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  1. segment_anything/__init__.py +15 -0
  2. segment_anything/__pycache__/__init__.cpython-310.pyc +0 -0
  3. segment_anything/__pycache__/__init__.cpython-36.pyc +0 -0
  4. segment_anything/__pycache__/automatic_mask_generator.cpython-310.pyc +0 -0
  5. segment_anything/__pycache__/automatic_mask_generator.cpython-36.pyc +0 -0
  6. segment_anything/__pycache__/build_sam.cpython-310.pyc +0 -0
  7. segment_anything/__pycache__/build_sam.cpython-36.pyc +0 -0
  8. segment_anything/__pycache__/predictor.cpython-310.pyc +0 -0
  9. segment_anything/__pycache__/predictor.cpython-36.pyc +0 -0
  10. segment_anything/automatic_mask_generator.py +372 -0
  11. segment_anything/build_sam.py +107 -0
  12. segment_anything/data_setups.py +80 -0
  13. segment_anything/modeling/__init__.py +11 -0
  14. segment_anything/modeling/__pycache__/__init__.cpython-310.pyc +0 -0
  15. segment_anything/modeling/__pycache__/__init__.cpython-36.pyc +0 -0
  16. segment_anything/modeling/__pycache__/common.cpython-310.pyc +0 -0
  17. segment_anything/modeling/__pycache__/common.cpython-36.pyc +0 -0
  18. segment_anything/modeling/__pycache__/image_encoder.cpython-310.pyc +0 -0
  19. segment_anything/modeling/__pycache__/image_encoder.cpython-36.pyc +0 -0
  20. segment_anything/modeling/__pycache__/mask_decoder.cpython-310.pyc +0 -0
  21. segment_anything/modeling/__pycache__/mask_decoder.cpython-36.pyc +0 -0
  22. segment_anything/modeling/__pycache__/prompt_encoder.cpython-310.pyc +0 -0
  23. segment_anything/modeling/__pycache__/prompt_encoder.cpython-36.pyc +0 -0
  24. segment_anything/modeling/__pycache__/sam.cpython-310.pyc +0 -0
  25. segment_anything/modeling/__pycache__/sam.cpython-36.pyc +0 -0
  26. segment_anything/modeling/__pycache__/transformer.cpython-310.pyc +0 -0
  27. segment_anything/modeling/__pycache__/transformer.cpython-36.pyc +0 -0
  28. segment_anything/modeling/common.py +43 -0
  29. segment_anything/modeling/image_encoder.py +395 -0
  30. segment_anything/modeling/mask_decoder.py +176 -0
  31. segment_anything/modeling/prompt_encoder.py +214 -0
  32. segment_anything/modeling/sam.py +174 -0
  33. segment_anything/modeling/transformer.py +240 -0
  34. segment_anything/predictor.py +269 -0
  35. segment_anything/utils/__init__.py +5 -0
  36. segment_anything/utils/__pycache__/__init__.cpython-310.pyc +0 -0
  37. segment_anything/utils/__pycache__/__init__.cpython-36.pyc +0 -0
  38. segment_anything/utils/__pycache__/amg.cpython-310.pyc +0 -0
  39. segment_anything/utils/__pycache__/amg.cpython-36.pyc +0 -0
  40. segment_anything/utils/__pycache__/transforms.cpython-310.pyc +0 -0
  41. segment_anything/utils/__pycache__/transforms.cpython-36.pyc +0 -0
  42. segment_anything/utils/amg.py +346 -0
  43. segment_anything/utils/onnx.py +144 -0
  44. segment_anything/utils/transforms.py +102 -0
segment_anything/__init__.py ADDED
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1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ from .build_sam import (
8
+ build_sam,
9
+ build_sam_vit_h,
10
+ build_sam_vit_l,
11
+ build_sam_vit_b,
12
+ sam_model_registry,
13
+ )
14
+ from .predictor import SamPredictor
15
+ from .automatic_mask_generator import SamAutomaticMaskGenerator
segment_anything/__pycache__/__init__.cpython-310.pyc ADDED
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segment_anything/__pycache__/__init__.cpython-36.pyc ADDED
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segment_anything/__pycache__/automatic_mask_generator.cpython-310.pyc ADDED
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segment_anything/__pycache__/automatic_mask_generator.cpython-36.pyc ADDED
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segment_anything/__pycache__/build_sam.cpython-310.pyc ADDED
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segment_anything/__pycache__/build_sam.cpython-36.pyc ADDED
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segment_anything/__pycache__/predictor.cpython-310.pyc ADDED
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segment_anything/__pycache__/predictor.cpython-36.pyc ADDED
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segment_anything/automatic_mask_generator.py ADDED
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1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import numpy as np
8
+ import torch
9
+ from torchvision.ops.boxes import batched_nms, box_area # type: ignore
10
+
11
+ from typing import Any, Dict, List, Optional, Tuple
12
+
13
+ from .modeling import Sam
14
+ from .predictor import SamPredictor
15
+ from .utils.amg import (
16
+ MaskData,
17
+ area_from_rle,
18
+ batch_iterator,
19
+ batched_mask_to_box,
20
+ box_xyxy_to_xywh,
21
+ build_all_layer_point_grids,
22
+ calculate_stability_score,
23
+ coco_encode_rle,
24
+ generate_crop_boxes,
25
+ is_box_near_crop_edge,
26
+ mask_to_rle_pytorch,
27
+ remove_small_regions,
28
+ rle_to_mask,
29
+ uncrop_boxes_xyxy,
30
+ uncrop_masks,
31
+ uncrop_points,
32
+ )
33
+
34
+
35
+ class SamAutomaticMaskGenerator:
36
+ def __init__(
37
+ self,
38
+ model: Sam,
39
+ points_per_side: Optional[int] = 32,
40
+ points_per_batch: int = 64,
41
+ pred_iou_thresh: float = 0.88,
42
+ stability_score_thresh: float = 0.95,
43
+ stability_score_offset: float = 1.0,
44
+ box_nms_thresh: float = 0.7,
45
+ crop_n_layers: int = 0,
46
+ crop_nms_thresh: float = 0.7,
47
+ crop_overlap_ratio: float = 512 / 1500,
48
+ crop_n_points_downscale_factor: int = 1,
49
+ point_grids: Optional[List[np.ndarray]] = None,
50
+ min_mask_region_area: int = 0,
51
+ output_mode: str = "binary_mask",
52
+ ) -> None:
53
+ """
54
+ Using a SAM model, generates masks for the entire image.
55
+ Generates a grid of point prompts over the image, then filters
56
+ low quality and duplicate masks. The default settings are chosen
57
+ for SAM with a ViT-H backbone.
58
+
59
+ Arguments:
60
+ model (Sam): The SAM model to use for mask prediction.
61
+ points_per_side (int or None): The number of points to be sampled
62
+ along one side of the image. The total number of points is
63
+ points_per_side**2. If None, 'point_grids' must provide explicit
64
+ point sampling.
65
+ points_per_batch (int): Sets the number of points run simultaneously
66
+ by the model. Higher numbers may be faster but use more GPU memory.
67
+ pred_iou_thresh (float): A filtering threshold in [0,1], using the
68
+ model's predicted mask quality.
69
+ stability_score_thresh (float): A filtering threshold in [0,1], using
70
+ the stability of the mask under changes to the cutoff used to binarize
71
+ the model's mask predictions.
72
+ stability_score_offset (float): The amount to shift the cutoff when
73
+ calculated the stability score.
74
+ box_nms_thresh (float): The box IoU cutoff used by non-maximal
75
+ suppression to filter duplicate masks.
76
+ crops_n_layers (int): If >0, mask prediction will be run again on
77
+ crops of the image. Sets the number of layers to run, where each
78
+ layer has 2**i_layer number of image crops.
79
+ crops_nms_thresh (float): The box IoU cutoff used by non-maximal
80
+ suppression to filter duplicate masks between different crops.
81
+ crop_overlap_ratio (float): Sets the degree to which crops overlap.
82
+ In the first crop layer, crops will overlap by this fraction of
83
+ the image length. Later layers with more crops scale down this overlap.
84
+ crop_n_points_downscale_factor (int): The number of points-per-side
85
+ sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
86
+ point_grids (list(np.ndarray) or None): A list over explicit grids
87
+ of points used for sampling, normalized to [0,1]. The nth grid in the
88
+ list is used in the nth crop layer. Exclusive with points_per_side.
89
+ min_mask_region_area (int): If >0, postprocessing will be applied
90
+ to remove disconnected regions and holes in masks with area smaller
91
+ than min_mask_region_area. Requires opencv.
92
+ output_mode (str): The form masks are returned in. Can be 'binary_mask',
93
+ 'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
94
+ For large resolutions, 'binary_mask' may consume large amounts of
95
+ memory.
96
+ """
97
+
98
+ assert (points_per_side is None) != (
99
+ point_grids is None
100
+ ), "Exactly one of points_per_side or point_grid must be provided."
101
+ if points_per_side is not None:
102
+ self.point_grids = build_all_layer_point_grids(
103
+ points_per_side,
104
+ crop_n_layers,
105
+ crop_n_points_downscale_factor,
106
+ )
107
+ elif point_grids is not None:
108
+ self.point_grids = point_grids
109
+ else:
110
+ raise ValueError("Can't have both points_per_side and point_grid be None.")
111
+
112
+ assert output_mode in [
113
+ "binary_mask",
114
+ "uncompressed_rle",
115
+ "coco_rle",
116
+ ], f"Unknown output_mode {output_mode}."
117
+ if output_mode == "coco_rle":
118
+ from pycocotools import mask as mask_utils # type: ignore # noqa: F401
119
+
120
+ if min_mask_region_area > 0:
121
+ import cv2 # type: ignore # noqa: F401
122
+
123
+ self.predictor = SamPredictor(model)
124
+ self.points_per_batch = points_per_batch
125
+ self.pred_iou_thresh = pred_iou_thresh
126
+ self.stability_score_thresh = stability_score_thresh
127
+ self.stability_score_offset = stability_score_offset
128
+ self.box_nms_thresh = box_nms_thresh
129
+ self.crop_n_layers = crop_n_layers
130
+ self.crop_nms_thresh = crop_nms_thresh
131
+ self.crop_overlap_ratio = crop_overlap_ratio
132
+ self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
133
+ self.min_mask_region_area = min_mask_region_area
134
+ self.output_mode = output_mode
135
+
136
+ @torch.no_grad()
137
+ def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
138
+ """
139
+ Generates masks for the given image.
140
+
141
+ Arguments:
142
+ image (np.ndarray): The image to generate masks for, in HWC uint8 format.
143
+
144
+ Returns:
145
+ list(dict(str, any)): A list over records for masks. Each record is
146
+ a dict containing the following keys:
147
+ segmentation (dict(str, any) or np.ndarray): The mask. If
148
+ output_mode='binary_mask', is an array of shape HW. Otherwise,
149
+ is a dictionary containing the RLE.
150
+ bbox (list(float)): The box around the mask, in XYWH format.
151
+ area (int): The area in pixels of the mask.
152
+ predicted_iou (float): The model's own prediction of the mask's
153
+ quality. This is filtered by the pred_iou_thresh parameter.
154
+ point_coords (list(list(float))): The point coordinates input
155
+ to the model to generate this mask.
156
+ stability_score (float): A measure of the mask's quality. This
157
+ is filtered on using the stability_score_thresh parameter.
158
+ crop_box (list(float)): The crop of the image used to generate
159
+ the mask, given in XYWH format.
160
+ """
161
+
162
+ # Generate masks
163
+ mask_data = self._generate_masks(image)
164
+
165
+ # Filter small disconnected regions and holes in masks
166
+ if self.min_mask_region_area > 0:
167
+ mask_data = self.postprocess_small_regions(
168
+ mask_data,
169
+ self.min_mask_region_area,
170
+ max(self.box_nms_thresh, self.crop_nms_thresh),
171
+ )
172
+
173
+ # Encode masks
174
+ if self.output_mode == "coco_rle":
175
+ mask_data["segmentations"] = [coco_encode_rle(rle) for rle in mask_data["rles"]]
176
+ elif self.output_mode == "binary_mask":
177
+ mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
178
+ else:
179
+ mask_data["segmentations"] = mask_data["rles"]
180
+
181
+ # Write mask records
182
+ curr_anns = []
183
+ for idx in range(len(mask_data["segmentations"])):
184
+ ann = {
185
+ "segmentation": mask_data["segmentations"][idx],
186
+ "area": area_from_rle(mask_data["rles"][idx]),
187
+ "bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
188
+ "predicted_iou": mask_data["iou_preds"][idx].item(),
189
+ "point_coords": [mask_data["points"][idx].tolist()],
190
+ "stability_score": mask_data["stability_score"][idx].item(),
191
+ "crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
192
+ }
193
+ curr_anns.append(ann)
194
+
195
+ return curr_anns
196
+
197
+ def _generate_masks(self, image: np.ndarray) -> MaskData:
198
+ orig_size = image.shape[:2]
199
+ crop_boxes, layer_idxs = generate_crop_boxes(
200
+ orig_size, self.crop_n_layers, self.crop_overlap_ratio
201
+ )
202
+
203
+ # Iterate over image crops
204
+ data = MaskData()
205
+ for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
206
+ crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
207
+ data.cat(crop_data)
208
+
209
+ # Remove duplicate masks between crops
210
+ if len(crop_boxes) > 1:
211
+ # Prefer masks from smaller crops
212
+ scores = 1 / box_area(data["crop_boxes"])
213
+ scores = scores.to(data["boxes"].device)
214
+ keep_by_nms = batched_nms(
215
+ data["boxes"].float(),
216
+ scores,
217
+ torch.zeros(len(data["boxes"])), # categories
218
+ iou_threshold=self.crop_nms_thresh,
219
+ )
220
+ data.filter(keep_by_nms)
221
+
222
+ data.to_numpy()
223
+ return data
224
+
225
+ def _process_crop(
226
+ self,
227
+ image: np.ndarray,
228
+ crop_box: List[int],
229
+ crop_layer_idx: int,
230
+ orig_size: Tuple[int, ...],
231
+ ) -> MaskData:
232
+ # Crop the image and calculate embeddings
233
+ x0, y0, x1, y1 = crop_box
234
+ cropped_im = image[y0:y1, x0:x1, :]
235
+ cropped_im_size = cropped_im.shape[:2]
236
+ self.predictor.set_image(cropped_im)
237
+
238
+ # Get points for this crop
239
+ points_scale = np.array(cropped_im_size)[None, ::-1]
240
+ points_for_image = self.point_grids[crop_layer_idx] * points_scale
241
+
242
+ # Generate masks for this crop in batches
243
+ data = MaskData()
244
+ for (points,) in batch_iterator(self.points_per_batch, points_for_image):
245
+ batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size)
246
+ data.cat(batch_data)
247
+ del batch_data
248
+ self.predictor.reset_image()
249
+
250
+ # Remove duplicates within this crop.
251
+ keep_by_nms = batched_nms(
252
+ data["boxes"].float(),
253
+ data["iou_preds"],
254
+ torch.zeros(len(data["boxes"])), # categories
255
+ iou_threshold=self.box_nms_thresh,
256
+ )
257
+ data.filter(keep_by_nms)
258
+
259
+ # Return to the original image frame
260
+ data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box)
261
+ data["points"] = uncrop_points(data["points"], crop_box)
262
+ data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))])
263
+
264
+ return data
265
+
266
+ def _process_batch(
267
+ self,
268
+ points: np.ndarray,
269
+ im_size: Tuple[int, ...],
270
+ crop_box: List[int],
271
+ orig_size: Tuple[int, ...],
272
+ ) -> MaskData:
273
+ orig_h, orig_w = orig_size
274
+
275
+ # Run model on this batch
276
+ transformed_points = self.predictor.transform.apply_coords(points, im_size)
277
+ in_points = torch.as_tensor(transformed_points, device=self.predictor.device)
278
+ in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)
279
+ masks, iou_preds, _ = self.predictor.predict_torch(
280
+ in_points[:, None, :],
281
+ in_labels[:, None],
282
+ multimask_output=True,
283
+ return_logits=True,
284
+ )
285
+
286
+ # Serialize predictions and store in MaskData
287
+ data = MaskData(
288
+ masks=masks.flatten(0, 1),
289
+ iou_preds=iou_preds.flatten(0, 1),
290
+ points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
291
+ )
292
+ del masks
293
+
294
+ # Filter by predicted IoU
295
+ if self.pred_iou_thresh > 0.0:
296
+ keep_mask = data["iou_preds"] > self.pred_iou_thresh
297
+ data.filter(keep_mask)
298
+
299
+ # Calculate stability score
300
+ data["stability_score"] = calculate_stability_score(
301
+ data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset
302
+ )
303
+ if self.stability_score_thresh > 0.0:
304
+ keep_mask = data["stability_score"] >= self.stability_score_thresh
305
+ data.filter(keep_mask)
306
+
307
+ # Threshold masks and calculate boxes
308
+ data["masks"] = data["masks"] > self.predictor.model.mask_threshold
309
+ data["boxes"] = batched_mask_to_box(data["masks"])
310
+
311
+ # Filter boxes that touch crop boundaries
312
+ keep_mask = ~is_box_near_crop_edge(data["boxes"], crop_box, [0, 0, orig_w, orig_h])
313
+ if not torch.all(keep_mask):
314
+ data.filter(keep_mask)
315
+
316
+ # Compress to RLE
317
+ data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w)
318
+ data["rles"] = mask_to_rle_pytorch(data["masks"])
319
+ del data["masks"]
320
+
321
+ return data
322
+
323
+ @staticmethod
324
+ def postprocess_small_regions(
325
+ mask_data: MaskData, min_area: int, nms_thresh: float
326
+ ) -> MaskData:
327
+ """
328
+ Removes small disconnected regions and holes in masks, then reruns
329
+ box NMS to remove any new duplicates.
330
+
331
+ Edits mask_data in place.
332
+
333
+ Requires open-cv as a dependency.
334
+ """
335
+ if len(mask_data["rles"]) == 0:
336
+ return mask_data
337
+
338
+ # Filter small disconnected regions and holes
339
+ new_masks = []
340
+ scores = []
341
+ for rle in mask_data["rles"]:
342
+ mask = rle_to_mask(rle)
343
+
344
+ mask, changed = remove_small_regions(mask, min_area, mode="holes")
345
+ unchanged = not changed
346
+ mask, changed = remove_small_regions(mask, min_area, mode="islands")
347
+ unchanged = unchanged and not changed
348
+
349
+ new_masks.append(torch.as_tensor(mask).unsqueeze(0))
350
+ # Give score=0 to changed masks and score=1 to unchanged masks
351
+ # so NMS will prefer ones that didn't need postprocessing
352
+ scores.append(float(unchanged))
353
+
354
+ # Recalculate boxes and remove any new duplicates
355
+ masks = torch.cat(new_masks, dim=0)
356
+ boxes = batched_mask_to_box(masks)
357
+ keep_by_nms = batched_nms(
358
+ boxes.float(),
359
+ torch.as_tensor(scores),
360
+ torch.zeros(len(boxes)), # categories
361
+ iou_threshold=nms_thresh,
362
+ )
363
+
364
+ # Only recalculate RLEs for masks that have changed
365
+ for i_mask in keep_by_nms:
366
+ if scores[i_mask] == 0.0:
367
+ mask_torch = masks[i_mask].unsqueeze(0)
368
+ mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0]
369
+ mask_data["boxes"][i_mask] = boxes[i_mask] # update res directly
370
+ mask_data.filter(keep_by_nms)
371
+
372
+ return mask_data
segment_anything/build_sam.py ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import torch
8
+
9
+ from functools import partial
10
+
11
+ from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer
12
+
13
+
14
+ def build_sam_vit_h(checkpoint=None):
15
+ return _build_sam(
16
+ encoder_embed_dim=1280,
17
+ encoder_depth=32,
18
+ encoder_num_heads=16,
19
+ encoder_global_attn_indexes=[7, 15, 23, 31],
20
+ checkpoint=checkpoint,
21
+ )
22
+
23
+
24
+ build_sam = build_sam_vit_h
25
+
26
+
27
+ def build_sam_vit_l(checkpoint=None):
28
+ return _build_sam(
29
+ encoder_embed_dim=1024,
30
+ encoder_depth=24,
31
+ encoder_num_heads=16,
32
+ encoder_global_attn_indexes=[5, 11, 17, 23],
33
+ checkpoint=checkpoint,
34
+ )
35
+
36
+
37
+ def build_sam_vit_b(checkpoint=None):
38
+ return _build_sam(
39
+ encoder_embed_dim=768,
40
+ encoder_depth=12,
41
+ encoder_num_heads=12,
42
+ encoder_global_attn_indexes=[2, 5, 8, 11],
43
+ checkpoint=checkpoint,
44
+ )
45
+
46
+
47
+ sam_model_registry = {
48
+ "default": build_sam,
49
+ "vit_h": build_sam,
50
+ "vit_l": build_sam_vit_l,
51
+ "vit_b": build_sam_vit_b,
52
+ }
53
+
54
+
55
+ def _build_sam(
56
+ encoder_embed_dim,
57
+ encoder_depth,
58
+ encoder_num_heads,
59
+ encoder_global_attn_indexes,
60
+ checkpoint=None,
61
+ ):
62
+ prompt_embed_dim = 256
63
+ image_size = 1024
64
+ vit_patch_size = 16
65
+ image_embedding_size = image_size // vit_patch_size
66
+ sam = Sam(
67
+ image_encoder=ImageEncoderViT(
68
+ depth=encoder_depth,
69
+ embed_dim=encoder_embed_dim,
70
+ img_size=image_size,
71
+ mlp_ratio=4,
72
+ norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
73
+ num_heads=encoder_num_heads,
74
+ patch_size=vit_patch_size,
75
+ qkv_bias=True,
76
+ use_rel_pos=True,
77
+ global_attn_indexes=encoder_global_attn_indexes,
78
+ window_size=14,
79
+ out_chans=prompt_embed_dim,
80
+ ),
81
+ prompt_encoder=PromptEncoder(
82
+ embed_dim=prompt_embed_dim,
83
+ image_embedding_size=(image_embedding_size, image_embedding_size),
84
+ input_image_size=(image_size, image_size),
85
+ mask_in_chans=16,
86
+ ),
87
+ mask_decoder=MaskDecoder(
88
+ num_multimask_outputs=3,
89
+ transformer=TwoWayTransformer(
90
+ depth=2,
91
+ embedding_dim=prompt_embed_dim,
92
+ mlp_dim=2048,
93
+ num_heads=8,
94
+ ),
95
+ transformer_dim=prompt_embed_dim,
96
+ iou_head_depth=3,
97
+ iou_head_hidden_dim=256,
98
+ ),
99
+ pixel_mean=[123.675, 116.28, 103.53],
100
+ pixel_std=[58.395, 57.12, 57.375],
101
+ )
102
+ sam.eval()
103
+ if checkpoint is not None:
104
+ with open(checkpoint, "rb") as f:
105
+ state_dict = torch.load(f)
106
+ sam.load_state_dict(state_dict)
107
+ return sam
segment_anything/data_setups.py ADDED
@@ -0,0 +1,80 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Make function to find classes in target directory
2
+ import os
3
+ import librosa
4
+ import torch
5
+ import numpy as np
6
+ from torchaudio.transforms import Resample
7
+
8
+ SAMPLE_RATE = 44100
9
+ AUDIO_LEN = 2.90
10
+
11
+ # Parameters to control the MelSpec generation
12
+ N_MELS = 128
13
+ F_MIN = 20
14
+ F_MAX = 16000
15
+ N_FFT = 1024
16
+ HOP_LEN = 512
17
+
18
+ # Make function to find classes in target directory
19
+ def find_classes(directory: str):
20
+ # 1. Get the class names by scanning the target directory
21
+ classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir())
22
+ # 2. Raise an error if class names not found
23
+ if not classes:
24
+ raise FileNotFoundError(f"Couldn't find any classes in {directory}.")
25
+ # 3. Crearte a dictionary of index labels (computers prefer numerical rather than string labels)
26
+ class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
27
+ return classes, class_to_idx
28
+
29
+ def resample(wav, sample_rate, new_sample_rate):
30
+ if wav.shape[0] >= 2:
31
+ wav = torch.mean(wav, dim=0)
32
+ else:
33
+ wav = wav.squeeze(0)
34
+ if sample_rate > new_sample_rate:
35
+ resampler = Resample(sample_rate, new_sample_rate)
36
+ wav = resampler(wav)
37
+ return wav
38
+
39
+ def mono_to_color(X, eps=1e-6, mean=None, std=None):
40
+ X = np.stack([X, X, X], axis=-1)
41
+ # Standardize
42
+ mean = mean or X.mean()
43
+ std = std or X.std()
44
+ X = (X - mean) / (std + eps)
45
+ # Normalize to [0, 255]
46
+ _min, _max = X.min(), X.max()
47
+ if (_max - _min) > eps:
48
+ V = np.clip(X, _min, _max)
49
+ V = 255 * (V - _min) / (_max - _min)
50
+ V = V.astype(np.uint8)
51
+ else:
52
+ V = np.zeros_like(X, dtype=np.uint8)
53
+ return V
54
+
55
+ def normalize(image, mean=None, std=None):
56
+ image = image / 255.0
57
+ if mean is not None and std is not None:
58
+ image = (image - mean) / std
59
+ return np.moveaxis(image, 2, 0).astype(np.float32)
60
+
61
+ def compute_melspec(wav, sample_rate=SAMPLE_RATE):
62
+ melspec = librosa.feature.melspectrogram(
63
+ y=wav,
64
+ sr=sample_rate,
65
+ n_fft=N_FFT,
66
+ fmin=F_MIN,
67
+ fmax=F_MAX,
68
+ n_mels=N_MELS,
69
+ hop_length=HOP_LEN
70
+ )
71
+ melspec = librosa.power_to_db(melspec).astype(np.float32)
72
+ return melspec
73
+
74
+ def audio_preprocess(wav, sample_rate):
75
+ wav = wav.numpy()
76
+ melspec = compute_melspec(wav, sample_rate)
77
+ image = mono_to_color(melspec)
78
+ image = normalize(image, mean=None, std=None)
79
+ image = torch.from_numpy(image)
80
+ return image
segment_anything/modeling/__init__.py ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ from .sam import Sam
8
+ from .image_encoder import ImageEncoderViT
9
+ from .mask_decoder import MaskDecoder
10
+ from .prompt_encoder import PromptEncoder
11
+ from .transformer import TwoWayTransformer
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segment_anything/modeling/common.py ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import torch
8
+ import torch.nn as nn
9
+
10
+ from typing import Type
11
+
12
+
13
+ class MLPBlock(nn.Module):
14
+ def __init__(
15
+ self,
16
+ embedding_dim: int,
17
+ mlp_dim: int,
18
+ act: Type[nn.Module] = nn.GELU,
19
+ ) -> None:
20
+ super().__init__()
21
+ self.lin1 = nn.Linear(embedding_dim, mlp_dim)
22
+ self.lin2 = nn.Linear(mlp_dim, embedding_dim)
23
+ self.act = act()
24
+
25
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
26
+ return self.lin2(self.act(self.lin1(x)))
27
+
28
+
29
+ # From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
30
+ # Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa
31
+ class LayerNorm2d(nn.Module):
32
+ def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
33
+ super().__init__()
34
+ self.weight = nn.Parameter(torch.ones(num_channels))
35
+ self.bias = nn.Parameter(torch.zeros(num_channels))
36
+ self.eps = eps
37
+
38
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
39
+ u = x.mean(1, keepdim=True)
40
+ s = (x - u).pow(2).mean(1, keepdim=True)
41
+ x = (x - u) / torch.sqrt(s + self.eps)
42
+ x = self.weight[:, None, None] * x + self.bias[:, None, None]
43
+ return x
segment_anything/modeling/image_encoder.py ADDED
@@ -0,0 +1,395 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import torch
8
+ import torch.nn as nn
9
+ import torch.nn.functional as F
10
+
11
+ from typing import Optional, Tuple, Type
12
+
13
+ from .common import LayerNorm2d, MLPBlock
14
+
15
+
16
+ # 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
17
+ class ImageEncoderViT(nn.Module):
18
+ def __init__(
19
+ self,
20
+ img_size: int = 1024,
21
+ patch_size: int = 16,
22
+ in_chans: int = 3,
23
+ embed_dim: int = 768,
24
+ depth: int = 12,
25
+ num_heads: int = 12,
26
+ mlp_ratio: float = 4.0,
27
+ out_chans: int = 256,
28
+ qkv_bias: bool = True,
29
+ norm_layer: Type[nn.Module] = nn.LayerNorm,
30
+ act_layer: Type[nn.Module] = nn.GELU,
31
+ use_abs_pos: bool = True,
32
+ use_rel_pos: bool = False,
33
+ rel_pos_zero_init: bool = True,
34
+ window_size: int = 0,
35
+ global_attn_indexes: Tuple[int, ...] = (),
36
+ ) -> None:
37
+ """
38
+ Args:
39
+ img_size (int): Input image size.
40
+ patch_size (int): Patch size.
41
+ in_chans (int): Number of input image channels.
42
+ embed_dim (int): Patch embedding dimension.
43
+ depth (int): Depth of ViT.
44
+ num_heads (int): Number of attention heads in each ViT block.
45
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
46
+ qkv_bias (bool): If True, add a learnable bias to query, key, value.
47
+ norm_layer (nn.Module): Normalization layer.
48
+ act_layer (nn.Module): Activation layer.
49
+ use_abs_pos (bool): If True, use absolute positional embeddings.
50
+ use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
51
+ rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
52
+ window_size (int): Window size for window attention blocks.
53
+ global_attn_indexes (list): Indexes for blocks using global attention.
54
+ """
55
+ super().__init__()
56
+ self.img_size = img_size
57
+
58
+ self.patch_embed = PatchEmbed(
59
+ kernel_size=(patch_size, patch_size),
60
+ stride=(patch_size, patch_size),
61
+ in_chans=in_chans,
62
+ embed_dim=embed_dim,
63
+ )
64
+
65
+ self.pos_embed: Optional[nn.Parameter] = None
66
+ if use_abs_pos:
67
+ # Initialize absolute positional embedding with pretrain image size.
68
+ self.pos_embed = nn.Parameter(
69
+ torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim)
70
+ )
71
+
72
+ self.blocks = nn.ModuleList()
73
+ for i in range(depth):
74
+ block = Block(
75
+ dim=embed_dim,
76
+ num_heads=num_heads,
77
+ mlp_ratio=mlp_ratio,
78
+ qkv_bias=qkv_bias,
79
+ norm_layer=norm_layer,
80
+ act_layer=act_layer,
81
+ use_rel_pos=use_rel_pos,
82
+ rel_pos_zero_init=rel_pos_zero_init,
83
+ window_size=window_size if i not in global_attn_indexes else 0,
84
+ input_size=(img_size // patch_size, img_size // patch_size),
85
+ )
86
+ self.blocks.append(block)
87
+
88
+ self.neck = nn.Sequential(
89
+ nn.Conv2d(
90
+ embed_dim,
91
+ out_chans,
92
+ kernel_size=1,
93
+ bias=False,
94
+ ),
95
+ LayerNorm2d(out_chans),
96
+ nn.Conv2d(
97
+ out_chans,
98
+ out_chans,
99
+ kernel_size=3,
100
+ padding=1,
101
+ bias=False,
102
+ ),
103
+ LayerNorm2d(out_chans),
104
+ )
105
+
106
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
107
+ x = self.patch_embed(x)
108
+ if self.pos_embed is not None:
109
+ x = x + self.pos_embed
110
+
111
+ for blk in self.blocks:
112
+ x = blk(x)
113
+
114
+ x = self.neck(x.permute(0, 3, 1, 2))
115
+
116
+ return x
117
+
118
+
119
+ class Block(nn.Module):
120
+ """Transformer blocks with support of window attention and residual propagation blocks"""
121
+
122
+ def __init__(
123
+ self,
124
+ dim: int,
125
+ num_heads: int,
126
+ mlp_ratio: float = 4.0,
127
+ qkv_bias: bool = True,
128
+ norm_layer: Type[nn.Module] = nn.LayerNorm,
129
+ act_layer: Type[nn.Module] = nn.GELU,
130
+ use_rel_pos: bool = False,
131
+ rel_pos_zero_init: bool = True,
132
+ window_size: int = 0,
133
+ input_size: Optional[Tuple[int, int]] = None,
134
+ ) -> None:
135
+ """
136
+ Args:
137
+ dim (int): Number of input channels.
138
+ num_heads (int): Number of attention heads in each ViT block.
139
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
140
+ qkv_bias (bool): If True, add a learnable bias to query, key, value.
141
+ norm_layer (nn.Module): Normalization layer.
142
+ act_layer (nn.Module): Activation layer.
143
+ use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
144
+ rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
145
+ window_size (int): Window size for window attention blocks. If it equals 0, then
146
+ use global attention.
147
+ input_size (int or None): Input resolution for calculating the relative positional
148
+ parameter size.
149
+ """
150
+ super().__init__()
151
+ self.norm1 = norm_layer(dim)
152
+ self.attn = Attention(
153
+ dim,
154
+ num_heads=num_heads,
155
+ qkv_bias=qkv_bias,
156
+ use_rel_pos=use_rel_pos,
157
+ rel_pos_zero_init=rel_pos_zero_init,
158
+ input_size=input_size if window_size == 0 else (window_size, window_size),
159
+ )
160
+
161
+ self.norm2 = norm_layer(dim)
162
+ self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
163
+
164
+ self.window_size = window_size
165
+
166
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
167
+ shortcut = x
168
+ x = self.norm1(x)
169
+ # Window partition
170
+ if self.window_size > 0:
171
+ H, W = x.shape[1], x.shape[2]
172
+ x, pad_hw = window_partition(x, self.window_size)
173
+
174
+ x = self.attn(x)
175
+ # Reverse window partition
176
+ if self.window_size > 0:
177
+ x = window_unpartition(x, self.window_size, pad_hw, (H, W))
178
+
179
+ x = shortcut + x
180
+ x = x + self.mlp(self.norm2(x))
181
+
182
+ return x
183
+
184
+
185
+ class Attention(nn.Module):
186
+ """Multi-head Attention block with relative position embeddings."""
187
+
188
+ def __init__(
189
+ self,
190
+ dim: int,
191
+ num_heads: int = 8,
192
+ qkv_bias: bool = True,
193
+ use_rel_pos: bool = False,
194
+ rel_pos_zero_init: bool = True,
195
+ input_size: Optional[Tuple[int, int]] = None,
196
+ ) -> None:
197
+ """
198
+ Args:
199
+ dim (int): Number of input channels.
200
+ num_heads (int): Number of attention heads.
201
+ qkv_bias (bool: If True, add a learnable bias to query, key, value.
202
+ rel_pos (bool): If True, add relative positional embeddings to the attention map.
203
+ rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
204
+ input_size (int or None): Input resolution for calculating the relative positional
205
+ parameter size.
206
+ """
207
+ super().__init__()
208
+ self.num_heads = num_heads
209
+ head_dim = dim // num_heads
210
+ self.scale = head_dim**-0.5
211
+
212
+ self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
213
+ self.proj = nn.Linear(dim, dim)
214
+
215
+ self.use_rel_pos = use_rel_pos
216
+ if self.use_rel_pos:
217
+ assert (
218
+ input_size is not None
219
+ ), "Input size must be provided if using relative positional encoding."
220
+ # initialize relative positional embeddings
221
+ self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
222
+ self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
223
+
224
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
225
+ B, H, W, _ = x.shape
226
+ # qkv with shape (3, B, nHead, H * W, C)
227
+ qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
228
+ # q, k, v with shape (B * nHead, H * W, C)
229
+ q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
230
+
231
+ attn = (q * self.scale) @ k.transpose(-2, -1)
232
+
233
+ if self.use_rel_pos:
234
+ attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
235
+
236
+ attn = attn.softmax(dim=-1)
237
+ x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
238
+ x = self.proj(x)
239
+
240
+ return x
241
+
242
+
243
+ def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
244
+ """
245
+ Partition into non-overlapping windows with padding if needed.
246
+ Args:
247
+ x (tensor): input tokens with [B, H, W, C].
248
+ window_size (int): window size.
249
+
250
+ Returns:
251
+ windows: windows after partition with [B * num_windows, window_size, window_size, C].
252
+ (Hp, Wp): padded height and width before partition
253
+ """
254
+ B, H, W, C = x.shape
255
+
256
+ pad_h = (window_size - H % window_size) % window_size
257
+ pad_w = (window_size - W % window_size) % window_size
258
+ if pad_h > 0 or pad_w > 0:
259
+ x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
260
+ Hp, Wp = H + pad_h, W + pad_w
261
+
262
+ x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
263
+ windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
264
+ return windows, (Hp, Wp)
265
+
266
+
267
+ def window_unpartition(
268
+ windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
269
+ ) -> torch.Tensor:
270
+ """
271
+ Window unpartition into original sequences and removing padding.
272
+ Args:
273
+ x (tensor): input tokens with [B * num_windows, window_size, window_size, C].
274
+ window_size (int): window size.
275
+ pad_hw (Tuple): padded height and width (Hp, Wp).
276
+ hw (Tuple): original height and width (H, W) before padding.
277
+
278
+ Returns:
279
+ x: unpartitioned sequences with [B, H, W, C].
280
+ """
281
+ Hp, Wp = pad_hw
282
+ H, W = hw
283
+ B = windows.shape[0] // (Hp * Wp // window_size // window_size)
284
+ x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
285
+ x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
286
+
287
+ if Hp > H or Wp > W:
288
+ x = x[:, :H, :W, :].contiguous()
289
+ return x
290
+
291
+
292
+ def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
293
+ """
294
+ Get relative positional embeddings according to the relative positions of
295
+ query and key sizes.
296
+ Args:
297
+ q_size (int): size of query q.
298
+ k_size (int): size of key k.
299
+ rel_pos (Tensor): relative position embeddings (L, C).
300
+
301
+ Returns:
302
+ Extracted positional embeddings according to relative positions.
303
+ """
304
+ max_rel_dist = int(2 * max(q_size, k_size) - 1)
305
+ # Interpolate rel pos if needed.
306
+ if rel_pos.shape[0] != max_rel_dist:
307
+ # Interpolate rel pos.
308
+ rel_pos_resized = F.interpolate(
309
+ rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
310
+ size=max_rel_dist,
311
+ mode="linear",
312
+ )
313
+ rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
314
+ else:
315
+ rel_pos_resized = rel_pos
316
+
317
+ # Scale the coords with short length if shapes for q and k are different.
318
+ q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
319
+ k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
320
+ relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
321
+
322
+ return rel_pos_resized[relative_coords.long()]
323
+
324
+
325
+ def add_decomposed_rel_pos(
326
+ attn: torch.Tensor,
327
+ q: torch.Tensor,
328
+ rel_pos_h: torch.Tensor,
329
+ rel_pos_w: torch.Tensor,
330
+ q_size: Tuple[int, int],
331
+ k_size: Tuple[int, int],
332
+ ) -> torch.Tensor:
333
+ """
334
+ Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
335
+ https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950
336
+ Args:
337
+ attn (Tensor): attention map.
338
+ q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
339
+ rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
340
+ rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
341
+ q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
342
+ k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
343
+
344
+ Returns:
345
+ attn (Tensor): attention map with added relative positional embeddings.
346
+ """
347
+ q_h, q_w = q_size
348
+ k_h, k_w = k_size
349
+ Rh = get_rel_pos(q_h, k_h, rel_pos_h)
350
+ Rw = get_rel_pos(q_w, k_w, rel_pos_w)
351
+
352
+ B, _, dim = q.shape
353
+ r_q = q.reshape(B, q_h, q_w, dim)
354
+ rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
355
+ rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
356
+
357
+ attn = (
358
+ attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
359
+ ).view(B, q_h * q_w, k_h * k_w)
360
+
361
+ return attn
362
+
363
+
364
+ class PatchEmbed(nn.Module):
365
+ """
366
+ Image to Patch Embedding.
367
+ """
368
+
369
+ def __init__(
370
+ self,
371
+ kernel_size: Tuple[int, int] = (16, 16),
372
+ stride: Tuple[int, int] = (16, 16),
373
+ padding: Tuple[int, int] = (0, 0),
374
+ in_chans: int = 3,
375
+ embed_dim: int = 768,
376
+ ) -> None:
377
+ """
378
+ Args:
379
+ kernel_size (Tuple): kernel size of the projection layer.
380
+ stride (Tuple): stride of the projection layer.
381
+ padding (Tuple): padding size of the projection layer.
382
+ in_chans (int): Number of input image channels.
383
+ embed_dim (int): embed_dim (int): Patch embedding dimension.
384
+ """
385
+ super().__init__()
386
+
387
+ self.proj = nn.Conv2d(
388
+ in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
389
+ )
390
+
391
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
392
+ x = self.proj(x)
393
+ # B C H W -> B H W C
394
+ x = x.permute(0, 2, 3, 1)
395
+ return x
segment_anything/modeling/mask_decoder.py ADDED
@@ -0,0 +1,176 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import torch
8
+ from torch import nn
9
+ from torch.nn import functional as F
10
+
11
+ from typing import List, Tuple, Type
12
+
13
+ from .common import LayerNorm2d
14
+
15
+
16
+ class MaskDecoder(nn.Module):
17
+ def __init__(
18
+ self,
19
+ *,
20
+ transformer_dim: int,
21
+ transformer: nn.Module,
22
+ num_multimask_outputs: int = 3,
23
+ activation: Type[nn.Module] = nn.GELU,
24
+ iou_head_depth: int = 3,
25
+ iou_head_hidden_dim: int = 256,
26
+ ) -> None:
27
+ """
28
+ Predicts masks given an image and prompt embeddings, using a
29
+ tranformer architecture.
30
+
31
+ Arguments:
32
+ transformer_dim (int): the channel dimension of the transformer
33
+ transformer (nn.Module): the transformer used to predict masks
34
+ num_multimask_outputs (int): the number of masks to predict
35
+ when disambiguating masks
36
+ activation (nn.Module): the type of activation to use when
37
+ upscaling masks
38
+ iou_head_depth (int): the depth of the MLP used to predict
39
+ mask quality
40
+ iou_head_hidden_dim (int): the hidden dimension of the MLP
41
+ used to predict mask quality
42
+ """
43
+ super().__init__()
44
+ self.transformer_dim = transformer_dim
45
+ self.transformer = transformer
46
+
47
+ self.num_multimask_outputs = num_multimask_outputs
48
+
49
+ self.iou_token = nn.Embedding(1, transformer_dim)
50
+ self.num_mask_tokens = num_multimask_outputs + 1
51
+ self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
52
+
53
+ self.output_upscaling = nn.Sequential(
54
+ nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
55
+ LayerNorm2d(transformer_dim // 4),
56
+ activation(),
57
+ nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
58
+ activation(),
59
+ )
60
+ self.output_hypernetworks_mlps = nn.ModuleList(
61
+ [
62
+ MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
63
+ for i in range(self.num_mask_tokens)
64
+ ]
65
+ )
66
+
67
+ self.iou_prediction_head = MLP(
68
+ transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
69
+ )
70
+
71
+ def forward(
72
+ self,
73
+ image_embeddings: torch.Tensor,
74
+ image_pe: torch.Tensor,
75
+ sparse_prompt_embeddings: torch.Tensor,
76
+ dense_prompt_embeddings: torch.Tensor,
77
+ multimask_output: bool,
78
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
79
+ """
80
+ Predict masks given image and prompt embeddings.
81
+
82
+ Arguments:
83
+ image_embeddings (torch.Tensor): the embeddings from the image encoder
84
+ image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
85
+ sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
86
+ dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
87
+ multimask_output (bool): Whether to return multiple masks or a single
88
+ mask.
89
+
90
+ Returns:
91
+ torch.Tensor: batched predicted masks
92
+ torch.Tensor: batched predictions of mask quality
93
+ """
94
+ masks, iou_pred = self.predict_masks(
95
+ image_embeddings=image_embeddings,
96
+ image_pe=image_pe,
97
+ sparse_prompt_embeddings=sparse_prompt_embeddings,
98
+ dense_prompt_embeddings=dense_prompt_embeddings,
99
+ )
100
+
101
+ # Select the correct mask or masks for outptu
102
+ if multimask_output:
103
+ mask_slice = slice(1, None)
104
+ else:
105
+ mask_slice = slice(0, 1)
106
+ masks = masks[:, mask_slice, :, :]
107
+ iou_pred = iou_pred[:, mask_slice]
108
+
109
+ # Prepare output
110
+ return masks, iou_pred
111
+
112
+ def predict_masks(
113
+ self,
114
+ image_embeddings: torch.Tensor,
115
+ image_pe: torch.Tensor,
116
+ sparse_prompt_embeddings: torch.Tensor,
117
+ dense_prompt_embeddings: torch.Tensor,
118
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
119
+ """Predicts masks. See 'forward' for more details."""
120
+ # Concatenate output tokens
121
+ output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
122
+ output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
123
+ tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
124
+
125
+ # Expand per-image data in batch direction to be per-mask
126
+ src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
127
+ src = src + dense_prompt_embeddings
128
+ pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
129
+ b, c, h, w = src.shape
130
+
131
+ # Run the transformer
132
+ hs, src = self.transformer(src, pos_src, tokens)
133
+ iou_token_out = hs[:, 0, :]
134
+ mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]
135
+
136
+ # Upscale mask embeddings and predict masks using the mask tokens
137
+ src = src.transpose(1, 2).view(b, c, h, w)
138
+ upscaled_embedding = self.output_upscaling(src)
139
+ hyper_in_list: List[torch.Tensor] = []
140
+ for i in range(self.num_mask_tokens):
141
+ hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
142
+ hyper_in = torch.stack(hyper_in_list, dim=1)
143
+ b, c, h, w = upscaled_embedding.shape
144
+ masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
145
+
146
+ # Generate mask quality predictions
147
+ iou_pred = self.iou_prediction_head(iou_token_out)
148
+
149
+ return masks, iou_pred
150
+
151
+
152
+ # Lightly adapted from
153
+ # https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
154
+ class MLP(nn.Module):
155
+ def __init__(
156
+ self,
157
+ input_dim: int,
158
+ hidden_dim: int,
159
+ output_dim: int,
160
+ num_layers: int,
161
+ sigmoid_output: bool = False,
162
+ ) -> None:
163
+ super().__init__()
164
+ self.num_layers = num_layers
165
+ h = [hidden_dim] * (num_layers - 1)
166
+ self.layers = nn.ModuleList(
167
+ nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
168
+ )
169
+ self.sigmoid_output = sigmoid_output
170
+
171
+ def forward(self, x):
172
+ for i, layer in enumerate(self.layers):
173
+ x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
174
+ if self.sigmoid_output:
175
+ x = F.sigmoid(x)
176
+ return x
segment_anything/modeling/prompt_encoder.py ADDED
@@ -0,0 +1,214 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import numpy as np
8
+ import torch
9
+ from torch import nn
10
+
11
+ from typing import Any, Optional, Tuple, Type
12
+
13
+ from .common import LayerNorm2d
14
+
15
+
16
+ class PromptEncoder(nn.Module):
17
+ def __init__(
18
+ self,
19
+ embed_dim: int,
20
+ image_embedding_size: Tuple[int, int],
21
+ input_image_size: Tuple[int, int],
22
+ mask_in_chans: int,
23
+ activation: Type[nn.Module] = nn.GELU,
24
+ ) -> None:
25
+ """
26
+ Encodes prompts for input to SAM's mask decoder.
27
+
28
+ Arguments:
29
+ embed_dim (int): The prompts' embedding dimension
30
+ image_embedding_size (tuple(int, int)): The spatial size of the
31
+ image embedding, as (H, W).
32
+ input_image_size (int): The padded size of the image as input
33
+ to the image encoder, as (H, W).
34
+ mask_in_chans (int): The number of hidden channels used for
35
+ encoding input masks.
36
+ activation (nn.Module): The activation to use when encoding
37
+ input masks.
38
+ """
39
+ super().__init__()
40
+ self.embed_dim = embed_dim
41
+ self.input_image_size = input_image_size
42
+ self.image_embedding_size = image_embedding_size
43
+ self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
44
+
45
+ self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners
46
+ point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
47
+ self.point_embeddings = nn.ModuleList(point_embeddings)
48
+ self.not_a_point_embed = nn.Embedding(1, embed_dim)
49
+
50
+ self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])
51
+ self.mask_downscaling = nn.Sequential(
52
+ nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
53
+ LayerNorm2d(mask_in_chans // 4),
54
+ activation(),
55
+ nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
56
+ LayerNorm2d(mask_in_chans),
57
+ activation(),
58
+ nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
59
+ )
60
+ self.no_mask_embed = nn.Embedding(1, embed_dim)
61
+
62
+ def get_dense_pe(self) -> torch.Tensor:
63
+ """
64
+ Returns the positional encoding used to encode point prompts,
65
+ applied to a dense set of points the shape of the image encoding.
66
+
67
+ Returns:
68
+ torch.Tensor: Positional encoding with shape
69
+ 1x(embed_dim)x(embedding_h)x(embedding_w)
70
+ """
71
+ return self.pe_layer(self.image_embedding_size).unsqueeze(0)
72
+
73
+ def _embed_points(
74
+ self,
75
+ points: torch.Tensor,
76
+ labels: torch.Tensor,
77
+ pad: bool,
78
+ ) -> torch.Tensor:
79
+ """Embeds point prompts."""
80
+ points = points + 0.5 # Shift to center of pixel
81
+ if pad:
82
+ padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)
83
+ padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
84
+ points = torch.cat([points, padding_point], dim=1)
85
+ labels = torch.cat([labels, padding_label], dim=1)
86
+ point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
87
+ point_embedding[labels == -1] = 0.0
88
+ point_embedding[labels == -1] += self.not_a_point_embed.weight
89
+ point_embedding[labels == 0] += self.point_embeddings[0].weight
90
+ point_embedding[labels == 1] += self.point_embeddings[1].weight
91
+ return point_embedding
92
+
93
+ def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
94
+ """Embeds box prompts."""
95
+ boxes = boxes + 0.5 # Shift to center of pixel
96
+ coords = boxes.reshape(-1, 2, 2)
97
+ corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
98
+ corner_embedding[:, 0, :] += self.point_embeddings[2].weight
99
+ corner_embedding[:, 1, :] += self.point_embeddings[3].weight
100
+ return corner_embedding
101
+
102
+ def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
103
+ """Embeds mask inputs."""
104
+ mask_embedding = self.mask_downscaling(masks)
105
+ return mask_embedding
106
+
107
+ def _get_batch_size(
108
+ self,
109
+ points: Optional[Tuple[torch.Tensor, torch.Tensor]],
110
+ boxes: Optional[torch.Tensor],
111
+ masks: Optional[torch.Tensor],
112
+ ) -> int:
113
+ """
114
+ Gets the batch size of the output given the batch size of the input prompts.
115
+ """
116
+ if points is not None:
117
+ return points[0].shape[0]
118
+ elif boxes is not None:
119
+ return boxes.shape[0]
120
+ elif masks is not None:
121
+ return masks.shape[0]
122
+ else:
123
+ return 1
124
+
125
+ def _get_device(self) -> torch.device:
126
+ return self.point_embeddings[0].weight.device
127
+
128
+ def forward(
129
+ self,
130
+ points: Optional[Tuple[torch.Tensor, torch.Tensor]],
131
+ boxes: Optional[torch.Tensor],
132
+ masks: Optional[torch.Tensor],
133
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
134
+ """
135
+ Embeds different types of prompts, returning both sparse and dense
136
+ embeddings.
137
+
138
+ Arguments:
139
+ points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
140
+ and labels to embed.
141
+ boxes (torch.Tensor or none): boxes to embed
142
+ masks (torch.Tensor or none): masks to embed
143
+
144
+ Returns:
145
+ torch.Tensor: sparse embeddings for the points and boxes, with shape
146
+ BxNx(embed_dim), where N is determined by the number of input points
147
+ and boxes.
148
+ torch.Tensor: dense embeddings for the masks, in the shape
149
+ Bx(embed_dim)x(embed_H)x(embed_W)
150
+ """
151
+ bs = self._get_batch_size(points, boxes, masks)
152
+ sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
153
+ if points is not None:
154
+ coords, labels = points
155
+ point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
156
+ sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
157
+ if boxes is not None:
158
+ box_embeddings = self._embed_boxes(boxes)
159
+ sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
160
+
161
+ if masks is not None:
162
+ dense_embeddings = self._embed_masks(masks)
163
+ else:
164
+ dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
165
+ bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
166
+ )
167
+
168
+ return sparse_embeddings, dense_embeddings
169
+
170
+
171
+ class PositionEmbeddingRandom(nn.Module):
172
+ """
173
+ Positional encoding using random spatial frequencies.
174
+ """
175
+
176
+ def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
177
+ super().__init__()
178
+ if scale is None or scale <= 0.0:
179
+ scale = 1.0
180
+ self.register_buffer(
181
+ "positional_encoding_gaussian_matrix",
182
+ scale * torch.randn((2, num_pos_feats)),
183
+ )
184
+
185
+ def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
186
+ """Positionally encode points that are normalized to [0,1]."""
187
+ # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
188
+ coords = 2 * coords - 1
189
+ coords = coords @ self.positional_encoding_gaussian_matrix
190
+ coords = 2 * np.pi * coords
191
+ # outputs d_1 x ... x d_n x C shape
192
+ return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
193
+
194
+ def forward(self, size: Tuple[int, int]) -> torch.Tensor:
195
+ """Generate positional encoding for a grid of the specified size."""
196
+ h, w = size
197
+ device: Any = self.positional_encoding_gaussian_matrix.device
198
+ grid = torch.ones((h, w), device=device, dtype=torch.float32)
199
+ y_embed = grid.cumsum(dim=0) - 0.5
200
+ x_embed = grid.cumsum(dim=1) - 0.5
201
+ y_embed = y_embed / h
202
+ x_embed = x_embed / w
203
+
204
+ pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
205
+ return pe.permute(2, 0, 1) # C x H x W
206
+
207
+ def forward_with_coords(
208
+ self, coords_input: torch.Tensor, image_size: Tuple[int, int]
209
+ ) -> torch.Tensor:
210
+ """Positionally encode points that are not normalized to [0,1]."""
211
+ coords = coords_input.clone()
212
+ coords[:, :, 0] = coords[:, :, 0] / image_size[1]
213
+ coords[:, :, 1] = coords[:, :, 1] / image_size[0]
214
+ return self._pe_encoding(coords.to(torch.float)) # B x N x C
segment_anything/modeling/sam.py ADDED
@@ -0,0 +1,174 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import torch
8
+ from torch import nn
9
+ from torch.nn import functional as F
10
+
11
+ from typing import Any, Dict, List, Tuple
12
+
13
+ from .image_encoder import ImageEncoderViT
14
+ from .mask_decoder import MaskDecoder
15
+ from .prompt_encoder import PromptEncoder
16
+
17
+
18
+ class Sam(nn.Module):
19
+ mask_threshold: float = 0.0
20
+ image_format: str = "RGB"
21
+
22
+ def __init__(
23
+ self,
24
+ image_encoder: ImageEncoderViT,
25
+ prompt_encoder: PromptEncoder,
26
+ mask_decoder: MaskDecoder,
27
+ pixel_mean: List[float] = [123.675, 116.28, 103.53],
28
+ pixel_std: List[float] = [58.395, 57.12, 57.375],
29
+ ) -> None:
30
+ """
31
+ SAM predicts object masks from an image and input prompts.
32
+
33
+ Arguments:
34
+ image_encoder (ImageEncoderViT): The backbone used to encode the
35
+ image into image embeddings that allow for efficient mask prediction.
36
+ prompt_encoder (PromptEncoder): Encodes various types of input prompts.
37
+ mask_decoder (MaskDecoder): Predicts masks from the image embeddings
38
+ and encoded prompts.
39
+ pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
40
+ pixel_std (list(float)): Std values for normalizing pixels in the input image.
41
+ """
42
+ super().__init__()
43
+ self.image_encoder = image_encoder
44
+ self.prompt_encoder = prompt_encoder
45
+ self.mask_decoder = mask_decoder
46
+ self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
47
+ self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
48
+
49
+ @property
50
+ def device(self) -> Any:
51
+ return self.pixel_mean.device
52
+
53
+ @torch.no_grad()
54
+ def forward(
55
+ self,
56
+ batched_input: List[Dict[str, Any]],
57
+ multimask_output: bool,
58
+ ) -> List[Dict[str, torch.Tensor]]:
59
+ """
60
+ Predicts masks end-to-end from provided images and prompts.
61
+ If prompts are not known in advance, using SamPredictor is
62
+ recommended over calling the model directly.
63
+
64
+ Arguments:
65
+ batched_input (list(dict)): A list over input images, each a
66
+ dictionary with the following keys. A prompt key can be
67
+ excluded if it is not present.
68
+ 'image': The image as a torch tensor in 3xHxW format,
69
+ already transformed for input to the model.
70
+ 'original_size': (tuple(int, int)) The original size of
71
+ the image before transformation, as (H, W).
72
+ 'point_coords': (torch.Tensor) Batched point prompts for
73
+ this image, with shape BxNx2. Already transformed to the
74
+ input frame of the model.
75
+ 'point_labels': (torch.Tensor) Batched labels for point prompts,
76
+ with shape BxN.
77
+ 'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
78
+ Already transformed to the input frame of the model.
79
+ 'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
80
+ in the form Bx1xHxW.
81
+ multimask_output (bool): Whether the model should predict multiple
82
+ disambiguating masks, or return a single mask.
83
+
84
+ Returns:
85
+ (list(dict)): A list over input images, where each element is
86
+ as dictionary with the following keys.
87
+ 'masks': (torch.Tensor) Batched binary mask predictions,
88
+ with shape BxCxHxW, where B is the number of input promts,
89
+ C is determiend by multimask_output, and (H, W) is the
90
+ original size of the image.
91
+ 'iou_predictions': (torch.Tensor) The model's predictions
92
+ of mask quality, in shape BxC.
93
+ 'low_res_logits': (torch.Tensor) Low resolution logits with
94
+ shape BxCxHxW, where H=W=256. Can be passed as mask input
95
+ to subsequent iterations of prediction.
96
+ """
97
+ input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
98
+ image_embeddings = self.image_encoder(input_images)
99
+
100
+ outputs = []
101
+ for image_record, curr_embedding in zip(batched_input, image_embeddings):
102
+ if "point_coords" in image_record:
103
+ points = (image_record["point_coords"], image_record["point_labels"])
104
+ else:
105
+ points = None
106
+ sparse_embeddings, dense_embeddings = self.prompt_encoder(
107
+ points=points,
108
+ boxes=image_record.get("boxes", None),
109
+ masks=image_record.get("mask_inputs", None),
110
+ )
111
+ low_res_masks, iou_predictions = self.mask_decoder(
112
+ image_embeddings=curr_embedding.unsqueeze(0),
113
+ image_pe=self.prompt_encoder.get_dense_pe(),
114
+ sparse_prompt_embeddings=sparse_embeddings,
115
+ dense_prompt_embeddings=dense_embeddings,
116
+ multimask_output=multimask_output,
117
+ )
118
+ masks = self.postprocess_masks(
119
+ low_res_masks,
120
+ input_size=image_record["image"].shape[-2:],
121
+ original_size=image_record["original_size"],
122
+ )
123
+ masks = masks > self.mask_threshold
124
+ outputs.append(
125
+ {
126
+ "masks": masks,
127
+ "iou_predictions": iou_predictions,
128
+ "low_res_logits": low_res_masks,
129
+ }
130
+ )
131
+ return outputs
132
+
133
+ def postprocess_masks(
134
+ self,
135
+ masks: torch.Tensor,
136
+ input_size: Tuple[int, ...],
137
+ original_size: Tuple[int, ...],
138
+ ) -> torch.Tensor:
139
+ """
140
+ Remove padding and upscale masks to the original image size.
141
+
142
+ Arguments:
143
+ masks (torch.Tensor): Batched masks from the mask_decoder,
144
+ in BxCxHxW format.
145
+ input_size (tuple(int, int)): The size of the image input to the
146
+ model, in (H, W) format. Used to remove padding.
147
+ original_size (tuple(int, int)): The original size of the image
148
+ before resizing for input to the model, in (H, W) format.
149
+
150
+ Returns:
151
+ (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
152
+ is given by original_size.
153
+ """
154
+ masks = F.interpolate(
155
+ masks,
156
+ (self.image_encoder.img_size, self.image_encoder.img_size),
157
+ mode="bilinear",
158
+ align_corners=False,
159
+ )
160
+ masks = masks[..., : input_size[0], : input_size[1]]
161
+ masks = F.interpolate(masks, original_size, mode="bilinear", align_corners=False)
162
+ return masks
163
+
164
+ def preprocess(self, x: torch.Tensor) -> torch.Tensor:
165
+ """Normalize pixel values and pad to a square input."""
166
+ # Normalize colors
167
+ x = (x - self.pixel_mean) / self.pixel_std
168
+
169
+ # Pad
170
+ h, w = x.shape[-2:]
171
+ padh = self.image_encoder.img_size - h
172
+ padw = self.image_encoder.img_size - w
173
+ x = F.pad(x, (0, padw, 0, padh))
174
+ return x
segment_anything/modeling/transformer.py ADDED
@@ -0,0 +1,240 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import torch
8
+ from torch import Tensor, nn
9
+
10
+ import math
11
+ from typing import Tuple, Type
12
+
13
+ from .common import MLPBlock
14
+
15
+
16
+ class TwoWayTransformer(nn.Module):
17
+ def __init__(
18
+ self,
19
+ depth: int,
20
+ embedding_dim: int,
21
+ num_heads: int,
22
+ mlp_dim: int,
23
+ activation: Type[nn.Module] = nn.ReLU,
24
+ attention_downsample_rate: int = 2,
25
+ ) -> None:
26
+ """
27
+ A transformer decoder that attends to an input image using
28
+ queries whose positional embedding is supplied.
29
+
30
+ Args:
31
+ depth (int): number of layers in the transformer
32
+ embedding_dim (int): the channel dimension for the input embeddings
33
+ num_heads (int): the number of heads for multihead attention. Must
34
+ divide embedding_dim
35
+ mlp_dim (int): the channel dimension internal to the MLP block
36
+ activation (nn.Module): the activation to use in the MLP block
37
+ """
38
+ super().__init__()
39
+ self.depth = depth
40
+ self.embedding_dim = embedding_dim
41
+ self.num_heads = num_heads
42
+ self.mlp_dim = mlp_dim
43
+ self.layers = nn.ModuleList()
44
+
45
+ for i in range(depth):
46
+ self.layers.append(
47
+ TwoWayAttentionBlock(
48
+ embedding_dim=embedding_dim,
49
+ num_heads=num_heads,
50
+ mlp_dim=mlp_dim,
51
+ activation=activation,
52
+ attention_downsample_rate=attention_downsample_rate,
53
+ skip_first_layer_pe=(i == 0),
54
+ )
55
+ )
56
+
57
+ self.final_attn_token_to_image = Attention(
58
+ embedding_dim, num_heads, downsample_rate=attention_downsample_rate
59
+ )
60
+ self.norm_final_attn = nn.LayerNorm(embedding_dim)
61
+
62
+ def forward(
63
+ self,
64
+ image_embedding: Tensor,
65
+ image_pe: Tensor,
66
+ point_embedding: Tensor,
67
+ ) -> Tuple[Tensor, Tensor]:
68
+ """
69
+ Args:
70
+ image_embedding (torch.Tensor): image to attend to. Should be shape
71
+ B x embedding_dim x h x w for any h and w.
72
+ image_pe (torch.Tensor): the positional encoding to add to the image. Must
73
+ have the same shape as image_embedding.
74
+ point_embedding (torch.Tensor): the embedding to add to the query points.
75
+ Must have shape B x N_points x embedding_dim for any N_points.
76
+
77
+ Returns:
78
+ torch.Tensor: the processed point_embedding
79
+ torch.Tensor: the processed image_embedding
80
+ """
81
+ # BxCxHxW -> BxHWxC == B x N_image_tokens x C
82
+ bs, c, h, w = image_embedding.shape
83
+ image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
84
+ image_pe = image_pe.flatten(2).permute(0, 2, 1)
85
+
86
+ # Prepare queries
87
+ queries = point_embedding
88
+ keys = image_embedding
89
+
90
+ # Apply transformer blocks and final layernorm
91
+ for layer in self.layers:
92
+ queries, keys = layer(
93
+ queries=queries,
94
+ keys=keys,
95
+ query_pe=point_embedding,
96
+ key_pe=image_pe,
97
+ )
98
+
99
+ # Apply the final attenion layer from the points to the image
100
+ q = queries + point_embedding
101
+ k = keys + image_pe
102
+ attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
103
+ queries = queries + attn_out
104
+ queries = self.norm_final_attn(queries)
105
+
106
+ return queries, keys
107
+
108
+
109
+ class TwoWayAttentionBlock(nn.Module):
110
+ def __init__(
111
+ self,
112
+ embedding_dim: int,
113
+ num_heads: int,
114
+ mlp_dim: int = 2048,
115
+ activation: Type[nn.Module] = nn.ReLU,
116
+ attention_downsample_rate: int = 2,
117
+ skip_first_layer_pe: bool = False,
118
+ ) -> None:
119
+ """
120
+ A transformer block with four layers: (1) self-attention of sparse
121
+ inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp
122
+ block on sparse inputs, and (4) cross attention of dense inputs to sparse
123
+ inputs.
124
+
125
+ Arguments:
126
+ embedding_dim (int): the channel dimension of the embeddings
127
+ num_heads (int): the number of heads in the attention layers
128
+ mlp_dim (int): the hidden dimension of the mlp block
129
+ activation (nn.Module): the activation of the mlp block
130
+ skip_first_layer_pe (bool): skip the PE on the first layer
131
+ """
132
+ super().__init__()
133
+ self.self_attn = Attention(embedding_dim, num_heads)
134
+ self.norm1 = nn.LayerNorm(embedding_dim)
135
+
136
+ self.cross_attn_token_to_image = Attention(
137
+ embedding_dim, num_heads, downsample_rate=attention_downsample_rate
138
+ )
139
+ self.norm2 = nn.LayerNorm(embedding_dim)
140
+
141
+ self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
142
+ self.norm3 = nn.LayerNorm(embedding_dim)
143
+
144
+ self.norm4 = nn.LayerNorm(embedding_dim)
145
+ self.cross_attn_image_to_token = Attention(
146
+ embedding_dim, num_heads, downsample_rate=attention_downsample_rate
147
+ )
148
+
149
+ self.skip_first_layer_pe = skip_first_layer_pe
150
+
151
+ def forward(
152
+ self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor
153
+ ) -> Tuple[Tensor, Tensor]:
154
+ # Self attention block
155
+ if self.skip_first_layer_pe:
156
+ queries = self.self_attn(q=queries, k=queries, v=queries)
157
+ else:
158
+ q = queries + query_pe
159
+ attn_out = self.self_attn(q=q, k=q, v=queries)
160
+ queries = queries + attn_out
161
+ queries = self.norm1(queries)
162
+
163
+ # Cross attention block, tokens attending to image embedding
164
+ q = queries + query_pe
165
+ k = keys + key_pe
166
+ attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
167
+ queries = queries + attn_out
168
+ queries = self.norm2(queries)
169
+
170
+ # MLP block
171
+ mlp_out = self.mlp(queries)
172
+ queries = queries + mlp_out
173
+ queries = self.norm3(queries)
174
+
175
+ # Cross attention block, image embedding attending to tokens
176
+ q = queries + query_pe
177
+ k = keys + key_pe
178
+ attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
179
+ keys = keys + attn_out
180
+ keys = self.norm4(keys)
181
+
182
+ return queries, keys
183
+
184
+
185
+ class Attention(nn.Module):
186
+ """
187
+ An attention layer that allows for downscaling the size of the embedding
188
+ after projection to queries, keys, and values.
189
+ """
190
+
191
+ def __init__(
192
+ self,
193
+ embedding_dim: int,
194
+ num_heads: int,
195
+ downsample_rate: int = 1,
196
+ ) -> None:
197
+ super().__init__()
198
+ self.embedding_dim = embedding_dim
199
+ self.internal_dim = embedding_dim // downsample_rate
200
+ self.num_heads = num_heads
201
+ assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim."
202
+
203
+ self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
204
+ self.k_proj = nn.Linear(embedding_dim, self.internal_dim)
205
+ self.v_proj = nn.Linear(embedding_dim, self.internal_dim)
206
+ self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
207
+
208
+ def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
209
+ b, n, c = x.shape
210
+ x = x.reshape(b, n, num_heads, c // num_heads)
211
+ return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head
212
+
213
+ def _recombine_heads(self, x: Tensor) -> Tensor:
214
+ b, n_heads, n_tokens, c_per_head = x.shape
215
+ x = x.transpose(1, 2)
216
+ return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C
217
+
218
+ def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
219
+ # Input projections
220
+ q = self.q_proj(q)
221
+ k = self.k_proj(k)
222
+ v = self.v_proj(v)
223
+
224
+ # Separate into heads
225
+ q = self._separate_heads(q, self.num_heads)
226
+ k = self._separate_heads(k, self.num_heads)
227
+ v = self._separate_heads(v, self.num_heads)
228
+
229
+ # Attention
230
+ _, _, _, c_per_head = q.shape
231
+ attn = q @ k.permute(0, 1, 3, 2) # B x N_heads x N_tokens x N_tokens
232
+ attn = attn / math.sqrt(c_per_head)
233
+ attn = torch.softmax(attn, dim=-1)
234
+
235
+ # Get output
236
+ out = attn @ v
237
+ out = self._recombine_heads(out)
238
+ out = self.out_proj(out)
239
+
240
+ return out
segment_anything/predictor.py ADDED
@@ -0,0 +1,269 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import numpy as np
8
+ import torch
9
+
10
+ from segment_anything.modeling import Sam
11
+
12
+ from typing import Optional, Tuple
13
+
14
+ from .utils.transforms import ResizeLongestSide
15
+
16
+
17
+ class SamPredictor:
18
+ def __init__(
19
+ self,
20
+ sam_model: Sam,
21
+ ) -> None:
22
+ """
23
+ Uses SAM to calculate the image embedding for an image, and then
24
+ allow repeated, efficient mask prediction given prompts.
25
+
26
+ Arguments:
27
+ sam_model (Sam): The model to use for mask prediction.
28
+ """
29
+ super().__init__()
30
+ self.model = sam_model
31
+ self.transform = ResizeLongestSide(sam_model.image_encoder.img_size)
32
+ self.reset_image()
33
+
34
+ def set_image(
35
+ self,
36
+ image: np.ndarray,
37
+ image_format: str = "RGB",
38
+ ) -> None:
39
+ """
40
+ Calculates the image embeddings for the provided image, allowing
41
+ masks to be predicted with the 'predict' method.
42
+
43
+ Arguments:
44
+ image (np.ndarray): The image for calculating masks. Expects an
45
+ image in HWC uint8 format, with pixel values in [0, 255].
46
+ image_format (str): The color format of the image, in ['RGB', 'BGR'].
47
+ """
48
+ assert image_format in [
49
+ "RGB",
50
+ "BGR",
51
+ ], f"image_format must be in ['RGB', 'BGR'], is {image_format}."
52
+ if image_format != self.model.image_format:
53
+ image = image[..., ::-1]
54
+
55
+ # Transform the image to the form expected by the model
56
+ input_image = self.transform.apply_image(image)
57
+ input_image_torch = torch.as_tensor(input_image, device=self.device)
58
+ input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]
59
+
60
+ self.set_torch_image(input_image_torch, image.shape[:2])
61
+
62
+ @torch.no_grad()
63
+ def set_torch_image(
64
+ self,
65
+ transformed_image: torch.Tensor,
66
+ original_image_size: Tuple[int, ...],
67
+ ) -> None:
68
+ """
69
+ Calculates the image embeddings for the provided image, allowing
70
+ masks to be predicted with the 'predict' method. Expects the input
71
+ image to be already transformed to the format expected by the model.
72
+
73
+ Arguments:
74
+ transformed_image (torch.Tensor): The input image, with shape
75
+ 1x3xHxW, which has been transformed with ResizeLongestSide.
76
+ original_image_size (tuple(int, int)): The size of the image
77
+ before transformation, in (H, W) format.
78
+ """
79
+ assert (
80
+ len(transformed_image.shape) == 4
81
+ and transformed_image.shape[1] == 3
82
+ and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size
83
+ ), f"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}."
84
+ self.reset_image()
85
+
86
+ self.original_size = original_image_size
87
+ self.input_size = tuple(transformed_image.shape[-2:])
88
+ input_image = self.model.preprocess(transformed_image)
89
+ self.features = self.model.image_encoder(input_image)
90
+ self.is_image_set = True
91
+
92
+ def predict(
93
+ self,
94
+ point_coords: Optional[np.ndarray] = None,
95
+ point_labels: Optional[np.ndarray] = None,
96
+ box: Optional[np.ndarray] = None,
97
+ mask_input: Optional[np.ndarray] = None,
98
+ multimask_output: bool = True,
99
+ return_logits: bool = False,
100
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
101
+ """
102
+ Predict masks for the given input prompts, using the currently set image.
103
+
104
+ Arguments:
105
+ point_coords (np.ndarray or None): A Nx2 array of point prompts to the
106
+ model. Each point is in (X,Y) in pixels.
107
+ point_labels (np.ndarray or None): A length N array of labels for the
108
+ point prompts. 1 indicates a foreground point and 0 indicates a
109
+ background point.
110
+ box (np.ndarray or None): A length 4 array given a box prompt to the
111
+ model, in XYXY format.
112
+ mask_input (np.ndarray): A low resolution mask input to the model, typically
113
+ coming from a previous prediction iteration. Has form 1xHxW, where
114
+ for SAM, H=W=256.
115
+ multimask_output (bool): If true, the model will return three masks.
116
+ For ambiguous input prompts (such as a single click), this will often
117
+ produce better masks than a single prediction. If only a single
118
+ mask is needed, the model's predicted quality score can be used
119
+ to select the best mask. For non-ambiguous prompts, such as multiple
120
+ input prompts, multimask_output=False can give better results.
121
+ return_logits (bool): If true, returns un-thresholded masks logits
122
+ instead of a binary mask.
123
+
124
+ Returns:
125
+ (np.ndarray): The output masks in CxHxW format, where C is the
126
+ number of masks, and (H, W) is the original image size.
127
+ (np.ndarray): An array of length C containing the model's
128
+ predictions for the quality of each mask.
129
+ (np.ndarray): An array of shape CxHxW, where C is the number
130
+ of masks and H=W=256. These low resolution logits can be passed to
131
+ a subsequent iteration as mask input.
132
+ """
133
+ if not self.is_image_set:
134
+ raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
135
+
136
+ # Transform input prompts
137
+ coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None
138
+ if point_coords is not None:
139
+ assert (
140
+ point_labels is not None
141
+ ), "point_labels must be supplied if point_coords is supplied."
142
+ point_coords = self.transform.apply_coords(point_coords, self.original_size)
143
+ coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device)
144
+ labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)
145
+ coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :]
146
+ if box is not None:
147
+ box = self.transform.apply_boxes(box, self.original_size)
148
+ box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device)
149
+ box_torch = box_torch[None, :]
150
+ if mask_input is not None:
151
+ mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device)
152
+ mask_input_torch = mask_input_torch[None, :, :, :]
153
+
154
+ masks, iou_predictions, low_res_masks = self.predict_torch(
155
+ coords_torch,
156
+ labels_torch,
157
+ box_torch,
158
+ mask_input_torch,
159
+ multimask_output,
160
+ return_logits=return_logits,
161
+ )
162
+
163
+ masks = masks[0].detach().cpu().numpy()
164
+ iou_predictions = iou_predictions[0].detach().cpu().numpy()
165
+ low_res_masks = low_res_masks[0].detach().cpu().numpy()
166
+ return masks, iou_predictions, low_res_masks
167
+
168
+ @torch.no_grad()
169
+ def predict_torch(
170
+ self,
171
+ point_coords: Optional[torch.Tensor],
172
+ point_labels: Optional[torch.Tensor],
173
+ boxes: Optional[torch.Tensor] = None,
174
+ mask_input: Optional[torch.Tensor] = None,
175
+ multimask_output: bool = True,
176
+ return_logits: bool = False,
177
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
178
+ """
179
+ Predict masks for the given input prompts, using the currently set image.
180
+ Input prompts are batched torch tensors and are expected to already be
181
+ transformed to the input frame using ResizeLongestSide.
182
+
183
+ Arguments:
184
+ point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the
185
+ model. Each point is in (X,Y) in pixels.
186
+ point_labels (torch.Tensor or None): A BxN array of labels for the
187
+ point prompts. 1 indicates a foreground point and 0 indicates a
188
+ background point.
189
+ box (np.ndarray or None): A Bx4 array given a box prompt to the
190
+ model, in XYXY format.
191
+ mask_input (np.ndarray): A low resolution mask input to the model, typically
192
+ coming from a previous prediction iteration. Has form Bx1xHxW, where
193
+ for SAM, H=W=256. Masks returned by a previous iteration of the
194
+ predict method do not need further transformation.
195
+ multimask_output (bool): If true, the model will return three masks.
196
+ For ambiguous input prompts (such as a single click), this will often
197
+ produce better masks than a single prediction. If only a single
198
+ mask is needed, the model's predicted quality score can be used
199
+ to select the best mask. For non-ambiguous prompts, such as multiple
200
+ input prompts, multimask_output=False can give better results.
201
+ return_logits (bool): If true, returns un-thresholded masks logits
202
+ instead of a binary mask.
203
+
204
+ Returns:
205
+ (torch.Tensor): The output masks in BxCxHxW format, where C is the
206
+ number of masks, and (H, W) is the original image size.
207
+ (torch.Tensor): An array of shape BxC containing the model's
208
+ predictions for the quality of each mask.
209
+ (torch.Tensor): An array of shape BxCxHxW, where C is the number
210
+ of masks and H=W=256. These low res logits can be passed to
211
+ a subsequent iteration as mask input.
212
+ """
213
+ if not self.is_image_set:
214
+ raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
215
+
216
+ if point_coords is not None:
217
+ points = (point_coords, point_labels)
218
+ else:
219
+ points = None
220
+
221
+ # Embed prompts
222
+ sparse_embeddings, dense_embeddings = self.model.prompt_encoder(
223
+ points=points,
224
+ boxes=boxes,
225
+ masks=mask_input,
226
+ )
227
+
228
+ # Predict masks
229
+ low_res_masks, iou_predictions = self.model.mask_decoder(
230
+ image_embeddings=self.features,
231
+ image_pe=self.model.prompt_encoder.get_dense_pe(),
232
+ sparse_prompt_embeddings=sparse_embeddings,
233
+ dense_prompt_embeddings=dense_embeddings,
234
+ multimask_output=multimask_output,
235
+ )
236
+
237
+ # Upscale the masks to the original image resolution
238
+ masks = self.model.postprocess_masks(low_res_masks, self.input_size, self.original_size)
239
+
240
+ if not return_logits:
241
+ masks = masks > self.model.mask_threshold
242
+
243
+ return masks, iou_predictions, low_res_masks
244
+
245
+ def get_image_embedding(self) -> torch.Tensor:
246
+ """
247
+ Returns the image embeddings for the currently set image, with
248
+ shape 1xCxHxW, where C is the embedding dimension and (H,W) are
249
+ the embedding spatial dimension of SAM (typically C=256, H=W=64).
250
+ """
251
+ if not self.is_image_set:
252
+ raise RuntimeError(
253
+ "An image must be set with .set_image(...) to generate an embedding."
254
+ )
255
+ assert self.features is not None, "Features must exist if an image has been set."
256
+ return self.features
257
+
258
+ @property
259
+ def device(self) -> torch.device:
260
+ return self.model.device
261
+
262
+ def reset_image(self) -> None:
263
+ """Resets the currently set image."""
264
+ self.is_image_set = False
265
+ self.features = None
266
+ self.orig_h = None
267
+ self.orig_w = None
268
+ self.input_h = None
269
+ self.input_w = None
segment_anything/utils/__init__.py ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
segment_anything/utils/__pycache__/__init__.cpython-310.pyc ADDED
Binary file (211 Bytes). View file
 
segment_anything/utils/__pycache__/__init__.cpython-36.pyc ADDED
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segment_anything/utils/__pycache__/amg.cpython-310.pyc ADDED
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segment_anything/utils/__pycache__/amg.cpython-36.pyc ADDED
Binary file (12.3 kB). View file
 
segment_anything/utils/__pycache__/transforms.cpython-310.pyc ADDED
Binary file (3.99 kB). View file
 
segment_anything/utils/__pycache__/transforms.cpython-36.pyc ADDED
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segment_anything/utils/amg.py ADDED
@@ -0,0 +1,346 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import numpy as np
8
+ import torch
9
+
10
+ import math
11
+ from copy import deepcopy
12
+ from itertools import product
13
+ from typing import Any, Dict, Generator, ItemsView, List, Tuple
14
+
15
+
16
+ class MaskData:
17
+ """
18
+ A structure for storing masks and their related data in batched format.
19
+ Implements basic filtering and concatenation.
20
+ """
21
+
22
+ def __init__(self, **kwargs) -> None:
23
+ for v in kwargs.values():
24
+ assert isinstance(
25
+ v, (list, np.ndarray, torch.Tensor)
26
+ ), "MaskData only supports list, numpy arrays, and torch tensors."
27
+ self._stats = dict(**kwargs)
28
+
29
+ def __setitem__(self, key: str, item: Any) -> None:
30
+ assert isinstance(
31
+ item, (list, np.ndarray, torch.Tensor)
32
+ ), "MaskData only supports list, numpy arrays, and torch tensors."
33
+ self._stats[key] = item
34
+
35
+ def __delitem__(self, key: str) -> None:
36
+ del self._stats[key]
37
+
38
+ def __getitem__(self, key: str) -> Any:
39
+ return self._stats[key]
40
+
41
+ def items(self) -> ItemsView[str, Any]:
42
+ return self._stats.items()
43
+
44
+ def filter(self, keep: torch.Tensor) -> None:
45
+ for k, v in self._stats.items():
46
+ if v is None:
47
+ self._stats[k] = None
48
+ elif isinstance(v, torch.Tensor):
49
+ self._stats[k] = v[torch.as_tensor(keep, device=v.device)]
50
+ elif isinstance(v, np.ndarray):
51
+ self._stats[k] = v[keep.detach().cpu().numpy()]
52
+ elif isinstance(v, list) and keep.dtype == torch.bool:
53
+ self._stats[k] = [a for i, a in enumerate(v) if keep[i]]
54
+ elif isinstance(v, list):
55
+ self._stats[k] = [v[i] for i in keep]
56
+ else:
57
+ raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
58
+
59
+ def cat(self, new_stats: "MaskData") -> None:
60
+ for k, v in new_stats.items():
61
+ if k not in self._stats or self._stats[k] is None:
62
+ self._stats[k] = deepcopy(v)
63
+ elif isinstance(v, torch.Tensor):
64
+ self._stats[k] = torch.cat([self._stats[k], v], dim=0)
65
+ elif isinstance(v, np.ndarray):
66
+ self._stats[k] = np.concatenate([self._stats[k], v], axis=0)
67
+ elif isinstance(v, list):
68
+ self._stats[k] = self._stats[k] + deepcopy(v)
69
+ else:
70
+ raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
71
+
72
+ def to_numpy(self) -> None:
73
+ for k, v in self._stats.items():
74
+ if isinstance(v, torch.Tensor):
75
+ self._stats[k] = v.detach().cpu().numpy()
76
+
77
+
78
+ def is_box_near_crop_edge(
79
+ boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0
80
+ ) -> torch.Tensor:
81
+ """Filter masks at the edge of a crop, but not at the edge of the original image."""
82
+ crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)
83
+ orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)
84
+ boxes = uncrop_boxes_xyxy(boxes, crop_box).float()
85
+ near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)
86
+ near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)
87
+ near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)
88
+ return torch.any(near_crop_edge, dim=1)
89
+
90
+
91
+ def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:
92
+ box_xywh = deepcopy(box_xyxy)
93
+ box_xywh[2] = box_xywh[2] - box_xywh[0]
94
+ box_xywh[3] = box_xywh[3] - box_xywh[1]
95
+ return box_xywh
96
+
97
+
98
+ def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:
99
+ assert len(args) > 0 and all(
100
+ len(a) == len(args[0]) for a in args
101
+ ), "Batched iteration must have inputs of all the same size."
102
+ n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)
103
+ for b in range(n_batches):
104
+ yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]
105
+
106
+
107
+ def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
108
+ """
109
+ Encodes masks to an uncompressed RLE, in the format expected by
110
+ pycoco tools.
111
+ """
112
+ # Put in fortran order and flatten h,w
113
+ b, h, w = tensor.shape
114
+ tensor = tensor.permute(0, 2, 1).flatten(1)
115
+
116
+ # Compute change indices
117
+ diff = tensor[:, 1:] ^ tensor[:, :-1]
118
+ change_indices = diff.nonzero()
119
+
120
+ # Encode run length
121
+ out = []
122
+ for i in range(b):
123
+ cur_idxs = change_indices[change_indices[:, 0] == i, 1]
124
+ cur_idxs = torch.cat(
125
+ [
126
+ torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),
127
+ cur_idxs + 1,
128
+ torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device),
129
+ ]
130
+ )
131
+ btw_idxs = cur_idxs[1:] - cur_idxs[:-1]
132
+ counts = [] if tensor[i, 0] == 0 else [0]
133
+ counts.extend(btw_idxs.detach().cpu().tolist())
134
+ out.append({"size": [h, w], "counts": counts})
135
+ return out
136
+
137
+
138
+ def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
139
+ """Compute a binary mask from an uncompressed RLE."""
140
+ h, w = rle["size"]
141
+ mask = np.empty(h * w, dtype=bool)
142
+ idx = 0
143
+ parity = False
144
+ for count in rle["counts"]:
145
+ mask[idx : idx + count] = parity
146
+ idx += count
147
+ parity ^= True
148
+ mask = mask.reshape(w, h)
149
+ return mask.transpose() # Put in C order
150
+
151
+
152
+ def area_from_rle(rle: Dict[str, Any]) -> int:
153
+ return sum(rle["counts"][1::2])
154
+
155
+
156
+ def calculate_stability_score(
157
+ masks: torch.Tensor, mask_threshold: float, threshold_offset: float
158
+ ) -> torch.Tensor:
159
+ """
160
+ Computes the stability score for a batch of masks. The stability
161
+ score is the IoU between the binary masks obtained by thresholding
162
+ the predicted mask logits at high and low values.
163
+ """
164
+ # One mask is always contained inside the other.
165
+ # Save memory by preventing unnecesary cast to torch.int64
166
+ intersections = (
167
+ (masks > (mask_threshold + threshold_offset))
168
+ .sum(-1, dtype=torch.int16)
169
+ .sum(-1, dtype=torch.int32)
170
+ )
171
+ unions = (
172
+ (masks > (mask_threshold - threshold_offset))
173
+ .sum(-1, dtype=torch.int16)
174
+ .sum(-1, dtype=torch.int32)
175
+ )
176
+ return intersections / unions
177
+
178
+
179
+ def build_point_grid(n_per_side: int) -> np.ndarray:
180
+ """Generates a 2D grid of points evenly spaced in [0,1]x[0,1]."""
181
+ offset = 1 / (2 * n_per_side)
182
+ points_one_side = np.linspace(offset, 1 - offset, n_per_side)
183
+ points_x = np.tile(points_one_side[None, :], (n_per_side, 1))
184
+ points_y = np.tile(points_one_side[:, None], (1, n_per_side))
185
+ points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2)
186
+ return points
187
+
188
+
189
+ def build_all_layer_point_grids(
190
+ n_per_side: int, n_layers: int, scale_per_layer: int
191
+ ) -> List[np.ndarray]:
192
+ """Generates point grids for all crop layers."""
193
+ points_by_layer = []
194
+ for i in range(n_layers + 1):
195
+ n_points = int(n_per_side / (scale_per_layer**i))
196
+ points_by_layer.append(build_point_grid(n_points))
197
+ return points_by_layer
198
+
199
+
200
+ def generate_crop_boxes(
201
+ im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float
202
+ ) -> Tuple[List[List[int]], List[int]]:
203
+ """
204
+ Generates a list of crop boxes of different sizes. Each layer
205
+ has (2**i)**2 boxes for the ith layer.
206
+ """
207
+ crop_boxes, layer_idxs = [], []
208
+ im_h, im_w = im_size
209
+ short_side = min(im_h, im_w)
210
+
211
+ # Original image
212
+ crop_boxes.append([0, 0, im_w, im_h])
213
+ layer_idxs.append(0)
214
+
215
+ def crop_len(orig_len, n_crops, overlap):
216
+ return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))
217
+
218
+ for i_layer in range(n_layers):
219
+ n_crops_per_side = 2 ** (i_layer + 1)
220
+ overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))
221
+
222
+ crop_w = crop_len(im_w, n_crops_per_side, overlap)
223
+ crop_h = crop_len(im_h, n_crops_per_side, overlap)
224
+
225
+ crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]
226
+ crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]
227
+
228
+ # Crops in XYWH format
229
+ for x0, y0 in product(crop_box_x0, crop_box_y0):
230
+ box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]
231
+ crop_boxes.append(box)
232
+ layer_idxs.append(i_layer + 1)
233
+
234
+ return crop_boxes, layer_idxs
235
+
236
+
237
+ def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
238
+ x0, y0, _, _ = crop_box
239
+ offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)
240
+ # Check if boxes has a channel dimension
241
+ if len(boxes.shape) == 3:
242
+ offset = offset.unsqueeze(1)
243
+ return boxes + offset
244
+
245
+
246
+ def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
247
+ x0, y0, _, _ = crop_box
248
+ offset = torch.tensor([[x0, y0]], device=points.device)
249
+ # Check if points has a channel dimension
250
+ if len(points.shape) == 3:
251
+ offset = offset.unsqueeze(1)
252
+ return points + offset
253
+
254
+
255
+ def uncrop_masks(
256
+ masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int
257
+ ) -> torch.Tensor:
258
+ x0, y0, x1, y1 = crop_box
259
+ if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:
260
+ return masks
261
+ # Coordinate transform masks
262
+ pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)
263
+ pad = (x0, pad_x - x0, y0, pad_y - y0)
264
+ return torch.nn.functional.pad(masks, pad, value=0)
265
+
266
+
267
+ def remove_small_regions(
268
+ mask: np.ndarray, area_thresh: float, mode: str
269
+ ) -> Tuple[np.ndarray, bool]:
270
+ """
271
+ Removes small disconnected regions and holes in a mask. Returns the
272
+ mask and an indicator of if the mask has been modified.
273
+ """
274
+ import cv2 # type: ignore
275
+
276
+ assert mode in ["holes", "islands"]
277
+ correct_holes = mode == "holes"
278
+ working_mask = (correct_holes ^ mask).astype(np.uint8)
279
+ n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
280
+ sizes = stats[:, -1][1:] # Row 0 is background label
281
+ small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
282
+ if len(small_regions) == 0:
283
+ return mask, False
284
+ fill_labels = [0] + small_regions
285
+ if not correct_holes:
286
+ fill_labels = [i for i in range(n_labels) if i not in fill_labels]
287
+ # If every region is below threshold, keep largest
288
+ if len(fill_labels) == 0:
289
+ fill_labels = [int(np.argmax(sizes)) + 1]
290
+ mask = np.isin(regions, fill_labels)
291
+ return mask, True
292
+
293
+
294
+ def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:
295
+ from pycocotools import mask as mask_utils # type: ignore
296
+
297
+ h, w = uncompressed_rle["size"]
298
+ rle = mask_utils.frPyObjects(uncompressed_rle, h, w)
299
+ rle["counts"] = rle["counts"].decode("utf-8") # Necessary to serialize with json
300
+ return rle
301
+
302
+
303
+ def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:
304
+ """
305
+ Calculates boxes in XYXY format around masks. Return [0,0,0,0] for
306
+ an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4.
307
+ """
308
+ # torch.max below raises an error on empty inputs, just skip in this case
309
+ if torch.numel(masks) == 0:
310
+ return torch.zeros(*masks.shape[:-2], 4, device=masks.device)
311
+
312
+ # Normalize shape to CxHxW
313
+ shape = masks.shape
314
+ h, w = shape[-2:]
315
+ if len(shape) > 2:
316
+ masks = masks.flatten(0, -3)
317
+ else:
318
+ masks = masks.unsqueeze(0)
319
+
320
+ # Get top and bottom edges
321
+ in_height, _ = torch.max(masks, dim=-1)
322
+ in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]
323
+ bottom_edges, _ = torch.max(in_height_coords, dim=-1)
324
+ in_height_coords = in_height_coords + h * (~in_height)
325
+ top_edges, _ = torch.min(in_height_coords, dim=-1)
326
+
327
+ # Get left and right edges
328
+ in_width, _ = torch.max(masks, dim=-2)
329
+ in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]
330
+ right_edges, _ = torch.max(in_width_coords, dim=-1)
331
+ in_width_coords = in_width_coords + w * (~in_width)
332
+ left_edges, _ = torch.min(in_width_coords, dim=-1)
333
+
334
+ # If the mask is empty the right edge will be to the left of the left edge.
335
+ # Replace these boxes with [0, 0, 0, 0]
336
+ empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)
337
+ out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)
338
+ out = out * (~empty_filter).unsqueeze(-1)
339
+
340
+ # Return to original shape
341
+ if len(shape) > 2:
342
+ out = out.reshape(*shape[:-2], 4)
343
+ else:
344
+ out = out[0]
345
+
346
+ return out
segment_anything/utils/onnx.py ADDED
@@ -0,0 +1,144 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import torch
8
+ import torch.nn as nn
9
+ from torch.nn import functional as F
10
+
11
+ from typing import Tuple
12
+
13
+ from ..modeling import Sam
14
+ from .amg import calculate_stability_score
15
+
16
+
17
+ class SamOnnxModel(nn.Module):
18
+ """
19
+ This model should not be called directly, but is used in ONNX export.
20
+ It combines the prompt encoder, mask decoder, and mask postprocessing of Sam,
21
+ with some functions modified to enable model tracing. Also supports extra
22
+ options controlling what information. See the ONNX export script for details.
23
+ """
24
+
25
+ def __init__(
26
+ self,
27
+ model: Sam,
28
+ return_single_mask: bool,
29
+ use_stability_score: bool = False,
30
+ return_extra_metrics: bool = False,
31
+ ) -> None:
32
+ super().__init__()
33
+ self.mask_decoder = model.mask_decoder
34
+ self.model = model
35
+ self.img_size = model.image_encoder.img_size
36
+ self.return_single_mask = return_single_mask
37
+ self.use_stability_score = use_stability_score
38
+ self.stability_score_offset = 1.0
39
+ self.return_extra_metrics = return_extra_metrics
40
+
41
+ @staticmethod
42
+ def resize_longest_image_size(
43
+ input_image_size: torch.Tensor, longest_side: int
44
+ ) -> torch.Tensor:
45
+ input_image_size = input_image_size.to(torch.float32)
46
+ scale = longest_side / torch.max(input_image_size)
47
+ transformed_size = scale * input_image_size
48
+ transformed_size = torch.floor(transformed_size + 0.5).to(torch.int64)
49
+ return transformed_size
50
+
51
+ def _embed_points(self, point_coords: torch.Tensor, point_labels: torch.Tensor) -> torch.Tensor:
52
+ point_coords = point_coords + 0.5
53
+ point_coords = point_coords / self.img_size
54
+ point_embedding = self.model.prompt_encoder.pe_layer._pe_encoding(point_coords)
55
+ point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding)
56
+
57
+ point_embedding = point_embedding * (point_labels != -1)
58
+ point_embedding = point_embedding + self.model.prompt_encoder.not_a_point_embed.weight * (
59
+ point_labels == -1
60
+ )
61
+
62
+ for i in range(self.model.prompt_encoder.num_point_embeddings):
63
+ point_embedding = point_embedding + self.model.prompt_encoder.point_embeddings[
64
+ i
65
+ ].weight * (point_labels == i)
66
+
67
+ return point_embedding
68
+
69
+ def _embed_masks(self, input_mask: torch.Tensor, has_mask_input: torch.Tensor) -> torch.Tensor:
70
+ mask_embedding = has_mask_input * self.model.prompt_encoder.mask_downscaling(input_mask)
71
+ mask_embedding = mask_embedding + (
72
+ 1 - has_mask_input
73
+ ) * self.model.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1)
74
+ return mask_embedding
75
+
76
+ def mask_postprocessing(self, masks: torch.Tensor, orig_im_size: torch.Tensor) -> torch.Tensor:
77
+ masks = F.interpolate(
78
+ masks,
79
+ size=(self.img_size, self.img_size),
80
+ mode="bilinear",
81
+ align_corners=False,
82
+ )
83
+
84
+ prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size)
85
+ masks = masks[..., : int(prepadded_size[0]), : int(prepadded_size[1])]
86
+
87
+ orig_im_size = orig_im_size.to(torch.int64)
88
+ h, w = orig_im_size[0], orig_im_size[1]
89
+ masks = F.interpolate(masks, size=(h, w), mode="bilinear", align_corners=False)
90
+ return masks
91
+
92
+ def select_masks(
93
+ self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int
94
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
95
+ # Determine if we should return the multiclick mask or not from the number of points.
96
+ # The reweighting is used to avoid control flow.
97
+ score_reweight = torch.tensor(
98
+ [[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)]
99
+ ).to(iou_preds.device)
100
+ score = iou_preds + (num_points - 2.5) * score_reweight
101
+ best_idx = torch.argmax(score, dim=1)
102
+ masks = masks[torch.arange(masks.shape[0]), best_idx, :, :].unsqueeze(1)
103
+ iou_preds = iou_preds[torch.arange(masks.shape[0]), best_idx].unsqueeze(1)
104
+
105
+ return masks, iou_preds
106
+
107
+ @torch.no_grad()
108
+ def forward(
109
+ self,
110
+ image_embeddings: torch.Tensor,
111
+ point_coords: torch.Tensor,
112
+ point_labels: torch.Tensor,
113
+ mask_input: torch.Tensor,
114
+ has_mask_input: torch.Tensor,
115
+ orig_im_size: torch.Tensor,
116
+ ):
117
+ sparse_embedding = self._embed_points(point_coords, point_labels)
118
+ dense_embedding = self._embed_masks(mask_input, has_mask_input)
119
+
120
+ masks, scores = self.model.mask_decoder.predict_masks(
121
+ image_embeddings=image_embeddings,
122
+ image_pe=self.model.prompt_encoder.get_dense_pe(),
123
+ sparse_prompt_embeddings=sparse_embedding,
124
+ dense_prompt_embeddings=dense_embedding,
125
+ )
126
+
127
+ if self.use_stability_score:
128
+ scores = calculate_stability_score(
129
+ masks, self.model.mask_threshold, self.stability_score_offset
130
+ )
131
+
132
+ if self.return_single_mask:
133
+ masks, scores = self.select_masks(masks, scores, point_coords.shape[1])
134
+
135
+ upscaled_masks = self.mask_postprocessing(masks, orig_im_size)
136
+
137
+ if self.return_extra_metrics:
138
+ stability_scores = calculate_stability_score(
139
+ upscaled_masks, self.model.mask_threshold, self.stability_score_offset
140
+ )
141
+ areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1)
142
+ return upscaled_masks, scores, stability_scores, areas, masks
143
+
144
+ return upscaled_masks, scores, masks
segment_anything/utils/transforms.py ADDED
@@ -0,0 +1,102 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Meta Platforms, Inc. and affiliates.
2
+ # All rights reserved.
3
+
4
+ # This source code is licensed under the license found in the
5
+ # LICENSE file in the root directory of this source tree.
6
+
7
+ import numpy as np
8
+ import torch
9
+ from torch.nn import functional as F
10
+ from torchvision.transforms.functional import resize, to_pil_image # type: ignore
11
+
12
+ from copy import deepcopy
13
+ from typing import Tuple
14
+
15
+
16
+ class ResizeLongestSide:
17
+ """
18
+ Resizes images to longest side 'target_length', as well as provides
19
+ methods for resizing coordinates and boxes. Provides methods for
20
+ transforming both numpy array and batched torch tensors.
21
+ """
22
+
23
+ def __init__(self, target_length: int) -> None:
24
+ self.target_length = target_length
25
+
26
+ def apply_image(self, image: np.ndarray) -> np.ndarray:
27
+ """
28
+ Expects a numpy array with shape HxWxC in uint8 format.
29
+ """
30
+ target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
31
+ return np.array(resize(to_pil_image(image), target_size))
32
+
33
+ def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
34
+ """
35
+ Expects a numpy array of length 2 in the final dimension. Requires the
36
+ original image size in (H, W) format.
37
+ """
38
+ old_h, old_w = original_size
39
+ new_h, new_w = self.get_preprocess_shape(
40
+ original_size[0], original_size[1], self.target_length
41
+ )
42
+ coords = deepcopy(coords).astype(float)
43
+ coords[..., 0] = coords[..., 0] * (new_w / old_w)
44
+ coords[..., 1] = coords[..., 1] * (new_h / old_h)
45
+ return coords
46
+
47
+ def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
48
+ """
49
+ Expects a numpy array shape Bx4. Requires the original image size
50
+ in (H, W) format.
51
+ """
52
+ boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)
53
+ return boxes.reshape(-1, 4)
54
+
55
+ def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:
56
+ """
57
+ Expects batched images with shape BxCxHxW and float format. This
58
+ transformation may not exactly match apply_image. apply_image is
59
+ the transformation expected by the model.
60
+ """
61
+ # Expects an image in BCHW format. May not exactly match apply_image.
62
+ target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
63
+ return F.interpolate(
64
+ image, target_size, mode="bilinear", align_corners=False, antialias=True
65
+ )
66
+
67
+ def apply_coords_torch(
68
+ self, coords: torch.Tensor, original_size: Tuple[int, ...]
69
+ ) -> torch.Tensor:
70
+ """
71
+ Expects a torch tensor with length 2 in the last dimension. Requires the
72
+ original image size in (H, W) format.
73
+ """
74
+ old_h, old_w = original_size
75
+ new_h, new_w = self.get_preprocess_shape(
76
+ original_size[0], original_size[1], self.target_length
77
+ )
78
+ coords = deepcopy(coords).to(torch.float)
79
+ coords[..., 0] = coords[..., 0] * (new_w / old_w)
80
+ coords[..., 1] = coords[..., 1] * (new_h / old_h)
81
+ return coords
82
+
83
+ def apply_boxes_torch(
84
+ self, boxes: torch.Tensor, original_size: Tuple[int, ...]
85
+ ) -> torch.Tensor:
86
+ """
87
+ Expects a torch tensor with shape Bx4. Requires the original image
88
+ size in (H, W) format.
89
+ """
90
+ boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)
91
+ return boxes.reshape(-1, 4)
92
+
93
+ @staticmethod
94
+ def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]:
95
+ """
96
+ Compute the output size given input size and target long side length.
97
+ """
98
+ scale = long_side_length * 1.0 / max(oldh, oldw)
99
+ newh, neww = oldh * scale, oldw * scale
100
+ neww = int(neww + 0.5)
101
+ newh = int(newh + 0.5)
102
+ return (newh, neww)