Zen DNN - Tests

Zen DNN - Tests#

Convolution Layer Test#

Steps:#

  1. create engin and stream

  2. create memory (source, weights, bias, & destination)

  3. create memory descriptor

  4. create convolution descriptor

  5. create convolution primitive descriptor

  6. create convolution primitive

  7. execute the convlution

Build & Run#

mkdir build-conv && cd build-conv
cmake ../conv && make
ZENDNN_LOG_OPTS=ALL:5 ZENDNN_VERBOSE=1 ./zendnn_conv cpu

NOTE: use export ZENDNN_LOG_OPTS=ALL:5 ZENDNN_VERBOSE=1 for developer information

MNIST FP32 Sample#

This C++ API example demonstrates how to build the MNIST neural network topology for forward-pass inference.

Some key take-aways include:#

  • Tensor implementation and submission to primitives.

  • Primitives creation.

  • Dependency between the primitive input and output data.

  • ‘Inference-only’ configurations.

NOTE: The example implements the MNIST layers as numbered primitives (for example, conv1, pool1, conv2).

Build & Run#

mkdir build-mnist && cd build-mnist
cmake ../mnist && make
ZENDNN_LOG_OPTS=ALL:5 ZENDNN_VERBOSE=1 ./zendnn_mnist_f32 cpu

MNIST FP32 Inference Application#

This C++ API example demonstrates how to build the MNIST neural network topology for forward-pass inference and test with images.

Some key take-aways include:#

  • How to load weights & bias tensors.

  • How to load user input

  • How to get network output for post processing

NOTE: The example implements the MNIST layers as numbered primitives (for example, conv1, pool1, conv2).

Build & Run#

mkdir build-mnist && cd build-mnist
cmake ../mnist_app && make
ZENDNN_LOG_OPTS=ALL:5 ZENDNN_VERBOSE=1 ./zendnn_mnist_app data/weights.bin images/input_data_3.bin
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