ptq_openvino_resnet50_8xb32_in1k_calib32xb32.py

_base_ = [
    'mmcls::resnet/resnet50_8xb32_in1k.py',
    '../../deploy_cfgs/mmcls/classification_openvino_dynamic-224x224.py'
]

_base_.val_dataloader.batch_size = 32

test_cfg = dict(
    type='mmrazor.PTQLoop',
    calibrate_dataloader=_base_.val_dataloader,
    calibrate_steps=32,
)

global_qconfig = dict(
    w_observer=dict(type='mmrazor.PerChannelMinMaxObserver'),
    a_observer=dict(type='mmrazor.MovingAverageMinMaxObserver'),
    w_fake_quant=dict(type='mmrazor.FakeQuantize'),
    a_fake_quant=dict(type='mmrazor.FakeQuantize'),
    w_qscheme=dict(
        qdtype='qint8', bit=8, is_symmetry=True, is_symmetric_range=True),
    a_qscheme=dict(
        qdtype='quint8', bit=8, is_symmetry=True, averaging_constant=0.1),
)

float_checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth'  # noqa: E501

model = dict(
    _delete_=True,
    type='mmrazor.MMArchitectureQuant',
    data_preprocessor=dict(
        type='mmcls.ClsDataPreprocessor',
        num_classes=1000,
        # RGB format normalization parameters
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        # convert image from BGR to RGB
        to_rgb=True),
    architecture=_base_.model,
    deploy_cfg=_base_.deploy_cfg,
    float_checkpoint=float_checkpoint,
    quantizer=dict(
        type='mmrazor.OpenVINOQuantizer',
        global_qconfig=global_qconfig,
        tracer=dict(
            type='mmrazor.CustomTracer',
            skipped_methods=[
                'mmcls.models.heads.ClsHead._get_loss',
                'mmcls.models.heads.ClsHead._get_predictions'
            ])))
model_wrapper_cfg = dict(type='mmrazor.MMArchitectureQuantDDP', )