Merge pull request #795 from will-jl944/develop_jf

Add quantization-aware training

dygraph/paddlex/cv/models/base.py

@@ -22,6 +22,7 @@ import yaml
 import json
 import paddle
 from paddle.io import DataLoader, DistributedBatchSampler
+from paddleslim import QAT
 from paddleslim.analysis import flops
 from paddleslim import L1NormFilterPruner, FPGMFilterPruner
 import paddlex
@@ -53,6 +54,8 @@ class BaseModel:
         self.completed_epochs = 0
         self.pruner = None
         self.pruning_ratios = None
+        self.quantizer = None
+        self.quant_config = None
 
     def net_initialize(self, pretrain_weights=None, save_dir='.'):
         if pretrain_weights is not None and \
@@ -122,6 +125,11 @@ class BaseModel:
         info['pruner_inputs'] = self.pruner.inputs
         return info
 
+    def get_quant_info(self):
+        info = dict()
+        info['quant_config'] = self.quant_config
+        return info
+
     def save_model(self, save_dir):
         if not osp.isdir(save_dir):
             if osp.exists(save_dir):
@@ -129,10 +137,11 @@ class BaseModel:
             os.makedirs(save_dir)
         model_info = self.get_model_info()
         model_info['status'] = self.status
+
         paddle.save(self.net.state_dict(),
-                    os.path.join(save_dir, 'model.pdparams'))
+                    osp.join(save_dir, 'model.pdparams'))
         paddle.save(self.optimizer.state_dict(),
-                    os.path.join(save_dir, 'model.pdopt'))
+                    osp.join(save_dir, 'model.pdopt'))
 
         with open(
                 osp.join(save_dir, 'model.yml'), encoding='utf-8',
@@ -151,6 +160,13 @@ class BaseModel:
                     mode='w') as f:
                 yaml.dump(pruning_info, f)
 
+        if self.status == 'Quantized' and self.quantizer is not None:
+            quant_info = self.get_quant_info()
+            with open(
+                    osp.join(save_dir, 'quant.yml'), encoding='utf-8',
+                    mode='w') as f:
+                yaml.dump(quant_info, f)
+
         # 模型保存成功的标志
         open(osp.join(save_dir, '.success'), 'w').close()
         logging.info("Model saved in {}.".format(save_dir))
@@ -400,8 +416,8 @@ class BaseModel:
 
         Args:
             pruned_flops(float): Ratio of FLOPs to be pruned.
-            save_dir(None or str, optional): If None, the pruned model will not be saved
-            Otherwise, the pruned model will be saved at save_dir. Defaults to None.
+            save_dir(None or str, optional): If None, the pruned model will not be saved.
+                Otherwise, the pruned model will be saved at save_dir. Defaults to None.
 
         """
         if self.status == "Pruned":
@@ -427,13 +443,57 @@ class BaseModel:
             self.save_model(save_dir)
             logging.info("Pruned model is saved at {}".format(save_dir))
 
+    def _prepare_qat(self, quant_config):
+        if quant_config is None:
+            # default quantization configuration
+            quant_config = {
+                # {None, 'PACT'}. Weight preprocess type. If None, no preprocessing is performed.
+                'weight_preprocess_type': None,
+                # {None, 'PACT'}. Activation preprocess type. If None, no preprocessing is performed.
+                'activation_preprocess_type': None,
+                # {'abs_max', 'channel_wise_abs_max', 'range_abs_max', 'moving_average_abs_max'}.
+                # Weight quantization type.
+                'weight_quantize_type': 'channel_wise_abs_max',
+                # {'abs_max', 'range_abs_max', 'moving_average_abs_max'}. Activation quantization type.
+                'activation_quantize_type': 'moving_average_abs_max',
+                # The number of bits of weights after quantization.
+                'weight_bits': 8,
+                # The number of bits of activation after quantization.
+                'activation_bits': 8,
+                # Data type after quantization, such as 'uint8', 'int8', etc.
+                'dtype': 'int8',
+                # Window size for 'range_abs_max' quantization.
+                'window_size': 10000,
+                # Decay coefficient of moving average.
+                'moving_rate': .9,
+                # Types of layers that will be quantized.
+                'quantizable_layer_type': ['Conv2D', 'Linear']
+            }
+        self.quant_config = quant_config
+        self.quantizer = QAT(config=self.quant_config)
+        logging.info("Preparing the model for quantization-aware training...")
+        self.quantizer.quantize(self.net)
+        logging.info("Model is ready for quantization-aware training.")
+        self.status = 'Quantized'
+
     def _export_inference_model(self, save_dir, image_shape=[-1, -1]):
         save_dir = osp.join(save_dir, 'inference_model')
         self.net.eval()
         self.test_inputs = self.get_test_inputs(image_shape)
-        static_net = paddle.jit.to_static(
-            self.net, input_spec=self.test_inputs)
-        paddle.jit.save(static_net, osp.join(save_dir, 'model'))
+
+        if self.status == 'Quantized':
+            self.quantizer.save_quantized_model(self.net,
+                                                osp.join(save_dir, 'model'),
+                                                self.test_inputs)
+            quant_info = self.get_quant_info()
+            with open(
+                    osp.join(save_dir, 'quant.yml'), encoding='utf-8',
+                    mode='w') as f:
+                yaml.dump(quant_info, f)
+        else:
+            static_net = paddle.jit.to_static(
+                self.net, input_spec=self.test_inputs)
+            paddle.jit.save(static_net, osp.join(save_dir, 'model'))
 
         if self.status == 'Pruned':
             pruning_info = self.get_pruning_info()

dygraph/paddlex/cv/models/classifier.py

@@ -242,6 +242,70 @@ class BaseClassifier(BaseModel):
             early_stop_patience=early_stop_patience,
             use_vdl=use_vdl)
 
+    def quant_aware_train(self,
+                          num_epochs,
+                          train_dataset,
+                          train_batch_size=64,
+                          eval_dataset=None,
+                          optimizer=None,
+                          save_interval_epochs=1,
+                          log_interval_steps=10,
+                          save_dir='output',
+                          learning_rate=.000025,
+                          warmup_steps=0,
+                          warmup_start_lr=0.0,
+                          lr_decay_epochs=(30, 60, 90),
+                          lr_decay_gamma=0.1,
+                          early_stop=False,
+                          early_stop_patience=5,
+                          use_vdl=True,
+                          quant_config=None):
+        """
+        Quantization-aware training.
+        Args:
+            num_epochs(int): The number of epochs.
+            train_dataset(paddlex.dataset): Training dataset.
+            train_batch_size(int, optional): Total batch size among all cards used in training. Defaults to 64.
+            eval_dataset(paddlex.dataset, optional):
+                Evaluation dataset. If None, the model will not be evaluated during training process. Defaults to None.
+            optimizer(paddle.optimizer.Optimizer or None, optional):
+                Optimizer used for training. If None, a default optimizer is used. Defaults to None.
+            save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1.
+            log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10.
+            save_dir(str, optional): Directory to save the model. Defaults to 'output'.
+            learning_rate(float, optional): Learning rate for training. Defaults to .025.
+            warmup_steps(int, optional): The number of steps of warm-up training. Defaults to 0.
+            warmup_start_lr(float, optional): Start learning rate of warm-up training. Defaults to 0..
+            lr_decay_epochs(List[int] or Tuple[int], optional):
+                Epoch milestones for learning rate decay. Defaults to (20, 60, 90).
+            lr_decay_gamma(float, optional): Gamma coefficient of learning rate decay, default .1.
+            early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False.
+            early_stop_patience(int, optional): Early stop patience. Defaults to 5.
+            use_vdl(bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True.
+            quant_config(dict or None, optional): Quantization configuration. If None, a default rule of thumb
+                configuration will be used. Defaults to None.
+
+        """
+        self._prepare_qat(quant_config)
+        self.train(
+            num_epochs=num_epochs,
+            train_dataset=train_dataset,
+            train_batch_size=train_batch_size,
+            eval_dataset=eval_dataset,
+            optimizer=optimizer,
+            save_interval_epochs=save_interval_epochs,
+            log_interval_steps=log_interval_steps,
+            save_dir=save_dir,
+            pretrain_weights=None,
+            learning_rate=learning_rate,
+            warmup_steps=warmup_steps,
+            warmup_start_lr=warmup_start_lr,
+            lr_decay_epochs=lr_decay_epochs,
+            lr_decay_gamma=lr_decay_gamma,
+            early_stop=early_stop,
+            early_stop_patience=early_stop_patience,
+            use_vdl=use_vdl)
+
     def evaluate(self, eval_dataset, batch_size=1, return_details=False):
         """
         Evaluate the model.
@@ -506,9 +570,7 @@ class MobileNetV3_small(BaseClassifier):
         model_name = 'MobileNetV3_small_x' + str(float(scale)).replace('.',
                                                                        '_')
         super(MobileNetV3_small, self).__init__(
-            model_name=model_name,
-            num_classes=num_classes,
-            lr_mult_list=[.1, .1, .2, .2, .3])
+            model_name=model_name, num_classes=num_classes)
 
 
 class MobileNetV3_small_ssld(BaseClassifier):

dygraph/paddlex/cv/models/detector.py

@@ -246,6 +246,76 @@ class BaseDetector(BaseModel):
             early_stop_patience=early_stop_patience,
             use_vdl=use_vdl)
 
+    def quant_aware_train(self,
+                          num_epochs,
+                          train_dataset,
+                          train_batch_size=64,
+                          eval_dataset=None,
+                          optimizer=None,
+                          save_interval_epochs=1,
+                          log_interval_steps=10,
+                          save_dir='output',
+                          learning_rate=.00001,
+                          warmup_steps=0,
+                          warmup_start_lr=0.0,
+                          lr_decay_epochs=(216, 243),
+                          lr_decay_gamma=0.1,
+                          metric=None,
+                          use_ema=False,
+                          early_stop=False,
+                          early_stop_patience=5,
+                          use_vdl=True,
+                          quant_config=None):
+        """
+        Quantization-aware training.
+        Args:
+            num_epochs(int): The number of epochs.
+            train_dataset(paddlex.dataset): Training dataset.
+            train_batch_size(int, optional): Total batch size among all cards used in training. Defaults to 64.
+            eval_dataset(paddlex.dataset, optional):
+                Evaluation dataset. If None, the model will not be evaluated during training process. Defaults to None.
+            optimizer(paddle.optimizer.Optimizer or None, optional):
+                Optimizer used for training. If None, a default optimizer is used. Defaults to None.
+            save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1.
+            log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10.
+            save_dir(str, optional): Directory to save the model. Defaults to 'output'.
+            learning_rate(float, optional): Learning rate for training. Defaults to .001.
+            warmup_steps(int, optional): The number of steps of warm-up training. Defaults to 0.
+            warmup_start_lr(float, optional): Start learning rate of warm-up training. Defaults to 0..
+            lr_decay_epochs(list or tuple, optional): Epoch milestones for learning rate decay. Defaults to (216, 243).
+            lr_decay_gamma(float, optional): Gamma coefficient of learning rate decay. Defaults to .1.
+            metric({'VOC', 'COCO', None}, optional):
+                Evaluation metric. If None, determine the metric according to the dataset format. Defaults to None.
+            use_ema(bool, optional): Whether to use exponential moving average strategy. Defaults to False.
+            early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False.
+            early_stop_patience(int, optional): Early stop patience. Defaults to 5.
+            use_vdl(bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True.
+            quant_config(dict or None, optional): Quantization configuration. If None, a default rule of thumb
+                configuration will be used. Defaults to None.
+
+        """
+        self._prepare_qat(quant_config)
+        self.train(
+            num_epochs=num_epochs,
+            train_dataset=train_dataset,
+            train_batch_size=train_batch_size,
+            eval_dataset=eval_dataset,
+            optimizer=optimizer,
+            save_interval_epochs=save_interval_epochs,
+            log_interval_steps=log_interval_steps,
+            save_dir=save_dir,
+            pretrain_weights=None,
+            learning_rate=learning_rate,
+            warmup_steps=warmup_steps,
+            warmup_start_lr=warmup_start_lr,
+            lr_decay_epochs=lr_decay_epochs,
+            lr_decay_gamma=lr_decay_gamma,
+            metric=metric,
+            use_ema=use_ema,
+            early_stop=early_stop,
+            early_stop_patience=early_stop_patience,
+            use_vdl=use_vdl)
+
     def evaluate(self,
                  eval_dataset,
                  batch_size=1,

dygraph/paddlex/cv/models/load_model.py

@@ -76,6 +76,13 @@ def load_model(model_dir):
                     ratios=model.pruning_ratios,
                     axis=paddleslim.dygraph.prune.filter_pruner.FILTER_DIM)
 
+        if status == 'Quantized':
+            with open(osp.join(model_dir, "quant.yml")) as f:
+                quant_info = yaml.load(f.read(), Loader=yaml.Loader)
+                quant_config = quant_info['quant_config']
+                model.quantizer = paddleslim.QAT(quant_config)
+                model.quantizer.quantize(model.net)
+
         if status == 'Infer':
             if model_info['Model'] in ['FasterRCNN', 'MaskRCNN']:
                 net_state_dict = paddle.load(

dygraph/paddlex/cv/models/segmenter.py

@@ -228,6 +228,60 @@ class BaseSegmenter(BaseModel):
             early_stop_patience=early_stop_patience,
             use_vdl=use_vdl)
 
+    def quant_aware_train(self,
+                          num_epochs,
+                          train_dataset,
+                          train_batch_size=2,
+                          eval_dataset=None,
+                          optimizer=None,
+                          save_interval_epochs=1,
+                          log_interval_steps=2,
+                          save_dir='output',
+                          learning_rate=0.0001,
+                          lr_decay_power=0.9,
+                          early_stop=False,
+                          early_stop_patience=5,
+                          use_vdl=True,
+                          quant_config=None):
+        """
+        Quantization-aware training.
+        Args:
+            num_epochs(int): The number of epochs.
+            train_dataset(paddlex.dataset): Training dataset.
+            train_batch_size(int, optional): Total batch size among all cards used in training. Defaults to 2.
+            eval_dataset(paddlex.dataset, optional):
+                Evaluation dataset. If None, the model will not be evaluated furing training process. Defaults to None.
+            optimizer(paddle.optimizer.Optimizer or None, optional):
+                Optimizer used in training. If None, a default optimizer is used. Defaults to None.
+            save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1.
+            log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10.
+            save_dir(str, optional): Directory to save the model. Defaults to 'output'.
+            learning_rate(float, optional): Learning rate for training. Defaults to .025.
+            lr_decay_power(float, optional): Learning decay power. Defaults to .9.
+            early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False.
+            early_stop_patience(int, optional): Early stop patience. Defaults to 5.
+            use_vdl(bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True.
+            quant_config(dict or None, optional): Quantization configuration. If None, a default rule of thumb
+                configuration will be used. Defaults to None.
+
+        """
+        self._prepare_qat(quant_config)
+        self.train(
+            num_epochs=num_epochs,
+            train_dataset=train_dataset,
+            train_batch_size=train_batch_size,
+            eval_dataset=eval_dataset,
+            optimizer=optimizer,
+            save_interval_epochs=save_interval_epochs,
+            log_interval_steps=log_interval_steps,
+            save_dir=save_dir,
+            pretrain_weights=None,
+            learning_rate=learning_rate,
+            lr_decay_power=lr_decay_power,
+            early_stop=early_stop,
+            early_stop_patience=early_stop_patience,
+            use_vdl=use_vdl)
+
     def evaluate(self, eval_dataset, batch_size=1, return_details=False):
         """
         Evaluate the model.

dygraph/tutorials/slim/quantize/image_classification/mobilenetv2_qat.py

@@ -0,0 +1,43 @@
+import paddlex as pdx
+from paddlex import transforms as T
+
+# 下载和解压蔬菜分类数据集
+veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
+pdx.utils.download_and_decompress(veg_dataset, path='./')
+
+# 定义训练和验证时的transforms
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+train_transforms = T.Compose(
+    [T.RandomCrop(crop_size=224), T.RandomHorizontalFlip(), T.Normalize()])
+
+eval_transforms = T.Compose([
+    T.ResizeByShort(short_size=256), T.CenterCrop(crop_size=224), T.Normalize()
+])
+
+# 定义训练和验证所用的数据集
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/imagenet.py#L21
+train_dataset = pdx.datasets.ImageNet(
+    data_dir='vegetables_cls',
+    file_list='vegetables_cls/train_list.txt',
+    label_list='vegetables_cls/labels.txt',
+    transforms=train_transforms,
+    shuffle=True)
+
+eval_dataset = pdx.datasets.ImageNet(
+    data_dir='vegetables_cls',
+    file_list='vegetables_cls/val_list.txt',
+    label_list='vegetables_cls/labels.txt',
+    transforms=eval_transforms)
+
+# 加载模型
+model = pdx.load_model('output/mobilenet_v2/best_model')
+
+# 在线量化
+model.quant_aware_train(
+    num_epochs=5,
+    train_dataset=train_dataset,
+    train_batch_size=32,
+    eval_dataset=eval_dataset,
+    learning_rate=0.000025,
+    save_dir='output/mobilenet_v2/quant',
+    use_vdl=True)

dygraph/tutorials/slim/quantize/image_classification/mobilenetv2_train.py

@@ -0,0 +1,47 @@
+import paddlex as pdx
+from paddlex import transforms as T
+
+# 下载和解压蔬菜分类数据集
+veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
+pdx.utils.download_and_decompress(veg_dataset, path='./')
+
+# 定义训练和验证时的transforms
+# API说明https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+train_transforms = T.Compose(
+    [T.RandomCrop(crop_size=224), T.RandomHorizontalFlip(), T.Normalize()])
+
+eval_transforms = T.Compose([
+    T.ResizeByShort(short_size=256), T.CenterCrop(crop_size=224), T.Normalize()
+])
+
+# 定义训练和验证所用的数据集
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/imagenet.py#L21
+train_dataset = pdx.datasets.ImageNet(
+    data_dir='vegetables_cls',
+    file_list='vegetables_cls/train_list.txt',
+    label_list='vegetables_cls/labels.txt',
+    transforms=train_transforms,
+    shuffle=True)
+
+eval_dataset = pdx.datasets.ImageNet(
+    data_dir='vegetables_cls',
+    file_list='vegetables_cls/val_list.txt',
+    label_list='vegetables_cls/labels.txt',
+    transforms=eval_transforms)
+
+# 初始化模型，并进行训练
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+num_classes = len(train_dataset.labels)
+model = pdx.models.MobileNetV3_large(num_classes=num_classes)
+
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/classifier.py#L153
+# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+model.train(
+    num_epochs=10,
+    train_dataset=train_dataset,
+    train_batch_size=32,
+    eval_dataset=eval_dataset,
+    lr_decay_epochs=[4, 6, 8],
+    learning_rate=0.025,
+    save_dir='output/mobilenet_v2',
+    use_vdl=True)

dygraph/tutorials/slim/quantize/object_detection/yolov3_qat.py

@@ -0,0 +1,55 @@
+import paddlex as pdx
+from paddlex import transforms as T
+
+# 下载和解压昆虫检测数据集
+dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'
+pdx.utils.download_and_decompress(dataset, path='./')
+
+# 定义训练和验证时的transforms
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+train_transforms = T.Compose([
+    T.MixupImage(mixup_epoch=250), T.RandomDistort(),
+    T.RandomExpand(im_padding_value=[123.675, 116.28, 103.53]), T.RandomCrop(),
+    T.RandomHorizontalFlip(), T.BatchRandomResize(
+        target_sizes=[320, 352, 384, 416, 448, 480, 512, 544, 576, 608],
+        interp='RANDOM'), T.Normalize(
+            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+eval_transforms = T.Compose([
+    T.Resize(
+        608, interp='CUBIC'), T.Normalize(
+            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# 定义训练和验证所用的数据集
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/voc.py#L29
+train_dataset = pdx.datasets.VOCDetection(
+    data_dir='insect_det',
+    file_list='insect_det/train_list.txt',
+    label_list='insect_det/labels.txt',
+    transforms=train_transforms,
+    shuffle=True)
+
+eval_dataset = pdx.datasets.VOCDetection(
+    data_dir='insect_det',
+    file_list='insect_det/val_list.txt',
+    label_list='insect_det/labels.txt',
+    transforms=eval_transforms,
+    shuffle=False)
+
+# 加载模型
+model = pdx.load_model('output/yolov3_darknet53/best_model')
+
+# 在线量化
+model.quant_aware_train(
+    num_epochs=50,
+    train_dataset=train_dataset,
+    train_batch_size=8,
+    eval_dataset=eval_dataset,
+    learning_rate=0.0001 / 8,
+    warmup_steps=100,
+    warmup_start_lr=0.0,
+    save_interval_epochs=1,
+    lr_decay_epochs=[30, 45],
+    save_dir='output/yolov3_darknet53/quant')

dygraph/tutorials/slim/quantize/object_detection/yolov3_train.py

@@ -0,0 +1,58 @@
+import paddlex as pdx
+from paddlex import transforms as T
+
+# 下载和解压昆虫检测数据集
+dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'
+pdx.utils.download_and_decompress(dataset, path='./')
+
+# 定义训练和验证时的transforms
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+train_transforms = T.Compose([
+    T.MixupImage(mixup_epoch=250), T.RandomDistort(),
+    T.RandomExpand(im_padding_value=[123.675, 116.28, 103.53]), T.RandomCrop(),
+    T.RandomHorizontalFlip(), T.BatchRandomResize(
+        target_sizes=[320, 352, 384, 416, 448, 480, 512, 544, 576, 608],
+        interp='RANDOM'), T.Normalize(
+            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+eval_transforms = T.Compose([
+    T.Resize(
+        608, interp='CUBIC'), T.Normalize(
+            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# 定义训练和验证所用的数据集
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/voc.py#L29
+train_dataset = pdx.datasets.VOCDetection(
+    data_dir='insect_det',
+    file_list='insect_det/train_list.txt',
+    label_list='insect_det/labels.txt',
+    transforms=train_transforms,
+    shuffle=True)
+
+eval_dataset = pdx.datasets.VOCDetection(
+    data_dir='insect_det',
+    file_list='insect_det/val_list.txt',
+    label_list='insect_det/labels.txt',
+    transforms=eval_transforms,
+    shuffle=False)
+
+# 初始化模型，并进行训练
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+num_classes = len(train_dataset.labels)
+model = pdx.models.YOLOv3(num_classes=num_classes, backbone='DarkNet53')
+
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/detector.py#L154
+# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+model.train(
+    num_epochs=270,
+    train_dataset=train_dataset,
+    train_batch_size=8,
+    eval_dataset=eval_dataset,
+    learning_rate=0.001 / 8,
+    warmup_steps=1000,
+    warmup_start_lr=0.0,
+    save_interval_epochs=5,
+    lr_decay_epochs=[216, 243],
+    save_dir='output/yolov3_darknet53')

dygraph/tutorials/slim/quantize/semantic_segmentation/unet_qat.py

@@ -0,0 +1,49 @@
+import paddlex as pdx
+from paddlex import transforms as T
+
+# 下载和解压视盘分割数据集
+optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
+pdx.utils.download_and_decompress(optic_dataset, path='./')
+
+# 定义训练和验证时的transforms
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+train_transforms = T.Compose([
+    T.Resize(target_size=512),
+    T.RandomHorizontalFlip(),
+    T.Normalize(
+        mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+])
+
+eval_transforms = T.Compose([
+    T.Resize(target_size=512),
+    T.Normalize(
+        mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+])
+
+# 定义训练和验证所用的数据集
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/seg_dataset.py#L22
+train_dataset = pdx.datasets.SegDataset(
+    data_dir='optic_disc_seg',
+    file_list='optic_disc_seg/train_list.txt',
+    label_list='optic_disc_seg/labels.txt',
+    transforms=train_transforms,
+    shuffle=True)
+
+eval_dataset = pdx.datasets.SegDataset(
+    data_dir='optic_disc_seg',
+    file_list='optic_disc_seg/val_list.txt',
+    label_list='optic_disc_seg/labels.txt',
+    transforms=eval_transforms,
+    shuffle=False)
+
+# 加载模型
+model = pdx.load_model('output/unet/best_model')
+
+# 在线量化
+model.quant_aware_train(
+    num_epochs=5,
+    train_dataset=train_dataset,
+    train_batch_size=4,
+    eval_dataset=eval_dataset,
+    learning_rate=0.001,
+    save_dir='output/unet/quant')

dygraph/tutorials/slim/quantize/semantic_segmentation/unet_train.py

@@ -0,0 +1,52 @@
+import paddlex as pdx
+from paddlex import transforms as T
+
+# 下载和解压视盘分割数据集
+optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
+pdx.utils.download_and_decompress(optic_dataset, path='./')
+
+# 定义训练和验证时的transforms
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+train_transforms = T.Compose([
+    T.Resize(target_size=512),
+    T.RandomHorizontalFlip(),
+    T.Normalize(
+        mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+])
+
+eval_transforms = T.Compose([
+    T.Resize(target_size=512),
+    T.Normalize(
+        mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+])
+
+# 定义训练和验证所用的数据集
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/seg_dataset.py#L22
+train_dataset = pdx.datasets.SegDataset(
+    data_dir='optic_disc_seg',
+    file_list='optic_disc_seg/train_list.txt',
+    label_list='optic_disc_seg/labels.txt',
+    transforms=train_transforms,
+    shuffle=True)
+
+eval_dataset = pdx.datasets.SegDataset(
+    data_dir='optic_disc_seg',
+    file_list='optic_disc_seg/val_list.txt',
+    label_list='optic_disc_seg/labels.txt',
+    transforms=eval_transforms,
+    shuffle=False)
+
+# 初始化模型，并进行训练
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+num_classes = len(train_dataset.labels)
+model = pdx.models.UNet(num_classes=num_classes)
+
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/segmenter.py#L150
+# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+model.train(
+    num_epochs=10,
+    train_dataset=train_dataset,
+    train_batch_size=4,
+    eval_dataset=eval_dataset,
+    learning_rate=0.01,
+    save_dir='output/unet')

dygraph/tutorials/train/image_classification/alexnet.py

@@ -42,6 +42,6 @@ model.train(
     train_batch_size=32,
     eval_dataset=eval_dataset,
     lr_decay_epochs=[4, 6, 8],
-    learning_rate=0.01,
+    learning_rate=0.001,
     save_dir='output/alexnet',
     use_vdl=True)