Add generalized Attacker API for arbitrary model support - #3
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Replace task_id-based dispatch with Attacker(model, dataset).run(): adapters split a model into (FeatureModel, TunedModel) halves selected by model type, with embed_shape auto-measured via a dummy forward pass. Covers all four CNN families (ConvNet/VGG/ResNet/ResNeXt). The attack engine, compiler abstraction, and main.py's task_id 0-5 path are unchanged. Adds mac_tvm_stub for GPU-less environments, smoke tests, an example, and README docs. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Replace task_id-based dispatch with Attacker(model, dataset).run(): adapters split a model into (FeatureModel, TunedModel) halves selected by model type, with embed_shape auto-measured via a dummy forward pass.
Covers all four CNN families (ConvNet/VGG/ResNet/ResNeXt). The attack engine, compiler abstraction, and main.py's task_id 0-5 path are unchanged.
Adds mac_tvm_stub for GPU-less environments, smoke tests, an example, and README docs.