Support Conv2D with non-zero spatial padding#269
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Previously any Conv2D with padding != [0,0] was rejected at parse time. Adds end-to-end support for symmetric spatial zero-padding (ONNX same-conv convention: kernel=3, pad=1 per side). parser/onnx.rs now parses and validates symmetric pads and returns [pad_h, pad_w] to load_conv instead of erroring. Convolution<T> gains input_padding field and new_with_padding constructor. Tensor<T> gains zero_pad_spatial and crop_to helpers. pad_conv in padding.rs computes effective shapes accounting for spatial padding before FFT sizing. Adds two new tests for padded convolution. Also fixes three pre-existing clippy lints: mem::replace -> mem::take in prover.rs, redundant / 1 in shape arithmetic, redundant as-usize casts in onnx.rs.
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The ONNX parser rejects any
Conv2Dwithpadding != [0,0], so the standard "same conv" pattern (kernel=3, padding=1) used by ResNet, VGG and most CNNs won't load.Zero-pads the input before the existing valid-conv circuit. Padding values are public zeros so no new constraint is needed; only
output_shapehas to account for the larger effective input.Convolution<T>andConvCtx<E>get aninput_padding: [usize; 2]field with#[serde(default)]so old serialized proofs still deserialize.