[Features]Quantize pipeline (open-mmlab#350)

* init demo * add customer_tracer * add quantizer * add fake_quant, loop, config * remove CPatcher in custome_tracer * demo_try * init version * modified base.py * pre-rebase * wip of adaround series * adaround experiment * trasfer to s2 * update api * point at sub_reconstruction * pre-checkout * export onnx * add customtracer * fix lint * move custom tracer * fix import * update * updated * retina loss & predict & tesnor DONE * for RFC * Customed FX initialize * add UT init * TDO: UTs * Successfully RUN * update loop * update loop docstrings * update quantizer docstrings * update qscheme docstrings * update qobserver docstrings * update tracer docstrings * update UTs init * update UTs init * fix bugs * fix lsq * refactor quantize pipeline * fix quant * WIP: debug qat * fix lsq bugs * fix qat, docstring in progress * TDO: UTs * fix bugs * fix lsq * refactor quantize pipeline * fix quant * WIP: debug qat * fix lsq bugs * fix qat, docstring in progress * fixed DefaultQconfigs name * fix bugs * add comments and fix typos * delete useless codes * fix bugs and add comments * rename prepare_module_dict * update lsq config Co-authored-by: humu789 <humu@pjlab.org.cn> Co-authored-by: huangpengsheng <huangpengsheng@sensetime.com> Co-authored-by: FreakieHuang <frank0huang@foxmail.com> Co-authored-by: pppppM <gjf_mail@126.com>
humu789 · Jan 9, 2023 · 6b1e482 · 6b1e482
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diff --git a/configs/quantization/ptq/adaround.py b/configs/quantization/ptq/adaround.py
@@ -1,12 +1,8 @@
-_base_ = ['mmcls::resnet/resnet18_8xb32_in1k.py']
+_base_ = ['mmcls::resnet/resnet18_8xb16_cifar10.py']
 
 test_cfg = dict(
-    _delete_=True,
     type='mmrazor.PTQLoop',
-    dataloader=_base_.test_dataloader,
-    evaluator=_base_.test_evaluator,
-    calibrate_dataloader=_base_.train_dataloader,
-    batch_num=32,
+
     # reconstruction_cfg=dict(
     #     pattern='layer',
     #     loss=dict(

diff --git a/configs/quantization/qat/demo.py b/configs/quantization/qat/demo.py
diff --git a/configs/quantization/qat/lsq_resnet18_8xb16_cifar10.py b/configs/quantization/qat/lsq_resnet18_8xb16_cifar10.py
@@ -0,0 +1,70 @@
+_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
+
+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),
+    architecture=resnet,
+    pretrained_ckpt=pretrained_ckpt,
+    quantizer=dict(
+        type='CustomQuantizer',
+        skipped_methods=[
+            'mmcls.models.heads.ClsHead._get_loss',
+            'mmcls.models.heads.ClsHead._get_predictions'
+        ],
+        qconfig=dict(
+            qtype='affine',
+            w_observer=dict(type='mmrazor.LSQObserver'),
+            a_observer=dict(type='mmrazor.LSQObserver'),
+            w_fake_quant=dict(type='mmrazor.LearnableFakeQuantize'),
+            a_fake_quant=dict(type='mmrazor.LearnableFakeQuantize'),
+            w_qscheme=dict(
+                bit=8,
+                is_symmetry=False,
+                is_per_channel=False,
+                is_pot_scale=False,
+            ),
+            a_qscheme=dict(
+                bit=8,
+                is_symmetry=False,
+                is_per_channel=False,
+                is_pot_scale=False),
+        )))
+
+optim_wrapper = dict(
+    optimizer=dict(type='SGD', lr=0.004, 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.GeneralQuantDDP',
+    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
diff --git a/configs/quantization/qat/lsq_resnet18_8xb32_in1k.py b/configs/quantization/qat/lsq_resnet18_8xb32_in1k.py
@@ -0,0 +1,75 @@
+_base_ = ['mmcls::resnet/resnet18_8xb32_in1k.py']
+
+train_cfg = dict(
+    _delete_=True,
+    type='mmrazor.QATEpochBasedLoop',
+    max_epochs=_base_.train_cfg.max_epochs)
+
+resnet = _base_.model
+ckpt = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth'  # noqa: E501
+resnet.init_cfg = dict(type='Pretrained', checkpoint=ckpt)
+
+model = dict(
+    _delete_=True,
+    _scope_='mmrazor',
+    type='GeneralQuant',
+    # data_preprocessor = dict(
+    #     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,
+    quantizer=dict(
+        type='CustomQuantizer',
+        skipped_methods=[
+            'mmcls.models.heads.ClsHead._get_loss',
+            'mmcls.models.heads.ClsHead._get_predictions'
+        ],
+        qconfig=dict(
+            qtype='affine',
+            w_observer=dict(type='mmrazor.MinMaxObserver'),
+            a_observer=dict(type='mmrazor.EMAMinMaxObserver'),
+            w_fake_quant=dict(type='mmrazor.LearnableFakeQuantize'),
+            a_fake_quant=dict(type='mmrazor.LearnableFakeQuantize'),
+            w_qscheme=dict(
+                bit=8,
+                is_symmetry=False,
+                is_per_channel=False,
+                is_pot_scale=False,
+            ),
+            a_qscheme=dict(
+                bit=8,
+                is_symmetry=False,
+                is_per_channel=False,
+                is_pot_scale=False),
+        )))
+
+optim_wrapper = dict(
+    optimizer=dict(type='SGD', lr=0.004, 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)
+
+default_hooks = dict(
+    checkpoint=dict(
+        type='CheckpointHook',
+        interval=5,
+        max_keep_ckpts=3,
+        out_dir='/mnt/petrelfs/caoweihan.p/training_ckpt/quant'))
+
+model_wrapper_cfg = dict(
+    type='mmrazor.GeneralQuantDDP',
+    broadcast_buffers=False,
+    find_unused_parameters=False)
+
+val_cfg = dict(_delete_=True, type='mmrazor.QATValLoop')
+test_cfg = val_cfg
diff --git a/configs/quantization/qat/lsq_resnet50_8xb16_cifar10.py b/configs/quantization/qat/lsq_resnet50_8xb16_cifar10.py