Refine ppdet download (open-mmlab#3628)

* refine download sh to py

* update QUICK_STARTED

dataset/coco/download_coco.py

@@ -0,0 +1,25 @@
+# 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
+
+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, 'coco')

dataset/fruit/download_fruit.py

@@ -0,0 +1,25 @@
+# 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
+
+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')

dataset/voc/download_voc.py

@@ -0,0 +1,25 @@
+# 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
+
+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, 'voc')

docs/INSTALL.md

@@ -110,15 +110,15 @@ On the other hand, to download the datasets, run the following commands:
 - COCO
 
 ```
-cd dataset/coco
-./download.sh
+export PYTHONPATH=$PYTHONPATH:.
+python dataset/coco/download_coco.py
 ```
 
 - Pascal VOC
 
 ```
-cd dataset/voc
-./download.sh
+export PYTHONPATH=$PYTHONPATH:.
+python dataset/voc/download_voc.py
 ```
 
 **Download datasets automatically:**

docs/INSTALL_cn.md

@@ -109,15 +109,15 @@ ln -sf <path/to/voc> <path/to/paddle_detection>/dataset/voc
 - COCO
 
 ```
-cd dataset/coco
-./download.sh
+export PYTHONPATH=$PYTHONPATH:.
+python dataset/coco/download_coco.py
 ```
 
 - Pascal VOC
 
 ```
-cd dataset/voc
-./download.sh
+export PYTHONPATH=$PYTHONPATH:.
+python dataset/voc/download_voc.py
 ```
 
 **自动下载数据集：**

docs/QUICK_STARTED.md

@@ -6,11 +6,11 @@ This tutorial fine-tunes a tiny dataset by pretrained detection model for users
 
 ## Data Preparation
 
-Dataset refers to [Kaggle](https://www.kaggle.com/mbkinaci/fruit-images-for-object-detection), which contains 240 images in train dataset and 60 images in test dataset. Data categories are apple, orange and banana. Download [here](https://dataset.bj.bcebos.com/PaddleDetection_demo/fruit-detection.tar) and uncompress the dataset after download, script for data preparation is located at [download.sh](../dataset/fruit/download.sh). Command is as follows:
+Dataset refers to [Kaggle](https://www.kaggle.com/mbkinaci/fruit-images-for-object-detection), which contains 240 images in train dataset and 60 images in test dataset. Data categories are apple, orange and banana. Download [here](https://dataset.bj.bcebos.com/PaddleDetection_demo/fruit-detection.tar) and uncompress the dataset after download, script for data preparation is located at [download_fruit.py](../dataset/fruit/download_fruit.py). Command is as follows:
 
 ```bash
-cd dataset/fruit
-sh download.sh
+export PYTHONPATH=$PYTHONPATH:.
+python dataset/fruit/download_fruit.py
 ```
 
 - **Note: before started, run the following command and specifiy the GPU**

docs/QUICK_STARTED_cn.md

@@ -6,11 +6,11 @@
 
 ## 数据准备
 
-数据集参考[Kaggle数据集](https://www.kaggle.com/mbkinaci/fruit-images-for-object-detection)，其中训练数据集240张图片，测试数据集60张图片，数据类别为3类：苹果，橘子，香蕉。[下载链接](https://dataset.bj.bcebos.com/PaddleDetection_demo/fruit-detection.tar)。数据下载后分别解压即可, 数据准备脚本位于[download.sh](../dataset/fruit/download.sh)。下载数据方式如下：
+数据集参考[Kaggle数据集](https://www.kaggle.com/mbkinaci/fruit-images-for-object-detection)，其中训练数据集240张图片，测试数据集60张图片，数据类别为3类：苹果，橘子，香蕉。[下载链接](https://dataset.bj.bcebos.com/PaddleDetection_demo/fruit-detection.tar)。数据下载后分别解压即可, 数据准备脚本位于[download_fruit.py](../dataset/fruit/download_fruit.py)。下载数据方式如下：
 
 ```bash
-cd dataset/fruit
-sh download.sh
+export PYTHONPATH=$PYTHONPATH:.
+python dataset/fruit/download_fruit.py
 ```
 
 - **注：在开始前，运行如下命令并指定GPU**

ppdet/utils/download.py

@@ -35,7 +35,7 @@
 WEIGHTS_HOME = osp.expanduser("~/.cache/paddle/weights")
 DATASET_HOME = osp.expanduser("~/.cache/paddle/dataset")
 
-# dict of {dataset_name: (downalod_info, sub_dirs)}
+# dict of {dataset_name: (download_info, sub_dirs)}
 # download info: (url, md5sum)
 DATASETS = {
     'coco': ([
@@ -60,6 +60,11 @@
             'http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar',
             'b6e924de25625d8de591ea690078ad9f', ),
     ], ["VOCdevkit/VOC_all"]),
+    'fruit': ([
+        (
+            'https://dataset.bj.bcebos.com/PaddleDetection_demo/fruit-detection.tar',
+            '374554a7633b1b68d6a5fbb7c061b8ba', ),
+    ], ["fruit-detection"]),
 }
 
 DOWNLOAD_RETRY_LIMIT = 3
@@ -103,25 +108,7 @@ def get_dataset_path(path, annotation, image_dir):
 
             # voc should merge dir and create list after download
             if name == 'voc':
-                logger.info("Download voc dataset successed, merge "
-                            "VOC2007 and VOC2012 to VOC_all...")
-                output_dir = osp.join(data_dir, dataset[1][0])
-                devkit_dir = "/".join(output_dir.split('/')[:-1])
-                years = ['2007', '2012']
-                # merge dir in output_tmp_dir at first, move to 
-                # output_dir after merge sucessed.
-                output_tmp_dir = osp.join(data_dir, 'tmp')
-                if osp.isdir(output_tmp_dir):
-                    shutil.rmtree(output_tmp_dir)
-                # NOTE(dengkaipeng): since using auto download VOC
-                # dataset, VOC default label list should be used, 
-                # do not generate label_list.txt here. For default
-                # label, see ../data/source/voc_loader.py
-                merge_and_create_list(devkit_dir, years, output_tmp_dir)
-                shutil.move(output_tmp_dir, output_dir)
-                # remove source directory VOC2007 and VOC2012
-                shutil.rmtree(osp.join(devkit_dir, "VOC2007"))
-                shutil.rmtree(osp.join(devkit_dir, "VOC2012"))
+                _merge_voc_dir(data_dir, dataset[1][0])
             return data_dir
 
     # not match any dataset in DATASETS
@@ -130,6 +117,28 @@ def get_dataset_path(path, annotation, image_dir):
                      "'voc' and 'coco' currently".format(path, osp.split(path)[-1]))
 
 
+def _merge_voc_dir(data_dir, output_subdir):
+    logger.info("Download voc dataset successed, merge "
+                "VOC2007 and VOC2012 to VOC_all...")
+    output_dir = osp.join(data_dir, output_subdir)
+    devkit_dir = "/".join(output_dir.split('/')[:-1])
+    years = ['2007', '2012']
+    # merge dir in output_tmp_dir at first, move to 
+    # output_dir after merge sucessed.
+    output_tmp_dir = osp.join(data_dir, 'tmp')
+    if osp.isdir(output_tmp_dir):
+        shutil.rmtree(output_tmp_dir)
+    # NOTE: since using auto download VOC
+    # dataset, VOC default label list should be used, 
+    # do not generate label_list.txt here. For default
+    # label, see ../data/source/voc_loader.py
+    merge_and_create_list(devkit_dir, years, output_tmp_dir)
+    shutil.move(output_tmp_dir, output_dir)
+    # remove source directory VOC2007 and VOC2012
+    shutil.rmtree(osp.join(devkit_dir, "VOC2007"))
+    shutil.rmtree(osp.join(devkit_dir, "VOC2012"))
+
+
 def map_path(url, root_dir):
     # parse path after download to decompress under root_dir
     fname = url.split('/')[-1]
@@ -173,6 +182,19 @@ def get_path(url, root_dir, md5sum=None):
     return fullpath
 
 
+def download_dataset(path, dataset=None):
+    if dataset not in DATASETS.keys():
+        logger.error("Unknown dataset {}, it should be "
+                     "{}".format(dataset, DATASETS.keys()))
+        return
+    dataset_info = DATASETS[dataset][0]
+    for info in dataset_info:
+        get_path(info[0], path, info[1])
+    if dataset == 'voc':
+        _merge_voc_dir(path, DATASETS[dataset][1][0])
+    logger.info("Download dataset {} finished.".format(dataset))
+
+
 def _dataset_exists(path, annotation, image_dir):
     """
     Check if user define dataset exists