[Feature] Add prepare_for_mmdeploy interface

configs/quantization/ptq/adaround.py → configs/quantization/ptq/ptq_openvino_resnet18_8xb16_cifar10.py

@@ -2,22 +2,14 @@
 
 test_cfg = dict(
     type='mmrazor.PTQLoop',
-
-    # reconstruction_cfg=dict(
-    #     pattern='layer',
-    #     loss=dict(
-    #         type='mmrazor.AdaRoundLoss',
-    #         iters=20000
-    #     )
-    # )
 )
 
 model = dict(
     _delete_=True,
-    type='mmrazor.GeneralQuant',
+    type='mmrazor.MMArchitectureQuant',
     architecture=_base_.model,
     quantizer=dict(
-        type='mmrazor.CustomQuantizer',
+        type='mmrazor.OpenvinoQuantizer',
         is_qat=False,
         skipped_methods=[
             'mmcls.models.heads.ClsHead._get_loss',
@@ -27,16 +19,16 @@
             qtype='affine',
             w_observer=dict(type='mmrazor.MSEObserver'),
             a_observer=dict(type='mmrazor.EMAMSEObserver'),
-            w_fake_quant=dict(type='mmrazor.AdaRoundFakeQuantize'),
+            w_fake_quant=dict(type='mmrazor.FakeQuantize'),
             a_fake_quant=dict(type='mmrazor.FakeQuantize'),
             w_qscheme=dict(
-                bit=2,
-                is_symmetry=False,
+                bit=8,
+                is_symmetry=True,
                 is_per_channel=True,
                 is_pot_scale=False,
             ),
             a_qscheme=dict(
-                bit=4,
+                bit=8,
                 is_symmetry=False,
                 is_per_channel=False,
                 is_pot_scale=False),

configs/quantization/qat/lsq_resnet18_8xb16_cifar10.py → configs/quantization/qat/lsq_openvino_resnet18_8xb16_cifar10.py

@@ -1,24 +1,16 @@
 _base_ = ['mmcls::resnet/resnet18_8xb16_cifar10.py']
 
 resnet = _base_.model
-pretrained_ckpt = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.pth'  # noqa: E501
+float_ckpt = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.pth'  # noqa: E501
 
 model = dict(
     _delete_=True,
     _scope_='mmrazor',
-    type='GeneralQuant',
-    data_preprocessor=dict(
-        type='mmcls.ClsDataPreprocessor',
-        num_classes=10,
-        # RGB format normalization parameters
-        mean=[125.307, 122.961, 113.8575],
-        std=[51.5865, 50.847, 51.255],
-        # loaded images are already RGB format
-        to_rgb=False),
+    type='MMArchitectureQuant',
     architecture=resnet,
-    pretrained_ckpt=pretrained_ckpt,
+    float_checkpoint=float_ckpt,
     quantizer=dict(
-        type='CustomQuantizer',
+        type='OpenvinoQuantizer',
         skipped_methods=[
             'mmcls.models.heads.ClsHead._get_loss',
             'mmcls.models.heads.ClsHead._get_predictions'
@@ -31,8 +23,8 @@
             a_fake_quant=dict(type='mmrazor.LearnableFakeQuantize'),
             w_qscheme=dict(
                 bit=8,
-                is_symmetry=False,
-                is_per_channel=False,
+                is_symmetry=True,
+                is_per_channel=True,
                 is_pot_scale=False,
             ),
             a_qscheme=dict(
@@ -55,16 +47,15 @@
     end=100)
 
 model_wrapper_cfg = dict(
-    type='mmrazor.GeneralQuantDDP',
+    type='mmrazor.MMArchitectureQuantDDP',
     broadcast_buffers=False,
     find_unused_parameters=True)
 
 # train, val, test setting
 train_cfg = dict(
     _delete_=True,
     type='mmrazor.QATEpochBasedLoop',
-    by_epoch=True,
     max_epochs=100,
     val_interval=1)
 val_cfg = dict(_delete_=True, type='mmrazor.QATValLoop')
-test_cfg = val_cfg
+# test_cfg = val_cfg