Use frozenset() (#18785)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

ultralytics/cfg/__init__.py

@@ -47,8 +47,8 @@ SOLUTION_MAP = {
 }
 
 # Define valid tasks and modes
-MODES = {"train", "val", "predict", "export", "track", "benchmark"}
+MODES = frozenset({"train", "val", "predict", "export", "track", "benchmark"})
-TASKS = {"detect", "segment", "classify", "pose", "obb"}
+TASKS = frozenset({"detect", "segment", "classify", "pose", "obb"})
 TASK2DATA = {
     "detect": "coco8.yaml",
     "segment": "coco8-seg.yaml",
@@ -70,7 +70,7 @@ TASK2METRIC = {
     "pose": "metrics/mAP50-95(P)",
     "obb": "metrics/mAP50-95(B)",
 }
-MODELS = {TASK2MODEL[task] for task in TASKS}
+MODELS = frozenset({TASK2MODEL[task] for task in TASKS})
 
 ARGV = sys.argv or ["", ""]  # sometimes sys.argv = []
 SOLUTIONS_HELP_MSG = f"""
@@ -144,7 +144,8 @@ CLI_HELP_MSG = f"""
     """
 
 # Define keys for arg type checks
-CFG_FLOAT_KEYS = {  # integer or float arguments, i.e. x=2 and x=2.0
+CFG_FLOAT_KEYS = frozenset(
+    {  # integer or float arguments, i.e. x=2 and x=2.0
         "warmup_epochs",
         "box",
         "cls",
@@ -154,8 +155,10 @@ CFG_FLOAT_KEYS = {  # integer or float arguments, i.e. x=2 and x=2.0
         "time",
         "workspace",
         "batch",
-}
+    }
-CFG_FRACTION_KEYS = {  # fractional float arguments with 0.0<=values<=1.0
+)
+CFG_FRACTION_KEYS = frozenset(
+    {  # fractional float arguments with 0.0<=values<=1.0
         "dropout",
         "lr0",
         "lrf",
@@ -178,8 +181,10 @@ CFG_FRACTION_KEYS = {  # fractional float arguments with 0.0<=values<=1.0
         "conf",
         "iou",
         "fraction",
-}
+    }
-CFG_INT_KEYS = {  # integer-only arguments
+)
+CFG_INT_KEYS = frozenset(
+    {  # integer-only arguments
         "epochs",
         "patience",
         "workers",
@@ -191,8 +196,10 @@ CFG_INT_KEYS = {  # integer-only arguments
         "line_width",
         "nbs",
         "save_period",
-}
+    }
-CFG_BOOL_KEYS = {  # boolean-only arguments
+)
+CFG_BOOL_KEYS = frozenset(
+    {  # boolean-only arguments
         "save",
         "exist_ok",
         "verbose",
@@ -227,7 +234,8 @@ CFG_BOOL_KEYS = {  # boolean-only arguments
         "nms",
         "profile",
         "multi_scale",
-}
+    }
+)
 
 
 def cfg2dict(cfg):
@@ -472,7 +480,7 @@ def check_dict_alignment(base: Dict, custom: Dict, e=None):
         - Prints detailed error messages for each mismatched key to help users correct their configurations.
     """
     custom = _handle_deprecation(custom)
-    base_keys, custom_keys = (set(x.keys()) for x in (base, custom))
+    base_keys, custom_keys = (frozenset(x.keys()) for x in (base, custom))
     if mismatched := [k for k in custom_keys if k not in base_keys]:
         from difflib import get_close_matches
 

ultralytics/nn/tasks.py

@@ -954,20 +954,8 @@ def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)
         LOGGER.info(f"\n{'':>3}{'from':>20}{'n':>3}{'params':>10}  {'module':<45}{'arguments':<30}")
     ch = [ch]
     layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
-    for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]):  # from, number, module, args
+    base_modules = frozenset(
-        m = (
+        {
-            getattr(torch.nn, m[3:])
-            if "nn." in m
-            else getattr(__import__("torchvision").ops, m[16:])
-            if "torchvision.ops." in m
-            else globals()[m]
-        )  # get module
-        for j, a in enumerate(args):
-            if isinstance(a, str):
-                with contextlib.suppress(ValueError):
-                    args[j] = locals()[a] if a in locals() else ast.literal_eval(a)
-        n = n_ = max(round(n * depth), 1) if n > 1 else n  # depth gain
-        if m in {
             Classify,
             Conv,
             ConvTranspose,
@@ -1001,18 +989,10 @@ def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)
             PSA,
             SCDown,
             C2fCIB,
-        }:
+        }
-            c1, c2 = ch[f], args[0]
+    )
-            if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)
+    repeat_modules = frozenset(  # modules with 'repeat' arguments
-                c2 = make_divisible(min(c2, max_channels) * width, 8)
+        {
-            if m is C2fAttn:
-                args[1] = make_divisible(min(args[1], max_channels // 2) * width, 8)  # embed channels
-                args[2] = int(
-                    max(round(min(args[2], max_channels // 2 // 32)) * width, 1) if args[2] > 1 else args[2]
-                )  # num heads
-
-            args = [c1, c2, *args[1:]]
-            if m in {
             BottleneckCSP,
             C1,
             C2,
@@ -1027,7 +1007,31 @@ def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)
             C2fPSA,
             C2fCIB,
             C2PSA,
-            }:
+        }
+    )
+    for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]):  # from, number, module, args
+        m = (
+            getattr(torch.nn, m[3:])
+            if "nn." in m
+            else getattr(__import__("torchvision").ops, m[16:])
+            if "torchvision.ops." in m
+            else globals()[m]
+        )  # get module
+        for j, a in enumerate(args):
+            if isinstance(a, str):
+                with contextlib.suppress(ValueError):
+                    args[j] = locals()[a] if a in locals() else ast.literal_eval(a)
+        n = n_ = max(round(n * depth), 1) if n > 1 else n  # depth gain
+        if m in base_modules:
+            c1, c2 = ch[f], args[0]
+            if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)
+                c2 = make_divisible(min(c2, max_channels) * width, 8)
+            if m is C2fAttn:  # set 1) embed channels and 2) num heads
+                args[1] = make_divisible(min(args[1], max_channels // 2) * width, 8)
+                args[2] = int(max(round(min(args[2], max_channels // 2 // 32)) * width, 1) if args[2] > 1 else args[2])
+
+            args = [c1, c2, *args[1:]]
+            if m in repeat_modules:
                 args.insert(2, n)  # number of repeats
                 n = 1
             if m is C3k2:  # for M/L/X sizes
@@ -1036,7 +1040,7 @@ def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)
                     args[3] = True
         elif m is AIFI:
             args = [ch[f], *args]
-        elif m in {HGStem, HGBlock}:
+        elif m in frozenset({HGStem, HGBlock}):
             c1, cm, c2 = ch[f], args[0], args[1]
             args = [c1, cm, c2, *args[2:]]
             if m is HGBlock:
@@ -1048,7 +1052,7 @@ def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)
             args = [ch[f]]
         elif m is Concat:
             c2 = sum(ch[x] for x in f)
-        elif m in {Detect, WorldDetect, Segment, Pose, OBB, ImagePoolingAttn, v10Detect}:
+        elif m in frozenset({Detect, WorldDetect, Segment, Pose, OBB, ImagePoolingAttn, v10Detect}):
             args.append([ch[x] for x in f])
             if m is Segment:
                 args[2] = make_divisible(min(args[2], max_channels) * width, 8)
@@ -1056,7 +1060,7 @@ def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)
                 m.legacy = legacy
         elif m is RTDETRDecoder:  # special case, channels arg must be passed in index 1
             args.insert(1, [ch[x] for x in f])
-        elif m in {CBLinear, TorchVision, Index}:
+        elif m in frozenset({CBLinear, TorchVision, Index}):
             c2 = args[0]
             c1 = ch[f]
             args = [c1, c2, *args[1:]]

ultralytics/trackers/utils/matching.py

@@ -55,8 +55,8 @@ def linear_assignment(cost_matrix: np.ndarray, thresh: float, use_lap: bool = Tr
             unmatched_a = list(np.arange(cost_matrix.shape[0]))
             unmatched_b = list(np.arange(cost_matrix.shape[1]))
         else:
-            unmatched_a = list(set(np.arange(cost_matrix.shape[0])) - set(matches[:, 0]))
+            unmatched_a = list(frozenset(np.arange(cost_matrix.shape[0])) - frozenset(matches[:, 0]))
-            unmatched_b = list(set(np.arange(cost_matrix.shape[1])) - set(matches[:, 1]))
+            unmatched_b = list(frozenset(np.arange(cost_matrix.shape[1])) - frozenset(matches[:, 1]))
 
     return matches, unmatched_a, unmatched_b
 

ultralytics/utils/__init__.py

@@ -1227,7 +1227,7 @@ class SettingsManager(JSONDict):
 
     def _validate_settings(self):
         """Validate the current settings and reset if necessary."""
-        correct_keys = set(self.keys()) == set(self.defaults.keys())
+        correct_keys = frozenset(self.keys()) == frozenset(self.defaults.keys())
         correct_types = all(isinstance(self.get(k), type(v)) for k, v in self.defaults.items())
         correct_version = self.get("settings_version", "") == self.version
 

ultralytics/utils/instance.py

@@ -407,7 +407,7 @@ class Instances:
 
         cat_boxes = np.concatenate([ins.bboxes for ins in instances_list], axis=axis)
         seg_len = [b.segments.shape[1] for b in instances_list]
-        if len(set(seg_len)) > 1:  # resample segments if there's different length
+        if len(frozenset(seg_len)) > 1:  # resample segments if there's different length
             max_len = max(seg_len)
             cat_segments = np.concatenate(
                 [