Cleanup redundant SAM forward() methods (#4591)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

tests/test_cuda.py

@@ -27,12 +27,8 @@ def test_checks():
 
 @pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
 def test_train():
-    YOLO(MODEL).train(data=DATA, imgsz=64, epochs=1, batch=-1, device=0)  # also test AutoBatch, requires imgsz>=64
+    device = 0 if CUDA_DEVICE_COUNT < 2 else [0, 1]
-
+    YOLO(MODEL).train(data=DATA, imgsz=64, epochs=1, batch=-1, device=device)  # also test AutoBatch, requires imgsz>=64
-
-@pytest.mark.skipif(CUDA_DEVICE_COUNT < 2, reason=f'DDP is not available, {CUDA_DEVICE_COUNT} device(s) found')
-def test_train_ddp():
-    YOLO(MODEL).train(data=DATA, imgsz=64, epochs=1, device=[0, 1])  # requires imgsz>=64
 
 
 @pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')

ultralytics/data/loaders.py

@@ -119,7 +119,7 @@ class LoadStreams:
         # Wait until a frame is available in each buffer
         while not all(self.imgs):
             if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord('q'):  # q to quit
-                cv2.destroyAllWindows()
+                self.close()
                 raise StopIteration
             time.sleep(1 / min(self.fps))
 

ultralytics/models/sam/modules/sam.py

@@ -6,11 +6,10 @@
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
 
-from typing import Any, Dict, List, Tuple
+from typing import List
 
 import torch
 from torch import nn
-from torch.nn import functional as F
 
 from .decoders import MaskDecoder
 from .encoders import ImageEncoderViT, PromptEncoder
@@ -31,6 +30,9 @@ class Sam(nn.Module):
         """
         SAM predicts object masks from an image and input prompts.
 
+        Note:
+            All forward() operations moved to SAMPredictor.
+
         Args:
           image_encoder (ImageEncoderViT): The backbone used to encode the image into image embeddings that allow for
             efficient mask prediction.
@@ -45,109 +47,3 @@ class Sam(nn.Module):
         self.mask_decoder = mask_decoder
         self.register_buffer('pixel_mean', torch.Tensor(pixel_mean).view(-1, 1, 1), False)
         self.register_buffer('pixel_std', torch.Tensor(pixel_std).view(-1, 1, 1), False)
-
-    @property
-    def device(self) -> Any:
-        return self.pixel_mean.device
-
-    @torch.no_grad()
-    def forward(
-        self,
-        batched_input: List[Dict[str, Any]],
-        multimask_output: bool,
-    ) -> List[Dict[str, torch.Tensor]]:
-        """
-        Predicts masks end-to-end from provided images and prompts. If prompts are not known in advance, using
-        SamPredictor is recommended over calling the model directly.
-
-        Args:
-          batched_input (list(dict)): A list over input images, each a dictionary with the following keys. A prompt
-              key can be excluded if it is not present.
-              'image': The image as a torch tensor in 3xHxW format, already transformed for input to the model.
-              'original_size': (tuple(int, int)) The original size of the image before transformation, as (H, W).
-              'point_coords': (torch.Tensor) Batched point prompts for this image, with shape BxNx2. Already
-                transformed to the input frame of the model.
-              'point_labels': (torch.Tensor) Batched labels for point prompts, with shape BxN.
-              'boxes': (torch.Tensor) Batched box inputs, with shape Bx4. Already transformed to the input frame of
-                the model.
-              'mask_inputs': (torch.Tensor) Batched mask inputs to the model, in the form Bx1xHxW.
-          multimask_output (bool): Whether the model should predict multiple disambiguating masks, or return a single
-            mask.
-
-        Returns:
-          (list(dict)): A list over input images, where each element is as dictionary with the following keys.
-              'masks': (torch.Tensor) Batched binary mask predictions, with shape BxCxHxW, where B is the number of
-                input prompts, C is determined by multimask_output, and (H, W) is the original size of the image.
-              'iou_predictions': (torch.Tensor) The model's predictions of mask quality, in shape BxC.
-              'low_res_logits': (torch.Tensor) Low resolution logits with shape BxCxHxW, where H=W=256. Can be passed
-                as mask input to subsequent iterations of prediction.
-        """
-        input_images = torch.stack([self.preprocess(x['image']) for x in batched_input], dim=0)
-        image_embeddings = self.image_encoder(input_images)
-
-        outputs = []
-        for image_record, curr_embedding in zip(batched_input, image_embeddings):
-            if 'point_coords' in image_record:
-                points = (image_record['point_coords'], image_record['point_labels'])
-            else:
-                points = None
-            sparse_embeddings, dense_embeddings = self.prompt_encoder(
-                points=points,
-                boxes=image_record.get('boxes', None),
-                masks=image_record.get('mask_inputs', None),
-            )
-            low_res_masks, iou_predictions = self.mask_decoder(
-                image_embeddings=curr_embedding.unsqueeze(0),
-                image_pe=self.prompt_encoder.get_dense_pe(),
-                sparse_prompt_embeddings=sparse_embeddings,
-                dense_prompt_embeddings=dense_embeddings,
-                multimask_output=multimask_output,
-            )
-            masks = self.postprocess_masks(
-                low_res_masks,
-                input_size=image_record['image'].shape[-2:],
-                original_size=image_record['original_size'],
-            )
-            masks = masks > self.mask_threshold
-            outputs.append({
-                'masks': masks,
-                'iou_predictions': iou_predictions,
-                'low_res_logits': low_res_masks, })
-        return outputs
-
-    def postprocess_masks(
-        self,
-        masks: torch.Tensor,
-        input_size: Tuple[int, ...],
-        original_size: Tuple[int, ...],
-    ) -> torch.Tensor:
-        """
-        Remove padding and upscale masks to the original image size.
-
-        Args:
-          masks (torch.Tensor): Batched masks from the mask_decoder, in BxCxHxW format.
-          input_size (tuple(int, int)): The size of the model input image, in (H, W) format. Used to remove padding.
-          original_size (tuple(int, int)): The original image size before resizing for input to the model, in (H, W).
-
-        Returns:
-          (torch.Tensor): Batched masks in BxCxHxW format, where (H, W) is given by original_size.
-        """
-        masks = F.interpolate(
-            masks,
-            (self.image_encoder.img_size, self.image_encoder.img_size),
-            mode='bilinear',
-            align_corners=False,
-        )
-        masks = masks[..., :input_size[0], :input_size[1]]
-        return F.interpolate(masks, original_size, mode='bilinear', align_corners=False)
-
-    def preprocess(self, x: torch.Tensor) -> torch.Tensor:
-        """Normalize pixel values and pad to a square input."""
-        # Normalize colors
-        x = (x - self.pixel_mean) / self.pixel_std
-
-        # Pad
-        h, w = x.shape[-2:]
-        padh = self.image_encoder.img_size - h
-        padw = self.image_encoder.img_size - w
-        return F.pad(x, (0, padw, 0, padh))

ultralytics/utils/checks.py

@@ -519,9 +519,13 @@ def cuda_device_count() -> int:
         # Run the nvidia-smi command and capture its output
         output = subprocess.check_output(['nvidia-smi', '--query-gpu=count', '--format=csv,noheader,nounits'],
                                          encoding='utf-8')
-        return int(output.strip())
+
-    except (subprocess.CalledProcessError, FileNotFoundError):
+        # Take the first line and strip any leading/trailing white space
-        # If the command fails or nvidia-smi is not found, assume no GPUs are available
+        first_line = output.strip().split('\n')[0]
+
+        return int(first_line)
+    except (subprocess.CalledProcessError, FileNotFoundError, ValueError):
+        # If the command fails, nvidia-smi is not found, or output is not an integer, assume no GPUs are available
         return 0