Ultralytics Code Refactor https://ultralytics.com/actions (#16047)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/data/converter.py

@@ -370,13 +370,10 @@ def convert_segment_masks_to_yolo_seg(masks_dir, output_dir, classes):
                 ├─ mask_yolo_03.txt
                 └─ mask_yolo_04.txt
     """
-    import os
-
     pixel_to_class_mapping = {i + 1: i for i in range(classes)}
-    for mask_filename in os.listdir(masks_dir):
-        if mask_filename.endswith(".png"):
-            mask_path = os.path.join(masks_dir, mask_filename)
-            mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)  # Read the mask image in grayscale
+    for mask_path in Path(masks_dir).iterdir():
+        if mask_path.suffix == ".png":
+            mask = cv2.imread(str(mask_path), cv2.IMREAD_GRAYSCALE)  # Read the mask image in grayscale
             img_height, img_width = mask.shape  # Get image dimensions
             LOGGER.info(f"Processing {mask_path} imgsz = {img_height} x {img_width}")
 
@@ -406,7 +403,7 @@ def convert_segment_masks_to_yolo_seg(masks_dir, output_dir, classes):
                             yolo_format.append(round(point[1] / img_height, 6))
                         yolo_format_data.append(yolo_format)
             # Save Ultralytics YOLO format data to file
-            output_path = os.path.join(output_dir, os.path.splitext(mask_filename)[0] + ".txt")
+            output_path = Path(output_dir) / f"{Path(mask_filename).stem}.txt"
             with open(output_path, "w") as file:
                 for item in yolo_format_data:
                     line = " ".join(map(str, item))

ultralytics/hub/google/__init__.py

@@ -136,12 +136,12 @@ class GCPRegions:
         sorted_results = sorted(results, key=lambda x: x[1])
 
         if verbose:
-            print(f"{'Region':<25} {'Location':<35} {'Tier':<5} {'Latency (ms)'}")
+            print(f"{'Region':<25} {'Location':<35} {'Tier':<5} Latency (ms)")
             for region, mean, std, min_, max_ in sorted_results:
                 tier, city, country = self.regions[region]
                 location = f"{city}, {country}"
                 if mean == float("inf"):
-                    print(f"{region:<25} {location:<35} {tier:<5} {'Timeout'}")
+                    print(f"{region:<25} {location:<35} {tier:<5} Timeout")
                 else:
                     print(f"{region:<25} {location:<35} {tier:<5} {mean:.0f} ± {std:.0f} ({min_:.0f} - {max_:.0f})")
             print(f"\nLowest latency region{'s' if top > 1 else ''}:")

ultralytics/hub/session.py

@@ -346,7 +346,7 @@ class HUBTrainingSession:
         """
         weights = Path(weights)
         if not weights.is_file():
-            last = weights.with_name("last" + weights.suffix)
+            last = weights.with_name(f"last{weights.suffix}")
             if final and last.is_file():
                 LOGGER.warning(
                     f"{PREFIX} WARNING ⚠️ Model 'best.pt' not found, copying 'last.pt' to 'best.pt' and uploading. "

ultralytics/models/fastsam/predict.py

@@ -93,7 +93,7 @@ class FastSAMPredictor(SegmentationPredictor):
                     else torch.zeros(len(result), dtype=torch.bool, device=self.device)
                 )
                 for point, label in zip(points, labels):
-                    point_idx[torch.nonzero(masks[:, point[1], point[0]], as_tuple=True)[0]] = True if label else False
+                    point_idx[torch.nonzero(masks[:, point[1], point[0]], as_tuple=True)[0]] = bool(label)
                 idx |= point_idx
             if texts is not None:
                 if isinstance(texts, str):

ultralytics/models/sam/modules/blocks.py

@@ -736,7 +736,7 @@ class PositionEmbeddingSine(nn.Module):
         self.num_pos_feats = num_pos_feats // 2
         self.temperature = temperature
         self.normalize = normalize
-        if scale is not None and normalize is False:
+        if scale is not None and not normalize:
             raise ValueError("normalize should be True if scale is passed")
         if scale is None:
             scale = 2 * math.pi
@@ -763,8 +763,7 @@ class PositionEmbeddingSine(nn.Module):
     def encode_boxes(self, x, y, w, h):
         """Encodes box coordinates and dimensions into positional embeddings for detection."""
         pos_x, pos_y = self._encode_xy(x, y)
-        pos = torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1)
-        return pos
+        return torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1)
 
     encode = encode_boxes  # Backwards compatibility
 
@@ -775,8 +774,7 @@ class PositionEmbeddingSine(nn.Module):
         assert bx == by and nx == ny and bx == bl and nx == nl
         pos_x, pos_y = self._encode_xy(x.flatten(), y.flatten())
         pos_x, pos_y = pos_x.reshape(bx, nx, -1), pos_y.reshape(by, ny, -1)
-        pos = torch.cat((pos_y, pos_x, labels[:, :, None]), dim=2)
-        return pos
+        return torch.cat((pos_y, pos_x, labels[:, :, None]), dim=2)
 
     @torch.no_grad()
     def forward(self, x: torch.Tensor):

ultralytics/models/sam/modules/decoders.py

@@ -435,9 +435,9 @@ class SAM2MaskDecoder(nn.Module):
             upscaled_embedding = act1(ln1(dc1(src) + feat_s1))
             upscaled_embedding = act2(dc2(upscaled_embedding) + feat_s0)
 
-        hyper_in_list: List[torch.Tensor] = []
-        for i in range(self.num_mask_tokens):
-            hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
+        hyper_in_list: List[torch.Tensor] = [
+            self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]) for i in range(self.num_mask_tokens)
+        ]
         hyper_in = torch.stack(hyper_in_list, dim=1)
         b, c, h, w = upscaled_embedding.shape
         masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
@@ -459,8 +459,7 @@ class SAM2MaskDecoder(nn.Module):
         stability_delta = self.dynamic_multimask_stability_delta
         area_i = torch.sum(mask_logits > stability_delta, dim=-1).float()
         area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float()
-        stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0)
-        return stability_scores
+        return torch.where(area_u > 0, area_i / area_u, 1.0)
 
     def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores):
         """

ultralytics/models/sam/modules/encoders.py

@@ -491,12 +491,11 @@ class ImageEncoder(nn.Module):
             features, pos = features[: -self.scalp], pos[: -self.scalp]
 
         src = features[-1]
-        output = {
+        return {
             "vision_features": src,
             "vision_pos_enc": pos,
             "backbone_fpn": features,
         }
-        return output
 
 
 class FpnNeck(nn.Module):
@@ -577,7 +576,7 @@ class FpnNeck(nn.Module):
 
             self.convs.append(current)
         self.fpn_interp_model = fpn_interp_model
-        assert fuse_type in ["sum", "avg"]
+        assert fuse_type in {"sum", "avg"}
         self.fuse_type = fuse_type
 
         # levels to have top-down features in its outputs

ultralytics/models/sam/modules/sam.py

@@ -671,15 +671,8 @@ class SAM2Model(torch.nn.Module):
                 t_rel = self.num_maskmem - t_pos  # how many frames before current frame
                 if t_rel == 1:
                     # for t_rel == 1, we take the last frame (regardless of r)
-                    if not track_in_reverse:
-                        # the frame immediately before this frame (i.e. frame_idx - 1)
-                        prev_frame_idx = frame_idx - t_rel
-                    else:
-                        # the frame immediately after this frame (i.e. frame_idx + 1)
-                        prev_frame_idx = frame_idx + t_rel
-                else:
-                    # for t_rel >= 2, we take the memory frame from every r-th frames
-                    if not track_in_reverse:
+                    prev_frame_idx = frame_idx + t_rel if track_in_reverse else frame_idx - t_rel
+                elif not track_in_reverse:
                     # first find the nearest frame among every r-th frames before this frame
                     # for r=1, this would be (frame_idx - 2)
                     prev_frame_idx = ((frame_idx - 2) // r) * r
@@ -739,7 +732,7 @@ class SAM2Model(torch.nn.Module):
                     if out is not None:
                         pos_and_ptrs.append((t_diff, out["obj_ptr"]))
                 # If we have at least one object pointer, add them to the across attention
-                if len(pos_and_ptrs) > 0:
+                if pos_and_ptrs:
                     pos_list, ptrs_list = zip(*pos_and_ptrs)
                     # stack object pointers along dim=0 into [ptr_seq_len, B, C] shape
                     obj_ptrs = torch.stack(ptrs_list, dim=0)
@@ -930,12 +923,11 @@ class SAM2Model(torch.nn.Module):
     def _use_multimask(self, is_init_cond_frame, point_inputs):
         """Determines whether to use multiple mask outputs in the SAM head based on configuration and inputs."""
         num_pts = 0 if point_inputs is None else point_inputs["point_labels"].size(1)
-        multimask_output = (
+        return (
             self.multimask_output_in_sam
             and (is_init_cond_frame or self.multimask_output_for_tracking)
             and (self.multimask_min_pt_num <= num_pts <= self.multimask_max_pt_num)
         )
-        return multimask_output
 
     def _apply_non_overlapping_constraints(self, pred_masks):
         """Applies non-overlapping constraints to masks, keeping highest scoring object per location."""

ultralytics/models/yolo/classify/predict.py

@@ -53,7 +53,7 @@ class ClassificationPredictor(BasePredictor):
         if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
             orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
 
-        results = []
-        for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]):
-            results.append(Results(orig_img, path=img_path, names=self.model.names, probs=pred))
-        return results
+        return [
+            Results(orig_img, path=img_path, names=self.model.names, probs=pred)
+            for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0])
+        ]

ultralytics/nn/modules/activation.py

@@ -18,5 +18,4 @@ class AGLU(nn.Module):
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Compute the forward pass of the Unified activation function."""
         lam = torch.clamp(self.lambd, min=0.0001)
-        y = torch.exp((1 / lam) * self.act((self.kappa * x) - torch.log(lam)))
-        return y  # for AGLU simply return y * input
+        return torch.exp((1 / lam) * self.act((self.kappa * x) - torch.log(lam)))

ultralytics/utils/__init__.py

@@ -1160,9 +1160,9 @@ def vscode_msg(ext="ultralytics.ultralytics-snippets") -> str:
     obs_file = path / ".obsolete"  # file tracks uninstalled extensions, while source directory remains
     installed = any(path.glob(f"{ext}*")) and ext not in (obs_file.read_text("utf-8") if obs_file.exists() else "")
     return (
-        f"{colorstr('VS Code:')} view Ultralytics VS Code Extension ⚡ at https://docs.ultralytics.com/integrations/vscode"
-        if not installed
-        else ""
+        ""
+        if installed
+        else f"{colorstr('VS Code:')} view Ultralytics VS Code Extension ⚡ at https://docs.ultralytics.com/integrations/vscode"
     )
 
 

ultralytics/utils/checks.py

@@ -226,8 +226,7 @@ def check_version(
     if not required:  # if required is '' or None
         return True
 
-    if "sys_platform" in required:  # i.e. required='<2.4.0,>=1.8.0; sys_platform == "win32"'
-        if (
+    if "sys_platform" in required and (  # i.e. required='<2.4.0,>=1.8.0; sys_platform == "win32"'
         (WINDOWS and "win32" not in required)
         or (LINUX and "linux" not in required)
         or (MACOS and "macos" not in required and "darwin" not in required)