nntc.config.yaml

---
name: WRN-50-2
gops: 22.818

model: $(home)/WRN-50-2.pb

time: true
precision: true

fp_batch_sizes: [1, 2, 4, 8]

fp_compile_options:
  python3 -m bmnett
    --model $(model)
    --net_name $(name)
    --input_names "Placeholder"
    --shapes [1,224,224,3]
    --target $(target)

cali:
  python3 -m ufw.cali.cali_model
    --model=$(model)
    --input_shapes="[1,224,224,3]"
    --net_name=$(name)
    --input_names=Placeholder
    --output_names=add_16
    --cali_iterations=100
    --cali_image_path=$(imagenet2012_cali_set)
    --cali_image_preprocess='resize_side=256;crop_h=224,crop_w=224;mean_value=123.675:116.28:103.53,scale=0.0171:0.0175:0.0174,bgr2rgb=True'
    --target=$(target)
    --test_iterations=1
    --debug_cmd="not_suspend;not_call_bmnetu"
    --postprocess_and_calc_score_class=topx_accuracy_for_classify

time_only_cali:
  python3 -m ufw.cali.cali_model
    --model=$(model)
    --input_shapes="[1,224,224,3]"
    --net_name=$(name)
    --input_names=Placeholder
    --output_names=add_16
    --cali_iterations=100
    --target=$(target)
    --test_iterations=1
    --debug_cmd="not_call_bmnetu;not_suspend;use_fake_data_to_test"

int8_model: $(workdir)/$(name)_bmnett_deploy_int8_unique_top.prototxt
int8_weight: $(workdir)/$(name)_bmnett.int8umodel
bmnetu_batch_sizes: [1, 2, 4, 8]
bmnetu_options:
  --model $(int8_model)
  --weight $(int8_weight)
  --target $(target)
  --cmp False
  --enable_profile True

dataset:
  image_path: $(imagenet2012_val_set)
  image_label: $(imagenet2012_caffe_val_ground_truth)
  mean: [123.675, 116.28, 103.53]
  scale: [0.0171, 0.0175, 0.0174]
  resize_dims: 256
  size: 224
  trans: False
  bgr2rgb: True

harness:
  type: topk
  args:
    - name: FP32
      bmodel: $(workdir)/1b.fp.compilation/compilation.bmodel
    - name: INT8
      bmodel: $(workdir)/1b.compilation/compilation.bmodel