Convolutional neural networks (CNNs) are rapidly evolving and being applied to a broad range of applications. Given a specific application, an increasing challenge is to search the appropriate CNN algorithm and efficiently map it to the target hardware. The FPGA-based accelerator has the advantage of reconfigurability and flexibility, and has achieved high-performance and low-power. Without a general compiler to automate the implementation, however, significant efforts and expertise are still required to customize the design for each CNN model. In this work, we present an RTL-level CNN compiler that automatically generates customized FPGA hardware for the inference tasks of various CNNs, in order to enable high-level fast prototyping of CNNs from software to FPGA and still keep the benefits of low-level hardware optimization. First, a general-purpose library of RTL modules is developed to model different operations at each layer. The implementation of each module is optimized at the RTL level. Given a CNN algorithm, its structure is abstracted to a directed acyclic graph (DAG) and then complied with RTL modules in the library. The integration and dataflow of physical modules are predefined in the top-level system template and reconfigured during compilation. The runtime control of layer-by-layer sequential computation is managed by the proposed execution schedule so that even highly irregular and complex network topology, e.g. ResNet, can be compiled. The proposed methodology is demonstrated with end-to-end FPGA implementations of various CNN algorithms (e.g. NiN, VGG-16, ResNet-50, and ResNet-152) on two standalone Intel FPGAs, Stratix V and Arria 10. The performance and overhead of the automated compilation are evaluated. The compiled FPGA accelerators exhibit superior performance compared to state-of-the-art automation-based works by >2× for various CNNs.