add maskrcnn quant tutorial

dygraph/tutorials/slim/quantize/instance_segmentation/mask_rcnn_qat.py

@@ -0,0 +1,47 @@
+import paddlex as pdx
+from paddlex import transforms as T
+
+# 下载和解压小度熊分拣数据集
+dataset = 'https://bj.bcebos.com/paddlex/datasets/xiaoduxiong_ins_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.RandomResizeByShort(
+        short_sizes=[640, 672, 704, 736, 768, 800],
+        max_size=1333,
+        interp='CUBIC'), T.RandomHorizontalFlip(), T.Normalize(
+            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+eval_transforms = T.Compose([
+    T.ResizeByShort(
+        short_size=800, max_size=1333, 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/develop/dygraph/paddlex/cv/datasets/coco.py#L26
+train_dataset = pdx.datasets.CocoDetection(
+    data_dir='xiaoduxiong_ins_det/JPEGImages',
+    ann_file='xiaoduxiong_ins_det/train.json',
+    transforms=train_transforms,
+    shuffle=True)
+eval_dataset = pdx.datasets.CocoDetection(
+    data_dir='xiaoduxiong_ins_det/JPEGImages',
+    ann_file='xiaoduxiong_ins_det/val.json',
+    transforms=eval_transforms)
+
+# 加载模型
+model = pdx.load_model('output/mask_rcnn_r50_fpn/best_model')
+
+# 在线量化
+model.quant_aware_train(
+    num_epochs=6,
+    train_dataset=train_dataset,
+    train_batch_size=1,
+    eval_dataset=eval_dataset,
+    learning_rate=0.000125,
+    save_dir='output/mask_rcnn_r50_fpn/quant',
+    use_vdl=True)

dygraph/tutorials/slim/quantize/instance_segmentation/mask_rcnn_train.py

@@ -0,0 +1,55 @@
+import paddlex as pdx
+from paddlex import transforms as T
+
+# 下载和解压小度熊分拣数据集
+dataset = 'https://bj.bcebos.com/paddlex/datasets/xiaoduxiong_ins_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.RandomResizeByShort(
+        short_sizes=[640, 672, 704, 736, 768, 800],
+        max_size=1333,
+        interp='CUBIC'), T.RandomHorizontalFlip(), T.Normalize(
+            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+eval_transforms = T.Compose([
+    T.ResizeByShort(
+        short_size=800, max_size=1333, 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/develop/dygraph/paddlex/cv/datasets/coco.py#L26
+train_dataset = pdx.datasets.CocoDetection(
+    data_dir='xiaoduxiong_ins_det/JPEGImages',
+    ann_file='xiaoduxiong_ins_det/train.json',
+    transforms=train_transforms,
+    shuffle=True)
+eval_dataset = pdx.datasets.CocoDetection(
+    data_dir='xiaoduxiong_ins_det/JPEGImages',
+    ann_file='xiaoduxiong_ins_det/val.json',
+    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.det.MaskRCNN(
+    num_classes=num_classes, backbone='ResNet50', with_fpn=True)
+
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/detector.py#L155
+# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+model.train(
+    num_epochs=12,
+    train_dataset=train_dataset,
+    train_batch_size=1,
+    eval_dataset=eval_dataset,
+    pretrain_weights='COCO',
+    learning_rate=0.00125,
+    lr_decay_epochs=[8, 11],
+    warmup_steps=10,
+    warmup_start_lr=0.0,
+    save_dir='output/mask_rcnn_r50_fpn',
+    use_vdl=True)

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

@@ -52,4 +52,5 @@ model.quant_aware_train(
     warmup_start_lr=0.0,
     save_interval_epochs=1,
     lr_decay_epochs=[30, 45],
-    save_dir='output/yolov3_darknet53/quant')
+    save_dir='output/yolov3_darknet53/quant',
+    use_vdl=True)

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

@@ -46,4 +46,5 @@ model.quant_aware_train(
     train_batch_size=4,
     eval_dataset=eval_dataset,
     learning_rate=0.001,
-    save_dir='output/unet/quant')
+    save_dir='output/unet/quant',
+    use_vdl=True)