Principle:Tencent Ncnn Model Loading
| Knowledge Sources | |
|---|---|
| Domains | Inference, Model_Deployment |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
Mechanism for deserializing a trained neural network's architecture and weights into an in-memory runtime representation suitable for inference execution.
Description
Model loading is the foundational step of any inference pipeline. It involves parsing a serialized description of the network graph (layer types, connectivity, parameters) and loading the associated weight data (convolution kernels, biases, batch normalization statistics) into memory. The network topology is typically stored in a human-readable or binary parameter file, while weights are stored in a separate binary file. Upon loading, the runtime instantiates the appropriate operator implementations for each layer, resolves blob (tensor) connectivity between layers, and optionally applies platform-specific optimizations such as SIMD packing or Vulkan shader selection.
This two-file design (parameter file + weight file) enables independent inspection and modification of the network structure without touching the weights, and vice versa. It also enables zero-copy weight referencing from memory-mapped files on resource-constrained devices.
Usage
Use this principle at the start of every inference pipeline. Model loading must occur before any preprocessing or forward-pass execution. It applies whenever deploying a pre-trained or fine-tuned neural network model for inference on CPU or GPU, across all platforms (desktop, mobile, embedded).
Theoretical Basis
Model loading follows a two-phase deserialization pattern:
Phase 1 — Topology Parsing:
- Read layer definitions sequentially from the parameter file
- For each layer: resolve its type, instantiate the corresponding operator, parse per-layer parameters (kernel size, stride, padding, etc.), and register input/output blob connections
Phase 2 — Weight Loading:
- Read weight data from the binary file in the same layer order
- For each layer that has learnable parameters: deserialize weights into the layer's internal storage, optionally quantizing or repacking for the target ISA
Pseudo-code:
// Abstract model loading algorithm
net = new Network()
for each layer_def in parse(param_file):
layer = create_layer(layer_def.type)
layer.load_params(layer_def.params)
net.add_layer(layer)
for each layer in net.layers:
if layer.has_weights:
layer.load_weights(weight_file)
The separation of topology and weights enables multiple loading strategies: file-based, in-memory, Android asset manager, or memory-mapped.