Merge branch 'release/v0.1.0'

README.md

-# PaddleSeg 语义分割库
+# PaddleSeg 图像分割库
 
 [![Build Status](https://travis-ci.org/PaddlePaddle/PaddleSeg.svg?branch=master)](https://travis-ci.org/PaddlePaddle/PaddleSeg)
 [![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
@@ -31,7 +31,7 @@ PaddleSeg支持多进程IO、多卡并行、跨卡Batch Norm同步等训练加
 
 ## 使用教程
 
-我们提供了一系列的使用教程，来说明如何使用PaddleSeg完成一个语义分割模型的训练、评估、部署。
+我们提供了一系列的使用教程，来说明如何使用PaddleSeg完成语义分割模型的训练、评估、部署。
 
 这一系列的文档被分为**快速入门**、**基础功能**、**预测部署**、**高级功能**四个部分，四个教程由浅至深地介绍PaddleSeg的设计思路和使用方法。
 
@@ -102,7 +102,7 @@ PaddleSeg在AI Studio平台上提供了在线体验的教程，欢迎体验：
 ##  交流与反馈
 * 欢迎您通过Github Issues来提交问题、报告与建议
 * 微信公众号：飞桨PaddlePaddle
-* QQ群: 796771754 
+* QQ群: 796771754
 
 <p align="center"><img width="200" height="200"  src="https://user-images.githubusercontent.com/45189361/64117959-1969de80-cdc9-11e9-84f7-e1c2849a004c.jpeg"/>&#8194;&#8194;&#8194;&#8194;&#8194;<img width="200" height="200" margin="500" src="./docs/imgs/qq_group2.png"/></p>
 <p align="center">  &#8194;&#8194;&#8194;微信公众号&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;官方技术交流QQ群</p>

configs/cityscape.yaml

-EVAL_CROP_SIZE: (2049, 1025) # (width, height), for unpadding rangescaling and stepscaling
-TRAIN_CROP_SIZE: (769, 769) # (width, height), for unpadding rangescaling and stepscaling
-AUG:
-    AUG_METHOD: "stepscaling" # choice unpadding rangescaling and stepscaling
-    FIX_RESIZE_SIZE: (640, 640) # (width, height), for unpadding
-    INF_RESIZE_VALUE: 500  # for rangescaling
-    MAX_RESIZE_VALUE: 600  # for rangescaling
-    MIN_RESIZE_VALUE: 400  # for rangescaling
-    MAX_SCALE_FACTOR: 2.0  # for stepscaling
-    MIN_SCALE_FACTOR: 0.5  # for stepscaling
-    SCALE_STEP_SIZE: 0.25  # for stepscaling
-    MIRROR: True
-BATCH_SIZE: 4
-DATASET:
-    DATA_DIR: "./dataset/cityscapes/"
-    IMAGE_TYPE: "rgb"  # choice rgb or rgba
-    NUM_CLASSES: 19
-    TEST_FILE_LIST: "dataset/cityscapes/val.list"
-    TRAIN_FILE_LIST: "dataset/cityscapes/train.list"
-    VAL_FILE_LIST: "dataset/cityscapes/val.list"
-    IGNORE_INDEX: 255
-FREEZE:
-    MODEL_FILENAME: "model"
-    PARAMS_FILENAME: "params"
-MODEL:
-    DEFAULT_NORM_TYPE: "gn"
-    MODEL_NAME: "deeplabv3p"
-    DEEPLAB:
-        ASPP_WITH_SEP_CONV: True
-        DECODER_USE_SEP_CONV: True
-TEST:
-    TEST_MODEL: "snapshots/cityscape_v5/final/"
-TRAIN:
-    MODEL_SAVE_DIR: "snapshots/cityscape_v7/"
-    PRETRAINED_MODEL_DIR: "pretrain/deeplabv3plus_gn_init"
-    SNAPSHOT_EPOCH: 10
-SOLVER:
-    LR: 0.001
-    LR_POLICY: "poly"
-    OPTIMIZER: "sgd"
-    NUM_EPOCHS: 700

configs/deeplabv3p_mobilenet-1-0_pet.yaml

+TRAIN_CROP_SIZE: (512, 512) # (width, height), for unpadding rangescaling and stepscaling
+EVAL_CROP_SIZE: (512, 512) # (width, height), for unpadding rangescaling and stepscaling
+AUG:
+    AUG_METHOD: "unpadding" # choice unpadding rangescaling and stepscaling
+    FIX_RESIZE_SIZE: (512, 512) # (width, height), for unpadding
+
+    INF_RESIZE_VALUE: 500  # for rangescaling
+    MAX_RESIZE_VALUE: 600  # for rangescaling
+    MIN_RESIZE_VALUE: 400  # for rangescaling
+
+    MAX_SCALE_FACTOR: 1.25  # for stepscaling
+    MIN_SCALE_FACTOR: 0.75  # for stepscaling
+    SCALE_STEP_SIZE: 0.25  # for stepscaling
+    MIRROR: True
+BATCH_SIZE: 4
+DATASET:
+    DATA_DIR: "./dataset/mini_pet/"
+    IMAGE_TYPE: "rgb"  # choice rgb or rgba
+    NUM_CLASSES: 3
+    TEST_FILE_LIST: "./dataset/mini_pet/file_list/test_list.txt"
+    TRAIN_FILE_LIST: "./dataset/mini_pet/file_list/train_list.txt"
+    VAL_FILE_LIST: "./dataset/mini_pet/file_list/val_list.txt"
+    VIS_FILE_LIST: "./dataset/mini_pet/file_list/test_list.txt"
+    IGNORE_INDEX: 255
+    SEPARATOR: " "
+FREEZE:
+    MODEL_FILENAME: "__model__"
+    PARAMS_FILENAME: "__params__"
+MODEL:
+    MODEL_NAME: "deeplabv3p"
+    DEFAULT_NORM_TYPE: "bn"
+    DEEPLAB:
+        BACKBONE: "mobilenet"
+        DEPTH_MULTIPLIER: 1.0
+        ENCODER_WITH_ASPP: False
+        ENABLE_DECODER: False
+TRAIN:
+    PRETRAINED_MODEL_DIR: "./pretrained_model/deeplabv3p_mobilenetv2-1-0_bn_cityscapes/"
+    MODEL_SAVE_DIR: "./saved_model/deeplabv3p_mobilenetv2-1-0_bn_pet/"
+    SNAPSHOT_EPOCH: 10
+TEST:
+    TEST_MODEL: "./saved_model/deeplabv3p_mobilenetv2-1-0_bn_pet/final"
+SOLVER:
+    NUM_EPOCHS: 100
+    LR: 0.005
+    LR_POLICY: "poly"
+    OPTIMIZER: "sgd"

configs/deeplabv3p_xception65_pet.yaml

+TRAIN_CROP_SIZE: (512, 512) # (width, height), for unpadding rangescaling and stepscaling
+EVAL_CROP_SIZE: (512, 512) # (width, height), for unpadding rangescaling and stepscaling
+AUG:
+    AUG_METHOD: "unpadding" # choice unpadding rangescaling and stepscaling
+    FIX_RESIZE_SIZE: (512, 512) # (width, height), for unpadding
+
+    INF_RESIZE_VALUE: 500  # for rangescaling
+    MAX_RESIZE_VALUE: 600  # for rangescaling
+    MIN_RESIZE_VALUE: 400  # for rangescaling
+
+    MAX_SCALE_FACTOR: 1.25  # for stepscaling
+    MIN_SCALE_FACTOR: 0.75  # for stepscaling
+    SCALE_STEP_SIZE: 0.25  # for stepscaling
+    MIRROR: True
+BATCH_SIZE: 4
+DATASET:
+    DATA_DIR: "./dataset/mini_pet/"
+    IMAGE_TYPE: "rgb"  # choice rgb or rgba
+    NUM_CLASSES: 3
+    TEST_FILE_LIST: "./dataset/mini_pet/file_list/test_list.txt"
+    TRAIN_FILE_LIST: "./dataset/mini_pet/file_list/train_list.txt"
+    VAL_FILE_LIST: "./dataset/mini_pet/file_list/val_list.txt"
+    VIS_FILE_LIST: "./dataset/mini_pet/file_list/test_list.txt"
+    IGNORE_INDEX: 255
+    SEPARATOR: " "
+FREEZE:
+    MODEL_FILENAME: "__model__"
+    PARAMS_FILENAME: "__params__"
+MODEL:
+    MODEL_NAME: "deeplabv3p"
+    DEFAULT_NORM_TYPE: "bn"
+    DEEPLAB:
+        BACKBONE: "xception_65"
+TRAIN:
+    PRETRAINED_MODEL_DIR: "./pretrained_model/deeplabv3p_xception65_bn_coco/"
+    MODEL_SAVE_DIR: "./saved_model/deeplabv3p_xception65_bn_pet/"
+    SNAPSHOT_EPOCH: 10
+TEST:
+    TEST_MODEL: "./saved_model/deeplabv3p_xception65_bn_pet/final"
+SOLVER:
+    NUM_EPOCHS: 100
+    LR: 0.005
+    LR_POLICY: "poly"
+    OPTIMIZER: "sgd"

configs/icnet_pet.yaml

+TRAIN_CROP_SIZE: (512, 512) # (width, height), for unpadding rangescaling and stepscaling
+EVAL_CROP_SIZE: (512, 512) # (width, height), for unpadding rangescaling and stepscaling
+AUG:
+    AUG_METHOD: "unpadding" # choice unpadding rangescaling and stepscaling
+    FIX_RESIZE_SIZE: (512, 512) # (width, height), for unpadding
+
+    INF_RESIZE_VALUE: 500  # for rangescaling
+    MAX_RESIZE_VALUE: 600  # for rangescaling
+    MIN_RESIZE_VALUE: 400  # for rangescaling
+
+    MAX_SCALE_FACTOR: 1.25  # for stepscaling
+    MIN_SCALE_FACTOR: 0.75  # for stepscaling
+    SCALE_STEP_SIZE: 0.25  # for stepscaling
+    MIRROR: True
+BATCH_SIZE: 4
+DATASET:
+    DATA_DIR: "./dataset/mini_pet/"
+    IMAGE_TYPE: "rgb"  # choice rgb or rgba
+    NUM_CLASSES: 3
+    TEST_FILE_LIST: "./dataset/mini_pet/file_list/test_list.txt"
+    TRAIN_FILE_LIST: "./dataset/mini_pet/file_list/train_list.txt"
+    VAL_FILE_LIST: "./dataset/mini_pet/file_list/val_list.txt"
+    VIS_FILE_LIST: "./dataset/mini_pet/file_list/test_list.txt"
+    IGNORE_INDEX: 255
+    SEPARATOR: " "
+FREEZE:
+    MODEL_FILENAME: "__model__"
+    PARAMS_FILENAME: "__params__"
+MODEL:
+    MODEL_NAME: "icnet"
+    DEFAULT_NORM_TYPE: "bn"
+    MULTI_LOSS_WEIGHT: "[1.0, 0.4, 0.16]"
+    ICNET:
+        DEPTH_MULTIPLIER: 0.5
+TRAIN:
+    PRETRAINED_MODEL_DIR: "./pretrained_model/icnet_bn_cityscapes/"
+    MODEL_SAVE_DIR: "./saved_model/icnet_pet/"
+    SNAPSHOT_EPOCH: 10
+TEST:
+    TEST_MODEL: "./saved_model/icnet_pet/final"
+SOLVER:
+    NUM_EPOCHS: 100
+    LR: 0.005
+    LR_POLICY: "poly"
+    OPTIMIZER: "sgd"

docs/check.md

@@ -51,7 +51,5 @@ python pdseg/check.py --cfg ${YAML_FILE_PATH}
 
 - 当`AUG.AUG_METHOD`为rangscaling时，`EVAL_CROP_SIZE`的宽高应不小于缩放后图像中最大的宽高。
 
-我们将计算并给出`EVAL_CROP_SIZE`的建议值。
-
 ### 10 数据增强参数`AUG.INF_RESIZE_VALUE`校验
 验证`AUG.INF_RESIZE_VALUE`是否在[`AUG.MIN_RESIZE_VALUE`~`AUG.MAX_RESIZE_VALUE`]范围内。若在范围内，则通过校验。

docs/model_zoo.md

@@ -28,7 +28,7 @@ PaddleSeg对所有内置的分割模型都提供了公开数据集下的预训
 
 | 模型 | 数据集合 | 下载地址 |Output Strid|multi-scale test| mIoU |
 |---|---|---|---|---|---|
-| DeepLabv3+/MobileNetv2/bn | COCO |[deeplabv3plus_coco_bn_init.tgz](https://bj.bcebos.com/v1/paddleseg/deeplabv3plus_coco_bn_init.tgz) | 16 | --| -- |
+| DeepLabv3+/MobileNetv2/bn | COCO |[deeplab_mobilenet_x1_0_coco.tgz](https://bj.bcebos.com/v1/paddleseg/deeplab_mobilenet_x1_0_coco.tgz) | 16 | --| -- |
 | DeeplabV3+/Xception65/bn | COCO | [xception65_coco.tgz](https://paddleseg.bj.bcebos.com/models/xception65_coco.tgz)| 16 | -- | -- |
 | U-Net/bn | COCO | [unet_coco.tgz](https://paddleseg.bj.bcebos.com/models/unet_coco_v3.tgz) | 16 | -- | -- |
 
@@ -39,7 +39,7 @@ train数据集合为Cityscapes训练集合，测试为Cityscapes的验证集合
 | 模型 | 数据集合 | 下载地址 |Output Stride| mutli-scale test| mIoU on val|
 |---|---|---|---|---|---|
 | DeepLabv3+/MobileNetv2/bn | Cityscapes |[mobilenet_cityscapes.tgz](https://paddleseg.bj.bcebos.com/models/mobilenet_cityscapes.tgz) |16|false| 0.698|
-| DeepLabv3+/Xception65/gn  | Cityscapes |[deeplabv3p_xception65_cityscapes.tgz](https://paddleseg.bj.bcebos.com/models/deeplabv3p_xception65_cityscapes.tgz) |16|false| 0.7804 |
-| DeepLabv3+/Xception65/bn | Cityscapes |[Xception65_deeplab_cityscapes.tgz](https://paddleseg.bj.bcebos.com/models/xception65_bn_cityscapes.tgz) | 16 | false | 0.7715 |
+| DeepLabv3+/Xception65/gn  | Cityscapes |[deeplabv3p_xception65_gn_cityscapes.tgz](https://paddleseg.bj.bcebos.com/models/deeplabv3p_xception65_cityscapes.tgz) |16|false| 0.7824 |
+| DeepLabv3+/Xception65/bn | Cityscapes |[deeplabv3p_xception65_bn_cityscapes_.tgz](https://paddleseg.bj.bcebos.com/models/xception65_bn_cityscapes.tgz) | 16 | false | 0.7930 |
 | ICNet/bn | Cityscapes |[icnet_cityscapes.tgz](https://paddleseg.bj.bcebos.com/models/icnet6831.tar.gz) |16|false| 0.6831 |
 | PSPNet/bn | Cityscapes |[pspnet50_cityscapes.tgz](https://paddleseg.bj.bcebos.com/models/pspnet50_cityscapes.tgz) |16|false| 0.6968 |

pdseg/check.py

@@ -266,7 +266,11 @@ def eval_crop_size_check(max_height, max_width, min_aspectratio, max_aspectratio
                     .format(cfg.EVAL_CROP_SIZE[0], cfg.EVAL_CROP_SIZE[1],
                             max_width_rangscaling, cfg.AUG.INF_RESIZE_VALUE))
     elif cfg.AUG.AUG_METHOD == "unpadding":
-        if cfg.EVAL_CROP_SIZE[0] >= cfg.AUG.FIX_RESIZE_SIZE[0] and cfg.EVAL_CROP_SIZE[1] >= cfg.AUG.FIX_RESIZE_SIZE[1]:
+        if len(cfg.AUG.FIX_RESIZE_SIZE) != 2:
+            logger.info(error_print("EVAL_CROP_SIZE check"))
+            logger.info("you set AUG.AUG_METHOD = 'unpadding', but AUG.FIX_RESIZE_SIZE is wrong. "
+                    "AUG.FIX_RESIZE_SIZE should be a tuple of length 2")
+        elif cfg.EVAL_CROP_SIZE[0] >= cfg.AUG.FIX_RESIZE_SIZE[0] and cfg.EVAL_CROP_SIZE[1] >= cfg.AUG.FIX_RESIZE_SIZE[1]:
             logger.info(correct_print("EVAL_CROP_SIZE check"))
         else:
             logger.info(error_print("EVAL_CROP_SIZE check"))

turtorial/finetune_deeplabv3plus.md

@@ -113,7 +113,7 @@ python pdseg/eval.py --use_gpu --cfg ./configs/test_deeplabv3p_pet.yaml
 
 ## 模型组合
 
-|预训练模型名称|BackBone|Norm|数据集|配置|
+|预训练模型名称|BackBone|Norm Type|数据集|配置|
 |-|-|-|-|-|
 |mobilenetv2-2-0_bn_imagenet|-|bn|ImageNet|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: mobilenet <br> MODEL.DEEPLAB.DEPTH_MULTIPLIER: 2.0 <br> MODEL.DEFAULT_NORM_TYPE: bn|
 |mobilenetv2-1-5_bn_imagenet|-|bn|ImageNet|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: mobilenet <br> MODEL.DEEPLAB.DEPTH_MULTIPLIER: 1.5 <br> MODEL.DEFAULT_NORM_TYPE: bn|