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Use AlexNet for wasi-nn example (#2474)

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This commit is contained in:
Andrew Brown
2020-12-04 07:07:19 -08:00
committed by GitHub
parent bfd10512c1
commit d07fffeb41
2 changed files with 37 additions and 16 deletions
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8
ci/run-wasi-nn-example.sh
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@@ -7,7 +7,7 @@
# executed with the Wasmtime CLI. # executed with the Wasmtime CLI.
set -e set -e
WASMTIME_DIR=$(dirname "$0" | xargs dirname) WASMTIME_DIR=$(dirname "$0" | xargs dirname)
FIXTURE=https://gist.github.com/abrown/c7847bf3701f9efbb2070da1878542c1/raw/07a9f163994b0ff8f0d7c5a5c9645ec3d8b24024 FIXTURE=https://github.com/intel/openvino-rs/raw/main/crates/openvino/tests/fixtures/alexnet
if [ -z "${1+x}" ]; then if [ -z "${1+x}" ]; then
# If no temporary directory is specified, create one. # If no temporary directory is specified, create one.
TMP_DIR=$(mktemp -d -t ci-XXXXXXXXXX) TMP_DIR=$(mktemp -d -t ci-XXXXXXXXXX)
@@ -26,9 +26,9 @@ source /opt/intel/openvino/bin/setupvars.sh
OPENVINO_INSTALL_DIR=/opt/intel/openvino cargo build -p wasmtime-cli --features wasi-nn OPENVINO_INSTALL_DIR=/opt/intel/openvino cargo build -p wasmtime-cli --features wasi-nn
# Download all necessary test fixtures to the temporary directory. # Download all necessary test fixtures to the temporary directory.
wget --no-clobber --directory-prefix=$TMP_DIR $FIXTURE/frozen_inference_graph.bin wget --no-clobber --directory-prefix=$TMP_DIR $FIXTURE/alexnet.bin
wget --no-clobber --directory-prefix=$TMP_DIR $FIXTURE/frozen_inference_graph.xml wget --no-clobber --directory-prefix=$TMP_DIR $FIXTURE/alexnet.xml
wget --no-clobber --directory-prefix=$TMP_DIR $FIXTURE/tensor-1x3x300x300-f32.bgr wget --no-clobber --directory-prefix=$TMP_DIR $FIXTURE/tensor-1x3x227x227-f32.bgr
# Now build an example that uses the wasi-nn API. # Now build an example that uses the wasi-nn API.
pushd $WASMTIME_DIR/crates/wasi-nn/examples/classification-example pushd $WASMTIME_DIR/crates/wasi-nn/examples/classification-example

45
crates/wasi-nn/examples/classification-example/src/main.rs
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@@ -3,11 +3,11 @@ use std::fs;
use wasi_nn; use wasi_nn;
pub fn main() { pub fn main() {
let xml = fs::read_to_string("fixture/frozen_inference_graph.xml").unwrap(); let xml = fs::read_to_string("fixture/alexnet.xml").unwrap();
println!("First 50 characters of graph: {}", &xml[..50]); println!("Read graph XML, first 50 characters: {}", &xml[..50]);
let weights = fs::read("fixture/frozen_inference_graph.bin").unwrap(); let weights = fs::read("fixture/alexnet.bin").unwrap();
println!("Size of weights: {}", weights.len()); println!("Read graph weights, size in bytes: {}", weights.len());
let graph = unsafe { let graph = unsafe {
wasi_nn::load( wasi_nn::load(
@@ -17,17 +17,17 @@ pub fn main() {
) )
.unwrap() .unwrap()
}; };
println!("Graph handle ID: {}", graph); println!("Loaded graph into wasi-nn with ID: {}", graph);
let context = unsafe { wasi_nn::init_execution_context(graph).unwrap() }; let context = unsafe { wasi_nn::init_execution_context(graph).unwrap() };
println!("Execution context ID: {}", context); println!("Created wasi-nn execution context with ID: {}", context);
// Load a tensor that precisely matches the graph input tensor (see // Load a tensor that precisely matches the graph input tensor (see
// `fixture/frozen_inference_graph.xml`). // `fixture/frozen_inference_graph.xml`).
let tensor_data = fs::read("fixture/tensor-1x3x300x300-f32.bgr").unwrap(); let tensor_data = fs::read("fixture/tensor-1x3x227x227-f32.bgr").unwrap();
println!("Tensor bytes: {}", tensor_data.len()); println!("Read input tensor, size in bytes: {}", tensor_data.len());
let tensor = wasi_nn::Tensor { let tensor = wasi_nn::Tensor {
dimensions: &[1, 3, 300, 300], dimensions: &[1, 3, 227, 227],
r#type: wasi_nn::TENSOR_TYPE_F32, r#type: wasi_nn::TENSOR_TYPE_F32,
data: &tensor_data, data: &tensor_data,
}; };
@@ -39,9 +39,10 @@ pub fn main() {
unsafe { unsafe {
wasi_nn::compute(context).unwrap(); wasi_nn::compute(context).unwrap();
} }
println!("Executed graph inference");
// Retrieve the output (TODO output looks incorrect). // Retrieve the output.
let mut output_buffer = vec![0f32; 1 << 20]; let mut output_buffer = vec![0f32; 1000];
unsafe { unsafe {
wasi_nn::get_output( wasi_nn::get_output(
context, context,
@@ -50,5 +51,25 @@ pub fn main() {
(output_buffer.len() * 4).try_into().unwrap(), (output_buffer.len() * 4).try_into().unwrap(),
); );
} }
println!("output tensor: {:?}", &output_buffer[..1000]) println!(
"Found results, sorted top 5: {:?}",
&sort_results(&output_buffer)[..5]
)
} }
// Sort the buffer of probabilities. The graph places the match probability for each class at the
// index for that class (e.g. the probability of class 42 is placed at buffer[42]). Here we convert
// to a wrapping InferenceResult and sort the results.
fn sort_results(buffer: &[f32]) -> Vec<InferenceResult> {
let mut results: Vec<InferenceResult> = buffer
.iter()
.enumerate()
.map(|(c, p)| InferenceResult(c, *p))
.collect();
results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
results
}
// A wrapper for class ID and match probabilities.
#[derive(Debug, PartialEq)]
struct InferenceResult(usize, f32);