rnet.mlir.config.yaml

---

name: mtcnn_rnet
gops: [0.003, 0.012]

shapes:
  - [1, 3, 24, 24]
  - [4, 3, 24, 24]

mlir_transform:
  model_transform.py
    --model_name $(name)
    --model_def $(home)/det2.prototxt
    --model_data $(home)/det2.caffemodel
    --test_input $(root)/dataset/samples/stock_market.jpg
    --input_shapes [$(shape_param)]
    --test_result $(name)_top_outputs.npz
    --mlir $(workdir)/$(name).mlir

deploy:
  - model_deploy.py  --mlir $(workdir)/$(name).mlir
      --quantize F32
      --chip $(target)
      --test_input $(workdir)/$(name)_in_f32.npz
      --test_reference $(name)_top_outputs.npz
      --tolerance 0.99,0.99
      --model $(workdir)/$(name)_$(target)_f32.bmodel
  - model_deploy.py  --mlir $(workdir)/$(name).mlir
      --quantize INT8
      --calibration_table $(home)/det2_cali_table
      --quant_input
      --quant_output
      --chip $(target)
      --test_input $(workdir)/$(name)_in_f32.npz
      --test_reference $(name)_top_outputs.npz
      --model $(workdir)/$(name)_$(target)_int8_sym.bmodel

BM1684X:
  +deploy:
    - model_deploy.py  --mlir $(workdir)/$(name).mlir
        --quantize F16
        --chip $(target)
        --test_input $(workdir)/$(name)_in_f32.npz
        --test_reference $(name)_top_outputs.npz
        --tolerance 0.95,0.85
        --model $(workdir)/$(name)_bm1684x_f16.bmodel
    - model_deploy.py  --mlir $(workdir)/$(name).mlir
        --quantize BF16
        --chip $(target)
        --test_input $(workdir)/$(name)_in_f32.npz
        --test_reference $(name)_top_outputs.npz
        --tolerance 0.95,0.85
        --model $(workdir)/$(name)_bm1684x_bf16.bmodel