EVALUATION_PLOT_NAMES = 'F1_curve', 'P_curve', 'R_curve', 'PR_curve', 'confusion_matrix'
@@ -35,6 +23,35 @@ LABEL_PLOT_NAMES = 'labels', 'labels_correlogram'
 _comet_image_prediction_count = 0
 
 
+def _get_comet_mode():
+    return os.getenv('COMET_MODE', 'online')
+
+
+def _get_comet_model_name():
+    return os.getenv('COMET_MODEL_NAME', 'YOLOv8')
+
+
+def _get_eval_batch_logging_interval():
+    return int(os.getenv('COMET_EVAL_BATCH_LOGGING_INTERVAL', 1))
+
+
+def _get_max_image_predictions_to_log():
+    return int(os.getenv('COMET_MAX_IMAGE_PREDICTIONS', 100))
+
+
+def _scale_confidence_score(score):
+    scale = float(os.getenv('COMET_MAX_CONFIDENCE_SCORE', 100.0))
+    return score * scale
+
+
+def _should_log_confusion_matrix():
+    return os.getenv('COMET_EVAL_LOG_CONFUSION_MATRIX', 'true').lower() == 'true'
+
+
+def _should_log_image_predictions():
+    return os.getenv('COMET_EVAL_LOG_IMAGE_PREDICTIONS', 'true').lower() == 'true'
+
+
 def _get_experiment_type(mode, project_name):
     """Return an experiment based on mode and project name."""
     if mode == 'offline':
@@ -48,13 +65,14 @@ def _create_experiment(args):
     if RANK not in (-1, 0):
         return
     try:
-        experiment = _get_experiment_type(COMET_MODE, args.project)
+        comet_mode = _get_comet_mode()
+        experiment = _get_experiment_type(comet_mode, args.project)
         experiment.log_parameters(vars(args))
         experiment.log_others({
-            'eval_batch_logging_interval': COMET_EVAL_BATCH_LOGGING_INTERVAL,
-            'log_confusion_matrix': COMET_EVAL_LOG_CONFUSION_MATRIX,
-            'log_image_predictions': COMET_EVAL_LOG_IMAGE_PREDICTIONS,
-            'max_image_predictions': COMET_MAX_IMAGE_PREDICTIONS, })
+            'eval_batch_logging_interval': _get_eval_batch_logging_interval(),
+            'log_confusion_matrix': _should_log_confusion_matrix(),
+            'log_image_predictions': _should_log_image_predictions(),
+            'max_image_predictions': _get_max_image_predictions_to_log(), })
         experiment.log_other('Created from', 'yolov8')
 
     except Exception as e:
@@ -74,7 +92,12 @@ def _fetch_trainer_metadata(trainer):
     save_interval = curr_epoch % save_period == 0
     save_assets = save and save_period > 0 and save_interval and not final_epoch
 
-    return dict(curr_epoch=curr_epoch, curr_step=curr_step, save_assets=save_assets, final_epoch=final_epoch)
+    return dict(
+        curr_epoch=curr_epoch,
+        curr_step=curr_step,
+        save_assets=save_assets,
+        final_epoch=final_epoch,
+    )
 
 
 def _scale_bounding_box_to_original_image_shape(box, resized_image_shape, original_image_shape, ratio_pad):
@@ -117,7 +140,10 @@ def _format_ground_truth_annotations_for_detection(img_idx, image_path, batch, c
     data = []
     for box, label in zip(bboxes, cls_labels):
         box = _scale_bounding_box_to_original_image_shape(box, resized_image_shape, original_image_shape, ratio_pad)
-        data.append({'boxes': [box], 'label': f'gt_{label}', 'score': COMET_MAX_CONFIDENCE_SCORE})
+        data.append({
+            'boxes': [box],
+            'label': f'gt_{label}',
+            'score': _scale_confidence_score(1.0), })
 
     return {'name': 'ground_truth', 'data': data}
 
@@ -135,7 +161,7 @@ def _format_prediction_annotations_for_detection(image_path, metadata, class_lab
     data = []
     for prediction in predictions:
         boxes = prediction['bbox']
-        score = prediction['score'] * COMET_MAX_CONFIDENCE_SCORE
+        score = _scale_confidence_score(prediction['score'])
         cls_label = prediction['category_id']
         if class_label_map:
             cls_label = str(class_label_map[cls_label])
@@ -207,13 +233,16 @@ def _log_image_predictions(experiment, validator, curr_step):
     dataloader = validator.dataloader
     class_label_map = validator.names
 
+    batch_logging_interval = _get_eval_batch_logging_interval()
+    max_image_predictions = _get_max_image_predictions_to_log()
+
     for batch_idx, batch in enumerate(dataloader):
-        if (batch_idx + 1) % COMET_EVAL_BATCH_LOGGING_INTERVAL != 0:
+        if (batch_idx + 1) % batch_logging_interval != 0:
             continue
 
         image_paths = batch['im_file']
         for img_idx, image_path in enumerate(image_paths):
-            if _comet_image_prediction_count >= COMET_MAX_IMAGE_PREDICTIONS:
+            if _comet_image_prediction_count >= max_image_predictions:
                 return
 
             image_path = Path(image_path)
@@ -244,8 +273,9 @@ def _log_plots(experiment, trainer):
 
 def _log_model(experiment, trainer):
     """Log the best-trained model to Comet.ml."""
+    model_name = _get_comet_model_name()
     experiment.log_model(
-        COMET_MODEL_NAME,
+        model_name,
         file_or_folder=str(trainer.best),
         file_name='best.pt',
         overwrite=True,
@@ -255,7 +285,8 @@ def _log_model(experiment, trainer):
 def on_pretrain_routine_start(trainer):
     """Creates or resumes a CometML experiment at the start of a YOLO pre-training routine."""
     experiment = comet_ml.get_global_experiment()
-    if not experiment:
+    is_alive = getattr(experiment, 'alive', False)
+    if not experiment or not is_alive:
         _create_experiment(trainer.args)
 
 
@@ -296,16 +327,16 @@ def on_fit_epoch_end(trainer):
         model_info = {
             'model/parameters': get_num_params(trainer.model),
             'model/GFLOPs': round(get_flops(trainer.model), 3),
-            'model/speed(ms)': round(trainer.validator.speed['inference'], 3)}
+            'model/speed(ms)': round(trainer.validator.speed['inference'], 3), }
         experiment.log_metrics(model_info, step=curr_step, epoch=curr_epoch)
 
     if not save_assets:
         return
 
     _log_model(experiment, trainer)
-    if COMET_EVAL_LOG_CONFUSION_MATRIX:
+    if _should_log_confusion_matrix():
         _log_confusion_matrix(experiment, trainer, curr_step, curr_epoch)
-    if COMET_EVAL_LOG_IMAGE_PREDICTIONS:
+    if _should_log_image_predictions():
         _log_image_predictions(experiment, trainer.validator, curr_step)
 
 

ultralytics/yolo/utils/downloads.py

@@ -17,7 +17,8 @@ from ultralytics.yolo.utils import LOGGER, checks, clean_url, emojis, is_online,
 
 GITHUB_ASSET_NAMES = [f'yolov8{k}{suffix}.pt' for k in 'nsmlx' for suffix in ('', '6', '-cls', '-seg', '-pose')] + \
                      [f'yolov5{k}u.pt' for k in 'nsmlx'] + \
-                     [f'yolov3{k}u.pt' for k in ('', '-spp', '-tiny')]
+                     [f'yolov3{k}u.pt' for k in ('', '-spp', '-tiny')] + \
+                     [f'sam_{k}.pt' for k in 'bl']
 GITHUB_ASSET_STEMS = [Path(k).stem for k in GITHUB_ASSET_NAMES]
 
 

ultralytics/yolo/utils/plotting.py

@@ -192,14 +192,27 @@ class Annotator:
         """Add rectangle to image (PIL-only)."""
         self.draw.rectangle(xy, fill, outline, width)
 
-    def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'):
+    def text(self, xy, text, txt_color=(255, 255, 255), anchor='top', box_style=False):
         """Adds text to an image using PIL or cv2."""
         if anchor == 'bottom':  # start y from font bottom
             w, h = self.font.getsize(text)  # text width, height
             xy[1] += 1 - h
         if self.pil:
+            if box_style:
+                w, h = self.font.getsize(text)
+                self.draw.rectangle((xy[0], xy[1], xy[0] + w + 1, xy[1] + h + 1), fill=txt_color)
+                # Using `txt_color` for background and draw fg with white color
+                txt_color = (255, 255, 255)
             self.draw.text(xy, text, fill=txt_color, font=self.font)
         else:
+            if box_style:
+                tf = max(self.lw - 1, 1)  # font thickness
+                w, h = cv2.getTextSize(text, 0, fontScale=self.lw / 3, thickness=tf)[0]  # text width, height
+                outside = xy[1] - h >= 3
+                p2 = xy[0] + w, xy[1] - h - 3 if outside else xy[1] + h + 3
+                cv2.rectangle(self.im, xy, p2, txt_color, -1, cv2.LINE_AA)  # filled
+                # Using `txt_color` for background and draw fg with white color
+                txt_color = (255, 255, 255)
             tf = max(self.lw - 1, 1)  # font thickness
             cv2.putText(self.im, text, xy, 0, self.lw / 3, txt_color, thickness=tf, lineType=cv2.LINE_AA)
 
@@ -283,7 +296,7 @@ def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False,
 def plot_images(images,
                 batch_idx,
                 cls,
-                bboxes,
+                bboxes=np.zeros(0, dtype=np.float32),
                 masks=np.zeros(0, dtype=np.uint8),
                 kpts=np.zeros((0, 51), dtype=np.float32),
                 paths=None,
@@ -337,27 +350,33 @@ def plot_images(images,
             annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
         if len(cls) > 0:
             idx = batch_idx == i
-
-            boxes = xywh2xyxy(bboxes[idx, :4]).T
             classes = cls[idx].astype('int')
-            labels = bboxes.shape[1] == 4  # labels if no conf column
-            conf = None if labels else bboxes[idx, 4]  # check for confidence presence (label vs pred)
 
-            if boxes.shape[1]:
-                if boxes.max() <= 1.01:  # if normalized with tolerance 0.01
-                    boxes[[0, 2]] *= w  # scale to pixels
-                    boxes[[1, 3]] *= h
-                elif scale < 1:  # absolute coords need scale if image scales
-                    boxes *= scale
-            boxes[[0, 2]] += x
-            boxes[[1, 3]] += y
-            for j, box in enumerate(boxes.T.tolist()):
-                c = classes[j]
-                color = colors(c)
-                c = names.get(c, c) if names else c
-                if labels or conf[j] > 0.25:  # 0.25 conf thresh
-                    label = f'{c}' if labels else f'{c} {conf[j]:.1f}'
-                    annotator.box_label(box, label, color=color)
+            if len(bboxes):
+                boxes = xywh2xyxy(bboxes[idx, :4]).T
+                labels = bboxes.shape[1] == 4  # labels if no conf column
+                conf = None if labels else bboxes[idx, 4]  # check for confidence presence (label vs pred)
+
+                if boxes.shape[1]:
+                    if boxes.max() <= 1.01:  # if normalized with tolerance 0.01
+                        boxes[[0, 2]] *= w  # scale to pixels
+                        boxes[[1, 3]] *= h
+                    elif scale < 1:  # absolute coords need scale if image scales
+                        boxes *= scale
+                boxes[[0, 2]] += x
+                boxes[[1, 3]] += y
+                for j, box in enumerate(boxes.T.tolist()):
+                    c = classes[j]
+                    color = colors(c)
+                    c = names.get(c, c) if names else c
+                    if labels or conf[j] > 0.25:  # 0.25 conf thresh
+                        label = f'{c}' if labels else f'{c} {conf[j]:.1f}'
+                        annotator.box_label(box, label, color=color)
+            elif len(classes):
+                for c in classes:
+                    color = colors(c)
+                    c = names.get(c, c) if names else c
+                    annotator.text((x, y), f'{c}', txt_color=color, box_style=True)
 
             # Plot keypoints
             if len(kpts):
@@ -403,11 +422,14 @@ def plot_images(images,
 
 
 @plt_settings()
-def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False):
+def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False, classify=False):
     """Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')."""
     import pandas as pd
     save_dir = Path(file).parent if file else Path(dir)
-    if segment:
+    if classify:
+        fig, ax = plt.subplots(2, 2, figsize=(6, 6), tight_layout=True)
+        index = [1, 4, 2, 3]
+    elif segment:
         fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True)
         index = [1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]
     elif pose:

ultralytics/yolo/utils/tal.py

@@ -225,7 +225,7 @@ class TaskAlignedAssigner(nn.Module):
         target_bboxes = gt_bboxes.view(-1, 4)[target_gt_idx]
 
         # Assigned target scores
-        target_labels.clamp(0)
+        target_labels.clamp_(0)
         target_scores = F.one_hot(target_labels, self.num_classes)  # (b, h*w, 80)
         fg_scores_mask = fg_mask[:, :, None].repeat(1, 1, self.num_classes)  # (b, h*w, 80)
         target_scores = torch.where(fg_scores_mask > 0, target_scores, 0)

ultralytics/yolo/v8/classify/predict.py

@@ -9,8 +9,14 @@ from ultralytics.yolo.utils import DEFAULT_CFG, ROOT
 
 class ClassificationPredictor(BasePredictor):
 
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        super().__init__(cfg, overrides, _callbacks)
+        self.args.task = 'classify'
+
     def preprocess(self, img):
         """Converts input image to model-compatible data type."""
+        if not isinstance(img, torch.Tensor):
+            img = torch.stack([self.transforms(im) for im in img], dim=0)
         img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
         return img.half() if self.model.fp16 else img.float()  # uint8 to fp16/32
 
@@ -19,7 +25,7 @@ class ClassificationPredictor(BasePredictor):
         results = []
         for i, pred in enumerate(preds):
             orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
-            path, _, _, _, _ = self.batch
+            path = self.batch[0]
             img_path = path[i] if isinstance(path, list) else path
             results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, probs=pred))
 

ultralytics/yolo/v8/classify/train.py

@@ -5,10 +5,11 @@ import torchvision
 
 from ultralytics.nn.tasks import ClassificationModel, attempt_load_one_weight
 from ultralytics.yolo import v8
-from ultralytics.yolo.data import build_classification_dataloader
+from ultralytics.yolo.data import ClassificationDataset, build_dataloader
 from ultralytics.yolo.engine.trainer import BaseTrainer
 from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, colorstr
-from ultralytics.yolo.utils.torch_utils import is_parallel, strip_optimizer
+from ultralytics.yolo.utils.plotting import plot_images, plot_results
+from ultralytics.yolo.utils.torch_utils import is_parallel, strip_optimizer, torch_distributed_zero_first
 
 
 class ClassificationTrainer(BaseTrainer):
@@ -71,14 +72,16 @@ class ClassificationTrainer(BaseTrainer):
 
         return  # dont return ckpt. Classification doesn't support resume
 
+    def build_dataset(self, img_path, mode='train'):
+        dataset = ClassificationDataset(root=img_path, imgsz=self.args.imgsz, augment=mode == 'train')
+        return dataset
+
     def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode='train'):
         """Returns PyTorch DataLoader with transforms to preprocess images for inference."""
-        loader = build_classification_dataloader(path=dataset_path,
-                                                 imgsz=self.args.imgsz,
-                                                 batch_size=batch_size if mode == 'train' else (batch_size * 2),
-                                                 augment=mode == 'train',
-                                                 rank=rank,
-                                                 workers=self.args.workers)
+        with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
+            dataset = self.build_dataset(dataset_path, mode)
+
+        loader = build_dataloader(dataset, batch_size, self.args.workers, rank=rank)
         # Attach inference transforms
         if mode != 'train':
             if is_parallel(self.model):
@@ -124,6 +127,10 @@ class ClassificationTrainer(BaseTrainer):
         """Resumes training from a given checkpoint."""
         pass
 
+    def plot_metrics(self):
+        """Plots metrics from a CSV file."""
+        plot_results(file=self.csv, classify=True)  # save results.png
+
     def final_eval(self):
         """Evaluate trained model and save validation results."""
         for f in self.last, self.best:
@@ -138,6 +145,13 @@ class ClassificationTrainer(BaseTrainer):
                 #     self.run_callbacks('on_fit_epoch_end')
         LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}")
 
+    def plot_training_samples(self, batch, ni):
+        """Plots training samples with their annotations."""
+        plot_images(images=batch['img'],
+                    batch_idx=torch.arange(len(batch['img'])),
+                    cls=batch['cls'].squeeze(-1),
+                    fname=self.save_dir / f'train_batch{ni}.jpg')
+
 
 def train(cfg=DEFAULT_CFG, use_python=False):
     """Train the YOLO classification model."""

ultralytics/yolo/v8/classify/val.py

@@ -1,9 +1,12 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-from ultralytics.yolo.data import build_classification_dataloader
+import torch
+
+from ultralytics.yolo.data import ClassificationDataset, build_dataloader
 from ultralytics.yolo.engine.validator import BaseValidator
 from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER
 from ultralytics.yolo.utils.metrics import ClassifyMetrics, ConfusionMatrix
+from ultralytics.yolo.utils.plotting import plot_images
 
 
 class ClassificationValidator(BaseValidator):
@@ -52,20 +55,36 @@ class ClassificationValidator(BaseValidator):
         self.metrics.process(self.targets, self.pred)
         return self.metrics.results_dict
 
+    def build_dataset(self, img_path):
+        dataset = ClassificationDataset(root=img_path, imgsz=self.args.imgsz, augment=False)
+        return dataset
+
     def get_dataloader(self, dataset_path, batch_size):
         """Builds and returns a data loader for classification tasks with given parameters."""
-        return build_classification_dataloader(path=dataset_path,
-                                               imgsz=self.args.imgsz,
-                                               batch_size=batch_size,
-                                               augment=False,
-                                               shuffle=False,
-                                               workers=self.args.workers)
+        dataset = self.build_dataset(dataset_path)
+        return build_dataloader(dataset, batch_size, self.args.workers, rank=-1)
 
     def print_results(self):
         """Prints evaluation metrics for YOLO object detection model."""
         pf = '%22s' + '%11.3g' * len(self.metrics.keys)  # print format
         LOGGER.info(pf % ('all', self.metrics.top1, self.metrics.top5))
 
+    def plot_val_samples(self, batch, ni):
+        """Plot validation image samples."""
+        plot_images(images=batch['img'],
+                    batch_idx=torch.arange(len(batch['img'])),
+                    cls=batch['cls'].squeeze(-1),
+                    fname=self.save_dir / f'val_batch{ni}_labels.jpg',
+                    names=self.names)
+
+    def plot_predictions(self, batch, preds, ni):
+        """Plots predicted bounding boxes on input images and saves the result."""
+        plot_images(batch['img'],
+                    batch_idx=torch.arange(len(batch['img'])),
+                    cls=torch.argmax(preds, dim=1),
+                    fname=self.save_dir / f'val_batch{ni}_pred.jpg',
+                    names=self.names)  # pred
+
 
 def val(cfg=DEFAULT_CFG, use_python=False):
     """Validate YOLO model using custom data."""

ultralytics/yolo/v8/detect/predict.py

@@ -9,13 +9,6 @@ from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops
 
 class DetectionPredictor(BasePredictor):
 
-    def preprocess(self, img):
-        """Convert an image to PyTorch tensor and normalize pixel values."""
-        img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
-        img = img.half() if self.model.fp16 else img.float()  # uint8 to fp16/32
-        img /= 255  # 0 - 255 to 0.0 - 1.0
-        return img
-
     def postprocess(self, preds, img, orig_imgs):
         """Postprocesses predictions and returns a list of Results objects."""
         preds = ops.non_max_suppression(preds,
@@ -30,7 +23,7 @@ class DetectionPredictor(BasePredictor):
             orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
             if not isinstance(orig_imgs, torch.Tensor):
                 pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
-            path, _, _, _, _ = self.batch
+            path = self.batch[0]
             img_path = path[i] if isinstance(path, list) else path
             results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))
         return results

ultralytics/yolo/v8/detect/train.py

@@ -7,41 +7,63 @@ import torch.nn as nn
 
 from ultralytics.nn.tasks import DetectionModel
 from ultralytics.yolo import v8
-from ultralytics.yolo.data import build_dataloader
+from ultralytics.yolo.data import build_dataloader, build_yolo_dataset
 from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader
 from ultralytics.yolo.engine.trainer import BaseTrainer
-from ultralytics.yolo.utils import DEFAULT_CFG, RANK, colorstr
+from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, colorstr
 from ultralytics.yolo.utils.loss import BboxLoss
 from ultralytics.yolo.utils.ops import xywh2xyxy
 from ultralytics.yolo.utils.plotting import plot_images, plot_labels, plot_results
 from ultralytics.yolo.utils.tal import TaskAlignedAssigner, dist2bbox, make_anchors
-from ultralytics.yolo.utils.torch_utils import de_parallel
+from ultralytics.yolo.utils.torch_utils import de_parallel, torch_distributed_zero_first
 
 
 # BaseTrainer python usage
 class DetectionTrainer(BaseTrainer):
 
+    def build_dataset(self, img_path, mode='train', batch=None):
+        """Build YOLO Dataset
+
+        Args:
+            img_path (str): Path to the folder containing images.
+            mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
+            batch_size (int, optional): Size of batches, this is for `rect`. Defaults to None.
+        """
+        gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
+        return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, rect=mode == 'val', stride=gs)
+
     def get_dataloader(self, dataset_path, batch_size, rank=0, mode='train'):
         """TODO: manage splits differently."""
         # Calculate stride - check if model is initialized
-        gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
-        return create_dataloader(path=dataset_path,
-                                 imgsz=self.args.imgsz,
-                                 batch_size=batch_size,
-                                 stride=gs,
-                                 hyp=vars(self.args),
-                                 augment=mode == 'train',
-                                 cache=self.args.cache,
-                                 pad=0 if mode == 'train' else 0.5,
-                                 rect=self.args.rect or mode == 'val',
-                                 rank=rank,
-                                 workers=self.args.workers,
-                                 close_mosaic=self.args.close_mosaic != 0,
-                                 prefix=colorstr(f'{mode}: '),
-                                 shuffle=mode == 'train',
-                                 seed=self.args.seed)[0] if self.args.v5loader else \
-            build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode,
-                             rect=mode == 'val', data_info=self.data)[0]
+        if self.args.v5loader:
+            LOGGER.warning("WARNING ⚠️ 'v5loader' feature is deprecated and will be removed soon. You can train using "
+                           'the default YOLOv8 dataloader instead, no argument is needed.')
+            gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
+            return create_dataloader(path=dataset_path,
+                                     imgsz=self.args.imgsz,
+                                     batch_size=batch_size,
+                                     stride=gs,
+                                     hyp=vars(self.args),
+                                     augment=mode == 'train',
+                                     cache=self.args.cache,
+                                     pad=0 if mode == 'train' else 0.5,
+                                     rect=self.args.rect or mode == 'val',
+                                     rank=rank,
+                                     workers=self.args.workers,
+                                     close_mosaic=self.args.close_mosaic != 0,
+                                     prefix=colorstr(f'{mode}: '),
+                                     shuffle=mode == 'train',
+                                     seed=self.args.seed)[0]
+        assert mode in ['train', 'val']
+        with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
+            dataset = self.build_dataset(dataset_path, mode, batch_size)
+        shuffle = mode == 'train'
+        if getattr(dataset, 'rect', False) and shuffle:
+            LOGGER.warning("WARNING ⚠️ 'rect=True' is incompatible with DataLoader shuffle, setting shuffle=False")
+            shuffle = False
+        workers = self.args.workers if mode == 'train' else self.args.workers * 2
+        dataloader = build_dataloader(dataset, batch_size, workers, shuffle, rank)
+        return dataloader
 
     def preprocess_batch(self, batch):
         """Preprocesses a batch of images by scaling and converting to float."""

ultralytics/yolo/v8/detect/val.py

@@ -6,7 +6,7 @@ from pathlib import Path
 import numpy as np
 import torch
 
-from ultralytics.yolo.data import build_dataloader
+from ultralytics.yolo.data import build_dataloader, build_yolo_dataset
 from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader
 from ultralytics.yolo.engine.validator import BaseValidator
 from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, colorstr, ops
@@ -171,24 +171,40 @@ class DetectionValidator(BaseValidator):
                 correct[matches[:, 1].astype(int), i] = True
         return torch.tensor(correct, dtype=torch.bool, device=detections.device)
 
+    def build_dataset(self, img_path, mode='val', batch=None):
+        """Build YOLO Dataset
+
+        Args:
+            img_path (str): Path to the folder containing images.
+            mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
+            batch_size (int, optional): Size of batches, this is for `rect`. Defaults to None.
+        """
+        gs = max(int(de_parallel(self.model).stride if self.model else 0), 32)
+        return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, stride=gs)
+
     def get_dataloader(self, dataset_path, batch_size):
         """TODO: manage splits differently."""
         # Calculate stride - check if model is initialized
-        gs = max(int(de_parallel(self.model).stride if self.model else 0), 32)
-        return create_dataloader(path=dataset_path,
-                                 imgsz=self.args.imgsz,
-                                 batch_size=batch_size,
-                                 stride=gs,
-                                 hyp=vars(self.args),
-                                 cache=False,
-                                 pad=0.5,
-                                 rect=self.args.rect,
-                                 workers=self.args.workers,
-                                 prefix=colorstr(f'{self.args.mode}: '),
-                                 shuffle=False,
-                                 seed=self.args.seed)[0] if self.args.v5loader else \
-            build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, data_info=self.data,
-                             mode='val')[0]
+        if self.args.v5loader:
+            LOGGER.warning("WARNING ⚠️ 'v5loader' feature is deprecated and will be removed soon. You can train using "
+                           'the default YOLOv8 dataloader instead, no argument is needed.')
+            gs = max(int(de_parallel(self.model).stride if self.model else 0), 32)
+            return create_dataloader(path=dataset_path,
+                                     imgsz=self.args.imgsz,
+                                     batch_size=batch_size,
+                                     stride=gs,
+                                     hyp=vars(self.args),
+                                     cache=False,
+                                     pad=0.5,
+                                     rect=self.args.rect,
+                                     workers=self.args.workers,
+                                     prefix=colorstr(f'{self.args.mode}: '),
+                                     shuffle=False,
+                                     seed=self.args.seed)[0]
+
+        dataset = self.build_dataset(dataset_path, batch=batch_size, mode='val')
+        dataloader = build_dataloader(dataset, batch_size, self.args.workers, shuffle=False, rank=-1)
+        return dataloader
 
     def plot_val_samples(self, batch, ni):
         """Plot validation image samples."""

ultralytics/yolo/v8/pose/predict.py

@@ -7,6 +7,10 @@ from ultralytics.yolo.v8.detect.predict import DetectionPredictor
 
 class PosePredictor(DetectionPredictor):
 
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        super().__init__(cfg, overrides, _callbacks)
+        self.args.task = 'pose'
+
     def postprocess(self, preds, img, orig_img):
         """Return detection results for a given input image or list of images."""
         preds = ops.non_max_suppression(preds,
@@ -24,7 +28,7 @@ class PosePredictor(DetectionPredictor):
             pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
             pred_kpts = pred[:, 6:].view(len(pred), *self.model.kpt_shape) if len(pred) else pred[:, 6:]
             pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, shape)
-            path, _, _, _, _ = self.batch
+            path = self.batch[0]
             img_path = path[i] if isinstance(path, list) else path
             results.append(
                 Results(orig_img=orig_img,

ultralytics/yolo/v8/segment/predict.py

@@ -9,6 +9,10 @@ from ultralytics.yolo.v8.detect.predict import DetectionPredictor
 
 class SegmentationPredictor(DetectionPredictor):
 
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        super().__init__(cfg, overrides, _callbacks)
+        self.args.task = 'segment'
+
     def postprocess(self, preds, img, orig_imgs):
         """TODO: filter by classes."""
         p = ops.non_max_suppression(preds[0],
@@ -22,7 +26,7 @@ class SegmentationPredictor(DetectionPredictor):
         proto = preds[1][-1] if len(preds[1]) == 3 else preds[1]  # second output is len 3 if pt, but only 1 if exported
         for i, pred in enumerate(p):
             orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
-            path, _, _, _, _ = self.batch
+            path = self.batch[0]
             img_path = path[i] if isinstance(path, list) else path
             if not len(pred):  # save empty boxes
                 results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6]))