{"record":{"id":"5216cc6c6d6cc476","repo":"huggingface/pytorch-image-models","slug":"schedule-spread-must-be-non-negative","errorCode":null,"errorMessage":"schedule_spread must be non-negative.","messagePattern":"schedule_spread must be non-negative\\.","errorType":"validation","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"timm/data/scheduled_sampler.py","lineNumber":78,"sourceCode":"        if not hasattr(sampler, '__len__'):\n            raise TypeError('ScheduledBatchSampler requires a sampler with a length.')\n        if len(sampler) <= 0:\n            raise ValueError('ScheduledBatchSampler requires a non-empty sampler.')\n        if not batch_sizes:\n            raise ValueError('batch_sizes must contain at least one value.')\n        if any(int(batch_size) != batch_size or batch_size <= 0 for batch_size in batch_sizes):\n            raise ValueError('All scheduled batch sizes must be positive integers.')\n        if num_batches is not None and (int(num_batches) != num_batches or num_batches <= 0):\n            raise ValueError('num_batches must be a positive integer when specified.')\n        if choice_schedule not in ('constant', 'progressive'):\n            raise ValueError(\"choice_schedule must be 'constant' or 'progressive'.\")\n        if choice_schedule == 'progressive':\n            if len(batch_sizes) < 2:\n                raise ValueError('A progressive schedule requires at least two choices.')\n            if schedule_epochs is None or int(schedule_epochs) != schedule_epochs or schedule_epochs <= 0:\n                raise ValueError('schedule_epochs must be a positive integer for a progressive schedule.')\n            if schedule_spread < 0:\n                raise ValueError('schedule_spread must be non-negative.')\n            if not 0 <= schedule_random_mix <= 1:\n                raise ValueError('schedule_random_mix must be between 0 and 1.')\n\n        self.sampler = sampler\n        self.batch_sizes = tuple(int(batch_size) for batch_size in batch_sizes)\n        self.choice_weights = self._normalize_choice_weights(choice_weights)\n        self._active_choices = tuple(\n            choice_index\n            for choice_index, choice_weight in enumerate(self.choice_weights)\n            if choice_weight > 0\n        )\n        self.seed = seed\n        self.drop_last = drop_last\n        self.shuffle_schedule = shuffle_schedule\n        self.choice_schedule = choice_schedule\n        self.schedule_epochs = int(schedule_epochs) if schedule_epochs is not None else None\n        self.schedule_spread = schedule_spread\n        self.schedule_random_mix = schedule_random_mix","sourceCodeStart":60,"sourceCodeEnd":96,"githubUrl":"https://github.com/huggingface/pytorch-image-models/blob/9a5261e31b3b5128526eb2658333b4c0a54464ae/timm/data/scheduled_sampler.py#L60-L96","documentation":"schedule_spread (how spread out/soft the transition between batch-size choices is during a progressive schedule) must be >= 0; negative values fail validation in the constructor.","triggerScenarios":"choice_schedule='progressive' with schedule_spread=-0.2 (or any negative number).","commonSituations":"Typo'd sign in a config; borrowed hyperparameters from a paper/codebase using a signed spread convention.","solutions":["Use a non-negative spread (default 0.65; larger values make transitions more gradual).","If you wanted sharp switching, set schedule_spread=0."],"exampleFix":"# before\nScheduledBatchSampler(s, batch_sizes=[64,256], choice_schedule='progressive', schedule_epochs=20, schedule_spread=-0.65)\n\n# after\nScheduledBatchSampler(s, batch_sizes=[64,256], choice_schedule='progressive', schedule_epochs=20, schedule_spread=0.65)","handlingStrategy":"validation","validationCode":"assert schedule_spread is None or schedule_spread >= 0","typeGuard":null,"tryCatchPattern":null,"preventionTips":["Use the documented default (0.65).","Treat spread as a non-negative softness knob; 0 for hard transitions."],"tags":["timm","sampler","schedule-spread","validation"],"backgroundTag":"invalid-numeric-config-value","analyzedSha":"9a5261e31b3b5128526eb2658333b4c0a54464ae","analyzedAt":"2026-08-27T02:34:25.417Z","schemaVersion":2},"datasetVersion":"2026-08-27T03:17:27.898Z"}