mlcommons-Deep-Autoencoder-DCASE2020-ToyCar-int8-onnx

MLCommons Deep Autoencoder DCASE 2020 ToyCar INT8 ONNX

INT8 ONNX version of MLCommons Deep Autoencoder DCASE 2020 ToyCar for anomaly detection.

• Quantization: Static INT8 quantization, QDQ format

Model Files

Parameter Summary

Stored initializer elements includes weights, biases, quantization scales, zero-points, and other constant tensors. It is not a trainable-parameter count.

Original Model Inference

pip install huggingface_hub numpy ai-edge-litert

import numpy as np
from ai_edge_litert.interpreter import Interpreter
from huggingface_hub import hf_hub_download

repo_id = "ketiswp/mlcommons-Deep-Autoencoder-DCASE2020-ToyCar-int8-onnx"
model_path = hf_hub_download(repo_id=repo_id, filename="source/model.tflite")
interpreter = Interpreter(model_path=model_path, num_threads=1)

for item in interpreter.get_input_details():
    signature = [int(value) for value in item.get("shape_signature", item["shape"])]
    shape = [value if value > 0 else 1 for value in signature]
    if shape != [int(value) for value in item["shape"]]:
        interpreter.resize_tensor_input(int(item["index"]), shape, strict=False)

interpreter.allocate_tensors()
for item in interpreter.get_input_details():
    value = np.zeros(tuple(int(dim) for dim in item["shape"]), dtype=item["dtype"])
    interpreter.set_tensor(int(item["index"]), value)

interpreter.invoke()
outputs = [interpreter.get_tensor(int(item["index"]))
           for item in interpreter.get_output_details()]
print([(value.shape, str(value.dtype)) for value in outputs])

Converted ONNX Inference

pip install huggingface_hub numpy onnxruntime

import numpy as np
import onnxruntime as ort
from huggingface_hub import hf_hub_download

repo_id = "ketiswp/mlcommons-Deep-Autoencoder-DCASE2020-ToyCar-int8-onnx"
model_path = hf_hub_download(repo_id=repo_id, filename="model.onnx")

options = ort.SessionOptions()
options.intra_op_num_threads = 1
options.inter_op_num_threads = 1
options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
session = ort.InferenceSession(
    model_path,
    sess_options=options,
    providers=["CPUExecutionProvider"],
)

dtype_by_ort_type = {
    "tensor(float)": np.float32,
    "tensor(double)": np.float64,
    "tensor(float16)": np.float16,
    "tensor(int64)": np.int64,
    "tensor(int32)": np.int32,
    "tensor(int16)": np.int16,
    "tensor(int8)": np.int8,
    "tensor(uint8)": np.uint8,
    "tensor(bool)": np.bool_,
}
feeds = {}
for item in session.get_inputs():
    shape = [dim if isinstance(dim, int) and dim > 0 else 1 for dim in item.shape]
    feeds[item.name] = np.zeros(shape, dtype=dtype_by_ort_type[item.type])

outputs = session.run(None, feeds)
print([(item.name, value.shape, str(value.dtype))
       for item, value in zip(session.get_outputs(), outputs)])

Paired Model

FP32 version

Source

• Original artifact
• Source project

Project Validation

FP32/quantized comparison, conversion results, and reproduction code

This repository also includes the Netron graph, ONNX Dialect MLIR, and static MLIR dependency graph for this model variant.