[Feature&Doc]Modify ptq pipeline and support lsq

configs/quantization/ptq/ptq_openvino_mbv2_8xb32_in1k_calib32xb32.py

@@ -22,6 +22,14 @@
 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,
     float_checkpoint=float_checkpoint,
     quantizer=dict(

configs/quantization/ptq/ptq_openvino_resnet18_8xb32_in1k_calib32xb32.py

@@ -24,6 +24,14 @@
 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,
     float_checkpoint=float_checkpoint,
     quantizer=dict(

configs/quantization/ptq/ptq_openvino_resnet50_8xb32_in1k_calib32xb32.py

@@ -24,6 +24,14 @@
 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,
     float_checkpoint=float_checkpoint,
     quantizer=dict(

configs/quantization/ptq/ptq_openvino_retina_r50_1x_coco_calib32xb32.py

@@ -0,0 +1,49 @@
+_base_ = ['mmdet::retinanet/retinanet_r50_fpn_1x_coco.py']
+
+train_dataloader = dict(batch_size=32)
+
+test_cfg = dict(
+    type='mmrazor.PTQLoop',
+    calibrate_dataloader=train_dataloader,
+    calibrate_steps=32,
+)
+
+retina = _base_.model
+float_checkpoint = 'https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.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='mmdet.DetDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True,
+        pad_size_divisor=32),
+    architecture=retina,
+    float_checkpoint=float_checkpoint,
+    quantizer=dict(
+        type='mmrazor.OpenVINOQuantizer',
+        global_qconfig=global_qconfig,
+        tracer=dict(
+            type='mmrazor.CustomTracer',
+            skipped_methods=[
+                'mmdet.models.dense_heads.base_dense_head.BaseDenseHead.predict_by_feat',  # noqa: E501
+                'mmdet.models.dense_heads.anchor_head.AnchorHead.loss_by_feat',
+            ])))
+
+model_wrapper_cfg = dict(
+    type='mmrazor.MMArchitectureQuantDDP',
+    broadcast_buffers=False,
+    find_unused_parameters=True)

configs/quantization/qat/lsq_openvino_resnet18_8xb32_in1k.py

@@ -0,0 +1,64 @@
+_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.LSQPerChannelObserver'),
+    a_observer=dict(type='mmrazor.LSQObserver'),
+    w_fake_quant=dict(type='mmrazor.LearnableFakeQuantize'),
+    a_fake_quant=dict(type='mmrazor.LearnableFakeQuantize'),
+    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='CosineAnnealingLR',
+    T_max=100,
+    by_epoch=True,
+    begin=0,
+    end=100)
+
+model_wrapper_cfg = dict(
+    type='mmrazor.MMArchitectureQuantDDP',
+    broadcast_buffers=False,
+    find_unused_parameters=True)
+
+# train, val, test setting
+train_cfg = dict(
+    _delete_=True,
+    type='mmrazor.LSQEpochBasedLoop',
+    max_epochs=100,
+    val_interval=1)
+val_cfg = dict(_delete_=True, type='mmrazor.QATValLoop')
+test_cfg = val_cfg

mmrazor/engine/__init__.py

@@ -3,14 +3,16 @@
 from .optimizers import SeparateOptimWrapperConstructor
 from .runner import (AutoSlimGreedySearchLoop, DartsEpochBasedTrainLoop,
                      DartsIterBasedTrainLoop, EvolutionSearchLoop,
-                     GreedySamplerTrainLoop, PTQLoop, QATEpochBasedLoop,
-                     SelfDistillValLoop, SingleTeacherDistillValLoop,
-                     SlimmableValLoop, SubnetValLoop)
+                     GreedySamplerTrainLoop, LSQEpochBasedLoop, PTQLoop,
+                     QATEpochBasedLoop, QATValLoop, SelfDistillValLoop,
+                     SingleTeacherDistillValLoop, SlimmableValLoop,
+                     SubnetValLoop)
 
 __all__ = [
     'SeparateOptimWrapperConstructor', 'DumpSubnetHook',
     'SingleTeacherDistillValLoop', 'DartsEpochBasedTrainLoop',
     'DartsIterBasedTrainLoop', 'SlimmableValLoop', 'EvolutionSearchLoop',
     'GreedySamplerTrainLoop', 'EstimateResourcesHook', 'SelfDistillValLoop',
-    'AutoSlimGreedySearchLoop', 'SubnetValLoop', 'PTQLoop', 'QATEpochBasedLoop'
+    'AutoSlimGreedySearchLoop', 'SubnetValLoop', 'PTQLoop',
+    'QATEpochBasedLoop', 'LSQEpochBasedLoop', 'QATValLoop'
 ]

mmrazor/engine/runner/__init__.py

@@ -4,7 +4,8 @@
 from .distill_val_loop import SelfDistillValLoop, SingleTeacherDistillValLoop
 from .evolution_search_loop import EvolutionSearchLoop
 from .iteprune_val_loop import ItePruneValLoop
-from .quantization_loops import PTQLoop, QATEpochBasedLoop
+from .quantization_loops import (LSQEpochBasedLoop, PTQLoop, QATEpochBasedLoop,
+                                 QATValLoop)
 from .slimmable_val_loop import SlimmableValLoop
 from .subnet_sampler_loop import GreedySamplerTrainLoop
 from .subnet_val_loop import SubnetValLoop
@@ -14,5 +15,5 @@
     'DartsIterBasedTrainLoop', 'SlimmableValLoop', 'EvolutionSearchLoop',
     'GreedySamplerTrainLoop', 'SubnetValLoop', 'SelfDistillValLoop',
     'ItePruneValLoop', 'AutoSlimGreedySearchLoop', 'QATEpochBasedLoop',
-    'PTQLoop'
+    'PTQLoop', 'LSQEpochBasedLoop', 'QATValLoop'
 ]