[Dygraph] quickstart doc (#2155)

* add quickstart doc, roadsign dataset

dygraph/configs/datasets/roadsign_voc.yml

+metric: VOC
+map_type: 11point
+num_classes: 4
+
+TrainDataset:
+  !VOCDataSet
+    dataset_dir: dataset/roadsign_voc
+    anno_path: train.txt
+    label_list: label_list.txt
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']
+
+EvalDataset:
+  !VOCDataSet
+    dataset_dir: dataset/roadsign_voc
+    anno_path: valid.txt
+    label_list: label_list.txt
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']
+
+TestDataset:
+  !ImageFolder
+    anno_path: dataset/roadsign_voc/label_list.txt

dygraph/configs/yolov3/yolov3_mobilenet_v1_roadsign.yml

+_BASE_: [
+  '../datasets/roadsign_voc.yml',
+  '../runtime.yml',
+  '_base_/yolov3_mobilenet_v1.yml',
+  '_base_/yolov3_reader.yml',
+]
+pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_mobilenet_v1_270e_coco.pdparams
+use_fine_grained_loss: false
+load_static_weights: false
+norm_type: sync_bn
+weights: output/yolov3_mobilenet_v1_roadsign/model_final
+
+YOLOv3Loss:
+  ignore_thresh: 0.7
+  label_smooth: true
+
+TrainReader:
+  inputs_def:
+    num_max_boxes: 50
+  sample_transforms:
+    - DecodeOp: {}
+    - MixupOp: {alpha: 1.5, beta: 1.5}
+    - RandomDistortOp: {}
+    - RandomExpandOp: {fill_value: [123.675, 116.28, 103.53]}
+    - RandomCropOp: {}
+    - RandomFlipOp: {}
+  batch_transforms:
+    - BatchRandomResizeOp:
+        target_size: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608]
+        random_size: True
+        random_interp: True
+        keep_ratio: False
+    - NormalizeBoxOp: {}
+    - PadBoxOp: {num_max_boxes: 50}
+    - BboxXYXY2XYWHOp: {}
+    - NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - PermuteOp: {}
+    - Gt2YoloTargetOp:
+        anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
+        anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]]
+        downsample_ratios: [32, 16, 8]
+        num_classes: 4
+  batch_size: 8
+  shuffle: true
+  drop_last: true
+
+snapshot_epoch: 5
+epoch: 40
+
+LearningRate:
+  base_lr: 0.0001
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [32, 36]
+  - !LinearWarmup
+    start_factor: 0.3333333333333333
+    steps: 100
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005
+    type: L2

dygraph/dataset/roadsign_voc/download_roadsign_voc.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import 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, 'roadsign_voc')

dygraph/dataset/roadsign_voc/label_list.txt

+speedlimit
+crosswalk
+trafficlight
+stop
\ No newline at end of file

dygraph/docs/tutorials/QUICK_STARTED_cn.md

+# 快速开始
+为了使得用户能够在很短时间内快速产出模型，掌握PaddleDetection的使用方式，这篇教程通过一个预训练检测模型对小数据集进行finetune。在较短时间内即可产出一个效果不错的模型。实际业务中，建议用户根据需要选择合适模型配置文件进行适配。
+
+- **设置显卡**
+```bash
+export CUDA_VISIBLE_DEVICES=0
+```
+
+## 一、快速体验
+```
+# 用PP-YOLO算法在COCO数据集上预训练模型预测一张图片
+python tools/infer.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml -o use_gpu=true weights=https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_1x_coco.pdparams --infer_img=demo/000000014439.jpg
+```
+结果如下图：
+
+![](../images/000000014439.jpg)
+
+
+## 二、准备数据
+数据集参考[Kaggle数据集](https://www.kaggle.com/andrewmvd/road-sign-detection) ，包含877张图像，数据类别4类：crosswalk，speedlimit，stop，trafficlight。
+将数据划分为训练集701张图和测试集176张图，[下载链接](https://paddlemodels.bj.bcebos.com/object_detection/roadsign_voc.tar).
+
+```
+# 注意：可跳过这步下载，后面训练会自动下载
+python dataset/roadsign_voc/download_roadsign_voc.py
+```
+
+## 三、训练、评估、预测
+### 1、训练
+```
+# 边训练边测试 CPU需要约1小时(use_gpu=false)，1080Ti GPU需要约10分钟。
+# -c 参数表示指定使用哪个配置文件
+# -o 参数表示指定配置文件中的全局变量（覆盖配置文件中的设置），这里设置使用gpu，
+# --eval 参数表示边训练边评估，会自动保存一个评估结果最的名为model_final.pdmodel的模型
+
+
+python tools/train.py -c configs/yolov3/yolov3_mobilenet_v1_roadsign.yml --eval -o use_gpu=true --weight_type finetune
+```
+
+如果想通过VisualDL实时观察loss变化曲线，在训练命令中添加--use_vdl=true，以及通过--vdl_log_dir设置日志保存路径。
+
+**但注意VisualDL需Python>=3.5**
+
+首先安装[VisualDL](https://github.com/PaddlePaddle/VisualDL)
+```
+python -m pip install visualdl -i https://mirror.baidu.com/pypi/simple
+```
+
+```
+python -u tools/train.py -c configs/yolov3/yolov3_mobilenet_v1_roadsign.yml \
+                        --use_vdl=true \
+                        --vdl_log_dir=vdl_dir/scalar \
+                        --eval
+```
+通过visualdl命令实时查看变化曲线：
+```
+visualdl --logdir vdl_dir/scalar/ --host <host_IP> --port <port_num>
+```
+
+### 2、评估
+```
+# 评估 默认使用训练过程中保存的model_final
+# -c 参数表示指定使用哪个配置文件
+# -o 参数表示指定配置文件中的全局变量（覆盖配置文件中的设置），需使用单卡评估
+
+python tools/eval.py -c configs/yolov3/yolov3_mobilenet_v1_roadsign.yml -o use_gpu=true
+```
+
+
+### 3、预测
+```
+# -c 参数表示指定使用哪个配置文件
+# -o 参数表示指定配置文件中的全局变量（覆盖配置文件中的设置）
+# --infer_img 参数指定预测图像路径
+# 预测结束后会在output文件夹中生成一张画有预测结果的同名图像
+
+python tools/infer.py -c configs/yolov3/yolov3_mobilenet_v1_roadsign.yml -o use_gpu=true --infer_img=demo/road554.png
+```
+
+结果如下图：
+
+![](../images/road554.png)

dygraph/ppdet/utils/checkpoint.py

@@ -164,9 +164,16 @@ def load_pretrain_weight(model,
     else:
         ignore_set = set()
         for name, weight in model_dict.items():
-            if name in param_state_dict:
-                if weight.shape != param_state_dict[name].shape:
+            if name in param_state_dict.keys():
+                if weight.shape != list(param_state_dict[name].shape):
+                    logger.info(
+                        '{} not used, shape {} unmatched with {} in model.'.
+                        format(name,
+                               list(param_state_dict[name].shape),
+                               weight.shape))
                     param_state_dict.pop(name, None)
+            else:
+                logger.info('Lack weight: {}'.format(name))
         model.set_dict(param_state_dict)
     return
 

dygraph/ppdet/utils/download.py

@@ -81,6 +81,12 @@ DATASETS = {
         'https://dataset.bj.bcebos.com/PaddleDetection_demo/fruit.tar',
         'baa8806617a54ccf3685fa7153388ae6', ), ],
               ['Annotations', 'JPEGImages']),
+    'roadsign_voc': ([(
+        'https://paddlemodels.bj.bcebos.com/object_detection/roadsign_voc.tar',
+        '8d629c0f880dd8b48de9aeff44bf1f3e', ), ], ['annotations', 'images']),
+    'roadsign_coco': ([(
+        'https://paddlemodels.bj.bcebos.com/object_detection/roadsign_coco.tar',
+        '49ce5a9b5ad0d6266163cd01de4b018e', ), ], ['annotations', 'images']),
     'objects365': (),
 }
 
@@ -173,7 +179,7 @@ def get_dataset_path(path, annotation, image_dir):
                     "https://www.objects365.org/download.html".format(name))
             data_dir = osp.join(DATASET_HOME, name)
             # For voc, only check dir VOCdevkit/VOC2012, VOCdevkit/VOC2007
-            if name == 'voc' or name == 'fruit':
+            if name in ['voc', 'fruit', 'roadsign_voc']:
                 exists = True
                 for sub_dir in dataset[1]:
                     check_dir = osp.join(data_dir, sub_dir)
@@ -185,7 +191,7 @@ def get_dataset_path(path, annotation, image_dir):
                     return data_dir
 
             # voc exist is checked above, voc is not exist here
-            check_exist = name != 'voc' and name != 'fruit'
+            check_exist = name != 'voc' and name != 'fruit' and name != 'roadsign_voc'
             for url, md5sum in dataset[0]:
                 get_path(url, data_dir, md5sum, check_exist)
 
@@ -195,10 +201,11 @@ def get_dataset_path(path, annotation, image_dir):
             return data_dir
 
     # not match any dataset in DATASETS
-    raise ValueError("Dataset {} is not valid and cannot parse dataset type "
-                     "'{}' for automaticly downloading, which only supports "
-                     "'voc' , 'coco', 'wider_face' and 'fruit' currently".
-                     format(path, osp.split(path)[-1]))
+    raise ValueError(
+        "Dataset {} is not valid and cannot parse dataset type "
+        "'{}' for automaticly downloading, which only supports "
+        "'voc' , 'coco', 'wider_face', 'fruit' and 'roadsign_voc' currently".
+        format(path, osp.split(path)[-1]))
 
 
 def create_voc_list(data_dir, devkit_subdir='VOCdevkit'):