diff --git a/README.md b/README.md index fb4e9b636d451222729d39bf6e195a2368314299..5bd121b34396b02d200d151f9cd87cc1b819db8e 100644 --- a/README.md +++ b/README.md @@ -1,72 +1,11 @@ -简体中文 | [English](README_en.md) - -文档：[https://paddledetection.readthedocs.io](https://paddledetection.readthedocs.io) - # PaddleDetection -飞桨推出的PaddleDetection是端到端目标检测开发套件，旨在帮助开发者更快更好地完成检测模型的训练、精度速度优化到部署全流程。PaddleDetection以模块化的设计实现了多种主流目标检测算法，并且提供了丰富的数据增强、网络组件、损失函数等模块，集成了模型压缩和跨平台高性能部署能力。目前基于PaddleDetection已经完成落地的项目涉及工业质检、遥感图像检测、无人巡检等多个领域。 - -**目前检测库下模型均要求使用PaddlePaddle 1.7及以上版本或适当的develop版本。** - -
- -
- - -## 简介 - -特性： - -- 模型丰富： - - PaddleDetection提供了丰富的模型，包含目标检测、实例分割、人脸检测等100+个预训练模型，涵盖多种数据集竞赛冠军方案、适合云端/边缘端设备部署的检测方案。 - -- 易部署: - - PaddleDetection的模型中使用的核心算子均通过C++或CUDA实现，同时基于PaddlePaddle的高性能推理引擎可以方便地部署在多种硬件平台上。 - -- 高灵活度： - - PaddleDetection通过模块化设计来解耦各个组件，基于配置文件可以轻松地搭建各种检测模型。 - -- 高性能： - - 基于PaddlePaddle框架的高性能内核，在模型训练速度、显存占用上有一定的优势。例如，YOLOv3的训练速度快于其他框架，在Tesla V100 16GB环境下，Mask-RCNN(ResNet50)可以单卡Batch Size可以达到4 (甚至到5)。 - +动态图版本的PaddleDetection, 支持的模型: -支持的模型结构： - -| | ResNet | ResNet-vd [1](#vd) | ResNeXt-vd | SENet | MobileNet | HRNet | Res2Net | -|--------------------|:------:|------------------------------:|:----------:|:-----:|:---------:|:------:| :--: | -| Faster R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ | ✗ | -| Faster R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ | -| Mask R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ | ✗ | -| Mask R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ | -| Cascade Faster-RCNN | ✓ | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ | -| Cascade Mask-RCNN | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | -| Libra R-CNN | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | -| RetinaNet | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | -| YOLOv3 | ✓ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | -| SSD | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | -| BlazeFace | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | -| Faceboxes | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | - -[1] [ResNet-vd](https://arxiv.org/pdf/1812.01187) 模型预测速度基本不变的情况下提高了精度。 - -更多的模型: - -- EfficientDet -- FCOS -- CornerNet-Squeeze -- YOLOv4 - -更多的Backone： - -- DarkNet -- VGG -- GCNet -- CBNet -- Hourglass +- Faster-RCNN (FPN) +- Mask-RCNN (FPN) +- Cascade RCNN +- YOLOv3 扩展特性： @@ -74,45 +13,15 @@ - [x] **Group Norm** - [x] **Modulated Deformable Convolution** - [x] **Deformable PSRoI Pooling** -- [x] **Non-local和GCNet** - -**注意:** Synchronized batch normalization 只能在多GPU环境下使用，不能在CPU环境或者单GPU环境下使用。 - -以下为选取各模型结构和骨干网络的代表模型COCO数据集精度mAP和单卡Tesla V100上预测速度(FPS)关系图。 - -
- -
- -**说明：** -- `CBResNet`为`Cascade-Faster-RCNN-CBResNet200vd-FPN`模型，COCO数据集mAP高达53.3% -- `Cascade-Faster-RCNN`为`Cascade-Faster-RCNN-ResNet50vd-DCN`，PaddleDetection将其优化到COCO数据mAP为47.8%时推理速度为20FPS -- PaddleDetection增强版`YOLOv3-ResNet50vd-DCN`在COCO数据集mAP高于原作10.6个绝对百分点，推理速度为61.3FPS，快于原作约70% -- 图中模型均可在[模型库](#模型库)中获取 ## 文档教程 -### 入门教程 +### 教程 - [安装说明](docs/tutorials/INSTALL_cn.md) - [快速开始](docs/tutorials/QUICK_STARTED_cn.md) - [训练/评估/预测流程](docs/tutorials/GETTING_STARTED_cn.md) - [常见问题汇总](docs/FAQ.md) - -### 进阶教程 -- [数据预处理及自定义数据集](docs/advanced_tutorials/READER.md) -- [搭建模型步骤](docs/advanced_tutorials/MODEL_TECHNICAL.md) -- [模型参数配置](docs/advanced_tutorials/config_doc): - - [配置模块设计和介绍](docs/advanced_tutorials/config_doc/CONFIG_cn.md) - - [RCNN模型参数说明](docs/advanced_tutorials/config_doc/RCNN_PARAMS_DOC.md) -- [迁移学习教程](docs/advanced_tutorials/TRANSFER_LEARNING_cn.md) -- [IPython Notebook demo](demo/mask_rcnn_demo.ipynb) -- [模型压缩](slim) - - [压缩benchmark](slim) - - [量化](slim/quantization) - - [剪枝](slim/prune) - - [蒸馏](slim/distillation) - - [神经网络搜索](slim/nas) - [推理部署](deploy) - [模型导出教程](docs/advanced_tutorials/deploy/EXPORT_MODEL.md) - [Python端推理部署](deploy/python) @@ -120,25 +29,7 @@ - [推理Benchmark](docs/advanced_tutorials/deploy/BENCHMARK_INFER_cn.md) ## 模型库 - - [模型库](docs/MODEL_ZOO_cn.md) -- [移动端模型](configs/mobile/README.md) -- [Anchor free模型](configs/anchor_free/README.md) -- [人脸检测模型](docs/featured_model/FACE_DETECTION.md) -- [YOLOv3增强模型](docs/featured_model/YOLOv3_ENHANCEMENT.md): COCO mAP高达43.6%，原论文精度为33.0% -- [行人检测预训练模型](docs/featured_model/CONTRIB_cn.md) -- [车辆检测预训练模型](docs/featured_model/CONTRIB_cn.md) -- [Objects365 2019 Challenge夺冠模型](docs/featured_model/champion_model/CACascadeRCNN.md) -- [Open Images 2019-Object Detction比赛最佳单模型](docs/featured_model/champion_model/OIDV5_BASELINE_MODEL.md) -- [服务器端实用目标检测模型](configs/rcnn_enhance/README.md): V100上速度20FPS时，COCO mAP高达47.8%。 - ## 许可证书 本项目的发布受[Apache 2.0 license](LICENSE)许可认证。 - -## 版本更新 -v0.3.0版本已经在`05/2020`发布，增加Anchor-free、EfficientDet和YOLOv4等多个模型，推出移动端、服务器端实用高效多个模型，例如移动端将YOLOv3-MobileNetv3加速3.5倍，服务器端优化两阶段模型，速度和精度具备较高性价比。重构预测部署功能，提升易用性，修复已知诸多bug等，详细内容请参考[版本更新文档](docs/CHANGELOG.md)。 - -## 如何贡献代码 - -我们非常欢迎你可以为PaddleDetection提供代码，也十分感谢你的反馈。 diff --git a/dataset/fddb/download.sh b/dataset/fddb/download.sh deleted file mode 100755 index 29375d791269d684a99dc82e0ab5973d6ebdf267..0000000000000000000000000000000000000000 --- a/dataset/fddb/download.sh +++ /dev/null @@ -1,31 +0,0 @@ -# All rights `PaddleDetection` reserved -# References: -# @TechReport{fddbTech, -# author = {Vidit Jain and Erik Learned-Miller}, -# title = {FDDB: A Benchmark for Face Detection in Unconstrained Settings}, -# institution = {University of Massachusetts, Amherst}, -# year = {2010}, -# number = {UM-CS-2010-009} -# } - -DIR="$( cd "$(dirname "$0")" ; pwd -P )" -cd "$DIR" - -# Download the data. -echo "Downloading..." -# external link to the Faces in the Wild data set and annotations file -wget http://tamaraberg.com/faceDataset/originalPics.tar.gz -wget http://vis-www.cs.umass.edu/fddb/FDDB-folds.tgz -wget http://vis-www.cs.umass.edu/fddb/evaluation.tgz - -# Extract the data. -echo "Extracting..." -tar -zxf originalPics.tar.gz -tar -zxf FDDB-folds.tgz -tar -zxf evaluation.tgz - -# Generate full image path list and groundtruth in FDDB-folds: -cd FDDB-folds -cat `ls|grep -v"ellipse"` > filePath.txt && cat *ellipse* > fddb_annotFile.txt -cd .. -echo "------------- All done! --------------" diff --git a/dataset/fruit/download_fruit.py b/dataset/fruit/download_fruit.py deleted file mode 100644 index 2db2e207210c4bab39e8dfdb3abe91a51c49af1f..0000000000000000000000000000000000000000 --- a/dataset/fruit/download_fruit.py +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright (c) 2019 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 sys -import os.path as osp -import logging -# add python path of PadleDetection to sys.path -parent_path = osp.abspath(osp.join(__file__, *(['..'] * 3))) -if parent_path not in sys.path: - sys.path.append(parent_path) - -from ppdet.utils.download import download_dataset - -logging.basicConfig(level=logging.INFO) - -download_path = osp.split(osp.realpath(sys.argv[0]))[0] -download_dataset(download_path, 'fruit') diff --git a/dataset/fruit/label_list.txt b/dataset/fruit/label_list.txt deleted file mode 100644 index 1f60d62c399939cd92e667c1fb938764b3ec2901..0000000000000000000000000000000000000000 --- a/dataset/fruit/label_list.txt +++ /dev/null @@ -1,3 +0,0 @@ -apple -banana -orange diff --git a/dataset/wider_face/download.sh b/dataset/wider_face/download.sh deleted file mode 100755 index 6c86a22c6826d88846a16fbd43f8b556d8610b8f..0000000000000000000000000000000000000000 --- a/dataset/wider_face/download.sh +++ /dev/null @@ -1,21 +0,0 @@ -# All rights `PaddleDetection` reserved -# References: -# @inproceedings{yang2016wider, -# Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou}, -# Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, -# Title = {WIDER FACE: A Face Detection Benchmark}, -# Year = {2016}} - -DIR="$( cd "$(dirname "$0")" ; pwd -P )" -cd "$DIR" - -# Download the data. -echo "Downloading..." -wget https://dataset.bj.bcebos.com/wider_face/WIDER_train.zip -wget https://dataset.bj.bcebos.com/wider_face/WIDER_val.zip -wget https://dataset.bj.bcebos.com/wider_face/wider_face_split.zip -# Extract the data. -echo "Extracting..." -unzip WIDER_train.zip -unzip WIDER_val.zip -unzip wider_face_split.zip diff --git a/ppdet/data/tests/test.yml b/ppdet/data/tests/test.yml deleted file mode 100644 index 885d5ab1ea5d1dfc28be3468f3d22beaaecee05e..0000000000000000000000000000000000000000 --- a/ppdet/data/tests/test.yml +++ /dev/null @@ -1,73 +0,0 @@ -TrainReader: - inputs_def: - fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd', 'gt_mask'] - dataset: - !COCODataSet - image_dir: val2017 - anno_path: annotations/instances_val2017.json - dataset_dir: dataset/coco - sample_num: 10 - sample_transforms: - - !DecodeImage - to_rgb: true - with_mixup: false - - !RandomFlipImage - is_mask_flip: true - is_normalized: false - prob: 0.5 - - !NormalizeImage - is_channel_first: false - is_scale: true - mean: [0.485,0.456,0.406] - std: [0.229, 0.224,0.225] - - !ResizeImage - interp: 1 - max_size: 1333 - target_size: 800 - use_cv2: true - - !Permute - channel_first: true - to_bgr: false - batch_transforms: - - !PadBatch - pad_to_stride: 32 - use_padded_im_info: false - batch_size: 1 - shuffle: true - worker_num: 2 - drop_last: false - use_process: false - -EvalReader: - inputs_def: - fields: ['image', 'im_info', 'im_id'] - dataset: - !COCODataSet - image_dir: val2017 - anno_path: annotations/instances_val2017.json - dataset_dir: dataset/coco - sample_num: 10 - sample_transforms: - - !DecodeImage - to_rgb: true - with_mixup: false - - !NormalizeImage - is_channel_first: false - is_scale: true - mean: [0.485,0.456,0.406] - std: [0.229, 0.224,0.225] - - !ResizeImage - interp: 1 - max_size: 1333 - target_size: 800 - use_cv2: true - - !Permute - channel_first: true - to_bgr: false - batch_transforms: - - !PadBatch - pad_to_stride: 32 - use_padded_im_info: true - batch_size: 1 - shuffle: false - drop_last: false diff --git a/ppdet/data/tests/test_dataset.py b/ppdet/data/tests/test_dataset.py deleted file mode 100644 index 86e87257df87a4cc0b8629e449a38f3a0cf2df37..0000000000000000000000000000000000000000 --- a/ppdet/data/tests/test_dataset.py +++ /dev/null @@ -1,150 +0,0 @@ -# Copyright (c) 2019 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 -import time -import unittest -import sys -import logging -import random -import copy -# add python path of PadleDetection to sys.path -parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 4))) -if parent_path not in sys.path: - sys.path.append(parent_path) - -from ppdet.data.parallel_map import ParallelMap - - -class MemorySource(object): - """ memory data source for testing - """ - - def __init__(self, samples): - self._epoch = -1 - - self._pos = -1 - self._drained = False - self._samples = samples - - def __iter__(self): - return self - - def __next__(self): - return self.next() - - def next(self): - if self._epoch < 0: - self.reset() - - if self._pos >= self.size(): - self._drained = True - raise StopIteration("no more data in " + str(self)) - else: - sample = copy.deepcopy(self._samples[self._pos]) - self._pos += 1 - return sample - - def reset(self): - if self._epoch < 0: - self._epoch = 0 - else: - self._epoch += 1 - - self._pos = 0 - self._drained = False - random.shuffle(self._samples) - - def size(self): - return len(self._samples) - - def drained(self): - assert self._epoch >= 0, "the first epoch has not started yet" - return self._pos >= self.size() - - def epoch_id(self): - return self._epoch - - -class TestDataset(unittest.TestCase): - """Test cases for ppdet.data.dataset - """ - - @classmethod - def setUpClass(cls): - """ setup - """ - pass - - @classmethod - def tearDownClass(cls): - """ tearDownClass """ - pass - - def test_next(self): - """ test next - """ - samples = list(range(10)) - mem_sc = MemorySource(samples) - - for i, d in enumerate(mem_sc): - self.assertTrue(d in samples) - - def test_transform_with_abnormal_worker(self): - """ test dataset transform with abnormally exit process - """ - samples = list(range(20)) - mem_sc = MemorySource(samples) - - def _worker(sample): - if sample == 3: - sys.exit(1) - - return 2 * sample - - test_worker = ParallelMap( - mem_sc, _worker, worker_num=2, use_process=True, memsize='2M') - - ct = 0 - for i, d in enumerate(test_worker): - ct += 1 - self.assertTrue(d / 2 in samples) - - self.assertEqual(len(samples) - 1, ct) - - def test_transform_with_delay_worker(self): - """ test dataset transform with delayed process - """ - samples = list(range(20)) - mem_sc = MemorySource(samples) - - def _worker(sample): - if sample == 3: - time.sleep(30) - - return 2 * sample - - test_worker = ParallelMap( - mem_sc, _worker, worker_num=2, use_process=True, memsize='2M') - - ct = 0 - for i, d in enumerate(test_worker): - ct += 1 - self.assertTrue(d / 2 in samples) - - self.assertEqual(len(samples), ct) - - -if __name__ == '__main__': - logging.basicConfig() - unittest.main() diff --git a/ppdet/data/tests/test_loader.py b/ppdet/data/tests/test_loader.py deleted file mode 100644 index 26c990a16f35e6d248fb4d6c9f38cd3bfd2410f8..0000000000000000000000000000000000000000 --- a/ppdet/data/tests/test_loader.py +++ /dev/null @@ -1,171 +0,0 @@ -# Copyright (c) 2019 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 unittest -import os -import sys -# add python path of PadleDetection to sys.path -parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 4))) -if parent_path not in sys.path: - sys.path.append(parent_path) - -from ppdet.data.source.coco import COCODataSet -from ppdet.data.reader import Reader -from ppdet.utils.download import get_path -from ppdet.utils.download import DATASET_HOME - -from ppdet.data.transform.operators import DecodeImage, ResizeImage, Permute -from ppdet.data.transform.batch_operators import PadBatch - -COCO_VAL_URL = 'http://images.cocodataset.org/zips/val2017.zip' -COCO_VAL_MD5SUM = '442b8da7639aecaf257c1dceb8ba8c80' -COCO_ANNO_URL = 'http://images.cocodataset.org/annotations/annotations_trainval2017.zip' -COCO_ANNO_MD5SUM = 'f4bbac642086de4f52a3fdda2de5fa2c' - - -class TestReader(unittest.TestCase): - @classmethod - def setUpClass(cls): - """ setup - """ - root_path = os.path.join(DATASET_HOME, 'coco') - _, _ = get_path(COCO_VAL_URL, root_path, COCO_VAL_MD5SUM) - _, _ = get_path(COCO_ANNO_URL, root_path, COCO_ANNO_MD5SUM) - cls.anno_path = 'annotations/instances_val2017.json' - cls.image_dir = 'val2017' - cls.root_path = root_path - - @classmethod - def tearDownClass(cls): - """ tearDownClass """ - pass - - def test_loader(self): - coco_loader = COCODataSet( - dataset_dir=self.root_path, - image_dir=self.image_dir, - anno_path=self.anno_path, - sample_num=10) - sample_trans = [ - DecodeImage(to_rgb=True), ResizeImage( - target_size=800, max_size=1333, interp=1), Permute(to_bgr=False) - ] - batch_trans = [PadBatch(pad_to_stride=32, use_padded_im_info=True), ] - - inputs_def = { - 'fields': [ - 'image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd', - 'gt_mask' - ], - } - data_loader = Reader( - coco_loader, - sample_transforms=sample_trans, - batch_transforms=batch_trans, - batch_size=2, - shuffle=True, - drop_empty=True, - inputs_def=inputs_def)() - for i in range(2): - for samples in data_loader: - for sample in samples: - im_shape = sample[0].shape - self.assertEqual(im_shape[0], 3) - self.assertEqual(im_shape[1] % 32, 0) - self.assertEqual(im_shape[2] % 32, 0) - - im_info_shape = sample[1].shape - self.assertEqual(im_info_shape[-1], 3) - - im_id_shape = sample[2].shape - self.assertEqual(im_id_shape[-1], 1) - - gt_bbox_shape = sample[3].shape - self.assertEqual(gt_bbox_shape[-1], 4) - - gt_class_shape = sample[4].shape - self.assertEqual(gt_class_shape[-1], 1) - self.assertEqual(gt_class_shape[0], gt_bbox_shape[0]) - - is_crowd_shape = sample[5].shape - self.assertEqual(is_crowd_shape[-1], 1) - self.assertEqual(is_crowd_shape[0], gt_bbox_shape[0]) - - mask = sample[6] - self.assertEqual(len(mask), gt_bbox_shape[0]) - self.assertEqual(mask[0][0].shape[-1], 2) - data_loader.reset() - - def test_loader_multi_threads(self): - coco_loader = COCODataSet( - dataset_dir=self.root_path, - image_dir=self.image_dir, - anno_path=self.anno_path, - sample_num=10) - sample_trans = [ - DecodeImage(to_rgb=True), ResizeImage( - target_size=800, max_size=1333, interp=1), Permute(to_bgr=False) - ] - batch_trans = [PadBatch(pad_to_stride=32, use_padded_im_info=True), ] - - inputs_def = { - 'fields': [ - 'image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd', - 'gt_mask' - ], - } - data_loader = Reader( - coco_loader, - sample_transforms=sample_trans, - batch_transforms=batch_trans, - batch_size=2, - shuffle=True, - drop_empty=True, - worker_num=2, - use_process=False, - bufsize=8, - inputs_def=inputs_def)() - for i in range(2): - for samples in data_loader: - for sample in samples: - im_shape = sample[0].shape - self.assertEqual(im_shape[0], 3) - self.assertEqual(im_shape[1] % 32, 0) - self.assertEqual(im_shape[2] % 32, 0) - - im_info_shape = sample[1].shape - self.assertEqual(im_info_shape[-1], 3) - - im_id_shape = sample[2].shape - self.assertEqual(im_id_shape[-1], 1) - - gt_bbox_shape = sample[3].shape - self.assertEqual(gt_bbox_shape[-1], 4) - - gt_class_shape = sample[4].shape - self.assertEqual(gt_class_shape[-1], 1) - self.assertEqual(gt_class_shape[0], gt_bbox_shape[0]) - - is_crowd_shape = sample[5].shape - self.assertEqual(is_crowd_shape[-1], 1) - self.assertEqual(is_crowd_shape[0], gt_bbox_shape[0]) - - mask = sample[6] - self.assertEqual(len(mask), gt_bbox_shape[0]) - self.assertEqual(mask[0][0].shape[-1], 2) - data_loader.reset() - - -if __name__ == '__main__': - unittest.main() diff --git a/ppdet/data/tests/test_loader_yaml.py b/ppdet/data/tests/test_loader_yaml.py deleted file mode 100644 index b0b526586d4e4a05c9e036a7ad864309b5ced403..0000000000000000000000000000000000000000 --- a/ppdet/data/tests/test_loader_yaml.py +++ /dev/null @@ -1,115 +0,0 @@ -# Copyright (c) 2019 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 unittest -import os -import yaml -import logging -import sys -# add python path of PadleDetection to sys.path -parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 4))) -if parent_path not in sys.path: - sys.path.append(parent_path) - -from ppdet.utils.download import get_path -from ppdet.utils.download import DATASET_HOME -from ppdet.core.workspace import load_config, merge_config - -from ppdet.data.reader import create_reader - -COCO_VAL_URL = 'http://images.cocodataset.org/zips/val2017.zip' -COCO_VAL_MD5SUM = '442b8da7639aecaf257c1dceb8ba8c80' -COCO_ANNO_URL = 'http://images.cocodataset.org/annotations/annotations_trainval2017.zip' -COCO_ANNO_MD5SUM = 'f4bbac642086de4f52a3fdda2de5fa2c' - -FORMAT = '[%(asctime)s-%(filename)s-%(levelname)s:%(message)s]' -logging.basicConfig(level=logging.INFO, format=FORMAT) -logger = logging.getLogger(__name__) - - -class TestReaderYAML(unittest.TestCase): - @classmethod - def setUpClass(cls): - """ setup - """ - root_path = os.path.join(DATASET_HOME, 'coco') - _, _ = get_path(COCO_VAL_URL, root_path, COCO_VAL_MD5SUM) - _, _ = get_path(COCO_ANNO_URL, root_path, COCO_ANNO_MD5SUM) - cls.anno_path = 'annotations/instances_val2017.json' - cls.image_dir = 'val2017' - cls.root_path = root_path - - @classmethod - def tearDownClass(cls): - """ tearDownClass """ - pass - - def test_loader_yaml(self): - cfg_file = 'ppdet/data/tests/test.yml' - cfg = load_config(cfg_file) - data_cfg = '[!COCODataSet {{image_dir: {0}, dataset_dir: {1}, ' \ - 'anno_path: {2}, sample_num: 10}}]'.format( - self.image_dir, self.root_path, self.anno_path) - dataset_ins = yaml.load(data_cfg, Loader=yaml.Loader) - update_train_cfg = {'TrainReader': {'dataset': dataset_ins[0]}} - update_test_cfg = {'EvalReader': {'dataset': dataset_ins[0]}} - merge_config(update_train_cfg) - merge_config(update_test_cfg) - - reader = create_reader(cfg['TrainReader'], 10)() - for samples in reader: - for sample in samples: - im_shape = sample[0].shape - self.assertEqual(im_shape[0], 3) - self.assertEqual(im_shape[1] % 32, 0) - self.assertEqual(im_shape[2] % 32, 0) - - im_info_shape = sample[1].shape - self.assertEqual(im_info_shape[-1], 3) - - im_id_shape = sample[2].shape - self.assertEqual(im_id_shape[-1], 1) - - gt_bbox_shape = sample[3].shape - self.assertEqual(gt_bbox_shape[-1], 4) - - gt_class_shape = sample[4].shape - self.assertEqual(gt_class_shape[-1], 1) - self.assertEqual(gt_class_shape[0], gt_bbox_shape[0]) - - is_crowd_shape = sample[5].shape - self.assertEqual(is_crowd_shape[-1], 1) - self.assertEqual(is_crowd_shape[0], gt_bbox_shape[0]) - - mask = sample[6] - self.assertEqual(len(mask), gt_bbox_shape[0]) - self.assertEqual(mask[0][0].shape[-1], 2) - - reader = create_reader(cfg['EvalReader'], 10)() - for samples in reader: - for sample in samples: - im_shape = sample[0].shape - self.assertEqual(im_shape[0], 3) - self.assertEqual(im_shape[1] % 32, 0) - self.assertEqual(im_shape[2] % 32, 0) - - im_info_shape = sample[1].shape - self.assertEqual(im_info_shape[-1], 3) - - im_id_shape = sample[2].shape - self.assertEqual(im_id_shape[-1], 1) - - -if __name__ == '__main__': - unittest.main() diff --git a/ppdet/ext_op/README.md b/ppdet/ext_op/README.md deleted file mode 100644 index 0516cc70289d5238d4f3cf9299b7c133529154df..0000000000000000000000000000000000000000 --- a/ppdet/ext_op/README.md +++ /dev/null @@ -1,77 +0,0 @@ -# 自定义OP的编译过程 - -**注意：** 编译自定义OP使用的gcc版本须与Paddle编译使用gcc版本一致，Paddle develop每日版本目前采用**gcc 4.8.2**版本编译，若使用每日版本，请使用**gcc 4.8.2**版本编译自定义OP，否则可能出现兼容性问题。 - -## 代码结构 - - - src: 扩展OP C++/CUDA 源码 - - cornerpool_lib.py: Python API封装 - - tests: 各OP单测程序 - - -## 编译自定义OP - -自定义op需要将实现的C++、CUDA代码编译成动态库，```src/mask.sh```中通过g++/nvcc编译，当然您也可以写Makefile或者CMake。 - -编译需要include PaddlePaddle的相关头文件，链接PaddlePaddle的lib库。 头文件和lib库可通过下面命令获取到: - -``` -# python ->>> import paddle ->>> print(paddle.sysconfig.get_include()) -/paddle/pyenv/local/lib/python2.7/site-packages/paddle/include ->>> print(paddle.sysconfig.get_lib()) -/paddle/pyenv/local/lib/python2.7/site-packages/paddle/libs -``` - -我们提供动态库编译脚本如下： - -``` -cd src -sh make.sh -``` - -最终编译会产出`cornerpool_lib.so` - -**说明：** 若使用源码编译安装PaddlePaddle的方式，编译过程中`cmake`未设置`WITH_MKLDNN`的方式， -编译自定义OP时会报错找不到`mkldnn.h`等文件，可在`make.sh`中删除编译命令中的`-DPADDLE_WITH_MKLDNN`选项。 - - -## 设置环境变量 - -需要将Paddle的核心库设置到`LD_LIBRARY_PATH`里, 先运行下面程序获取路径: - -``` -import paddle -print(paddle.sysconfig.get_lib()) -``` - -可通过如下方式添加动态库路径: - -``` -export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:`python -c 'import paddle; print(paddle.sysconfig.get_lib())'` -``` - - - -## 执行单测 - -执行下列单测，确保自定义算子可在网络中正确使用： - -``` -# 回到 ext_op 目录，运行单测 -cd .. -python test/test_corner_pool.py -``` - -单测运行成功会输出提示信息，如下所示： - -``` -. ----------------------------------------------------------------------- -Ran 4 test in 2.858s - -OK -``` - -更多关于如何在框架外部自定义 C++ OP，可阅读[官网说明文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_usage/index_cn.html) diff --git a/ppdet/ext_op/__init__.py b/ppdet/ext_op/__init__.py deleted file mode 100644 index 5d38f757f866ce724d4e079484e45797a9e3bdb9..0000000000000000000000000000000000000000 --- a/ppdet/ext_op/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. -# -#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. - -from . import cornerpool_lib -from .cornerpool_lib import * - -__all__ = cornerpool_lib.__all__ diff --git a/ppdet/ext_op/cornerpool_lib.py b/ppdet/ext_op/cornerpool_lib.py deleted file mode 100644 index 4cae5a088c66bd945f4987a9201fe0914d54f83c..0000000000000000000000000000000000000000 --- a/ppdet/ext_op/cornerpool_lib.py +++ /dev/null @@ -1,255 +0,0 @@ -import os -import paddle.fluid as fluid - -use_cpp = False - -file_dir = os.path.dirname(os.path.abspath(__file__)) -try: - fluid.load_op_library(os.path.join(file_dir, 'src/cornerpool_lib.so')) - use_cpp = True -except: - print( - 'Warning: cornerpool_lib.so not found, use python version instead which may drop the inference speed. Compile in ppdet/ext_op at first if you need cpp version.' - ) - -from paddle.fluid.layer_helper import LayerHelper - -__all__ = [ - 'bottom_pool', - 'top_pool', - 'right_pool', - 'left_pool', -] - - -def cornerpool_op(layer_type, input, name): - helper = LayerHelper(layer_type, input=input, name=name) - dtype = helper.input_dtype() - output = helper.create_variable_for_type_inference(dtype) - max_map = helper.create_variable_for_type_inference(dtype) - helper.append_op( - type=layer_type, - inputs={"X": input}, - outputs={"Output": output, - "MaxMap": max_map}) - return output - - -def bottom_pool(input, is_test=False, name=None): - """ - This layer calculates the bottom pooling output based on the input. - Scan the input from top to bottm for the vertical max-pooling. - The output has the same shape with input. - Args: - input(Variable): This input is a Tensor with shape [N, C, H, W]. - The data type is float32 or float64. - Returns: - Variable(Tensor): The output of bottom_pool, with shape [N, C, H, W]. - The data type is float32 or float64. - Examples: - ..code-block:: python - import paddle.fluid as fluid - import cornerpool_lib - input = fluid.data( - name='input', shape=[2, 64, 10, 10], dtype='float32') - output = corner_pool.bottom_pool(input) - """ - if is_test: - if use_cpp: - output = cornerpool_op("bottom_pool", input, name) - return output - - def cond(i, output): - return i < H - - def body(i, output): - cur = fluid.layers.slice(output, [2], [i], [H]) - next = fluid.layers.slice(output, [2], [0], [H - i]) - max_v = fluid.layers.elementwise_max(cur, next) - orig = fluid.layers.slice(output, [2], [0], [i]) - output = fluid.layers.concat([orig, max_v], axis=2) - i = i * 2 - return [i, output] - - H = fluid.layers.shape(input)[2] - i = fluid.layers.fill_constant(shape=[1], dtype='int32', value=1) - output = input - output = fluid.layers.while_loop(cond, body, [i, output]) - return output[-1] - - H = input.shape[2] - i = 1 - output = input - while i < H: - cur = output[:, :, i:, :] - next = output[:, :, :H - i, :] - max_v = fluid.layers.elementwise_max(cur, next) - output = fluid.layers.concat([output[:, :, :i, :], max_v], axis=2) - i *= 2 - - return output - - -def top_pool(input, is_test=False, name=None): - """ - This layer calculates the top pooling output based on the input. - Scan the input from bottom to top for the vertical max-pooling. - The output has the same shape with input. - Args: - input(Variable): This input is a Tensor with shape [N, C, H, W]. - The data type is float32 or float64. - Returns: - Variable(Tensor): The output of top_pool, with shape [N, C, H, W]. - The data type is float32 or float64. - Examples: - ..code-block:: python - import paddle.fluid as fluid - import cornerpool_lib - input = fluid.data( - name='input', shape=[2, 64, 10, 10], dtype='float32') - output = corner_pool.top_pool(input) - """ - if is_test: - if use_cpp: - output = cornerpool_op("top_pool", input, name) - return output - - def cond(i, output): - return i < H - - def body(i, output): - cur = fluid.layers.slice(output, [2], [0], [H - i]) - next = fluid.layers.slice(output, [2], [i], [H]) - max_v = fluid.layers.elementwise_max(cur, next) - orig = fluid.layers.slice(output, [2], [H - i], [H]) - output = fluid.layers.concat([max_v, orig], axis=2) - i = i * 2 - return [i, output] - - H = fluid.layers.shape(input)[2] - i = fluid.layers.fill_constant(shape=[1], dtype='int32', value=1) - output = input - output = fluid.layers.while_loop(cond, body, [i, output]) - return output[-1] - - H = input.shape[2] - i = 1 - output = input - while i < H: - cur = output[:, :, :H - i, :] - next = output[:, :, i:, :] - max_v = fluid.layers.elementwise_max(cur, next) - output = fluid.layers.concat([max_v, output[:, :, H - i:, :]], axis=2) - i *= 2 - - return output - - -def right_pool(input, is_test=False, name=None): - """ - This layer calculates the right pooling output based on the input. - Scan the input from left to right for the horizontal max-pooling. - The output has the same shape with input. - Args: - input(Variable): This input is a Tensor with shape [N, C, H, W]. - The data type is float32 or float64. - Returns: - Variable(Tensor): The output of right_pool, with shape [N, C, H, W]. - The data type is float32 or float64. - Examples: - ..code-block:: python - import paddle.fluid as fluid - import cornerpool_lib - input = fluid.data( - name='input', shape=[2, 64, 10, 10], dtype='float32') - output = corner_pool.right_pool(input) - """ - if is_test: - if use_cpp: - output = cornerpool_op("right_pool", input, name) - return output - - def cond(i, output): - return i < W - - def body(i, output): - cur = fluid.layers.slice(output, [3], [i], [W]) - next = fluid.layers.slice(output, [3], [0], [W - i]) - max_v = fluid.layers.elementwise_max(cur, next) - orig = fluid.layers.slice(output, [3], [0], [i]) - output = fluid.layers.concat([orig, max_v], axis=-1) - i = i * 2 - return [i, output] - - W = fluid.layers.shape(input)[3] - i = fluid.layers.fill_constant(shape=[1], dtype='int32', value=1) - output = input - output = fluid.layers.while_loop(cond, body, [i, output]) - return output[-1] - - W = input.shape[3] - i = 1 - output = input - while i < W: - cur = output[:, :, :, i:] - next = output[:, :, :, :W - i] - max_v = fluid.layers.elementwise_max(cur, next) - output = fluid.layers.concat([output[:, :, :, :i], max_v], axis=-1) - i *= 2 - - return output - - -def left_pool(input, is_test=False, name=None): - """ - This layer calculates the left pooling output based on the input. - Scan the input from right to left for the horizontal max-pooling. - The output has the same shape with input. - Args: - input(Variable): This input is a Tensor with shape [N, C, H, W]. - The data type is float32 or float64. - Returns: - Variable(Tensor): The output of left_pool, with shape [N, C, H, W]. - The data type is float32 or float64. - Examples: - ..code-block:: python - import paddle.fluid as fluid - import cornerpool_lib - input = fluid.data( - name='input', shape=[2, 64, 10, 10], dtype='float32') - output = corner_pool.left_pool(input) - """ - if is_test: - if use_cpp: - output = cornerpool_op("left_pool", input, name) - return output - - def cond(i, output): - return i < W - - def body(i, output): - cur = fluid.layers.slice(output, [3], [0], [W - i]) - next = fluid.layers.slice(output, [3], [i], [W]) - max_v = fluid.layers.elementwise_max(cur, next) - orig = fluid.layers.slice(output, [3], [W - i], [W]) - output = fluid.layers.concat([max_v, orig], axis=-1) - i = i * 2 - return [i, output] - - W = fluid.layers.shape(input)[3] - i = fluid.layers.fill_constant(shape=[1], dtype='int32', value=1) - output = input - output = fluid.layers.while_loop(cond, body, [i, output]) - return output[-1] - - W = input.shape[3] - i = 1 - output = input - while i < W: - cur = output[:, :, :, :W - i] - next = output[:, :, :, i:] - max_v = fluid.layers.elementwise_max(cur, next) - output = fluid.layers.concat([max_v, output[:, :, :, W - i:]], axis=-1) - i *= 2 - - return output diff --git a/ppdet/ext_op/src/bottom_pool_op.cc b/ppdet/ext_op/src/bottom_pool_op.cc deleted file mode 100644 index 6a867d1f127a34a9f93a0d0719a5dba039466f24..0000000000000000000000000000000000000000 --- a/ppdet/ext_op/src/bottom_pool_op.cc +++ /dev/null @@ -1,101 +0,0 @@ -/* Copyright (c) 2019 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. */ - -#include "paddle/fluid/framework/op_registry.h" -namespace paddle { -namespace operators { - -using Tensor = framework::Tensor; - -class BottomPoolOp : public framework::OperatorWithKernel { -public: - using framework::OperatorWithKernel::OperatorWithKernel; - - void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); - ctx->ShareDim("X", /*->*/ "MaxMap"); - ctx->ShareDim("X", /*->*/ "Output"); - } - -protected: - framework::OpKernelType GetExpectedKernelType( - const framework::ExecutionContext& ctx) const override { - return framework::OpKernelType(ctx.Input("X")->type(), - ctx.GetPlace()); - } -}; - -class BottomPoolOpMaker : public framework::OpProtoAndCheckerMaker { -public: - void Make() override { - AddInput("X", - "Input with shape (batch, C, H, W)"); - AddOutput("MaxMap", "Max map with index of maximum value of input"); - AddOutput("Output", "output with same shape as input(X)"); - AddComment( - R"Doc( -This operatio calculates the bottom pooling output based on the input. -Scan the input from top to bottom for the vertical max-pooling. -The output has the same shape with input. - )Doc"); - } -}; - -class BottomPoolOpGrad : public framework::OperatorWithKernel { -public: - using framework::OperatorWithKernel::OperatorWithKernel; - -protected: - void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); - PADDLE_ENFORCE(ctx->HasInput("MaxMap"), "Input(MaxMap) should not be null"); - PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Output")), - "Input(Output@GRAD) should not be null"); - auto out_grad_name = framework::GradVarName("Output"); - ctx->ShareDim(out_grad_name, framework::GradVarName("X")); - } - - framework::OpKernelType GetExpectedKernelType( - const framework::ExecutionContext& ctx) const override { - return framework::OpKernelType( - ctx.Input(framework::GradVarName("Output"))->type(), - ctx.GetPlace()); - } -}; - -template -class BottomPoolGradDescMaker : public framework::SingleGradOpMaker { -public: - using framework::SingleGradOpMaker::SingleGradOpMaker; - -protected: - void Apply(GradOpPtr op) const override { - op->SetType("bottom_pool_grad"); - op->SetInput("X", this->Input("X")); - op->SetInput(framework::GradVarName("Output"), this->OutputGrad("Output")); - op->SetInput("MaxMap", this->Output("MaxMap")); - op->SetOutput(framework::GradVarName("X"), this->InputGrad("X")); - op->SetAttrMap(this->Attrs()); - } -}; - -} // namespace operators -} // namespace paddle - -namespace ops = paddle::operators; -REGISTER_OPERATOR(bottom_pool, - ops::BottomPoolOp, - ops::BottomPoolOpMaker, - ops::BottomPoolGradDescMaker, - ops::BottomPoolGradDescMaker); -REGISTER_OPERATOR(bottom_pool_grad, ops::BottomPoolOpGrad); diff --git a/ppdet/ext_op/src/bottom_pool_op.cu b/ppdet/ext_op/src/bottom_pool_op.cu deleted file mode 100644 index 4912ec3c0effb2d924111203168da821aae16b19..0000000000000000000000000000000000000000 --- a/ppdet/ext_op/src/bottom_pool_op.cu +++ /dev/null @@ -1,104 +0,0 @@ -/* Copyright (c) 2019 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. */ - -#include "paddle/fluid/framework/op_registry.h" -#include "paddle/fluid/platform/cuda_primitives.h" -#include "paddle/fluid/memory/memory.h" -#include -#include "util.cu.h" - -namespace paddle { -namespace operators { - -using Tensor = framework::Tensor; - -static constexpr int kNumCUDAThreads = 512; -static constexpr int kNumMaximumNumBlocks = 4096; - -static inline int NumBlocks(const int N) { - return std::min((N + kNumCUDAThreads - 1) / kNumCUDAThreads, - kNumMaximumNumBlocks); -} - -template -class BottomPoolOpCUDAKernel : public framework::OpKernel { -public: - void Compute(const framework::ExecutionContext &ctx) const override { - PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), - "This kernel only runs on GPU device."); - auto *x = ctx.Input("X"); - auto *max_map = ctx.Output("MaxMap"); - auto *output = ctx.Output("Output"); - auto *x_data = x->data(); - auto x_dims = x->dims(); - int NC_num = x_dims[0] * x_dims[1]; - int height = x_dims[2]; - int width = x_dims[3]; - int num = x->numel(); - auto& dev_ctx = ctx.cuda_device_context(); - - int *max_map_data = max_map->mutable_data(x_dims, dev_ctx.GetPlace()); - T *output_data = output->mutable_data(x_dims, dev_ctx.GetPlace()); - auto gpu_place = boost::get(dev_ctx.GetPlace()); - - int threads = kNumCUDAThreads; - int blocks = NumBlocks(num / height); - - auto max_val_ptr = memory::Alloc(gpu_place, num / height * sizeof(T)); - T* max_val_data = reinterpret_cast(max_val_ptr->ptr()); - auto max_ind_ptr = memory::Alloc(gpu_place, num / height * sizeof(int)); - int* max_ind_data = reinterpret_cast(max_ind_ptr->ptr()); - - GetMaxInfo<<>>(x->data(), NC_num, height, width, 2, false, max_val_data, max_ind_data, max_map_data); - - blocks = NumBlocks(num); - ScatterAddFw<<>>(x->data(), max_map_data, NC_num, height, width, 2, output_data); - } -}; - -template -class BottomPoolGradOpCUDAKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& ctx) const override { - auto* x = ctx.Input("X"); - auto* max_map = ctx.Input("MaxMap"); - auto* out_grad = ctx.Input(framework::GradVarName("Output")); - auto* in_grad = ctx.Output(framework::GradVarName("X")); - auto x_dims = x->dims(); - - auto& dev_ctx = ctx.cuda_device_context(); - T* in_grad_data = in_grad->mutable_data(x_dims, dev_ctx.GetPlace()); - auto gpu_place = boost::get(dev_ctx.GetPlace()); - - int threads = kNumCUDAThreads; - int NC_num = x_dims[0] * x_dims[1]; - int height = x_dims[2]; - int width = x_dims[3]; - int grad_num = in_grad->numel(); - int blocks = NumBlocks(grad_num); - FillConstant<<>>(in_grad_data, 0, grad_num); - - ScatterAddBw<<>>(out_grad->data(), max_map->data(), NC_num, height, width, 2, in_grad_data); - } -}; - -} // namespace operators -} // namespace paddle - -namespace ops = paddle::operators; -REGISTER_OP_CUDA_KERNEL(bottom_pool, - ops::BottomPoolOpCUDAKernel, - ops::BottomPoolOpCUDAKernel); -REGISTER_OP_CUDA_KERNEL(bottom_pool_grad, - ops::BottomPoolGradOpCUDAKernel, - ops::BottomPoolGradOpCUDAKernel); diff --git a/ppdet/ext_op/src/left_pool_op.cc b/ppdet/ext_op/src/left_pool_op.cc deleted file mode 100644 index c2a8f169fe0f11b7cebfdb16fb38e111a820df48..0000000000000000000000000000000000000000 --- a/ppdet/ext_op/src/left_pool_op.cc +++ /dev/null @@ -1,101 +0,0 @@ -/* Copyright (c) 2019 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. */ - -#include "paddle/fluid/framework/op_registry.h" -namespace paddle { -namespace operators { - -using Tensor = framework::Tensor; - -class LeftPoolOp : public framework::OperatorWithKernel { -public: - using framework::OperatorWithKernel::OperatorWithKernel; - - void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); - ctx->ShareDim("X", /*->*/ "MaxMap"); - ctx->ShareDim("X", /*->*/ "Output"); - } - -protected: - framework::OpKernelType GetExpectedKernelType( - const framework::ExecutionContext& ctx) const override { - return framework::OpKernelType(ctx.Input("X")->type(), - ctx.GetPlace()); - } -}; - -class LeftPoolOpMaker : public framework::OpProtoAndCheckerMaker { -public: - void Make() override { - AddInput("X", - "Input with shape (batch, C, H, W)"); - AddOutput("MaxMap", "Max map with index of maximum value of input"); - AddOutput("Output", "output with same shape as input(X)"); - AddComment( - R"Doc( -This operatio calculates the left pooling output based on the input. -Scan the input from right to left for the horizontal max-pooling. -The output has the same shape with input. - )Doc"); - } -}; - -class LeftPoolOpGrad : public framework::OperatorWithKernel { -public: - using framework::OperatorWithKernel::OperatorWithKernel; - -protected: - void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); - PADDLE_ENFORCE(ctx->HasInput("MaxMap"), "Input(MaxMap) should not be null"); - PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Output")), - "Input(Output@GRAD) should not be null"); - auto out_grad_name = framework::GradVarName("Output"); - ctx->ShareDim(out_grad_name, framework::GradVarName("X")); - } - - framework::OpKernelType GetExpectedKernelType( - const framework::ExecutionContext& ctx) const override { - return framework::OpKernelType( - ctx.Input(framework::GradVarName("Output"))->type(), - ctx.GetPlace()); - } -}; - -template -class LeftPoolGradDescMaker : public framework::SingleGradOpMaker { -public: - using framework::SingleGradOpMaker::SingleGradOpMaker; - -protected: - void Apply(GradOpPtr op) const override { - op->SetType("left_pool_grad"); - op->SetInput("X", this->Input("X")); - op->SetInput(framework::GradVarName("Output"), this->OutputGrad("Output")); - op->SetInput("MaxMap", this->Output("MaxMap")); - op->SetOutput(framework::GradVarName("X"), this->InputGrad("X")); - op->SetAttrMap(this->Attrs()); - } -}; - -} // namespace operators -} // namespace paddle - -namespace ops = paddle::operators; -REGISTER_OPERATOR(left_pool, - ops::LeftPoolOp, - ops::LeftPoolOpMaker, - ops::LeftPoolGradDescMaker, - ops::LeftPoolGradDescMaker); -REGISTER_OPERATOR(left_pool_grad, ops::LeftPoolOpGrad); diff --git a/ppdet/ext_op/src/left_pool_op.cu b/ppdet/ext_op/src/left_pool_op.cu deleted file mode 100644 index a5e9323adc6e268bf6572cf5470dec841664b6ec..0000000000000000000000000000000000000000 --- a/ppdet/ext_op/src/left_pool_op.cu +++ /dev/null @@ -1,106 +0,0 @@ -/* Copyright (c) 2019 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. */ - -#include "paddle/fluid/framework/op_registry.h" -#include "paddle/fluid/platform/cuda_primitives.h" -#include "paddle/fluid/memory/memory.h" -#include -#include "util.cu.h" - -namespace paddle { -namespace operators { - -using Tensor = framework::Tensor; - -static constexpr int kNumCUDAThreads = 512; -static constexpr int kNumMaximumNumBlocks = 4096; - -static inline int NumBlocks(const int N) { - return std::min((N + kNumCUDAThreads - 1) / kNumCUDAThreads, - kNumMaximumNumBlocks); -} - -template -class LeftPoolOpCUDAKernel : public framework::OpKernel { -public: - void Compute(const framework::ExecutionContext &ctx) const override { - PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), - "This kernel only runs on GPU device."); - auto *x = ctx.Input("X"); - auto *max_map = ctx.Output("MaxMap"); - auto *output = ctx.Output("Output"); - auto *x_data = x->data(); - auto x_dims = x->dims(); - int NC_num = x_dims[0] * x_dims[1]; - int height = x_dims[2]; - int width = x_dims[3]; - int num = x->numel(); - auto& dev_ctx = ctx.cuda_device_context(); - - int *max_map_data = max_map->mutable_data(x_dims, dev_ctx.GetPlace()); - T *output_data = output->mutable_data(x_dims, dev_ctx.GetPlace()); - auto gpu_place = boost::get(dev_ctx.GetPlace()); - - int threads = kNumCUDAThreads; - int blocks = NumBlocks(num / width); - - auto max_val_ptr = memory::Alloc(gpu_place, num / width * sizeof(T)); - T* max_val_data = reinterpret_cast(max_val_ptr->ptr()); - auto max_ind_ptr = memory::Alloc(gpu_place, num / width * sizeof(int)); - int* max_ind_data = reinterpret_cast(max_ind_ptr->ptr()); - - GetMaxInfo<<>>(x->data(), NC_num, height, width, 3, true, max_val_data, max_ind_data, max_map_data); - - blocks = NumBlocks(num); - ScatterAddFw<<>>(x->data(), max_map_data, NC_num, height, width, 3, output_data); - - } -}; - -template -class LeftPoolGradOpCUDAKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& ctx) const override { - auto* x = ctx.Input("X"); - auto* max_map = ctx.Input("MaxMap"); - auto* out_grad = ctx.Input(framework::GradVarName("Output")); - auto* in_grad = ctx.Output(framework::GradVarName("X")); - auto x_dims = x->dims(); - - auto& dev_ctx = ctx.cuda_device_context(); - T* in_grad_data = in_grad->mutable_data(x_dims, dev_ctx.GetPlace()); - auto gpu_place = boost::get(dev_ctx.GetPlace()); - - int threads = kNumCUDAThreads; - int NC_num = x_dims[0] * x_dims[1]; - int height = x_dims[2]; - int width = x_dims[3]; - int grad_num = in_grad->numel(); - int blocks = NumBlocks(grad_num); - FillConstant<<>>(in_grad_data, 0, grad_num); - - ScatterAddBw<<>>(out_grad->data(), max_map->data(), NC_num, height, width, 3, in_grad_data); - } -}; - - -} // namespace operators -} // namespace paddle - -namespace ops = paddle::operators; -REGISTER_OP_CUDA_KERNEL(left_pool, - ops::LeftPoolOpCUDAKernel, - ops::LeftPoolOpCUDAKernel); -REGISTER_OP_CUDA_KERNEL(left_pool_grad, - ops::LeftPoolGradOpCUDAKernel, - ops::LeftPoolGradOpCUDAKernel); diff --git a/ppdet/ext_op/src/make.sh b/ppdet/ext_op/src/make.sh deleted file mode 100755 index ffc66034f89587806044972335b1a40354407264..0000000000000000000000000000000000000000 --- a/ppdet/ext_op/src/make.sh +++ /dev/null @@ -1,21 +0,0 @@ -include_dir=$( python -c 'import paddle; print(paddle.sysconfig.get_include())' ) -lib_dir=$( python -c 'import paddle; print(paddle.sysconfig.get_lib())' ) - -echo $include_dir -echo $lib_dir - -OPS='bottom_pool_op top_pool_op right_pool_op left_pool_op' -for op in ${OPS} -do -nvcc ${op}.cu -c -o ${op}.cu.o -ccbin cc -DPADDLE_WITH_CUDA -DEIGEN_USE_GPU -DPADDLE_USE_DSO -DPADDLE_WITH_MKLDNN -Xcompiler -fPIC -std=c++11 -Xcompiler -fPIC -w --expt-relaxed-constexpr -O0 -g -DNVCC \ - -I ${include_dir}/third_party/ \ - -I ${include_dir} -done - -g++ bottom_pool_op.cc bottom_pool_op.cu.o top_pool_op.cc top_pool_op.cu.o right_pool_op.cc right_pool_op.cu.o left_pool_op.cc left_pool_op.cu.o -o cornerpool_lib.so -DPADDLE_WITH_MKLDNN -shared -fPIC -std=c++11 -O0 -g \ - -I ${include_dir}/third_party/ \ - -I ${include_dir} \ - -L ${lib_dir} \ - -L /usr/local/cuda/lib64 -lpaddle_framework -lcudart - -rm *.cu.o diff --git a/ppdet/ext_op/src/right_pool_op.cc b/ppdet/ext_op/src/right_pool_op.cc deleted file mode 100644 index 6bf74a1b08878724e388ae66ffe34c1ae0c65ec8..0000000000000000000000000000000000000000 --- a/ppdet/ext_op/src/right_pool_op.cc +++ /dev/null @@ -1,101 +0,0 @@ -/* Copyright (c) 2019 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. */ - -#include "paddle/fluid/framework/op_registry.h" -namespace paddle { -namespace operators { - -using Tensor = framework::Tensor; - -class RightPoolOp : public framework::OperatorWithKernel { -public: - using framework::OperatorWithKernel::OperatorWithKernel; - - void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); - ctx->ShareDim("X", /*->*/ "MaxMap"); - ctx->ShareDim("X", /*->*/ "Output"); - } - -protected: - framework::OpKernelType GetExpectedKernelType( - const framework::ExecutionContext& ctx) const override { - return framework::OpKernelType(ctx.Input("X")->type(), - ctx.GetPlace()); - } -}; - -class RightPoolOpMaker : public framework::OpProtoAndCheckerMaker { -public: - void Make() override { - AddInput("X", - "Input with shape (batch, C, H, W)"); - AddOutput("MaxMap", "Max map with index of maximum value of input"); - AddOutput("Output", "output with same shape as input(X)"); - AddComment( - R"Doc( -This operatio calculates the right pooling output based on the input. -Scan the input from left to right or the horizontal max-pooling. -The output has the same shape with input. - )Doc"); - } -}; - -class RightPoolOpGrad : public framework::OperatorWithKernel { -public: - using framework::OperatorWithKernel::OperatorWithKernel; - -protected: - void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); - PADDLE_ENFORCE(ctx->HasInput("MaxMap"), "Input(MaxMap) should not be null"); - PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Output")), - "Input(Output@GRAD) should not be null"); - auto out_grad_name = framework::GradVarName("Output"); - ctx->ShareDim(out_grad_name, framework::GradVarName("X")); - } - - framework::OpKernelType GetExpectedKernelType( - const framework::ExecutionContext& ctx) const override { - return framework::OpKernelType( - ctx.Input(framework::GradVarName("Output"))->type(), - ctx.GetPlace()); - } -}; - -template -class RightPoolGradDescMaker : public framework::SingleGradOpMaker { -public: - using framework::SingleGradOpMaker::SingleGradOpMaker; - -protected: - void Apply(GradOpPtr op) const override { - op->SetType("right_pool_grad"); - op->SetInput("X", this->Input("X")); - op->SetInput(framework::GradVarName("Output"), this->OutputGrad("Output")); - op->SetInput("MaxMap", this->Output("MaxMap")); - op->SetOutput(framework::GradVarName("X"), this->InputGrad("X")); - op->SetAttrMap(this->Attrs()); - } -}; - -} // namespace operators -} // namespace paddle - -namespace ops = paddle::operators; -REGISTER_OPERATOR(right_pool, - ops::RightPoolOp, - ops::RightPoolOpMaker, - ops::RightPoolGradDescMaker, - ops::RightPoolGradDescMaker); -REGISTER_OPERATOR(right_pool_grad, ops::RightPoolOpGrad); diff --git a/ppdet/ext_op/src/right_pool_op.cu b/ppdet/ext_op/src/right_pool_op.cu deleted file mode 100644 index 08a52ecf1eec9816c8a29e50452344d522b75c49..0000000000000000000000000000000000000000 --- a/ppdet/ext_op/src/right_pool_op.cu +++ /dev/null @@ -1,105 +0,0 @@ -/* Copyright (c) 2019 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. */ - -#include "paddle/fluid/framework/op_registry.h" -#include "paddle/fluid/platform/cuda_primitives.h" -#include "paddle/fluid/memory/memory.h" -#include -#include "util.cu.h" - -namespace paddle { -namespace operators { - -using Tensor = framework::Tensor; - -static constexpr int kNumCUDAThreads = 512; -static constexpr int kNumMaximumNumBlocks = 4096; - -static inline int NumBlocks(const int N) { - return std::min((N + kNumCUDAThreads - 1) / kNumCUDAThreads, - kNumMaximumNumBlocks); -} - -template -class RightPoolOpCUDAKernel : public framework::OpKernel { -public: - void Compute(const framework::ExecutionContext &ctx) const override { - PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), - "This kernel only runs on GPU device."); - auto *x = ctx.Input("X"); - auto *max_map = ctx.Output("MaxMap"); - auto *output = ctx.Output("Output"); - auto *x_data = x->data(); - auto x_dims = x->dims(); - int NC_num = x_dims[0] * x_dims[1]; - int height = x_dims[2]; - int width = x_dims[3]; - int num = x->numel(); - auto& dev_ctx = ctx.cuda_device_context(); - - int *max_map_data = max_map->mutable_data(x_dims, dev_ctx.GetPlace()); - T *output_data = output->mutable_data(x_dims, dev_ctx.GetPlace()); - auto gpu_place = boost::get(dev_ctx.GetPlace()); - - int threads = kNumCUDAThreads; - int blocks = NumBlocks(num / width); - - auto max_val_ptr = memory::Alloc(gpu_place, num / width * sizeof(T)); - T* max_val_data = reinterpret_cast(max_val_ptr->ptr()); - auto max_ind_ptr = memory::Alloc(gpu_place, num / width * sizeof(int)); - int* max_ind_data = reinterpret_cast(max_ind_ptr->ptr()); - - GetMaxInfo<<>>(x->data(), NC_num, height, width, 3, false, max_val_data, max_ind_data, max_map_data); - - blocks = NumBlocks(num); - ScatterAddFw<<>>(x->data(), max_map_data, NC_num, height, width, 3, output_data); - - } -}; - -template -class RightPoolGradOpCUDAKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& ctx) const override { - auto* x = ctx.Input("X"); - auto* max_map = ctx.Input("MaxMap"); - auto* out_grad = ctx.Input(framework::GradVarName("Output")); - auto* in_grad = ctx.Output(framework::GradVarName("X")); - auto x_dims = x->dims(); - - auto& dev_ctx = ctx.cuda_device_context(); - T* in_grad_data = in_grad->mutable_data(x_dims, dev_ctx.GetPlace()); - auto gpu_place = boost::get(dev_ctx.GetPlace()); - - int threads = kNumCUDAThreads; - int NC_num = x_dims[0] * x_dims[1]; - int height = x_dims[2]; - int width = x_dims[3]; - int grad_num = in_grad->numel(); - int blocks = NumBlocks(grad_num); - FillConstant<<>>(in_grad_data, 0, grad_num); - - ScatterAddBw<<>>(out_grad->data(), max_map->data(), NC_num, height, width, 3, in_grad_data); - } -}; - -} // namespace operators -} // namespace paddle - -namespace ops = paddle::operators; -REGISTER_OP_CUDA_KERNEL(right_pool, - ops::RightPoolOpCUDAKernel, - ops::RightPoolOpCUDAKernel); -REGISTER_OP_CUDA_KERNEL(right_pool_grad, - ops::RightPoolGradOpCUDAKernel, - ops::RightPoolGradOpCUDAKernel); diff --git a/ppdet/ext_op/src/top_pool_op.cc b/ppdet/ext_op/src/top_pool_op.cc deleted file mode 100755 index 29cba6660193c3bfe861dc81b03edbbb9368ea19..0000000000000000000000000000000000000000 --- a/ppdet/ext_op/src/top_pool_op.cc +++ /dev/null @@ -1,102 +0,0 @@ -/* Copyright (c) 2019 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. */ - -#include "paddle/fluid/framework/op_registry.h" -namespace paddle { -namespace operators { - -using Tensor = framework::Tensor; - -class TopPoolOp : public framework::OperatorWithKernel { -public: - using framework::OperatorWithKernel::OperatorWithKernel; - - void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); - ctx->ShareDim("X", /*->*/ "MaxMap"); - ctx->ShareDim("X", /*->*/ "Output"); - } - -protected: - framework::OpKernelType GetExpectedKernelType( - const framework::ExecutionContext& ctx) const override { - return framework::OpKernelType(ctx.Input("X")->type(), - ctx.GetPlace()); - } -}; - -class TopPoolOpMaker : public framework::OpProtoAndCheckerMaker { -public: - void Make() override { - AddInput("X", - "Input with shape (batch, C, H, W)"); - AddOutput("MaxMap", "Max map with index of maximum value of input"); - AddOutput("Output", "Output with same shape as input(X)"); - AddComment( - R"Doc( -This operatio calculates the top pooling output based on the input. -Scan the input from bottom to top for the vertical max-pooling. -The output has the same shape with input. - )Doc"); - } -}; - -class TopPoolOpGrad : public framework::OperatorWithKernel { -public: - using framework::OperatorWithKernel::OperatorWithKernel; - -protected: - void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); - PADDLE_ENFORCE(ctx->HasInput("MaxMap"), "Input(MaxMap) should not be null"); - PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Output")), - "Input(Output@GRAD) should not be null"); - - auto out_grad_name = framework::GradVarName("Output"); - ctx->ShareDim(out_grad_name, framework::GradVarName("X")); - } - - framework::OpKernelType GetExpectedKernelType( - const framework::ExecutionContext& ctx) const override { - return framework::OpKernelType( - ctx.Input(framework::GradVarName("Output"))->type(), - ctx.GetPlace()); - } -}; - -template -class TopPoolGradDescMaker : public framework::SingleGradOpMaker { - public: - using framework::SingleGradOpMaker::SingleGradOpMaker; - - protected: - void Apply(GradOpPtr op) const override { - op->SetType("top_pool_grad"); - op->SetInput("X", this->Input("X")); - op->SetInput(framework::GradVarName("Output"), this->OutputGrad("Output")); - op->SetInput("MaxMap", this->Output("MaxMap")); - op->SetOutput(framework::GradVarName("X"), this->InputGrad("X")); - op->SetAttrMap(this->Attrs()); - } -}; - -} // namespace operators -} // namespace paddle - -namespace ops = paddle::operators; -REGISTER_OPERATOR(top_pool, - ops::TopPoolOp, - ops::TopPoolOpMaker, - ops::TopPoolGradDescMaker, - ops::TopPoolGradDescMaker); -REGISTER_OPERATOR(top_pool_grad, ops::TopPoolOpGrad); diff --git a/ppdet/ext_op/src/top_pool_op.cu b/ppdet/ext_op/src/top_pool_op.cu deleted file mode 100755 index f6237fe798098ad0c6cb25597de849e65383ab78..0000000000000000000000000000000000000000 --- a/ppdet/ext_op/src/top_pool_op.cu +++ /dev/null @@ -1,104 +0,0 @@ -/* Copyright (c) 2019 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 - -GUnless 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. */ - -#include "paddle/fluid/framework/op_registry.h" -#include "paddle/fluid/platform/cuda_primitives.h" -#include "paddle/fluid/memory/memory.h" -#include -#include "util.cu.h" - -namespace paddle { -namespace operators { - -using Tensor = framework::Tensor; - -static constexpr int kNumCUDAThreads = 512; -static constexpr int kNumMaximumNumBlocks = 4096; - -static inline int NumBlocks(const int N) { - return std::min((N + kNumCUDAThreads - 1) / kNumCUDAThreads, - kNumMaximumNumBlocks); -} - -template -class TopPoolOpCUDAKernel : public framework::OpKernel { -public: - void Compute(const framework::ExecutionContext &ctx) const override { - PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), - "This kernel only runs on GPU device."); - auto *x = ctx.Input("X"); - auto *max_map = ctx.Output("MaxMap"); - auto *output = ctx.Output("Output"); - auto *x_data = x->data(); - auto x_dims = x->dims(); - int NC_num = x_dims[0] * x_dims[1]; - int height = x_dims[2]; - int width = x_dims[3]; - int num = x->numel(); - auto& dev_ctx = ctx.cuda_device_context(); - - int *max_map_data = max_map->mutable_data(x_dims, dev_ctx.GetPlace()); - T *output_data = output->mutable_data(x_dims, dev_ctx.GetPlace()); - auto gpu_place = boost::get(dev_ctx.GetPlace()); - - int threads = kNumCUDAThreads; - int blocks = NumBlocks(num / height); - - auto max_val_ptr = memory::Alloc(gpu_place, num / height * sizeof(T)); - T* max_val_data = reinterpret_cast(max_val_ptr->ptr()); - auto max_ind_ptr = memory::Alloc(gpu_place, num / height * sizeof(int)); - int* max_ind_data = reinterpret_cast(max_ind_ptr->ptr()); - - GetMaxInfo<<>>(x->data(), NC_num, height, width, 2, true, max_val_data, max_ind_data, max_map_data); - - blocks = NumBlocks(num); - ScatterAddFw<<>>(x->data(), max_map_data, NC_num, height, width, 2, output_data); - } -}; - -template -class TopPoolGradOpCUDAKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& ctx) const override { - auto* x = ctx.Input("X"); - auto* max_map = ctx.Input("MaxMap"); - auto* out_grad = ctx.Input(framework::GradVarName("Output")); - auto* in_grad = ctx.Output(framework::GradVarName("X")); - auto x_dims = x->dims(); - auto& dev_ctx = ctx.cuda_device_context(); - T* in_grad_data = in_grad->mutable_data(x_dims, dev_ctx.GetPlace()); - auto gpu_place = boost::get(dev_ctx.GetPlace()); - - int threads = kNumCUDAThreads; - int NC_num = x_dims[0] * x_dims[1]; - int height = x_dims[2]; - int width = x_dims[3]; - int grad_num = in_grad->numel(); - int blocks = NumBlocks(grad_num); - FillConstant<<>>(in_grad_data, 0, grad_num); - - ScatterAddBw<<>>(out_grad->data(), max_map->data(), NC_num, height, width, 2, in_grad_data); - } -}; - -} // namespace operators -} // namespace paddle - -namespace ops = paddle::operators; -REGISTER_OP_CUDA_KERNEL(top_pool, - ops::TopPoolOpCUDAKernel, - ops::TopPoolOpCUDAKernel); -REGISTER_OP_CUDA_KERNEL(top_pool_grad, - ops::TopPoolGradOpCUDAKernel, - ops::TopPoolGradOpCUDAKernel); diff --git a/ppdet/ext_op/src/util.cu.h b/ppdet/ext_op/src/util.cu.h deleted file mode 100644 index 615e45a7891fca7f89eea5824b4691d33a1fe5ef..0000000000000000000000000000000000000000 --- a/ppdet/ext_op/src/util.cu.h +++ /dev/null @@ -1,223 +0,0 @@ -/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. -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. */ - -#include "paddle/fluid/framework/op_registry.h" -#include "paddle/fluid/platform/cuda_primitives.h" -#include "paddle/fluid/memory/memory.h" -#include - -namespace paddle { -namespace operators { - -using framework::Tensor; - -#define CUDA_1D_KERNEL_LOOP(i, n) \ - for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ - i += blockDim.x * gridDim.x) - -template -__global__ void FillConstant(T* x, int num, int fill_num) { - CUDA_1D_KERNEL_LOOP(i, fill_num) { - x[i] = static_cast(num); - } -} - -template -__global__ void SliceOnAxis(const T* x, const int NC_num, const int H, const int W, - const int axis, const int start, const int end, - T* output) { - int HW_num = H * W; - int length = axis == 2 ? W : H; - int sliced_len = end - start; - int cur_HW_num = length * sliced_len; - // slice input on H or W (axis is 2 or 3) - CUDA_1D_KERNEL_LOOP(i, NC_num * cur_HW_num) { - int NC_id = i / cur_HW_num; - int HW_id = i % cur_HW_num; - if (axis == 2){ - output[i] = x[NC_id * HW_num + start * W + HW_id]; - } else if (axis == 3) { - int col = HW_id % sliced_len; - int row = HW_id / sliced_len; - output[i] = x[NC_id * HW_num + row * W + start + col]; - } - } -} - -template -__global__ void MaxOut(const T* input, const int next_ind, const int NC_num, - const int H, const int W, const int axis, - const int start, const int end, T* output) { - int HW_num = H * W; - int length = axis == 2 ? W : H; - T cur = static_cast(0.); - T next = static_cast(0.); - T max_v = static_cast(0.); - int sliced_len = end - start; - int cur_HW_num = length * sliced_len; - // compare cur and next and assign max values to output - CUDA_1D_KERNEL_LOOP(i, NC_num * cur_HW_num) { - int NC_id = i / cur_HW_num; - int HW_id = i % cur_HW_num; - - if (axis == 2){ - cur = input[NC_id * HW_num + start * W + HW_id]; - next = input[NC_id * HW_num + next_ind * W + HW_id]; - max_v = cur > next ? cur : next; - output[NC_id * HW_num + start * W + HW_id] = max_v; - } else if (axis == 3) { - int col = HW_id % sliced_len; - int row = HW_id / sliced_len; - cur = input[NC_id * HW_num + row * W + start + col]; - next = input[NC_id * HW_num + row * W + next_ind + col]; - max_v = cur > next ? cur : next; - output[NC_id * HW_num + row * W + start + col] = max_v; - } - __syncthreads(); - } -} - -template -__global__ void UpdateMaxInfo(const T* input, const int NC_num, - const int H, const int W, const int axis, - const int index, T* max_val, int* max_ind) { - int length = axis == 2 ? W : H; - int HW_num = H * W; - T val = static_cast(0.); - CUDA_1D_KERNEL_LOOP(i, NC_num * length) { - int NC_id = i / length; - int length_id = i % length; - if (axis == 2) { - val = input[NC_id * HW_num + index * W + length_id]; - } else if (axis == 3) { - val = input[NC_id * HW_num + length_id * W + index]; - } - if (val > max_val[i]) { - max_val[i] = val; - max_ind[i] = index; - } - __syncthreads(); - } -} - -template -__global__ void ScatterAddOnAxis(const T* input, const int start, const int* max_ind, const int NC_num, const int H, const int W, const int axis, T* output) { - int length = axis == 2 ? W : H; - int HW_num = H * W; - CUDA_1D_KERNEL_LOOP(i, NC_num * length) { - int NC_id = i / length; - int length_id = i % length; - int id_ = max_ind[i]; - if (axis == 2) { - platform::CudaAtomicAdd(output + NC_id * HW_num + id_ * W + length_id, input[NC_id * HW_num + start * W + length_id]); - //output[NC_id * HW_num + id_ * W + length_id] += input[NC_id * HW_num + start * W + length_id]; - } else if (axis == 3) { - platform::CudaAtomicAdd(output + NC_id * HW_num + length_id * W + id_, input[NC_id * HW_num + length_id * W + start]); - //output[NC_id * HW_num + length_id * W + id_] += input[NC_id * HW_num + length_id * W + start]; - } - __syncthreads(); - } -} - -template -__global__ void GetMaxInfo(const T* input, const int NC_num, - const int H, const int W, const int axis, - const bool reverse, T* max_val, int* max_ind, - int* max_map) { - int start = 0; - int end = axis == 2 ? H: W; - int s = reverse ? end-1 : start; - int e = reverse ? start-1 : end; - int step = reverse ? -1 : 1; - int len = axis == 2 ? W : H; - int loc = 0; - T val = static_cast(0.); - for (int i = s; ; ) { - if (i == s) { - CUDA_1D_KERNEL_LOOP(j, NC_num * len) { - int NC_id = j / len; - int len_id = j % len; - if (axis == 2) { - loc = NC_id * H * W + i * W + len_id; - } else if (axis == 3){ - loc = NC_id * H * W + len_id * W + i; - } - max_ind[j] = i; - max_map[loc] = max_ind[j]; - max_val[j] = input[loc]; - __syncthreads(); - } - } else { - CUDA_1D_KERNEL_LOOP(j, NC_num * len) { - int NC_id = j / len; - int len_id = j % len; - - if (axis == 2) { - loc = NC_id * H * W + i * W + len_id; - } else if (axis == 3){ - loc = NC_id * H * W + len_id * W + i; - } - val = input[loc]; - T max_v = max_val[j]; - if (val > max_v) { - max_val[j] = val; - max_map[loc] = i; - max_ind[j] = i; - } else { - max_map[loc] = max_ind[j]; - } - __syncthreads(); - } - } - i += step; - if (s < e && i >= e) break; - if (s > e && i <= e) break; - } -} - -template -__global__ void ScatterAddFw(const T* input, const int* max_map, const int NC_num, const int H, const int W, const int axis, T* output){ - CUDA_1D_KERNEL_LOOP(i, NC_num * H * W) { - int loc = max_map[i]; - int NC_id = i / (H * W); - int len_id = 0; - if (axis == 2) { - len_id = i % W; - output[i] = input[NC_id * H * W + loc * W + len_id]; - } else { - len_id = i % (H * W) / W; - output[i] = input[NC_id * H * W + len_id * W + loc]; - } - } -} - -template -__global__ void ScatterAddBw(const T* input, const int* max_map, const int NC_num, const int H, const int W, const int axis, T* output){ - CUDA_1D_KERNEL_LOOP(i, NC_num * H * W) { - int loc = max_map[i]; - int NC_id = i / (H * W); - int len_id = 0; - int offset = 0; - if (axis == 2) { - len_id = i % W; - offset = NC_id * H * W + loc * W + len_id; - } else { - len_id = i % (H * W) / W; - offset = NC_id * H * W + len_id * W + loc; - } - platform::CudaAtomicAdd(output + offset, input[i]); - } -} - -} // namespace operators -} // namespace paddle diff --git a/ppdet/ext_op/test/test_corner_pool.py b/ppdet/ext_op/test/test_corner_pool.py deleted file mode 100755 index 211aaaded08ba62d47505300e79ef895550621d4..0000000000000000000000000000000000000000 --- a/ppdet/ext_op/test/test_corner_pool.py +++ /dev/null @@ -1,123 +0,0 @@ -# Copyright (c) 2019 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. - -from __future__ import print_function - -import unittest -import numpy as np -import paddle.fluid as fluid -import os -import sys -# add python path of PadleDetection to sys.path -parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 4))) -if parent_path not in sys.path: - sys.path.append(parent_path) - -from ppdet.ext_op import cornerpool_lib - - -def bottom_pool_np(x): - height = x.shape[2] - output = x.copy() - for ind in range(height): - cur = output[:, :, ind:height, :] - next = output[:, :, :height - ind, :] - output[:, :, ind:height, :] = np.maximum(cur, next) - return output - - -def top_pool_np(x): - height = x.shape[2] - output = x.copy() - for ind in range(height): - cur = output[:, :, :height - ind, :] - next = output[:, :, ind:height, :] - output[:, :, :height - ind, :] = np.maximum(cur, next) - return output - - -def right_pool_np(x): - width = x.shape[3] - output = x.copy() - for ind in range(width): - cur = output[:, :, :, ind:width] - next = output[:, :, :, :width - ind] - output[:, :, :, ind:width] = np.maximum(cur, next) - return output - - -def left_pool_np(x): - width = x.shape[3] - output = x.copy() - for ind in range(width): - cur = output[:, :, :, :width - ind] - next = output[:, :, :, ind:width] - output[:, :, :, :width - ind] = np.maximum(cur, next) - return output - - -class TestRightPoolOp(unittest.TestCase): - def funcmap(self): - self.func_map = { - 'bottom_x': [cornerpool_lib.bottom_pool, bottom_pool_np], - 'top_x': [cornerpool_lib.top_pool, top_pool_np], - 'right_x': [cornerpool_lib.right_pool, right_pool_np], - 'left_x': [cornerpool_lib.left_pool, left_pool_np] - } - - def setup(self): - self.name = 'right_x' - - def test_check_output(self): - self.funcmap() - self.setup() - x_shape = (2, 10, 16, 16) - x_type = "float64" - - sp = fluid.Program() - tp = fluid.Program() - place = fluid.CUDAPlace(0) - - with fluid.program_guard(tp, sp): - x = fluid.data(name=self.name, shape=x_shape, dtype=x_type) - y = self.func_map[self.name][0](x) - - np.random.seed(0) - x_np = np.random.uniform(-1000, 1000, x_shape).astype(x_type) - - out_np = self.func_map[self.name][1](x_np) - - exe = fluid.Executor(place) - outs = exe.run(tp, feed={self.name: x_np}, fetch_list=[y]) - - self.assertTrue(np.allclose(outs, out_np)) - - -class TestTopPoolOp(TestRightPoolOp): - def setup(self): - self.name = 'top_x' - - -class TestBottomPoolOp(TestRightPoolOp): - def setup(self): - self.name = 'bottom_x' - - -class TestLeftPoolOp(TestRightPoolOp): - def setup(self): - self.name = 'left_x' - - -if __name__ == "__main__": - unittest.main() diff --git a/ppdet/py_op/post_processing.py b/ppdet/py_op/post_processing.py deleted file mode 100755 index 841b500aee568898063ccb6613e300f327c7d89e..0000000000000000000000000000000000000000 --- a/ppdet/py_op/post_processing.py +++ /dev/null @@ -1,187 +0,0 @@ -import six -import os -import numpy as np -from numba import jit -from .bbox import nms - - -@jit -def box_decoder(deltas, boxes, weights, bbox_clip=4.13): - if boxes.shape[0] == 0: - return np.zeros((0, deltas.shape[1]), dtype=deltas.dtype) - boxes = boxes.astype(deltas.dtype, copy=False) - - widths = boxes[:, 2] - boxes[:, 0] + 1.0 - heights = boxes[:, 3] - boxes[:, 1] + 1.0 - ctr_x = boxes[:, 0] + 0.5 * widths - ctr_y = boxes[:, 1] + 0.5 * heights - - wx, wy, ww, wh = weights - dx = deltas[:, 0::4] * wx - dy = deltas[:, 1::4] * wy - dw = deltas[:, 2::4] * ww - dh = deltas[:, 3::4] * wh - - # Prevent sending too large values into np.exp() - dw = np.minimum(dw, bbox_clip) - dh = np.minimum(dh, bbox_clip) - - pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis] - pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis] - pred_w = np.exp(dw) * widths[:, np.newaxis] - pred_h = np.exp(dh) * heights[:, np.newaxis] - - pred_boxes = np.zeros(deltas.shape, dtype=deltas.dtype) - # x1 - pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w - # y1 - pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h - # x2 (note: "- 1" is correct; don't be fooled by the asymmetry) - pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w - 1 - # y2 (note: "- 1" is correct; don't be fooled by the asymmetry) - pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h - 1 - - return pred_boxes - - -@jit -def clip_tiled_boxes(boxes, im_shape): - """Clip boxes to image boundaries. im_shape is [height, width] and boxes - has shape (N, 4 * num_tiled_boxes).""" - assert boxes.shape[1] % 4 == 0, \ - 'boxes.shape[1] is {:d}, but must be divisible by 4.'.format( - boxes.shape[1] - ) - # x1 >= 0 - boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0) - # y1 >= 0 - boxes[:, 1::4] = np.maximum(np.minimum(boxes[:, 1::4], im_shape[0] - 1), 0) - # x2 < im_shape[1] - boxes[:, 2::4] = np.maximum(np.minimum(boxes[:, 2::4], im_shape[1] - 1), 0) - # y2 < im_shape[0] - boxes[:, 3::4] = np.maximum(np.minimum(boxes[:, 3::4], im_shape[0] - 1), 0) - return boxes - - -#@jit -def get_nmsed_box(rpn_rois, - confs, - locs, - class_nums, - im_info, - bbox_reg_weights=[0.1, 0.1, 0.2, 0.2], - score_thresh=0.05, - nms_thresh=0.5, - detections_per_im=100): - box_nums = [0, rpn_rois.shape[0]] - variance_v = np.array(bbox_reg_weights) - rpn_rois_v = np.array(rpn_rois) - confs_v = np.array(confs) - locs_v = np.array(locs) - - im_results = [[] for _ in range(len(box_nums) - 1)] - new_box_nums = [0] - for i in range(len(box_nums) - 1): - start = box_nums[i] - end = box_nums[i + 1] - if start == end: - continue - - locs_n = locs_v[start:end, :] # box delta - rois_n = rpn_rois_v[start:end, :] # box - rois_n = rois_n / im_info[i][2] # scale - rois_n = box_decoder(locs_n, rois_n, variance_v) - rois_n = clip_tiled_boxes(rois_n, im_info[i][:2] / im_info[i][2]) - cls_boxes = [[] for _ in range(class_nums)] - scores_n = confs_v[start:end, :] - for j in range(1, class_nums): - inds = np.where(scores_n[:, j] > TEST.score_thresh)[0] - scores_j = scores_n[inds, j] - rois_j = rois_n[inds, j * 4:(j + 1) * 4] - dets_j = np.hstack((scores_j[:, np.newaxis], rois_j)).astype( - np.float32, copy=False) - keep = nms(dets_j, TEST.nms_thresh) - nms_dets = dets_j[keep, :] - #add labels - label = np.array([j for _ in range(len(keep))]) - nms_dets = np.hstack((label[:, np.newaxis], nms_dets)).astype( - np.float32, copy=False) - cls_boxes[j] = nms_dets - - # Limit to max_per_image detections **over all classes** - image_scores = np.hstack( - [cls_boxes[j][:, 1] for j in range(1, class_nums)]) - if len(image_scores) > detections_per_im: - image_thresh = np.sort(image_scores)[-detections_per_im] - for j in range(1, class_nums): - keep = np.where(cls_boxes[j][:, 1] >= image_thresh)[0] - cls_boxes[j] = cls_boxes[j][keep, :] - im_results_n = np.vstack([cls_boxes[j] for j in range(1, class_nums)]) - im_results[i] = im_results_n - new_box_nums.append(len(im_results_n) + new_box_nums[-1]) - labels = im_results_n[:, 0] - scores = im_results_n[:, 1] - boxes = im_results_n[:, 2:] - im_results = np.vstack([im_results[k] for k in range(len(box_nums) - 1)]) - return new_box_nums, im_results - - -@jit -def get_dt_res(batch_size, box_nums, nmsed_out, data, num_id_to_cat_id_map): - dts_res = [] - nmsed_out_v = np.array(nmsed_out) - if nmsed_out_v.shape == ( - 1, - 1, ): - return dts_res - assert (len(box_nums) == batch_size + 1), \ - "Error Tensor offset dimension. Box Nums({}) vs. batch_size({})"\ - .format(len(box_nums), batch_size) - k = 0 - for i in range(batch_size): - dt_num_this_img = box_nums[i + 1] - box_nums[i] - image_id = int(data[i][-1]) - image_width = int(data[i][1][1]) - image_height = int(data[i][1][2]) - for j in range(dt_num_this_img): - dt = nmsed_out_v[k] - k = k + 1 - num_id, score, xmin, ymin, xmax, ymax = dt.tolist() - category_id = num_id_to_cat_id_map[num_id] - w = xmax - xmin + 1 - h = ymax - ymin + 1 - bbox = [xmin, ymin, w, h] - dt_res = { - 'image_id': image_id, - 'category_id': category_id, - 'bbox': bbox, - 'score': score - } - dts_res.append(dt_res) - return dts_res - - -@jit -def get_segms_res(batch_size, box_nums, segms_out, data, num_id_to_cat_id_map): - segms_res = [] - segms_out_v = np.array(segms_out) - k = 0 - for i in range(batch_size): - dt_num_this_img = box_nums[i + 1] - box_nums[i] - image_id = int(data[i][-1]) - for j in range(dt_num_this_img): - dt = segms_out_v[k] - k = k + 1 - segm, num_id, score = dt.tolist() - cat_id = num_id_to_cat_id_map[num_id] - if six.PY3: - if 'counts' in segm: - segm['counts'] = segm['counts'].decode("utf8") - segm_res = { - 'image_id': image_id, - 'category_id': cat_id, - 'segmentation': segm, - 'score': score - } - segms_res.append(segm_res) - return segms_res diff --git a/ppdet/utils/data_structure.py b/ppdet/utils/data_structure.py deleted file mode 100644 index a600af32b5d810885c817b8a11f1c6aad7c2f262..0000000000000000000000000000000000000000 --- a/ppdet/utils/data_structure.py +++ /dev/null @@ -1,64 +0,0 @@ -import numpy as np - - -class BufferDict(dict): - def __init__(self, **kwargs): - super(BufferDict, self).__init__(**kwargs) - - def __getitem__(self, key): - if key in self.keys(): - return super(BufferDict, self).__getitem__(key) - else: - raise Exception("The %s is not in global inputs dict" % key) - - def __setitem__(self, key, value): - if key not in self.keys(): - super(BufferDict, self).__setitem__(key, value) - else: - raise Exception("The %s is already in global inputs dict" % key) - - def update(self, *args, **kwargs): - for k, v in dict(*args, **kwargs).items(): - self[k] = v - - def update_v(self, key, value): - if key in self.keys(): - super(BufferDict, self).__setitem__(key, value) - else: - raise Exception("The %s is not in global inputs dict" % key) - - def get(self, key): - return self.__getitem__(key) - - def set(self, key, value): - return self.__setitem__(key, value) - - def debug(self, dshape=True, dvalue=True, dtype=False): - if self['open_debug']: - if 'debug_names' not in self.keys(): - ditems = self.keys() - else: - ditems = self['debug_names'] - - infos = {} - for k in ditems: - if type(k) is dict: - i_d = {} - for i, j in k.items(): - if type(j) is list: - for jj in j: - i_d[jj] = self.get_debug_info(self[i][jj]) - infos[i] = i_d - else: - infos[k] = self.get_debug_info(self[k]) - print(infos) - - def get_debug_info(self, v, dshape=True, dvalue=True, dtype=False): - info = [] - if dshape == True and hasattr(v, 'shape'): - info.append(v.shape) - if dvalue == True and hasattr(v, 'numpy'): - info.append(np.mean(np.abs(v.numpy()))) - if dtype == True: - info.append(type(v)) - return info diff --git a/ppdet/utils/oid_eval.py b/ppdet/utils/oid_eval.py deleted file mode 100644 index 0a5a0c534cff10d8b5c399181f1b3631abff2a0b..0000000000000000000000000000000000000000 --- a/ppdet/utils/oid_eval.py +++ /dev/null @@ -1,543 +0,0 @@ -# Copyright (c) 2019 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. - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import os -import sys -import numpy as np - -from .coco_eval import bbox2out - -import logging -logger = logging.getLogger(__name__) - -__all__ = ['bbox2out', 'get_category_info'] - - -def get_category_info(anno_file=None, - with_background=True, - use_default_label=False): - clsid2catid = {k: k for k in range(1, 501)} - - catid2name = { - 0: "background", - 1: "Infant bed", - 2: "Rose", - 3: "Flag", - 4: "Flashlight", - 5: "Sea turtle", - 6: "Camera", - 7: "Animal", - 8: "Glove", - 9: "Crocodile", - 10: "Cattle", - 11: "House", - 12: "Guacamole", - 13: "Penguin", - 14: "Vehicle registration plate", - 15: "Bench", - 16: "Ladybug", - 17: "Human nose", - 18: "Watermelon", - 19: "Flute", - 20: "Butterfly", - 21: "Washing machine", - 22: "Raccoon", - 23: "Segway", - 24: "Taco", - 25: "Jellyfish", - 26: "Cake", - 27: "Pen", - 28: "Cannon", - 29: "Bread", - 30: "Tree", - 31: "Shellfish", - 32: "Bed", - 33: "Hamster", - 34: "Hat", - 35: "Toaster", - 36: "Sombrero", - 37: "Tiara", - 38: "Bowl", - 39: "Dragonfly", - 40: "Moths and butterflies", - 41: "Antelope", - 42: "Vegetable", - 43: "Torch", - 44: "Building", - 45: "Power plugs and sockets", - 46: "Blender", - 47: "Billiard table", - 48: "Cutting board", - 49: "Bronze sculpture", - 50: "Turtle", - 51: "Broccoli", - 52: "Tiger", - 53: "Mirror", - 54: "Bear", - 55: "Zucchini", - 56: "Dress", - 57: "Volleyball", - 58: "Guitar", - 59: "Reptile", - 60: "Golf cart", - 61: "Tart", - 62: "Fedora", - 63: "Carnivore", - 64: "Car", - 65: "Lighthouse", - 66: "Coffeemaker", - 67: "Food processor", - 68: "Truck", - 69: "Bookcase", - 70: "Surfboard", - 71: "Footwear", - 72: "Bench", - 73: "Necklace", - 74: "Flower", - 75: "Radish", - 76: "Marine mammal", - 77: "Frying pan", - 78: "Tap", - 79: "Peach", - 80: "Knife", - 81: "Handbag", - 82: "Laptop", - 83: "Tent", - 84: "Ambulance", - 85: "Christmas tree", - 86: "Eagle", - 87: "Limousine", - 88: "Kitchen & dining room table", - 89: "Polar bear", - 90: "Tower", - 91: "Football", - 92: "Willow", - 93: "Human head", - 94: "Stop sign", - 95: "Banana", - 96: "Mixer", - 97: "Binoculars", - 98: "Dessert", - 99: "Bee", - 100: "Chair", - 101: "Wood-burning stove", - 102: "Flowerpot", - 103: "Beaker", - 104: "Oyster", - 105: "Woodpecker", - 106: "Harp", - 107: "Bathtub", - 108: "Wall clock", - 109: "Sports uniform", - 110: "Rhinoceros", - 111: "Beehive", - 112: "Cupboard", - 113: "Chicken", - 114: "Man", - 115: "Blue jay", - 116: "Cucumber", - 117: "Balloon", - 118: "Kite", - 119: "Fireplace", - 120: "Lantern", - 121: "Missile", - 122: "Book", - 123: "Spoon", - 124: "Grapefruit", - 125: "Squirrel", - 126: "Orange", - 127: "Coat", - 128: "Punching bag", - 129: "Zebra", - 130: "Billboard", - 131: "Bicycle", - 132: "Door handle", - 133: "Mechanical fan", - 134: "Ring binder", - 135: "Table", - 136: "Parrot", - 137: "Sock", - 138: "Vase", - 139: "Weapon", - 140: "Shotgun", - 141: "Glasses", - 142: "Seahorse", - 143: "Belt", - 144: "Watercraft", - 145: "Window", - 146: "Giraffe", - 147: "Lion", - 148: "Tire", - 149: "Vehicle", - 150: "Canoe", - 151: "Tie", - 152: "Shelf", - 153: "Picture frame", - 154: "Printer", - 155: "Human leg", - 156: "Boat", - 157: "Slow cooker", - 158: "Croissant", - 159: "Candle", - 160: "Pancake", - 161: "Pillow", - 162: "Coin", - 163: "Stretcher", - 164: "Sandal", - 165: "Woman", - 166: "Stairs", - 167: "Harpsichord", - 168: "Stool", - 169: "Bus", - 170: "Suitcase", - 171: "Human mouth", - 172: "Juice", - 173: "Skull", - 174: "Door", - 175: "Violin", - 176: "Chopsticks", - 177: "Digital clock", - 178: "Sunflower", - 179: "Leopard", - 180: "Bell pepper", - 181: "Harbor seal", - 182: "Snake", - 183: "Sewing machine", - 184: "Goose", - 185: "Helicopter", - 186: "Seat belt", - 187: "Coffee cup", - 188: "Microwave oven", - 189: "Hot dog", - 190: "Countertop", - 191: "Serving tray", - 192: "Dog bed", - 193: "Beer", - 194: "Sunglasses", - 195: "Golf ball", - 196: "Waffle", - 197: "Palm tree", - 198: "Trumpet", - 199: "Ruler", - 200: "Helmet", - 201: "Ladder", - 202: "Office building", - 203: "Tablet computer", - 204: "Toilet paper", - 205: "Pomegranate", - 206: "Skirt", - 207: "Gas stove", - 208: "Cookie", - 209: "Cart", - 210: "Raven", - 211: "Egg", - 212: "Burrito", - 213: "Goat", - 214: "Kitchen knife", - 215: "Skateboard", - 216: "Salt and pepper shakers", - 217: "Lynx", - 218: "Boot", - 219: "Platter", - 220: "Ski", - 221: "Swimwear", - 222: "Swimming pool", - 223: "Drinking straw", - 224: "Wrench", - 225: "Drum", - 226: "Ant", - 227: "Human ear", - 228: "Headphones", - 229: "Fountain", - 230: "Bird", - 231: "Jeans", - 232: "Television", - 233: "Crab", - 234: "Microphone", - 235: "Home appliance", - 236: "Snowplow", - 237: "Beetle", - 238: "Artichoke", - 239: "Jet ski", - 240: "Stationary bicycle", - 241: "Human hair", - 242: "Brown bear", - 243: "Starfish", - 244: "Fork", - 245: "Lobster", - 246: "Corded phone", - 247: "Drink", - 248: "Saucer", - 249: "Carrot", - 250: "Insect", - 251: "Clock", - 252: "Castle", - 253: "Tennis racket", - 254: "Ceiling fan", - 255: "Asparagus", - 256: "Jaguar", - 257: "Musical instrument", - 258: "Train", - 259: "Cat", - 260: "Rifle", - 261: "Dumbbell", - 262: "Mobile phone", - 263: "Taxi", - 264: "Shower", - 265: "Pitcher", - 266: "Lemon", - 267: "Invertebrate", - 268: "Turkey", - 269: "High heels", - 270: "Bust", - 271: "Elephant", - 272: "Scarf", - 273: "Barrel", - 274: "Trombone", - 275: "Pumpkin", - 276: "Box", - 277: "Tomato", - 278: "Frog", - 279: "Bidet", - 280: "Human face", - 281: "Houseplant", - 282: "Van", - 283: "Shark", - 284: "Ice cream", - 285: "Swim cap", - 286: "Falcon", - 287: "Ostrich", - 288: "Handgun", - 289: "Whiteboard", - 290: "Lizard", - 291: "Pasta", - 292: "Snowmobile", - 293: "Light bulb", - 294: "Window blind", - 295: "Muffin", - 296: "Pretzel", - 297: "Computer monitor", - 298: "Horn", - 299: "Furniture", - 300: "Sandwich", - 301: "Fox", - 302: "Convenience store", - 303: "Fish", - 304: "Fruit", - 305: "Earrings", - 306: "Curtain", - 307: "Grape", - 308: "Sofa bed", - 309: "Horse", - 310: "Luggage and bags", - 311: "Desk", - 312: "Crutch", - 313: "Bicycle helmet", - 314: "Tick", - 315: "Airplane", - 316: "Canary", - 317: "Spatula", - 318: "Watch", - 319: "Lily", - 320: "Kitchen appliance", - 321: "Filing cabinet", - 322: "Aircraft", - 323: "Cake stand", - 324: "Candy", - 325: "Sink", - 326: "Mouse", - 327: "Wine", - 328: "Wheelchair", - 329: "Goldfish", - 330: "Refrigerator", - 331: "French fries", - 332: "Drawer", - 333: "Treadmill", - 334: "Picnic basket", - 335: "Dice", - 336: "Cabbage", - 337: "Football helmet", - 338: "Pig", - 339: "Person", - 340: "Shorts", - 341: "Gondola", - 342: "Honeycomb", - 343: "Doughnut", - 344: "Chest of drawers", - 345: "Land vehicle", - 346: "Bat", - 347: "Monkey", - 348: "Dagger", - 349: "Tableware", - 350: "Human foot", - 351: "Mug", - 352: "Alarm clock", - 353: "Pressure cooker", - 354: "Human hand", - 355: "Tortoise", - 356: "Baseball glove", - 357: "Sword", - 358: "Pear", - 359: "Miniskirt", - 360: "Traffic sign", - 361: "Girl", - 362: "Roller skates", - 363: "Dinosaur", - 364: "Porch", - 365: "Human beard", - 366: "Submarine sandwich", - 367: "Screwdriver", - 368: "Strawberry", - 369: "Wine glass", - 370: "Seafood", - 371: "Racket", - 372: "Wheel", - 373: "Sea lion", - 374: "Toy", - 375: "Tea", - 376: "Tennis ball", - 377: "Waste container", - 378: "Mule", - 379: "Cricket ball", - 380: "Pineapple", - 381: "Coconut", - 382: "Doll", - 383: "Coffee table", - 384: "Snowman", - 385: "Lavender", - 386: "Shrimp", - 387: "Maple", - 388: "Cowboy hat", - 389: "Goggles", - 390: "Rugby ball", - 391: "Caterpillar", - 392: "Poster", - 393: "Rocket", - 394: "Organ", - 395: "Saxophone", - 396: "Traffic light", - 397: "Cocktail", - 398: "Plastic bag", - 399: "Squash", - 400: "Mushroom", - 401: "Hamburger", - 402: "Light switch", - 403: "Parachute", - 404: "Teddy bear", - 405: "Winter melon", - 406: "Deer", - 407: "Musical keyboard", - 408: "Plumbing fixture", - 409: "Scoreboard", - 410: "Baseball bat", - 411: "Envelope", - 412: "Adhesive tape", - 413: "Briefcase", - 414: "Paddle", - 415: "Bow and arrow", - 416: "Telephone", - 417: "Sheep", - 418: "Jacket", - 419: "Boy", - 420: "Pizza", - 421: "Otter", - 422: "Office supplies", - 423: "Couch", - 424: "Cello", - 425: "Bull", - 426: "Camel", - 427: "Ball", - 428: "Duck", - 429: "Whale", - 430: "Shirt", - 431: "Tank", - 432: "Motorcycle", - 433: "Accordion", - 434: "Owl", - 435: "Porcupine", - 436: "Sun hat", - 437: "Nail", - 438: "Scissors", - 439: "Swan", - 440: "Lamp", - 441: "Crown", - 442: "Piano", - 443: "Sculpture", - 444: "Cheetah", - 445: "Oboe", - 446: "Tin can", - 447: "Mango", - 448: "Tripod", - 449: "Oven", - 450: "Mouse", - 451: "Barge", - 452: "Coffee", - 453: "Snowboard", - 454: "Common fig", - 455: "Salad", - 456: "Marine invertebrates", - 457: "Umbrella", - 458: "Kangaroo", - 459: "Human arm", - 460: "Measuring cup", - 461: "Snail", - 462: "Loveseat", - 463: "Suit", - 464: "Teapot", - 465: "Bottle", - 466: "Alpaca", - 467: "Kettle", - 468: "Trousers", - 469: "Popcorn", - 470: "Centipede", - 471: "Spider", - 472: "Sparrow", - 473: "Plate", - 474: "Bagel", - 475: "Personal care", - 476: "Apple", - 477: "Brassiere", - 478: "Bathroom cabinet", - 479: "studio couch", - 480: "Computer keyboard", - 481: "Table tennis racket", - 482: "Sushi", - 483: "Cabinetry", - 484: "Street light", - 485: "Towel", - 486: "Nightstand", - 487: "Rabbit", - 488: "Dolphin", - 489: "Dog", - 490: "Jug", - 491: "Wok", - 492: "Fire hydrant", - 493: "Human eye", - 494: "Skyscraper", - 495: "Backpack", - 496: "Potato", - 497: "Paper towel", - 498: "Lifejacket", - 499: "Bicycle wheel", - 500: "Toilet", - } - - if not with_background: - clsid2catid = {k - 1: v for k, v in clsid2catid.items()} - return clsid2catid, catid2name diff --git a/ppdet/utils/widerface_eval_utils.py b/ppdet/utils/widerface_eval_utils.py deleted file mode 100644 index 7f35c2431076cd654114539b290663e9dccbd950..0000000000000000000000000000000000000000 --- a/ppdet/utils/widerface_eval_utils.py +++ /dev/null @@ -1,229 +0,0 @@ -# Copyright (c) 2019 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. - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import numpy as np - -from ppdet.data.source.widerface import widerface_label -from ppdet.utils.coco_eval import bbox2out - -import logging -logger = logging.getLogger(__name__) - -__all__ = [ - 'get_shrink', 'bbox_vote', 'save_widerface_bboxes', 'save_fddb_bboxes', - 'to_chw_bgr', 'bbox2out', 'get_category_info' -] - - -def to_chw_bgr(image): - """ - Transpose image from HWC to CHW and from RBG to BGR. - Args: - image (np.array): an image with HWC and RBG layout. - """ - # HWC to CHW - if len(image.shape) == 3: - image = np.swapaxes(image, 1, 2) - image = np.swapaxes(image, 1, 0) - # RBG to BGR - image = image[[2, 1, 0], :, :] - return image - - -def bbox_vote(det): - order = det[:, 4].ravel().argsort()[::-1] - det = det[order, :] - if det.shape[0] == 0: - dets = np.array([[10, 10, 20, 20, 0.002]]) - det = np.empty(shape=[0, 5]) - while det.shape[0] > 0: - # IOU - area = (det[:, 2] - det[:, 0] + 1) * (det[:, 3] - det[:, 1] + 1) - xx1 = np.maximum(det[0, 0], det[:, 0]) - yy1 = np.maximum(det[0, 1], det[:, 1]) - xx2 = np.minimum(det[0, 2], det[:, 2]) - yy2 = np.minimum(det[0, 3], det[:, 3]) - w = np.maximum(0.0, xx2 - xx1 + 1) - h = np.maximum(0.0, yy2 - yy1 + 1) - inter = w * h - o = inter / (area[0] + area[:] - inter) - - # nms - merge_index = np.where(o >= 0.3)[0] - det_accu = det[merge_index, :] - det = np.delete(det, merge_index, 0) - if merge_index.shape[0] <= 1: - if det.shape[0] == 0: - try: - dets = np.row_stack((dets, det_accu)) - except: - dets = det_accu - continue - det_accu[:, 0:4] = det_accu[:, 0:4] * np.tile(det_accu[:, -1:], (1, 4)) - max_score = np.max(det_accu[:, 4]) - det_accu_sum = np.zeros((1, 5)) - det_accu_sum[:, 0:4] = np.sum(det_accu[:, 0:4], - axis=0) / np.sum(det_accu[:, -1:]) - det_accu_sum[:, 4] = max_score - try: - dets = np.row_stack((dets, det_accu_sum)) - except: - dets = det_accu_sum - dets = dets[0:750, :] - # Only keep 0.3 or more - keep_index = np.where(dets[:, 4] >= 0.01)[0] - dets = dets[keep_index, :] - return dets - - -def get_shrink(height, width): - """ - Args: - height (int): image height. - width (int): image width. - """ - # avoid out of memory - max_shrink_v1 = (0x7fffffff / 577.0 / (height * width))**0.5 - max_shrink_v2 = ((678 * 1024 * 2.0 * 2.0) / (height * width))**0.5 - - def get_round(x, loc): - str_x = str(x) - if '.' in str_x: - str_before, str_after = str_x.split('.') - len_after = len(str_after) - if len_after >= 3: - str_final = str_before + '.' + str_after[0:loc] - return float(str_final) - else: - return x - - max_shrink = get_round(min(max_shrink_v1, max_shrink_v2), 2) - 0.3 - if max_shrink >= 1.5 and max_shrink < 2: - max_shrink = max_shrink - 0.1 - elif max_shrink >= 2 and max_shrink < 3: - max_shrink = max_shrink - 0.2 - elif max_shrink >= 3 and max_shrink < 4: - max_shrink = max_shrink - 0.3 - elif max_shrink >= 4 and max_shrink < 5: - max_shrink = max_shrink - 0.4 - elif max_shrink >= 5: - max_shrink = max_shrink - 0.5 - elif max_shrink <= 0.1: - max_shrink = 0.1 - - shrink = max_shrink if max_shrink < 1 else 1 - return shrink, max_shrink - - -def save_widerface_bboxes(image_path, bboxes_scores, output_dir): - image_name = image_path.split('/')[-1] - image_class = image_path.split('/')[-2] - odir = os.path.join(output_dir, image_class) - if not os.path.exists(odir): - os.makedirs(odir) - - ofname = os.path.join(odir, '%s.txt' % (image_name[:-4])) - f = open(ofname, 'w') - f.write('{:s}\n'.format(image_class + '/' + image_name)) - f.write('{:d}\n'.format(bboxes_scores.shape[0])) - for box_score in bboxes_scores: - xmin, ymin, xmax, ymax, score = box_score - f.write('{:.1f} {:.1f} {:.1f} {:.1f} {:.3f}\n'.format(xmin, ymin, ( - xmax - xmin + 1), (ymax - ymin + 1), score)) - f.close() - logger.info("The predicted result is saved as {}".format(ofname)) - - -def save_fddb_bboxes(bboxes_scores, - output_dir, - output_fname='pred_fddb_res.txt'): - if not os.path.exists(output_dir): - os.makedirs(output_dir) - predict_file = os.path.join(output_dir, output_fname) - f = open(predict_file, 'w') - for image_path, dets in bboxes_scores.iteritems(): - f.write('{:s}\n'.format(image_path)) - f.write('{:d}\n'.format(dets.shape[0])) - for box_score in dets: - xmin, ymin, xmax, ymax, score = box_score - width, height = xmax - xmin, ymax - ymin - f.write('{:.1f} {:.1f} {:.1f} {:.1f} {:.3f}\n' - .format(xmin, ymin, width, height, score)) - logger.info("The predicted result is saved as {}".format(predict_file)) - return predict_file - - -def get_category_info(anno_file=None, - with_background=True, - use_default_label=False): - if use_default_label or anno_file is None \ - or not os.path.exists(anno_file): - logger.info("Not found annotation file {}, load " - "wider-face categories.".format(anno_file)) - return widerfaceall_category_info(with_background) - else: - logger.info("Load categories from {}".format(anno_file)) - return get_category_info_from_anno(anno_file, with_background) - - -def get_category_info_from_anno(anno_file, with_background=True): - """ - Get class id to category id map and category id - to category name map from annotation file. - Args: - anno_file (str): annotation file path - with_background (bool, default True): - whether load background as class 0. - """ - cats = [] - with open(anno_file) as f: - for line in f.readlines(): - cats.append(line.strip()) - - if cats[0] != 'background' and with_background: - cats.insert(0, 'background') - if cats[0] == 'background' and not with_background: - cats = cats[1:] - - clsid2catid = {i: i for i in range(len(cats))} - catid2name = {i: name for i, name in enumerate(cats)} - - return clsid2catid, catid2name - - -def widerfaceall_category_info(with_background=True): - """ - Get class id to category id map and category id - to category name map of mixup wider_face dataset - - Args: - with_background (bool, default True): - whether load background as class 0. - """ - label_map = widerface_label(with_background) - label_map = sorted(label_map.items(), key=lambda x: x[1]) - cats = [l[0] for l in label_map] - - if with_background: - cats.insert(0, 'background') - - clsid2catid = {i: i for i in range(len(cats))} - catid2name = {i: name for i, name in enumerate(cats)} - - return clsid2catid, catid2name diff --git a/tools/train.py b/tools/train.py index 121bb1d576ab39db91d9616ce715b57fe2ef37f6..115c38ef5bd81d852d3de5ede628bb25b3dafc70 100755 --- a/tools/train.py +++ b/tools/train.py @@ -29,6 +29,7 @@ import random import datetime import numpy as np from collections import deque + import paddle from ppdet.core.workspace import load_config, merge_config, create from ppdet.utils.stats import TrainingStats @@ -37,6 +38,7 @@ from ppdet.utils.cli import ArgsParser from ppdet.utils.checkpoint import load_weight, load_pretrain_weight, save_model from export_model import dygraph_to_static from paddle.distributed import ParallelEnv + import logging FORMAT = '%(asctime)s-%(levelname)s: %(message)s' logging.basicConfig(level=logging.INFO, format=FORMAT) @@ -71,16 +73,6 @@ def parse_args(): default=None, type=str, help="Evaluation directory, default is current directory.") - parser.add_argument( - "--use_tb", - type=bool, - default=False, - help="whether to record the data to Tensorboard.") - parser.add_argument( - '--tb_log_dir', - type=str, - default="tb_log_dir/scalar", - help='Tensorboard logging directory for scalar.') parser.add_argument( "--enable_ce", type=bool, @@ -89,13 +81,6 @@ def parse_args(): "This flag is only used for internal test.") parser.add_argument( "--use_gpu", action='store_true', default=False, help="data parallel") - - parser.add_argument( - '--is_profiler', - type=int, - default=0, - help='The switch of profiler tools. (used for benchmark)') - args = parser.parse_args() return args