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qat_openvino_resnet18_10e_8xb32_in1k.py
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qat_openvino_resnet18_10e_8xb32_in1k.py
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_base_ = ['mmcls::resnet/resnet18_8xb32_in1k.py']
resnet = _base_.model
float_checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth' # noqa: E501
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),
)
model = dict(
_delete_=True,
_scope_='mmrazor',
type='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=resnet,
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'
])))
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.0001, momentum=0.9, weight_decay=0.0001))
# learning policy
param_scheduler = dict(
_delete_=True, type='ConstantLR', factor=1.0, by_epoch=True)
model_wrapper_cfg = dict(
type='mmrazor.MMArchitectureQuantDDP',
broadcast_buffers=False,
find_unused_parameters=False)
# train, val, test setting
train_cfg = dict(
_delete_=True,
type='mmrazor.QATEpochBasedLoop',
max_epochs=10,
val_interval=1)
val_cfg = dict(_delete_=True, type='mmrazor.QATValLoop')
# Make sure the buffer such as min_val/max_val in saved checkpoint is the same
# among different rank.
default_hooks = dict(sync=dict(type='SyncBuffersHook'))