Use frozenset() (#18785)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher
GitHub
parent
fb3e5adfd7
commit
9341c1df76
5 changed files with 143 additions and 131 deletions
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@ -47,8 +47,8 @@ SOLUTION_MAP = {
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}
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# Define valid tasks and modes
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MODES = {"train", "val", "predict", "export", "track", "benchmark"}
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TASKS = {"detect", "segment", "classify", "pose", "obb"}
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MODES = frozenset({"train", "val", "predict", "export", "track", "benchmark"})
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TASKS = frozenset({"detect", "segment", "classify", "pose", "obb"})
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TASK2DATA = {
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"detect": "coco8.yaml",
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"segment": "coco8-seg.yaml",
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@ -70,7 +70,7 @@ TASK2METRIC = {
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"pose": "metrics/mAP50-95(P)",
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"obb": "metrics/mAP50-95(B)",
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}
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MODELS = {TASK2MODEL[task] for task in TASKS}
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MODELS = frozenset({TASK2MODEL[task] for task in TASKS})
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ARGV = sys.argv or ["", ""] # sometimes sys.argv = []
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SOLUTIONS_HELP_MSG = f"""
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@ -144,7 +144,8 @@ CLI_HELP_MSG = f"""
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"""
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# Define keys for arg type checks
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CFG_FLOAT_KEYS = { # integer or float arguments, i.e. x=2 and x=2.0
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CFG_FLOAT_KEYS = frozenset(
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{ # integer or float arguments, i.e. x=2 and x=2.0
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"warmup_epochs",
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"box",
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"cls",
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@ -155,7 +156,9 @@ CFG_FLOAT_KEYS = { # integer or float arguments, i.e. x=2 and x=2.0
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"workspace",
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"batch",
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}
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CFG_FRACTION_KEYS = { # fractional float arguments with 0.0<=values<=1.0
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)
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CFG_FRACTION_KEYS = frozenset(
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{ # fractional float arguments with 0.0<=values<=1.0
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"dropout",
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"lr0",
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"lrf",
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@ -179,7 +182,9 @@ CFG_FRACTION_KEYS = { # fractional float arguments with 0.0<=values<=1.0
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"iou",
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"fraction",
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}
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CFG_INT_KEYS = { # integer-only arguments
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)
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CFG_INT_KEYS = frozenset(
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{ # integer-only arguments
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"epochs",
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"patience",
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"workers",
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@ -192,7 +197,9 @@ CFG_INT_KEYS = { # integer-only arguments
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"nbs",
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"save_period",
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}
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CFG_BOOL_KEYS = { # boolean-only arguments
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)
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CFG_BOOL_KEYS = frozenset(
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{ # boolean-only arguments
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"save",
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"exist_ok",
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"verbose",
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@ -228,6 +235,7 @@ CFG_BOOL_KEYS = { # boolean-only arguments
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"profile",
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"multi_scale",
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}
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)
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def cfg2dict(cfg):
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@ -472,7 +480,7 @@ def check_dict_alignment(base: Dict, custom: Dict, e=None):
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- Prints detailed error messages for each mismatched key to help users correct their configurations.
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"""
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custom = _handle_deprecation(custom)
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base_keys, custom_keys = (set(x.keys()) for x in (base, custom))
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base_keys, custom_keys = (frozenset(x.keys()) for x in (base, custom))
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if mismatched := [k for k in custom_keys if k not in base_keys]:
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from difflib import get_close_matches
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@ -954,20 +954,8 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
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LOGGER.info(f"\n{'':>3}{'from':>20}{'n':>3}{'params':>10} {'module':<45}{'arguments':<30}")
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ch = [ch]
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layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
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for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]): # from, number, module, args
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m = (
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getattr(torch.nn, m[3:])
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if "nn." in m
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else getattr(__import__("torchvision").ops, m[16:])
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if "torchvision.ops." in m
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else globals()[m]
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) # get module
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for j, a in enumerate(args):
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if isinstance(a, str):
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with contextlib.suppress(ValueError):
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args[j] = locals()[a] if a in locals() else ast.literal_eval(a)
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n = n_ = max(round(n * depth), 1) if n > 1 else n # depth gain
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if m in {
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base_modules = frozenset(
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{
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Classify,
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Conv,
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ConvTranspose,
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@ -1001,18 +989,10 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
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PSA,
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SCDown,
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C2fCIB,
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}:
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c1, c2 = ch[f], args[0]
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if c2 != nc: # if c2 not equal to number of classes (i.e. for Classify() output)
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c2 = make_divisible(min(c2, max_channels) * width, 8)
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if m is C2fAttn:
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args[1] = make_divisible(min(args[1], max_channels // 2) * width, 8) # embed channels
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args[2] = int(
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max(round(min(args[2], max_channels // 2 // 32)) * width, 1) if args[2] > 1 else args[2]
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) # num heads
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args = [c1, c2, *args[1:]]
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if m in {
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}
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)
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repeat_modules = frozenset( # modules with 'repeat' arguments
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{
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BottleneckCSP,
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C1,
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C2,
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@ -1027,7 +1007,31 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
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C2fPSA,
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C2fCIB,
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C2PSA,
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}:
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}
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)
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for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]): # from, number, module, args
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m = (
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getattr(torch.nn, m[3:])
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if "nn." in m
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else getattr(__import__("torchvision").ops, m[16:])
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if "torchvision.ops." in m
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else globals()[m]
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) # get module
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for j, a in enumerate(args):
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if isinstance(a, str):
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with contextlib.suppress(ValueError):
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args[j] = locals()[a] if a in locals() else ast.literal_eval(a)
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n = n_ = max(round(n * depth), 1) if n > 1 else n # depth gain
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if m in base_modules:
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c1, c2 = ch[f], args[0]
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if c2 != nc: # if c2 not equal to number of classes (i.e. for Classify() output)
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c2 = make_divisible(min(c2, max_channels) * width, 8)
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if m is C2fAttn: # set 1) embed channels and 2) num heads
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args[1] = make_divisible(min(args[1], max_channels // 2) * width, 8)
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args[2] = int(max(round(min(args[2], max_channels // 2 // 32)) * width, 1) if args[2] > 1 else args[2])
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args = [c1, c2, *args[1:]]
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if m in repeat_modules:
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args.insert(2, n) # number of repeats
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n = 1
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if m is C3k2: # for M/L/X sizes
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@ -1036,7 +1040,7 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
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args[3] = True
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elif m is AIFI:
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args = [ch[f], *args]
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elif m in {HGStem, HGBlock}:
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elif m in frozenset({HGStem, HGBlock}):
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c1, cm, c2 = ch[f], args[0], args[1]
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args = [c1, cm, c2, *args[2:]]
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if m is HGBlock:
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@ -1048,7 +1052,7 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
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args = [ch[f]]
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elif m is Concat:
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c2 = sum(ch[x] for x in f)
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elif m in {Detect, WorldDetect, Segment, Pose, OBB, ImagePoolingAttn, v10Detect}:
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elif m in frozenset({Detect, WorldDetect, Segment, Pose, OBB, ImagePoolingAttn, v10Detect}):
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args.append([ch[x] for x in f])
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if m is Segment:
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args[2] = make_divisible(min(args[2], max_channels) * width, 8)
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@ -1056,7 +1060,7 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
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m.legacy = legacy
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elif m is RTDETRDecoder: # special case, channels arg must be passed in index 1
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args.insert(1, [ch[x] for x in f])
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elif m in {CBLinear, TorchVision, Index}:
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elif m in frozenset({CBLinear, TorchVision, Index}):
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c2 = args[0]
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c1 = ch[f]
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args = [c1, c2, *args[1:]]
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@ -55,8 +55,8 @@ def linear_assignment(cost_matrix: np.ndarray, thresh: float, use_lap: bool = Tr
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unmatched_a = list(np.arange(cost_matrix.shape[0]))
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unmatched_b = list(np.arange(cost_matrix.shape[1]))
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else:
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unmatched_a = list(set(np.arange(cost_matrix.shape[0])) - set(matches[:, 0]))
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unmatched_b = list(set(np.arange(cost_matrix.shape[1])) - set(matches[:, 1]))
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unmatched_a = list(frozenset(np.arange(cost_matrix.shape[0])) - frozenset(matches[:, 0]))
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unmatched_b = list(frozenset(np.arange(cost_matrix.shape[1])) - frozenset(matches[:, 1]))
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return matches, unmatched_a, unmatched_b
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@ -1227,7 +1227,7 @@ class SettingsManager(JSONDict):
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def _validate_settings(self):
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"""Validate the current settings and reset if necessary."""
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correct_keys = set(self.keys()) == set(self.defaults.keys())
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correct_keys = frozenset(self.keys()) == frozenset(self.defaults.keys())
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correct_types = all(isinstance(self.get(k), type(v)) for k, v in self.defaults.items())
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correct_version = self.get("settings_version", "") == self.version
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@ -407,7 +407,7 @@ class Instances:
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cat_boxes = np.concatenate([ins.bboxes for ins in instances_list], axis=axis)
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seg_len = [b.segments.shape[1] for b in instances_list]
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if len(set(seg_len)) > 1: # resample segments if there's different length
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if len(frozenset(seg_len)) > 1: # resample segments if there's different length
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max_len = max(seg_len)
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cat_segments = np.concatenate(
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[
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