`ultralytics 8.0.89` SAM predict and auto-annotate (#2298)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com> Co-authored-by: Paula Derrenger <107626595+pderrenger@users.noreply.github.com> Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com> Co-authored-by: Snyk bot <snyk-bot@snyk.io> Co-authored-by: Laughing-q <1185102784@qq.com>single_channel
Glenn Jocher
2 years ago
committed by
GitHub
parent
3e118f6170
commit
243fc4b1fe
@ -1,9 +1,10 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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__version__ = '8.0.88'
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__version__ = '8.0.89'
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from ultralytics.hub import start
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from ultralytics.vit.sam import SAM
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from ultralytics.yolo.engine.model import YOLO
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from ultralytics.yolo.utils.checks import check_yolo as checks
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__all__ = '__version__', 'YOLO', 'checks', 'start' # allow simpler import
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__all__ = '__version__', 'YOLO', 'SAM', 'checks', 'start' # allow simpler import
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from .sam import SAM # noqa
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from .build import build_sam # noqa
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from .model import SAM # noqa
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from .modules.prompt_predictor import PromptPredictor # noqa
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import math
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from copy import deepcopy
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from itertools import product
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from typing import Any, Dict, Generator, ItemsView, List, Tuple
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import numpy as np
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import torch
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class MaskData:
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"""
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A structure for storing masks and their related data in batched format.
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Implements basic filtering and concatenation.
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"""
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def __init__(self, **kwargs) -> None:
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"""Initialize a MaskData object, ensuring all values are supported types."""
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for v in kwargs.values():
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assert isinstance(
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v, (list, np.ndarray, torch.Tensor)), 'MaskData only supports list, numpy arrays, and torch tensors.'
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self._stats = dict(**kwargs)
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def __setitem__(self, key: str, item: Any) -> None:
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"""Set an item in the MaskData object, ensuring it is a supported type."""
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assert isinstance(
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item, (list, np.ndarray, torch.Tensor)), 'MaskData only supports list, numpy arrays, and torch tensors.'
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self._stats[key] = item
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def __delitem__(self, key: str) -> None:
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"""Delete an item from the MaskData object."""
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del self._stats[key]
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def __getitem__(self, key: str) -> Any:
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"""Get an item from the MaskData object."""
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return self._stats[key]
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def items(self) -> ItemsView[str, Any]:
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"""Return an ItemsView of the MaskData object."""
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return self._stats.items()
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def filter(self, keep: torch.Tensor) -> None:
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"""Filter the MaskData object based on the given boolean tensor."""
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for k, v in self._stats.items():
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if v is None:
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self._stats[k] = None
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elif isinstance(v, torch.Tensor):
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self._stats[k] = v[torch.as_tensor(keep, device=v.device)]
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elif isinstance(v, np.ndarray):
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self._stats[k] = v[keep.detach().cpu().numpy()]
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elif isinstance(v, list) and keep.dtype == torch.bool:
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self._stats[k] = [a for i, a in enumerate(v) if keep[i]]
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elif isinstance(v, list):
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self._stats[k] = [v[i] for i in keep]
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else:
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raise TypeError(f'MaskData key {k} has an unsupported type {type(v)}.')
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def cat(self, new_stats: 'MaskData') -> None:
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"""Concatenate a new MaskData object to the current one."""
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for k, v in new_stats.items():
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if k not in self._stats or self._stats[k] is None:
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self._stats[k] = deepcopy(v)
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elif isinstance(v, torch.Tensor):
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self._stats[k] = torch.cat([self._stats[k], v], dim=0)
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elif isinstance(v, np.ndarray):
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self._stats[k] = np.concatenate([self._stats[k], v], axis=0)
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elif isinstance(v, list):
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self._stats[k] = self._stats[k] + deepcopy(v)
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else:
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raise TypeError(f'MaskData key {k} has an unsupported type {type(v)}.')
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def to_numpy(self) -> None:
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"""Convert all torch tensors in the MaskData object to numpy arrays."""
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for k, v in self._stats.items():
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if isinstance(v, torch.Tensor):
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self._stats[k] = v.detach().cpu().numpy()
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def is_box_near_crop_edge(boxes: torch.Tensor,
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crop_box: List[int],
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orig_box: List[int],
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atol: float = 20.0) -> torch.Tensor:
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"""Return a boolean tensor indicating if boxes are near the crop edge."""
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crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)
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orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)
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boxes = uncrop_boxes_xyxy(boxes, crop_box).float()
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near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)
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near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)
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near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)
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return torch.any(near_crop_edge, dim=1)
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def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:
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"""Convert bounding boxes from XYXY format to XYWH format."""
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box_xywh = deepcopy(box_xyxy)
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box_xywh[2] = box_xywh[2] - box_xywh[0]
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box_xywh[3] = box_xywh[3] - box_xywh[1]
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return box_xywh
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def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:
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"""Yield batches of data from the input arguments."""
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assert args and all(len(a) == len(args[0]) for a in args), 'Batched iteration must have same-size inputs.'
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n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)
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for b in range(n_batches):
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yield [arg[b * batch_size:(b + 1) * batch_size] for arg in args]
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def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
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"""Encode masks as uncompressed RLEs in the format expected by pycocotools."""
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# Put in fortran order and flatten h,w
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b, h, w = tensor.shape
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tensor = tensor.permute(0, 2, 1).flatten(1)
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# Compute change indices
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diff = tensor[:, 1:] ^ tensor[:, :-1]
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change_indices = diff.nonzero()
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# Encode run length
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out = []
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for i in range(b):
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cur_idxs = change_indices[change_indices[:, 0] == i, 1]
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cur_idxs = torch.cat([
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torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),
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cur_idxs + 1,
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torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device), ])
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btw_idxs = cur_idxs[1:] - cur_idxs[:-1]
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counts = [] if tensor[i, 0] == 0 else [0]
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counts.extend(btw_idxs.detach().cpu().tolist())
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out.append({'size': [h, w], 'counts': counts})
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return out
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def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
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"""Compute a binary mask from an uncompressed RLE."""
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h, w = rle['size']
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mask = np.empty(h * w, dtype=bool)
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idx = 0
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parity = False
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for count in rle['counts']:
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mask[idx:idx + count] = parity
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idx += count
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parity ^= True
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mask = mask.reshape(w, h)
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return mask.transpose() # Put in C order
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def area_from_rle(rle: Dict[str, Any]) -> int:
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"""Calculate the area of a mask from its uncompressed RLE."""
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return sum(rle['counts'][1::2])
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def calculate_stability_score(masks: torch.Tensor, mask_threshold: float, threshold_offset: float) -> torch.Tensor:
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"""
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Computes the stability score for a batch of masks. The stability
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score is the IoU between the binary masks obtained by thresholding
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the predicted mask logits at high and low values.
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"""
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# One mask is always contained inside the other.
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# Save memory by preventing unnecessary cast to torch.int64
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intersections = ((masks > (mask_threshold + threshold_offset)).sum(-1, dtype=torch.int16).sum(-1,
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dtype=torch.int32))
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unions = ((masks > (mask_threshold - threshold_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32))
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return intersections / unions
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def build_point_grid(n_per_side: int) -> np.ndarray:
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"""Generate a 2D grid of evenly spaced points in the range [0,1]x[0,1]."""
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offset = 1 / (2 * n_per_side)
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points_one_side = np.linspace(offset, 1 - offset, n_per_side)
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points_x = np.tile(points_one_side[None, :], (n_per_side, 1))
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points_y = np.tile(points_one_side[:, None], (1, n_per_side))
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return np.stack([points_x, points_y], axis=-1).reshape(-1, 2)
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def build_all_layer_point_grids(n_per_side: int, n_layers: int, scale_per_layer: int) -> List[np.ndarray]:
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"""Generate point grids for all crop layers."""
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return [build_point_grid(int(n_per_side / (scale_per_layer ** i))) for i in range(n_layers + 1)]
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def generate_crop_boxes(im_size: Tuple[int, ...], n_layers: int,
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overlap_ratio: float) -> Tuple[List[List[int]], List[int]]:
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"""Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer."""
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crop_boxes, layer_idxs = [], []
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im_h, im_w = im_size
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short_side = min(im_h, im_w)
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# Original image
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crop_boxes.append([0, 0, im_w, im_h])
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layer_idxs.append(0)
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def crop_len(orig_len, n_crops, overlap):
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"""Crops bounding boxes to the size of the input image."""
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return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))
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for i_layer in range(n_layers):
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n_crops_per_side = 2 ** (i_layer + 1)
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overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))
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crop_w = crop_len(im_w, n_crops_per_side, overlap)
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crop_h = crop_len(im_h, n_crops_per_side, overlap)
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crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]
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crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]
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# Crops in XYWH format
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for x0, y0 in product(crop_box_x0, crop_box_y0):
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box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]
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crop_boxes.append(box)
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layer_idxs.append(i_layer + 1)
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return crop_boxes, layer_idxs
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def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
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"""Uncrop bounding boxes by adding the crop box offset."""
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x0, y0, _, _ = crop_box
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offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)
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# Check if boxes has a channel dimension
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if len(boxes.shape) == 3:
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offset = offset.unsqueeze(1)
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return boxes + offset
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def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
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"""Uncrop points by adding the crop box offset."""
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x0, y0, _, _ = crop_box
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offset = torch.tensor([[x0, y0]], device=points.device)
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# Check if points has a channel dimension
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if len(points.shape) == 3:
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offset = offset.unsqueeze(1)
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return points + offset
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def uncrop_masks(masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int) -> torch.Tensor:
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"""Uncrop masks by padding them to the original image size."""
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x0, y0, x1, y1 = crop_box
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if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:
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return masks
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# Coordinate transform masks
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pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)
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pad = (x0, pad_x - x0, y0, pad_y - y0)
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return torch.nn.functional.pad(masks, pad, value=0)
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def remove_small_regions(mask: np.ndarray, area_thresh: float, mode: str) -> Tuple[np.ndarray, bool]:
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"""Remove small disconnected regions or holes in a mask, returning the mask and a modification indicator."""
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import cv2 # type: ignore
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assert mode in {'holes', 'islands'}
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correct_holes = mode == 'holes'
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working_mask = (correct_holes ^ mask).astype(np.uint8)
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n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
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sizes = stats[:, -1][1:] # Row 0 is background label
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small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
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if not small_regions:
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return mask, False
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fill_labels = [0] + small_regions
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if not correct_holes:
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fill_labels = [i for i in range(n_labels) if i not in fill_labels]
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# If every region is below threshold, keep largest
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if not fill_labels:
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fill_labels = [int(np.argmax(sizes)) + 1]
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mask = np.isin(regions, fill_labels)
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return mask, True
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def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:
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"""Encode uncompressed RLE (run-length encoding) to COCO RLE format."""
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from pycocotools import mask as mask_utils # type: ignore
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h, w = uncompressed_rle['size']
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rle = mask_utils.frPyObjects(uncompressed_rle, h, w)
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rle['counts'] = rle['counts'].decode('utf-8') # Necessary to serialize with json
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return rle
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def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:
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"""
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Calculates boxes in XYXY format around masks. Return [0,0,0,0] for
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an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4.
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"""
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# torch.max below raises an error on empty inputs, just skip in this case
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if torch.numel(masks) == 0:
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return torch.zeros(*masks.shape[:-2], 4, device=masks.device)
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# Normalize shape to CxHxW
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shape = masks.shape
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h, w = shape[-2:]
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masks = masks.flatten(0, -3) if len(shape) > 2 else masks.unsqueeze(0)
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# Get top and bottom edges
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in_height, _ = torch.max(masks, dim=-1)
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in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]
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bottom_edges, _ = torch.max(in_height_coords, dim=-1)
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in_height_coords = in_height_coords + h * (~in_height)
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top_edges, _ = torch.min(in_height_coords, dim=-1)
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# Get left and right edges
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in_width, _ = torch.max(masks, dim=-2)
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in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]
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right_edges, _ = torch.max(in_width_coords, dim=-1)
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in_width_coords = in_width_coords + w * (~in_width)
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left_edges, _ = torch.min(in_width_coords, dim=-1)
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# If the mask is empty the right edge will be to the left of the left edge.
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# Replace these boxes with [0, 0, 0, 0]
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empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)
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out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)
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out = out * (~empty_filter).unsqueeze(-1)
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# Return to original shape
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return out.reshape(*shape[:-2], 4) if len(shape) > 2 else out[0]
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@ -0,0 +1,92 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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from copy import deepcopy
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from typing import Tuple
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import numpy as np
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import torch
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from torch.nn import functional as F
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from torchvision.transforms.functional import resize, to_pil_image # type: ignore
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class ResizeLongestSide:
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"""
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Resizes images to the longest side 'target_length', as well as provides
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methods for resizing coordinates and boxes. Provides methods for
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transforming both numpy array and batched torch tensors.
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"""
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def __init__(self, target_length: int) -> None:
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self.target_length = target_length
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def apply_image(self, image: np.ndarray) -> np.ndarray:
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"""
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Expects a numpy array with shape HxWxC in uint8 format.
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"""
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target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
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return np.array(resize(to_pil_image(image), target_size))
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def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
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"""
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Expects a numpy array of length 2 in the final dimension. Requires the
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original image size in (H, W) format.
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"""
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old_h, old_w = original_size
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new_h, new_w = self.get_preprocess_shape(original_size[0], original_size[1], self.target_length)
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coords = deepcopy(coords).astype(float)
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coords[..., 0] = coords[..., 0] * (new_w / old_w)
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coords[..., 1] = coords[..., 1] * (new_h / old_h)
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return coords
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def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
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"""
|
||||
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)
|
||||
@ -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)
|
||||
@ -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")
|
||||
@ -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
|
||||
@ -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
|
||||
@ -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
|
||||
@ -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
|
||||
@ -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))
|
||||
@ -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
|
||||
@ -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)
|
||||
@ -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')
|
||||
|
||||
@ -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')
|
||||
Loading…
Reference in new issue