add qat tutorials

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

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+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')
+
+# 量化训练
+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,
+    infer_image_shape=[224, 224])

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

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+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

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+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')
+
+# 量化训练
+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=1000,
+    warmup_start_lr=0.0,
+    save_interval_epochs=5,
+    lr_decay_epochs=[30, 45],
+    save_dir='output/yolov3_darknet53/quant')

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

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+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

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+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')
+
+# 量化训练
+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

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+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')