This example is used to demonstrate how to utilize LPOT builtin dataloader and metric to enabling quantization without coding effort.
-
Download the FP32 model wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_6/mobilenet_v1_1.0_224_frozen.pb
-
Update the root of dataset in conf.yaml The configuration will create a dataloader of Imagenet and it will do Bilinear resampling to resize the image to 224x224. And it will create a TopK metric function for evaluation.
quantization: # optional. tuning constraints on model-wise for advance user to reduce tuning space.
calibration:
sampling_size: 20 # optional. default value is 100. used to set how many samples should be used in calibration.
dataloader:
dataset:
ImageRecord:
root: <DATASET>/TF_imagenet/val/ # NOTE: modify to calibration dataset location if needed
transform:
BilinearImagenet:
height: 224
width: 224
model_wise: # optional. tuning constraints on model-wise for advance user to reduce tuning space.
activation:
algorithm: minmax
weight:
granularity: per_channel
evaluation: # optional. required if user doesn't provide eval_func in lpot.Quantization.
accuracy: # optional. required if user doesn't provide eval_func in lpot.Quantization.
metric:
topk: 1 # built-in metrics are topk, map, f1, allow user to register new metric.
dataloader:
batch_size: 32
dataset:
ImageRecord:
root: <DATASET>/TF_imagenet/val/ # NOTE: modify to evaluation dataset location if needed
transform:
BilinearImagenet:
height: 224
width: 224
- Run quantization We only need to add the following lines for quantization to create an int8 model.
from lpot.experimental import Quantization, common
quantizer = Quantization('./conf.yaml')
quantizer.model = common.Model("./mobilenet_v1_1.0_224_frozen.pb")
quantized_model = quantizer()
- Run quantization and evaluation:
python test.py