Merge branch 'develop' of https://github.com/PaddlePaddle/PaddleSeg i…

…nto develop

README.md

@@ -47,7 +47,7 @@ Welcome to PaddleSeg! PaddleSeg is an end-to-end image segmentation development
 - [x] Cityscapes
 - [x] Pascal VOC
 - [x] ADE20K
-- [ ] Pascal Context
+- [x] Pascal Context
 - [ ] COCO stuff
 
 ## Installation
@@ -101,3 +101,24 @@ python train.py --config configs/quick_start/bisenet_optic_disc_512x512_1k.yml
 * Thanks [jm12138](https://github.com/jm12138) for contributing U<sup>2</sup>-Net.
 * Thanks [zjhellofss](https://github.com/zjhellofss) (Fu Shenshen) for contributing Attention U-Net, and Dice Loss.
 * Thanks [liuguoyu666](https://github.com/liguoyu666) for contributing U-Net++.
+
+## Citation
+If you find our project useful in your research, please consider citing:
+
+```latex
+@misc{liu2021paddleseg,
+      title={PaddleSeg: A High-Efficient Development Toolkit for Image Segmentation}, 
+      author={Yi Liu and Lutao Chu and Guowei Chen and Zewu Wu and Zeyu Chen and Baohua Lai and Yuying Hao},
+      year={2021},
+      eprint={2101.06175},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+
+@misc{paddleseg2019,
+    title={PaddleSeg, End-to-end image segmentation kit based on PaddlePaddle},
+    author={PaddlePaddle Authors},
+    howpublished = {\url{https://github.com/PaddlePaddle/PaddleSeg}},
+    year={2019}
+}
+```

README_CN.md

@@ -47,7 +47,7 @@ PaddleSeg是基于飞桨[PaddlePaddle](https://www.paddlepaddle.org.cn)开发的
 - [x] Cityscapes
 - [x] Pascal VOC
 - [x] ADE20K
-- [ ] Pascal Context
+- [x] Pascal Context
 - [ ] COCO stuff
 
 ## 安装
@@ -98,3 +98,25 @@ python train.py --config configs/quick_start/bisenet_optic_disc_512x512_1k.yml
 * 非常感谢[jm12138](https://github.com/jm12138)贡献U<sup>2</sup>-Net模型。
 * 非常感谢[zjhellofss](https://github.com/zjhellofss)（傅莘莘）贡献Attention U-Net模型，和Dice loss损失函数。
 * 非常感谢[liuguoyu666](https://github.com/liguoyu666)贡献U-Net++模型。
+
+## 学术引用
+
+如果我们的项目在学术上帮助到你，请考虑以下引用：
+
+```latex
+@misc{liu2021paddleseg,
+      title={PaddleSeg: A High-Efficient Development Toolkit for Image Segmentation}, 
+      author={Yi Liu and Lutao Chu and Guowei Chen and Zewu Wu and Zeyu Chen and Baohua Lai and Yuying Hao},
+      year={2021},
+      eprint={2101.06175},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+
+@misc{paddleseg2019,
+    title={PaddleSeg, End-to-end image segmentation kit based on PaddlePaddle},
+    author={PaddlePaddle Authors},
+    howpublished = {\url{https://github.com/PaddlePaddle/PaddleSeg}},
+    year={2019}
+}
+```

configs/_base_/pascal_context.yml

@@ -0,0 +1,50 @@
+batch_size: 4
+iters: 40000
+
+train_dataset:
+  type: PascalContext
+  dataset_root: data/VOC2010/
+  transforms:
+    - type: ResizeStepScaling
+      min_scale_factor: 0.5
+      max_scale_factor: 2.0
+      scale_step_size: 0.25
+    - type: RandomPaddingCrop
+      crop_size: [520, 520]
+    - type: RandomHorizontalFlip
+    - type: RandomDistort
+      brightness_range: 0.4
+      contrast_range: 0.4
+      saturation_range: 0.4
+    - type: Normalize
+  mode: train
+
+val_dataset:
+  type: PascalContext
+  dataset_root: data/VOC2010/
+  transforms:
+    - type: Padding
+      target_size: [520, 520]
+    - type: Normalize
+  mode: val
+
+
+optimizer:
+  type: sgd
+  momentum: 0.9
+  weight_decay: 4.0e-5
+
+learning_rate:
+  value: 0.001
+  decay:
+    type: poly
+    power: 0.9
+    end_lr: 0.0
+
+loss:
+  types:
+    - type: CrossEntropyLoss
+  coef: [1]
+
+
+

docs/data_prepare.md

@@ -48,6 +48,30 @@ export PYTHONPATH=`pwd`
 [ADE20K](http://sceneparsing.csail.mit.edu/)由MIT发布的可用于场景感知、分割和多物体识别等多种任务的数据集。
 其涵盖了150个语义类别，包括训练集20210张，验证集2000张。
 
+## 关于Pascal Context数据集
+Pascal Context是基于PASCAL VOC 2010数据集额外标注的像素级别的语义分割数据集。我们提供的转换脚本支持59个类别，其中训练集4996, 验证集5104张.
+
+
+在使用Pascal Context数据集前， 请先下载[VOC2010](http://host.robots.ox.ac.uk/pascal/VOC/voc2010/VOCtrainval_03-May-2010.tar)，随后自行前往[Pascal-Context主页](https://www.cs.stanford.edu/~roozbeh/pascal-context/)下载数据集及[标注](https://codalabuser.blob.core.windows.net/public/trainval_merged.json)
+我们建议您将数据集存放于`PaddleSeg/data`中，以便与我们配置文件完全兼容。数据集下载后请组织成如下结构：
+
+    VOC2010
+    |
+    |--Annotations
+    |
+    |--ImageSets
+    |
+    |--SegmentationClass
+    |  
+    |--JPEGImages
+    |
+    |--SegmentationObject
+    |
+    |--trainval_merged.json
+
+其中，标注图像的标签从1，2依次取值，不可间隔。若有需要忽略的像素，则按0进行标注。在使用Pascal Context数据集时，需要安装[Detail](https://github.com/zhanghang1989/detail-api).
+
+
 ## 自定义数据集
 
 如果您需要使用自定义数据集进行训练，请按照以下步骤准备数据.

paddleseg/core/train.py

@@ -20,7 +20,7 @@
 import paddle
 import paddle.nn.functional as F
 
-from paddleseg.utils import Timer, calculate_eta, resume, logger
+from paddleseg.utils import TimeAverager, calculate_eta, resume, logger
 from paddleseg.core.val import evaluate
 
 
@@ -112,24 +112,23 @@ def train(model,
         from visualdl import LogWriter
         log_writer = LogWriter(save_dir)
 
-    timer = Timer()
     avg_loss = 0.0
     avg_loss_list = []
     iters_per_epoch = len(batch_sampler)
     best_mean_iou = -1.0
     best_model_iter = -1
-    train_reader_cost = 0.0
-    train_batch_cost = 0.0
+    reader_cost_averager = TimeAverager()
+    batch_cost_averager = TimeAverager()
     save_models = deque()
-    timer.start()
+    batch_start = time.time()
 
     iter = start_iter
     while iter < iters:
         for data in loader:
             iter += 1
             if iter > iters:
                 break
-            train_reader_cost += timer.elapsed_time()
+            reader_cost_averager.record(time.time() - batch_start)
             images = data[0]
             labels = data[1].astype('int64')
             edges = None
@@ -160,24 +159,24 @@ def train(model,
             else:
                 for i in range(len(loss_list)):
                     avg_loss_list[i] += loss_list[i]
-            train_batch_cost += timer.elapsed_time()
+            batch_cost_averager.record(
+                    time.time() - batch_start,
+                    num_samples=batch_size)
 
             if (iter) % log_iters == 0 and local_rank == 0:
                 avg_loss /= log_iters
                 avg_loss_list = [
                     l.numpy()[0] / log_iters for l in avg_loss_list
                 ]
-                avg_train_reader_cost = train_reader_cost / log_iters
-                avg_train_batch_cost = train_batch_cost / log_iters
-                train_reader_cost = 0.0
-                train_batch_cost = 0.0
                 remain_iters = iters - iter
+                avg_train_batch_cost = batch_cost_averager.get_average()
+                avg_train_reader_cost = reader_cost_averager.get_average()
                 eta = calculate_eta(remain_iters, avg_train_batch_cost)
                 logger.info(
-                    "[TRAIN] epoch={}, iter={}/{}, loss={:.4f}, lr={:.6f}, batch_cost={:.4f}, reader_cost={:.4f} | ETA {}"
+                    "[TRAIN] epoch={}, iter={}/{}, loss={:.4f}, lr={:.6f}, batch_cost={:.4f}, reader_cost={:.5f}, ips={:.4f} samples/sec | ETA {}"
                     .format((iter - 1) // iters_per_epoch + 1, iter, iters,
                             avg_loss, lr, avg_train_batch_cost,
-                            avg_train_reader_cost, eta))
+                            avg_train_reader_cost, batch_cost_averager.get_ips_average(), eta))
                 if use_vdl:
                     log_writer.add_scalar('Train/loss', avg_loss, iter)
                     # Record all losses if there are more than 2 losses.
@@ -196,6 +195,8 @@ def train(model,
                                           avg_train_reader_cost, iter)
                 avg_loss = 0.0
                 avg_loss_list = []
+                reader_cost_averager.reset()
+                batch_cost_averager.reset()
 
             if (iter % save_interval == 0
                     or iter == iters) and (val_dataset is not None):
@@ -233,7 +234,7 @@ def train(model,
                     if use_vdl:
                         log_writer.add_scalar('Evaluate/mIoU', mean_iou, iter)
                         log_writer.add_scalar('Evaluate/Acc', acc, iter)
-            timer.restart()
+            batch_start = time.time()
 
     # Calculate flops.
     if local_rank == 0:

paddleseg/core/val.py

@@ -15,10 +15,11 @@
 import os
 
 import numpy as np
+import time
 import paddle
 import paddle.nn.functional as F
 
-from paddleseg.utils import metrics, Timer, calculate_eta, logger, progbar
+from paddleseg.utils import metrics, TimeAverager, calculate_eta, logger, progbar
 from paddleseg.core import infer
 
 np.set_printoptions(suppress=True)
@@ -80,10 +81,12 @@ def evaluate(model,
     logger.info("Start evaluating (total_samples={}, total_iters={})...".format(
         len(eval_dataset), total_iters))
     progbar_val = progbar.Progbar(target=total_iters, verbose=1)
-    timer = Timer()
+    reader_cost_averager = TimeAverager()
+    batch_cost_averager = TimeAverager()
+    batch_start = time.time()
     with paddle.no_grad():
         for iter, (im, label) in enumerate(loader):
-            reader_cost = timer.elapsed_time()
+            reader_cost_averager.record(time.time() - batch_start)
             label = label.astype('int64')
 
             ori_shape = label.shape[-2:]
@@ -141,12 +144,18 @@ def evaluate(model,
                 intersect_area_all = intersect_area_all + intersect_area
                 pred_area_all = pred_area_all + pred_area
                 label_area_all = label_area_all + label_area
-            batch_cost = timer.elapsed_time()
-            timer.restart()
+            batch_cost_averager.record(
+                    time.time() - batch_start,
+                    num_samples=len(label))
+            batch_cost = batch_cost_averager.get_average()
+            reader_cost = reader_cost_averager.get_average()
 
             if local_rank == 0:
                 progbar_val.update(iter + 1, [('batch_cost', batch_cost),
                                               ('reader cost', reader_cost)])
+            reader_cost_averager.reset()
+            batch_cost_averager.reset()
+            batch_start = time.time()
 
     class_iou, miou = metrics.mean_iou(intersect_area_all, pred_area_all,
                                        label_area_all)

paddleseg/datasets/__init__.py

@@ -17,3 +17,4 @@
 from .voc import PascalVOC
 from .ade import ADE20K
 from .optic_disc_seg import OpticDiscSeg
+from .pascal_context import PascalContext

paddleseg/datasets/pascal_context.py

@@ -0,0 +1,77 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+from PIL import Image
+from paddleseg.datasets import Dataset
+from paddleseg.cvlibs import manager
+from paddleseg.transforms import Compose
+
+
+@manager.DATASETS.add_component
+class PascalContext(Dataset):
+    """
+    PascalVOC2010 dataset `http://host.robots.ox.ac.uk/pascal/VOC/`.
+    If you want to use pascal context dataset, please run the convert_voc2010.py in tools firstly.
+
+    Args:
+        transforms (list): Transforms for image.
+        dataset_root (str): The dataset directory. Default: None
+        mode (str): Which part of dataset to use. it is one of ('train', 'trainval', 'context', 'val').
+            If you want to set mode to 'context', please make sure the dataset have been augmented. Default: 'train'.
+    """
+
+    def __init__(self, transforms=None, dataset_root=None, mode='train'):
+        self.dataset_root = dataset_root
+        self.transforms = Compose(transforms)
+        mode = mode.lower()
+        self.mode = mode
+        self.file_list = list()
+        self.num_classes = 59
+        self.ignore_index = 255
+
+        if mode not in ['train', 'trainval', 'val']:
+            raise ValueError(
+                "`mode` should be one of ('train', 'trainval', 'val') in PascalContext dataset, but got {}."
+                .format(mode))
+
+        if self.transforms is None:
+            raise ValueError("`transforms` is necessary, but it is None.")
+        if self.dataset_root is None:
+            raise ValueError(
+                "The dataset is not Found or the folder structure is nonconfoumance.")
+
+        image_set_dir = os.path.join(self.dataset_root, 'ImageSets','Segmentation')
+
+        if mode == 'train':
+            file_path = os.path.join(image_set_dir, 'train_context.txt')
+        elif mode == 'val':
+            file_path = os.path.join(image_set_dir, 'val_context.txt')
+        elif mode == 'trainval':
+            file_path = os.path.join(image_set_dir, 'trainval_context.txt')
+        if not os.path.exists(file_path):
+                raise RuntimeError(
+                    "PASCAL-Context annotations are not ready, "
+                    "Please make sure voc_context.py has been properly run.")
+
+        img_dir = os.path.join(self.dataset_root,  'JPEGImages')
+        label_dir = os.path.join(self.dataset_root,  'Context')
+
+        with open(file_path, 'r') as f:
+            for line in f:
+                line = line.strip()
+                image_path = os.path.join(img_dir, ''.join([line, '.jpg']))
+                label_path = os.path.join(label_dir, ''.join([line, '.png']))
+                self.file_list.append([image_path, label_path])

paddleseg/utils/__init__.py

@@ -17,5 +17,5 @@
 from . import metrics
 from .env import seg_env, get_sys_env
 from .utils import *
-from .timer import Timer, calculate_eta
+from .timer import TimeAverager, calculate_eta
 from . import visualize