Autofocus plays a pivotal role in synthetic aperture radar (SAR) imaging, which is particularly crucial for high-resolution applications. Traditional autofocus methods face inherent limitations, such as reliance on strong isolated scatterers within the scene or prohibitive computational complexity, making them inadequate for the practical demands of the rapid and batch processing of massive SAR imagery. Although recent deep learning (DL)-based methods have improved focusing efficiency, they predominantly rely on predefined high-order polynomial models for phase error estimation, which inevitably introduces parameter redundancy, restricts the model’s adaptability to complex phase errors, and is highly prone to inducing severe oscillations at the aperture boundaries. To overcome these limitations, this article proposes DeepAutofocus, a non-parametric deep learning framework for SAR autofocus. First, we design a physics-inspired subaperture correlation Transformer architecture. A multiscale convolutional neural network (CNN) is utilized to extract local spatial features, which are then transformed into serialized subaperture image patches. Subsequently, the self-attention mechanism of the Transformer is employed to capture the long-range dependencies across subapertures. Second, we introduce a data-driven cubic B-spline reconstruction strategy that directly estimates phase errors without depending on predefined high-order polynomial models, thereby eliminating boundary oscillations while maintaining high flexibility. Extensive experiments on both simulated and real measured satellite datasets demonstrate that, compared to traditional autofocus algorithms and existing DL-based methods, the proposed DeepAutofocus achieves superior focusing performance under various complex phase error conditions.