EclipseAidge/ds_cnn

DS-CNN

DS-CNN model from MLCommons repository https://github.com/mlcommons/tiny/tree/master/benchmark/training/keyword_spotting

ONNX version exported from .pb model doing

# setup environment
python3.10 -m venv pytf
source pytf/bin/activate

# install latest officially compatible versions
pip install tensorflow==2.15.1 tf2onnx==1.16.1

# use most recent opset officially supported
python -m tf2onnx.convert \
  --saved-model <path/to/dir> \
  --output converted_ds_cnn.onnx --opset 18

This version input format is NHWC. The following Python code fuses MatMul+Add to Gemm and folds the first Reshape operator.

import onnx
import onnxruntime

import aidge_core as ai
import aidge_onnx

model_onnx = onnx.load_model("converted_ds_cnn.onnx")
model_onnx_clean_nhwc = aidge_onnx.onnx_cleaner.clean_onnx(
    model_onnx, {"input_1": [[1, 49, 10, 1]]}, "test_clean", opset_version=18
)
model = aidge_onnx.convert_onnx_to_aidge(model_onnx_clean_nhwc)

to_replace: set[ai.Node] = set(
    [
        model.get_node("StatefulPartitionedCall_functional_1_conv2d_BiasAdd__6"),
        model.get_node("new_shape__103_out0"),
    ]
)

model.replace(to_replace, set())
model.set_mandatory_inputs_first()

model.forward_dims(dims=[[1, 1, 49, 10]], allow_data_dependency=True)
model_onnx_clean_nchw = aidge_onnx.convert_aidge_to_onnx(model, "ds_cnn", opset=18)
onnx.save_model(model_onnx_clean_nchw, "ds_cnn.onnx")

Aidge support

Note: We tested this network for the following features. If you encounter any error please open an issue. Features not tested in CI may not be functional.

Model

• operators: 43 (9 types)
  • AvgPooling2D: 1
  • Conv2D: 4
  • FC: 1
  • PaddedConv2D: 1
  • PaddedConvDepthWise2D: 4
  • Producer: 21
  • ReLU: 9
  • Reshape: 1
  • Softmax: 1

Google Speech Commands v2

• Opset: 18
• Source: Google
• Input
  • size: [N, 1, 49, 10]
  • format: [N, C, H, W]
  • preprocessing:
    • ?
• Output
  • size: [N, 12]