fix format

PaddleCV/3d_vision/M3D-RPN/README.md

@@ -5,53 +5,53 @@
 ## Introduction
 
 
-Monocular 3D region proposal network for object detection accepted to ICCV 2019 (Oral), detailed in [arXiv report](https://arxiv.org/abs/1907.06038). 
+Monocular 3D region proposal network for object detection accepted to ICCV 2019 (Oral), detailed in [arXiv report](https://arxiv.org/abs/1907.06038).
+
 
 
-
 
 ## Setup
 
 - **Cuda & Python**
 
-    In this project we utilize PaddlePaddle1.8 with Python 3, Cuda 9, and a few Anaconda packages. 
+    In this project we utilize PaddlePaddle1.8 with Python 3, Cuda 9, and a few Anaconda packages.
 
 - **Data**
 
-    Download the full [KITTI](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) detection dataset. Then place a softlink (or the actual data) in  *M3D-RPN/data/kitti*. 
+    Download the full [KITTI](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) detection dataset. Then place a softlink (or the actual data) in  *M3D-RPN/data/kitti*.
 
-	```
+    ```
     cd M3D-RPN
-	ln -s /path/to/kitti dataset/kitti
-	```
+    ln -s /path/to/kitti dataset/kitti
+    ```
 
-	Then use the following scripts to extract the data splits, which use softlinks to the above directory for efficient storage. 
+    Then use the following scripts to extract the data splits, which use softlinks to the above directory for efficient storage.
 
     ```
     python dataset/kitti_split1/setup_split.py
     python dataset/kitti_split2/setup_split.py
     ```
-    
+
     Next, build the KITTI devkit eval for each split.
 
-	```
-	sh dataset/kitti_split1/devkit/cpp/build.sh
-	sh dataset/kitti_split2/devkit/cpp/build.sh
-	```
-    
+    ```
+    sh dataset/kitti_split1/devkit/cpp/build.sh
+    sh dataset/kitti_split2/devkit/cpp/build.sh
+    ```
+
     Lastly, build the nms modules
-
+
+    ```
+    cd lib/nms
+    make
     ```
-	cd lib/nms
-	make
-	```
 
 ## Training
 
 
-Training is split into a warmup and main configurations. Review the configurations in *config* for details. 
+Training is split into a warmup and main configurations. Review the configurations in *config* for details.
 
-``` 
+```
 // First train the warmup (without depth-aware)
 python train.py --config=kitti_3d_multi_warmup
 
@@ -63,11 +63,10 @@ python train.py --config=kitti_3d_multi_main
 
 ## Testing
 
-We provide models for the main experiments on val1 data splits available to download here [M3D-RPN-release.tar](https://pan.baidu.com/s/1VQa5hGzIbauLOQi-0kR9Hg), passward:ls39. 
+We provide models for the main experiments on val1 data splits available to download here [M3D-RPN-release.tar](https://pan.baidu.com/s/1VQa5hGzIbauLOQi-0kR9Hg), passward:ls39.
 
-Testing requires paths to the configuration file and model weights, exposed variables near the top *test.py*. To test a configuration and model, simply update the variables and run the test file as below. 
+Testing requires paths to the configuration file and model weights, exposed variables near the top *test.py*. To test a configuration and model, simply update the variables and run the test file as below.
 
 ```
 python test.py --conf_path M3D-RPN-release/conf.pkl --weights_path M3D-RPN-release/iter50000.0_params.pdparams
 ```
-

PaddleCV/3d_vision/M3D-RPN/config/kitti_3d_multi_main.py

@@ -4,18 +4,19 @@
 from easydict import EasyDict as edict
 import numpy as np
 
+
 def Config():
     """
     config
     """
     conf = edict()
-        
+
     # ----------------------------------------
     #  general
     # ----------------------------------------
 
     conf.model = 'model_3d_dilate_depth_aware'
-    
+
     # solver settings
     conf.solver_type = 'sgd'
     conf.lr = 0.004
@@ -25,21 +26,21 @@ def Config():
     conf.snapshot_iter = 10000
     conf.display = 20
     conf.do_test = True
-    
+
     # sgd parameters
     conf.lr_policy = 'poly'
     conf.lr_steps = None
     conf.lr_target = conf.lr * 0.00001
-    
+
     # random
     conf.rng_seed = 2
     conf.cuda_seed = 2
-    
+
     # misc network
     conf.image_means = [0.485, 0.456, 0.406]
     conf.image_stds = [0.229, 0.224, 0.225]
     conf.feat_stride = 16
-    
+
     conf.has_3d = True
 
     # ----------------------------------------
@@ -51,27 +52,32 @@ def Config():
     conf.crop_size = [512, 1760]
     conf.mirror_prob = 0.50
     conf.distort_prob = -1
-    
+
     # datasets
     conf.dataset_test = 'kitti_split1'
-    conf.datasets_train = [{'name': 'kitti_split1', 'anno_fmt': 'kitti_det', 'im_ext': '.png', 'scale': 1}]
+    conf.datasets_train = [{
+        'name': 'kitti_split1',
+        'anno_fmt': 'kitti_det',
+        'im_ext': '.png',
+        'scale': 1
+    }]
     conf.use_3d_for_2d = True
-    
+
     # percent expected height ranges based on test_scale
     # used for anchor selection 
     conf.percent_anc_h = [0.0625, 0.75]
-    
+
     # labels settings
     conf.min_gt_h = conf.test_scale * conf.percent_anc_h[0]
     conf.max_gt_h = conf.test_scale * conf.percent_anc_h[1]
     conf.min_gt_vis = 0.65
     conf.ilbls = ['Van', 'ignore']
     conf.lbls = ['Car', 'Pedestrian', 'Cyclist']
-    
+
     # ----------------------------------------
     #  detection sampling
     # ----------------------------------------
-    
+
     # detection sampling
     conf.batch_size = 2
     conf.fg_image_ratio = 1.0
@@ -101,22 +107,23 @@ def Config():
     # ----------------------------------------
     #  anchor settings
     # ----------------------------------------
-    
+
     # clustering settings
     conf.cluster_anchors = 0
     conf.even_anchors = 0
     conf.expand_anchors = 0
-                             
+
     conf.anchors = None
 
     conf.bbox_means = None
     conf.bbox_stds = None
-    
+
     # initialize anchors
-    base = (conf.max_gt_h / conf.min_gt_h) ** (1 / (12 - 1))
-    conf.anchor_scales = np.array([conf.min_gt_h * (base ** i) for i in range(0, 12)])
+    base = (conf.max_gt_h / conf.min_gt_h)**(1 / (12 - 1))
+    conf.anchor_scales = np.array(
+        [conf.min_gt_h * (base**i) for i in range(0, 12)])
     conf.anchor_ratios = np.array([0.5, 1.0, 1.5])
-    
+
     # loss logic
     conf.hard_negatives = True
     conf.focal_loss = 0
@@ -125,15 +132,14 @@ def Config():
     conf.bbox_2d_lambda = 0
     conf.bbox_3d_lambda = 1
     conf.bbox_3d_proj_lambda = 0.0
-    
+
     conf.hill_climbing = True
-    
+
     conf.bins = 32
-    
+
     # visdom
     conf.visdom_port = 8100
-    
+
     conf.pretrained = 'paddle.pdparams'
 
     return conf
-

PaddleCV/3d_vision/M3D-RPN/config/kitti_3d_multi_warmup.py

@@ -5,12 +5,13 @@
 from easydict import EasyDict as edict
 import numpy as np
 
+
 def Config():
     """
     config
     """
     conf = edict()
-        
+
     # ----------------------------------------
     #  general
     # ----------------------------------------
@@ -25,21 +26,21 @@ def Config():
     conf.snapshot_iter = 10000
     conf.display = 20
     conf.do_test = True
-    
+
     # sgd parameters
     conf.lr_policy = 'poly'
     conf.lr_steps = None
     conf.lr_target = conf.lr * 0.00001
-    
+
     # random
     conf.rng_seed = 2
     conf.cuda_seed = 2
-    
+
     # misc network
     conf.image_means = [0.485, 0.456, 0.406]
     conf.image_stds = [0.229, 0.224, 0.225]
     conf.feat_stride = 16
-    
+
     conf.has_3d = True
 
     # ----------------------------------------
@@ -51,27 +52,32 @@ def Config():
     conf.crop_size = [512, 1760]
     conf.mirror_prob = 0.50
     conf.distort_prob = -1
-    
+
     # datasets
     conf.dataset_test = 'kitti_split1'
-    conf.datasets_train = [{'name': 'kitti_split1', 'anno_fmt': 'kitti_det', 'im_ext': '.png', 'scale': 1}]
+    conf.datasets_train = [{
+        'name': 'kitti_split1',
+        'anno_fmt': 'kitti_det',
+        'im_ext': '.png',
+        'scale': 1
+    }]
     conf.use_3d_for_2d = True
-    
+
     # percent expected height ranges based on test_scale
     # used for anchor selection 
     conf.percent_anc_h = [0.0625, 0.75]
-    
+
     # labels settings
     conf.min_gt_h = conf.test_scale * conf.percent_anc_h[0]
     conf.max_gt_h = conf.test_scale * conf.percent_anc_h[1]
     conf.min_gt_vis = 0.65
     conf.ilbls = ['Van', 'ignore']
     conf.lbls = ['Car', 'Pedestrian', 'Cyclist']
-    
+
     # ----------------------------------------
     #  detection sampling
     # ----------------------------------------
-    
+
     # detection sampling
     conf.batch_size = 2
     conf.fg_image_ratio = 1.0
@@ -101,22 +107,23 @@ def Config():
     # ----------------------------------------
     #  anchor settings
     # ----------------------------------------
-    
+
     # clustering settings
     conf.cluster_anchors = 0
     conf.even_anchors = 0
     conf.expand_anchors = 0
-                             
+
     conf.anchors = None
 
     conf.bbox_means = None
     conf.bbox_stds = None
-    
+
     # initialize anchors
-    base = (conf.max_gt_h / conf.min_gt_h) ** (1 / (12 - 1))
-    conf.anchor_scales = np.array([conf.min_gt_h * (base ** i) for i in range(0, 12)])
+    base = (conf.max_gt_h / conf.min_gt_h)**(1 / (12 - 1))
+    conf.anchor_scales = np.array(
+        [conf.min_gt_h * (base**i) for i in range(0, 12)])
     conf.anchor_ratios = np.array([0.5, 1.0, 1.5])
-    
+
     # loss logic
     conf.hard_negatives = True
     conf.focal_loss = 0
@@ -125,13 +132,12 @@ def Config():
     conf.bbox_2d_lambda = 0
     conf.bbox_3d_lambda = 1
     conf.bbox_3d_proj_lambda = 0.0
-    
+
     conf.hill_climbing = True
 
     conf.pretrained = 'pretrained_model/densenet.pdparams'
-    
+
     # visdom
     conf.visdom_port = 8100
 
     return conf
-