fix qat configs

configs/quantization/qat/lsq_openvino_resnet18_8xb32_100e_in1k.py

@@ -0,0 +1,67 @@
+_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')
+
+# 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'))

configs/quantization/qat/lsq_openvino_resnet18_8xb32_in1k.py → configs/quantization/qat/lsq_openvino_resnet18_8xb32_10e_in1k.py

@@ -56,6 +56,7 @@
     max_epochs=10,
     val_interval=1)
 val_cfg = dict(_delete_=True, type='mmrazor.QATValLoop')
-# test_cfg = val_cfg
 
+# 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'))

configs/quantization/qat/qat_openvino_resnet18_10e_8xb32_in1k.py

@@ -0,0 +1,62 @@
+_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'))