From 3d211ff839d792da23c88661a4963aebf8cfc306 Mon Sep 17 00:00:00 2001
From: HIT-cwh <2892770585@qq.com>
Date: Thu, 13 Apr 2023 14:13:14 +0800
Subject: [PATCH 1/8] fix quantization loop

---
 mmrazor/engine/runner/quantization_loops.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/mmrazor/engine/runner/quantization_loops.py b/mmrazor/engine/runner/quantization_loops.py
index 18caf06f5..7fb3d5fd9 100644
--- a/mmrazor/engine/runner/quantization_loops.py
+++ b/mmrazor/engine/runner/quantization_loops.py
@@ -90,7 +90,7 @@ def run(self):
                     and self._epoch >= self.val_begin
                     and self._epoch % self.val_interval == 0):
                 # observer disabled during evaluation
-                self.prepare_for_val()
+
                 self.runner.val_loop.run()
 
         self.runner.call_hook('after_train')
@@ -102,16 +102,21 @@ def run_epoch(self) -> None:
 
         # The initialized _epoch equals to 0 so _epoch + 1
         # equal to the current epoch
-        if self._epoch + 1 >= self.disable_observer_begin:
+        if (self.disable_observer_begin > 0
+                and self._epoch + 1 >= self.disable_observer_begin):
             self.runner.model.apply(disable_observer)
 
-        if self._epoch + 1 >= self.freeze_bn_begin:
+        if (self.freeze_bn_begin > 0
+                and self._epoch + 1 >= self.freeze_bn_begin):
             self.runner.model.apply(freeze_bn_stats)
 
         for idx, data_batch in enumerate(self.dataloader):
             self.run_iter(idx, data_batch)
 
         self.runner.model.sync_qparams(src_mode='loss')
+        # Make sure the registered buffer such as `observer_enabled` is
+        # correct in the saved checkpoint.
+        self.prepare_for_val()
         self.runner.call_hook('after_train_epoch')
         self._epoch += 1
 

From eaa84f4103e623bc3adea8e14d38412669325b60 Mon Sep 17 00:00:00 2001
From: HIT-cwh <2892770585@qq.com>
Date: Fri, 14 Apr 2023 15:10:47 +0800
Subject: [PATCH 2/8] fix quant loop

---
 .../qat/lsq_openvino_resnet18_8xb32_in1k.py   | 13 ++--
 mmrazor/engine/runner/quantization_loops.py   | 59 ++++++++++++-------
 2 files changed, 42 insertions(+), 30 deletions(-)

diff --git a/configs/quantization/qat/lsq_openvino_resnet18_8xb32_in1k.py b/configs/quantization/qat/lsq_openvino_resnet18_8xb32_in1k.py
index 0b79232f8..ea3485f27 100644
--- a/configs/quantization/qat/lsq_openvino_resnet18_8xb32_in1k.py
+++ b/configs/quantization/qat/lsq_openvino_resnet18_8xb32_in1k.py
@@ -42,12 +42,7 @@
 
 # learning policy
 param_scheduler = dict(
-    _delete_=True,
-    type='CosineAnnealingLR',
-    T_max=100,
-    by_epoch=True,
-    begin=0,
-    end=100)
+    _delete_=True, type='ConstantLR', factor=1.0, by_epoch=True)
 
 model_wrapper_cfg = dict(
     type='mmrazor.MMArchitectureQuantDDP',
@@ -58,7 +53,9 @@
 train_cfg = dict(
     _delete_=True,
     type='mmrazor.LSQEpochBasedLoop',
-    max_epochs=100,
+    max_epochs=10,
     val_interval=1)
 val_cfg = dict(_delete_=True, type='mmrazor.QATValLoop')
-test_cfg = val_cfg
+# test_cfg = val_cfg
+
+default_hooks = dict(sync=dict(type='SyncBuffersHook'))
diff --git a/mmrazor/engine/runner/quantization_loops.py b/mmrazor/engine/runner/quantization_loops.py
index 7fb3d5fd9..86448bdbb 100644
--- a/mmrazor/engine/runner/quantization_loops.py
+++ b/mmrazor/engine/runner/quantization_loops.py
@@ -11,11 +11,13 @@
     from torch.nn.intrinsic.qat import freeze_bn_stats
 except ImportError:
     from mmrazor.utils import get_placeholder
+
     disable_observer = get_placeholder('torch>=1.13')
     enable_fake_quant = get_placeholder('torch>=1.13')
     enable_observer = get_placeholder('torch>=1.13')
     freeze_bn_stats = get_placeholder('torch>=1.13')
 
+from mmengine.dist import all_reduce_params
 from torch.utils.data import DataLoader
 
 from mmrazor.models import register_torch_fake_quants, register_torch_observers
@@ -69,7 +71,18 @@ def prepare_for_run_epoch(self):
         """Toggle the state of the observers and fake quantizers before qat
         training."""
         self.runner.model.apply(enable_fake_quant)
-        self.runner.model.apply(enable_observer)
+
+        # The initialized _epoch equals to 0 so _epoch + 1
+        # equal to the current epoch
+        if (self.disable_observer_begin > 0
+                and self._epoch + 1 >= self.disable_observer_begin):
+            self.runner.model.apply(disable_observer)
+        else:
+            self.runner.model.apply(enable_observer)
+
+        if (self.freeze_bn_begin > 0
+                and self._epoch + 1 >= self.freeze_bn_begin):
+            self.runner.model.apply(freeze_bn_stats)
 
     def prepare_for_val(self):
         """Toggle the state of the observers and fake quantizers before
@@ -89,8 +102,6 @@ def run(self):
             if (self.runner.val_loop is not None
                     and self._epoch >= self.val_begin
                     and self._epoch % self.val_interval == 0):
-                # observer disabled during evaluation
-
                 self.runner.val_loop.run()
 
         self.runner.call_hook('after_train')
@@ -100,16 +111,6 @@ def run_epoch(self) -> None:
         self.runner.call_hook('before_train_epoch')
         self.runner.model.train()
 
-        # The initialized _epoch equals to 0 so _epoch + 1
-        # equal to the current epoch
-        if (self.disable_observer_begin > 0
-                and self._epoch + 1 >= self.disable_observer_begin):
-            self.runner.model.apply(disable_observer)
-
-        if (self.freeze_bn_begin > 0
-                and self._epoch + 1 >= self.freeze_bn_begin):
-            self.runner.model.apply(freeze_bn_stats)
-
         for idx, data_batch in enumerate(self.dataloader):
             self.run_iter(idx, data_batch)
 
@@ -165,7 +166,11 @@ def __init__(
     def prepare_for_run_epoch(self):
         """Toggle the state of the observers and fake quantizers before qat
         training."""
-        pass
+        if (self.freeze_bn_begin > 0
+                and self._epoch + 1 >= self.freeze_bn_begin):
+            self.runner.model.apply(freeze_bn_stats)
+
+        self.runner.model.apply(enable_param_learning)
 
     def prepare_for_val(self):
         """Toggle the state of the observers and fake quantizers before
@@ -177,20 +182,30 @@ def run_epoch(self) -> None:
         self.runner.call_hook('before_train_epoch')
         self.runner.model.train()
 
-        # TODO freeze bn
-        if self._epoch + 1 >= self.freeze_bn_begin:
-            self.runner.model.apply(freeze_bn_stats)
-
         for idx, data_batch in enumerate(self.dataloader):
             if self.is_first_batch:
-                # lsq init
-                self.is_first_batch = False
+                # lsq observer init
                 self.runner.model.apply(enable_static_estimate)
-            else:
-                self.runner.model.apply(enable_param_learning)
+
             self.run_iter(idx, data_batch)
 
+            if self.is_first_batch:
+                # In the first batch, scale in LearnableFakeQuantize is
+                # calculated through lsq observer. As the values of `scale` of
+                # different observers in different rank are usually different,
+                # we have to sync the `scale` here.
+                all_reduce_params(self.runner.model.parameters(), op='mean')
+                # Change back to param learning mode
+                self.is_first_batch = False
+                self.runner.model.apply(enable_param_learning)
+
+            if idx > 100:
+                break
+
         self.runner.model.sync_qparams(src_mode='loss')
+        # Make sure the registered buffer such as `observer_enabled` is
+        # correct in the saved checkpoint.
+        self.prepare_for_val()
         self.runner.call_hook('after_train_epoch')
         self._epoch += 1
 

From 1c82c86763a50f68711b1adff0ac8a929c4169c0 Mon Sep 17 00:00:00 2001
From: HIT-cwh <2892770585@qq.com>
Date: Fri, 14 Apr 2023 15:20:39 +0800
Subject: [PATCH 3/8] fix quant loop

---
 mmrazor/engine/runner/quantization_loops.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/mmrazor/engine/runner/quantization_loops.py b/mmrazor/engine/runner/quantization_loops.py
index 86448bdbb..ec078c42f 100644
--- a/mmrazor/engine/runner/quantization_loops.py
+++ b/mmrazor/engine/runner/quantization_loops.py
@@ -17,7 +17,7 @@
     enable_observer = get_placeholder('torch>=1.13')
     freeze_bn_stats = get_placeholder('torch>=1.13')
 
-from mmengine.dist import all_reduce_params
+from mmengine.dist import all_reduce_params, is_distributed
 from torch.utils.data import DataLoader
 
 from mmrazor.models import register_torch_fake_quants, register_torch_observers
@@ -162,6 +162,7 @@ def __init__(
             dynamic_intervals=dynamic_intervals)
 
         self.is_first_batch = True
+        self.distributed = is_distributed()
 
     def prepare_for_run_epoch(self):
         """Toggle the state of the observers and fake quantizers before qat
@@ -194,7 +195,10 @@ def run_epoch(self) -> None:
                 # calculated through lsq observer. As the values of `scale` of
                 # different observers in different rank are usually different,
                 # we have to sync the `scale` here.
-                all_reduce_params(self.runner.model.parameters(), op='mean')
+                if self.distributed:
+                    all_reduce_params(
+                        self.runner.model.parameters(), op='mean')
+
                 # Change back to param learning mode
                 self.is_first_batch = False
                 self.runner.model.apply(enable_param_learning)

From c36dfb88c28c39d8707c88b0a80e446b4b2b4912 Mon Sep 17 00:00:00 2001
From: HIT-cwh <2892770585@qq.com>
Date: Fri, 14 Apr 2023 20:28:55 +0800
Subject: [PATCH 4/8] fix qat configs

---
 .../lsq_openvino_resnet18_8xb32_100e_in1k.py  | 67 +++++++++++++++++++
 ...> lsq_openvino_resnet18_8xb32_10e_in1k.py} |  3 +-
 .../qat_openvino_resnet18_10e_8xb32_in1k.py   | 62 +++++++++++++++++
 3 files changed, 131 insertions(+), 1 deletion(-)
 create mode 100644 configs/quantization/qat/lsq_openvino_resnet18_8xb32_100e_in1k.py
 rename configs/quantization/qat/{lsq_openvino_resnet18_8xb32_in1k.py => lsq_openvino_resnet18_8xb32_10e_in1k.py} (95%)
 create mode 100644 configs/quantization/qat/qat_openvino_resnet18_10e_8xb32_in1k.py

diff --git a/configs/quantization/qat/lsq_openvino_resnet18_8xb32_100e_in1k.py b/configs/quantization/qat/lsq_openvino_resnet18_8xb32_100e_in1k.py
new file mode 100644
index 000000000..55896c2c9
--- /dev/null
+++ b/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'))
diff --git a/configs/quantization/qat/lsq_openvino_resnet18_8xb32_in1k.py b/configs/quantization/qat/lsq_openvino_resnet18_8xb32_10e_in1k.py
similarity index 95%
rename from configs/quantization/qat/lsq_openvino_resnet18_8xb32_in1k.py
rename to configs/quantization/qat/lsq_openvino_resnet18_8xb32_10e_in1k.py
index ea3485f27..287a36fc9 100644
--- a/configs/quantization/qat/lsq_openvino_resnet18_8xb32_in1k.py
+++ b/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'))
diff --git a/configs/quantization/qat/qat_openvino_resnet18_10e_8xb32_in1k.py b/configs/quantization/qat/qat_openvino_resnet18_10e_8xb32_in1k.py
new file mode 100644
index 000000000..261af7abb
--- /dev/null
+++ b/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'))

From aa9ef39c11965dccc567721f4e96ea679c249170 Mon Sep 17 00:00:00 2001
From: humu789 <88702197+humu789@users.noreply.github.com>
Date: Thu, 13 Apr 2023 19:25:35 +0800
Subject: [PATCH 5/8] [Bug] Fix ci converage setting (#508)

fix ci converage
---
 .github/workflows/build.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 4b99bced4..9ed4cb002 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -119,7 +119,7 @@ jobs:
           coverage report -m
       # Upload coverage report for python3.8 && pytorch1.12.0 cpu
       - name: Upload coverage to Codecov
-        if: ${{matrix.torch == '1.12.0' && matrix.python-version == '3.8'}}
+        if: ${{matrix.torch == '1.13.0' && matrix.python-version == '3.8'}}
         uses: codecov/codecov-action@v2
         with:
           file: ./coverage.xml

From b238341eff90dcec22e2e2b04e2f47346f946920 Mon Sep 17 00:00:00 2001
From: humu789 <88702197+humu789@users.noreply.github.com>
Date: Mon, 17 Apr 2023 06:13:43 +0800
Subject: [PATCH 6/8] [Bug] Fix codecov (#509)

* remove codecov in requirements

* try to fix ci

* del adaround loss
---
 .github/workflows/build.yml            |  2 +-
 mmrazor/models/losses/adaround_loss.py | 87 --------------------------
 requirements/tests.txt                 |  2 +-
 3 files changed, 2 insertions(+), 89 deletions(-)
 delete mode 100644 mmrazor/models/losses/adaround_loss.py

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 9ed4cb002..2c2b8ed21 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -120,7 +120,7 @@ jobs:
       # Upload coverage report for python3.8 && pytorch1.12.0 cpu
       - name: Upload coverage to Codecov
         if: ${{matrix.torch == '1.13.0' && matrix.python-version == '3.8'}}
-        uses: codecov/codecov-action@v2
+        uses: codecov/codecov-action@v3
         with:
           file: ./coverage.xml
           flags: unittests
diff --git a/mmrazor/models/losses/adaround_loss.py b/mmrazor/models/losses/adaround_loss.py
deleted file mode 100644
index 76c97977d..000000000
--- a/mmrazor/models/losses/adaround_loss.py
+++ /dev/null
@@ -1,87 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-from mmengine.logging import print_log
-
-from mmrazor.registry import MODELS
-
-_ADAROUND_SUPPORT_TYPE = (torch.nn.Conv2d, torch.nn.Linear)
-
-
-@MODELS.register_module()
-class AdaRoundLoss(nn.Module):
-    r'''loss function to calculate mse reconstruction loss and relaxation loss
-    use some tempdecay to balance the two losses.
-    '''
-
-    def __init__(self,
-                 weight: float = 1.,
-                 iters: int = 10000,
-                 beta_range: tuple = (20, 2),
-                 warm_up: float = 0.0,
-                 p: float = 2.):
-        self.weight = weight
-        self.loss_start = iters * warm_up
-        self.p = p
-
-        self.temp_decay = LinearTempDecay(
-            iters,
-            warm_up=warm_up,
-            start_beta=beta_range[0],
-            end_beta=beta_range[1])
-        self.count = 0
-
-    def forward(self, subgraph, pred, tgt):
-        """Compute the total loss for adaptive rounding: rec_loss is the
-        quadratic output reconstruction loss, round_loss is a regularization
-        term to optimize the rounding policy.
-
-        :param pred: output from quantized model
-        :param tgt: output from FP model
-        :return: total loss function
-        """
-
-        def lp_loss(pred, tgt, p=2.0):
-            """loss function measured in L_p Norm."""
-            return (pred - tgt).abs().pow(p).sum(1).mean()
-
-        self.count += 1
-        rec_loss = lp_loss(pred, tgt, p=self.p)
-
-        beta = self.temp_decay(self.count)
-        if self.count < self.loss_start:
-            round_loss = 0
-        else:
-            round_loss = 0
-            for layer in subgraph.modules():
-                if isinstance(layer, _ADAROUND_SUPPORT_TYPE):
-                    round_vals = layer.weight_fake_quant.rectified_sigmoid()
-                    round_loss += self.weight * (1 - (
-                        (round_vals - .5).abs() * 2).pow(beta)).sum()
-
-        total_loss = rec_loss + round_loss
-        if self.count % 500 == 0:
-            print_log('Total loss:\t{:.3f} (rec_loss:{:.3f}, '
-                      'round_loss:{:.3f})\tbeta={:.2f}\tcount={}'.format(
-                          float(total_loss), float(rec_loss),
-                          float(round_loss), beta, self.count))
-        return total_loss
-
-
-class LinearTempDecay:
-
-    def __init__(self, t_max=10000, warm_up=0.2, start_beta=20, end_beta=2):
-        self.t_max = t_max
-        self.start_decay = warm_up * t_max
-        self.start_beta = start_beta
-        self.end_beta = end_beta
-
-    def __call__(self, t):
-        if t < self.start_decay:
-            return self.start_beta
-        elif t > self.t_max:
-            return self.end_beta
-        else:
-            rel_t = (t - self.start_decay) / (self.t_max - self.start_decay)
-            return self.end_beta + (self.start_beta - self.end_beta) * \
-                max(0.0, (1 - rel_t))
diff --git a/requirements/tests.txt b/requirements/tests.txt
index e38249fcd..5980dc303 100644
--- a/requirements/tests.txt
+++ b/requirements/tests.txt
@@ -1,4 +1,4 @@
-codecov
+coverage
 flake8
 interrogate
 isort==4.3.21

From af4e6b5396520e54099c9f3d41d9e41e1ff3fa43 Mon Sep 17 00:00:00 2001
From: HIT-cwh <2892770585@qq.com>
Date: Fri, 14 Apr 2023 20:38:31 +0800
Subject: [PATCH 7/8] add freeze_bn_begin to lsq

---
 .../quantization/qat/lsq_openvino_resnet18_8xb32_100e_in1k.py  | 3 ++-
 .../quantization/qat/lsq_openvino_resnet18_8xb32_10e_in1k.py   | 3 ++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/configs/quantization/qat/lsq_openvino_resnet18_8xb32_100e_in1k.py b/configs/quantization/qat/lsq_openvino_resnet18_8xb32_100e_in1k.py
index 55896c2c9..00e424141 100644
--- a/configs/quantization/qat/lsq_openvino_resnet18_8xb32_100e_in1k.py
+++ b/configs/quantization/qat/lsq_openvino_resnet18_8xb32_100e_in1k.py
@@ -59,7 +59,8 @@
     _delete_=True,
     type='mmrazor.LSQEpochBasedLoop',
     max_epochs=100,
-    val_interval=1)
+    val_interval=1,
+    freeze_bn_begin=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
diff --git a/configs/quantization/qat/lsq_openvino_resnet18_8xb32_10e_in1k.py b/configs/quantization/qat/lsq_openvino_resnet18_8xb32_10e_in1k.py
index 287a36fc9..f931ddaf5 100644
--- a/configs/quantization/qat/lsq_openvino_resnet18_8xb32_10e_in1k.py
+++ b/configs/quantization/qat/lsq_openvino_resnet18_8xb32_10e_in1k.py
@@ -54,7 +54,8 @@
     _delete_=True,
     type='mmrazor.LSQEpochBasedLoop',
     max_epochs=10,
-    val_interval=1)
+    val_interval=1,
+    freeze_bn_begin=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

From 6f8302f784a31b8037bdacaad5535b8039db38e1 Mon Sep 17 00:00:00 2001
From: HIT-cwh <2892770585@qq.com>
Date: Mon, 17 Apr 2023 14:57:20 +0800
Subject: [PATCH 8/8] delete useless codes

---
 mmrazor/engine/runner/quantization_loops.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/mmrazor/engine/runner/quantization_loops.py b/mmrazor/engine/runner/quantization_loops.py
index ec078c42f..764c8605d 100644
--- a/mmrazor/engine/runner/quantization_loops.py
+++ b/mmrazor/engine/runner/quantization_loops.py
@@ -203,9 +203,6 @@ def run_epoch(self) -> None:
                 self.is_first_batch = False
                 self.runner.model.apply(enable_param_learning)
 
-            if idx > 100:
-                break
-
         self.runner.model.sync_qparams(src_mode='loss')
         # Make sure the registered buffer such as `observer_enabled` is
         # correct in the saved checkpoint.