update faster rcnn example with logging and cpu make (apache#6486)

* update rcnn for logging and cpu make
* remove deprecated files
* update pycocoutils
* use logging for output
* support cpu make and setup.py

* fix proposal op

example/rcnn/README.md

@@ -1,5 +1,7 @@
 # Faster R-CNN in MXNet with distributed implementation and data parallelization
 
+![example detections](https://cloud.githubusercontent.com/assets/13162287/22101032/92085dc0-de6c-11e6-9228-67e72606ddbc.png)
+
 ## Why?
 There exist good implementations of Faster R-CNN yet they lack support for recent 
 ConvNet architectures. The aim of reproducing it from scratch is to fully utilize 
@@ -43,9 +45,8 @@ MXNet engines and parallelization for object detection.
 | Faster R-CNN end-to-end | VGG16 | COCO train | COCO val | 21.2 | 22.8 |
 | Faster R-CNN end-to-end | ResNet-101 | COCO train | COCO val | 27.2 | 26.1 |
 
-All reference results are from original publications.
-All VOC experiments are conducted in MXNet-v0.9.1-nnvm. MXNet-v0.8 have similar results.
-All COCO experiments are conducted in MXNet-v0.8.
+The above experiments were conducted at [mx-rcnn](https://github.com/precedenceguo/mx-rcnn/tree/6a1ab0eec5035a10a1efb5fc8c9d6c54e101b4d0)
+using [a MXNet fork, based on MXNet 0.9.1 nnvm pre-release](https://github.com/precedenceguo/mxnet/tree/simple).
 
 ## I'm Feeling Lucky
 * Prepare: `bash script/additional_deps.sh`
@@ -56,9 +57,8 @@ All COCO experiments are conducted in MXNet-v0.8.
 ## Getting started
 See if `bash script/additional_deps.sh` will do the following for you.
 * Suppose `HOME` represents where this file is located. All commands, unless stated otherwise, should be started from `HOME`.
-  Executing scripts in `script` must also be from `HOME`.
 * Install python package `cython easydict matplotlib scikit-image`.
-* Install MXNet Python Interface. Open `python` type `import mxnet` to confirm.
+* Install MXNet version v0.9.5 or higher and MXNet Python Interface. Open `python` type `import mxnet` to confirm.
 * Run `make` in `HOME`.
 
 Command line arguments have the same meaning as in mxnet/example/image-classification.
@@ -82,7 +82,7 @@ Refer to `script/vgg_voc07.sh` and other experiments for examples.
 
 ### Prepare Training Data
 See `bash script/get_voc.sh` and `bash script/get_coco.sh` will do the following for you.
-* Make a folder `data` in `HOME`. `data` folder will be used to place the training data folder `VOCdevkit` and `coco`. 
+* Make a folder `data` in `HOME`. `data` folder will be used to place the training data folder `VOCdevkit` and `coco`.
 * Download and extract [Pascal VOC data](http://host.robots.ox.ac.uk/pascal/VOC/), place the `VOCdevkit` folder in `HOME/data`.
 * Download and extract [coco dataset](http://mscoco.org/dataset/), place all images to `coco/images` and annotation jsons to `data/annotations`.
 
@@ -94,6 +94,7 @@ See `bash script/get_voc.sh` and `bash script/get_coco.sh` will do the following
 ### Prepare Pretrained Models
 See if `bash script/get_pretrained_model.sh` will do this for you. If not,
 * Make a folder `model` in `HOME`. `model` folder will be used to place model checkpoints along the training process. 
+  It is recommended to set `model` as a symbolic link to somewhere else in hard disk.
 * Download VGG16 pretrained model `vgg16-0000.params` from [MXNet model gallery](https://github.com/dmlc/mxnet-model-gallery/blob/master/imagenet-1k-vgg.md) to `model` folder.
 * Download ResNet pretrained model `resnet-101-0000.params` from [ResNet](https://github.com/tornadomeet/ResNet) to `model` folder.
 
@@ -174,7 +175,7 @@ History of this implementation is:
 * Faster R-CNN with end-to-end training and module testing (v4)
 * Faster R-CNN with accelerated training and resnet (v5)  
 
-mxnet/example/rcnn was v1, v2 and v3.5.
+mxnet/example/rcnn was v1, v2, v3.5 and now v5.
 
 ## References
 1. Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. In Neural Information Processing Systems, Workshop on Machine Learning Systems, 2015
@@ -186,3 +187,4 @@ mxnet/example/rcnn was v1, v2 and v3.5.
 7. Karen Simonyan, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
 8. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Deep Residual Learning for Image Recognition". In Computer Vision and Pattern Recognition, IEEE Conference on, 2016.
 9. Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. "Microsoft COCO: Common Objects in Context" In European Conference on Computer Vision, pp. 740-755. Springer International Publishing, 2014.
+

example/rcnn/demo.py

@@ -1,9 +1,9 @@
-from __future__ import print_function
 import argparse
 import os
 import cv2
 import mxnet as mx
 import numpy as np
+from rcnn.logger import logger
 from rcnn.config import config
 from rcnn.symbol import get_vgg_test, get_vgg_rpn_test
 from rcnn.io.image import resize, transform
@@ -104,17 +104,18 @@ def demo_net(predictor, image_name, vis=False):
     boxes_this_image = [[]] + [all_boxes[j] for j in range(1, len(CLASSES))]
 
     # print results
-    print('class ---- [[x1, x2, y1, y2, confidence]]')
+    logger.info('---class---')
+    logger.info('[[x1, x2, y1, y2, confidence]]')
     for ind, boxes in enumerate(boxes_this_image):
         if len(boxes) > 0:
-            print('---------', CLASSES[ind], '---------')
-            print(boxes)
+            logger.info('---%s---' % CLASSES[ind])
+            logger.info('%s' % boxes)
 
     if vis:
         vis_all_detection(data_dict['data'].asnumpy(), boxes_this_image, CLASSES, im_scale)
     else:
         result_file = image_name.replace('.', '_result.')
-        print('results saved to %s' % result_file)
+        logger.info('results saved to %s' % result_file)
         im = draw_all_detection(data_dict['data'].asnumpy(), boxes_this_image, CLASSES, im_scale)
         cv2.imwrite(result_file, im)
 

example/rcnn/rcnn/core/tester.py

@@ -1,11 +1,11 @@
-from __future__ import print_function
 import cPickle
 import os
 import time
 import mxnet as mx
 import numpy as np
 
 from module import MutableModule
+from rcnn.logger import logger
 from rcnn.config import config
 from rcnn.io import image
 from rcnn.processing.bbox_transform import bbox_pred, clip_boxes
@@ -79,9 +79,9 @@ def generate_proposals(predictor, test_data, imdb, vis=False, thresh=0.):
         if vis:
             vis_all_detection(data_dict['data'].asnumpy(), [dets], ['obj'], scale)
 
-        print('generating %d/%d' % (i + 1, imdb.num_images),
-              'proposal %d' % (dets.shape[0]),
-              'data %.4fs net %.4fs' % (t1, t2))
+        logger.info('generating %d/%d ' % (i + 1, imdb.num_images) +
+                    'proposal %d ' % (dets.shape[0]) +
+                    'data %.4fs net %.4fs' % (t1, t2))
         i += 1
 
     assert len(imdb_boxes) == imdb.num_images, 'calculations not complete'
@@ -100,7 +100,7 @@ def generate_proposals(predictor, test_data, imdb, vis=False, thresh=0.):
         with open(full_rpn_file, 'wb') as f:
             cPickle.dump(original_boxes, f, cPickle.HIGHEST_PROTOCOL)
 
-    print('wrote rpn proposals to {}'.format(rpn_file))
+    logger.info('wrote rpn proposals to %s' % rpn_file)
     return imdb_boxes
 
 
@@ -189,7 +189,7 @@ def pred_eval(predictor, test_data, imdb, vis=False, thresh=1e-3):
 
         t3 = time.time() - t
         t = time.time()
-        print('testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s'.format(i, imdb.num_images, t1, t2, t3))
+        logger.info('testing %d/%d data %.4fs net %.4fs post %.4fs' % (i, imdb.num_images, t1, t2, t3))
         i += 1
 
     det_file = os.path.join(imdb.cache_path, imdb.name + '_detections.pkl')

example/rcnn/rcnn/cython/setup.py

@@ -55,7 +55,13 @@ def locate_cuda():
             raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v))
 
     return cudaconfig
-CUDA = locate_cuda()
+
+
+# Test if cuda could be foun
+try:
+    CUDA = locate_cuda()
+except EnvironmentError:
+    CUDA = None
 
 
 # Obtain the numpy include directory.  This logic works across numpy versions.
@@ -123,25 +129,32 @@ def build_extensions(self):
         extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]},
         include_dirs = [numpy_include]
     ),
-    Extension('gpu_nms',
-        ['nms_kernel.cu', 'gpu_nms.pyx'],
-        library_dirs=[CUDA['lib64']],
-        libraries=['cudart'],
-        language='c++',
-        runtime_library_dirs=[CUDA['lib64']],
-        # this syntax is specific to this build system
-        # we're only going to use certain compiler args with nvcc and not with
-        # gcc the implementation of this trick is in customize_compiler() below
-        extra_compile_args={'gcc': ["-Wno-unused-function"],
-                            'nvcc': ['-arch=sm_35',
-                                     '--ptxas-options=-v',
-                                     '-c',
-                                     '--compiler-options',
-                                     "'-fPIC'"]},
-        include_dirs = [numpy_include, CUDA['include']]
-    ),
 ]
 
+if CUDA is not None:
+    ext_modules.append(
+        Extension('gpu_nms',
+            ['nms_kernel.cu', 'gpu_nms.pyx'],
+            library_dirs=[CUDA['lib64']],
+            libraries=['cudart'],
+            language='c++',
+            runtime_library_dirs=[CUDA['lib64']],
+            # this syntax is specific to this build system
+            # we're only going to use certain compiler args with nvcc and not with
+            # gcc the implementation of this trick is in customize_compiler() below
+            extra_compile_args={'gcc': ["-Wno-unused-function"],
+                                'nvcc': ['-arch=sm_35',
+                                         '--ptxas-options=-v',
+                                         '-c',
+                                         '--compiler-options',
+                                         "'-fPIC'"]},
+            include_dirs = [numpy_include, CUDA['include']]
+        )
+    )
+else:
+    print('Skipping GPU_NMS')
+
+
 setup(
     name='frcnn_cython',
     ext_modules=ext_modules,

example/rcnn/rcnn/dataset/coco.py

@@ -1,10 +1,10 @@
-from __future__ import print_function
 import cPickle
 import cv2
 import os
 import json
 import numpy as np
 
+from ..logger import logger
 from imdb import IMDB
 
 # coco api
@@ -38,7 +38,7 @@ def __init__(self, image_set, root_path, data_path):
         # load image file names
         self.image_set_index = self._load_image_set_index()
         self.num_images = len(self.image_set_index)
-        print('num_images', self.num_images)
+        logger.info('%s num_images %d' % (self.name, self.num_images))
 
         # deal with data name
         view_map = {'minival2014': 'val2014',
@@ -68,13 +68,13 @@ def gt_roidb(self):
         if os.path.exists(cache_file):
             with open(cache_file, 'rb') as fid:
                 roidb = cPickle.load(fid)
-            print('{} gt roidb loaded from {}'.format(self.name, cache_file))
+            logger.info('%s gt roidb loaded from %s' % (self.name, cache_file))
             return roidb
 
         gt_roidb = [self._load_coco_annotation(index) for index in self.image_set_index]
         with open(cache_file, 'wb') as fid:
             cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL)
-        print('wrote gt roidb to {}'.format(cache_file))
+        logger.info('%s wrote gt roidb to %s' % (self.name, cache_file))
 
         return gt_roidb
 
@@ -155,10 +155,10 @@ def _write_coco_results(self, detections, res_file):
         for cls_ind, cls in enumerate(self.classes):
             if cls == '__background__':
                 continue
-            print('Collecting %s results (%d/%d)' % (cls, cls_ind, self.num_classes - 1))
+            logger.info('collecting %s results (%d/%d)' % (cls, cls_ind, self.num_classes - 1))
             coco_cat_id = self._class_to_coco_ind[cls]
             results.extend(self._coco_results_one_category(detections[cls_ind], coco_cat_id))
-        print('Writing results json to %s' % res_file)
+        logger.info('writing results json to %s' % res_file)
         with open(res_file, 'w') as f:
             json.dump(results, f, sort_keys=True, indent=4)
 
@@ -192,7 +192,7 @@ def _do_python_eval(self, res_file, res_folder):
         eval_file = os.path.join(res_folder, 'detections_%s_results.pkl' % self.image_set)
         with open(eval_file, 'wb') as f:
             cPickle.dump(coco_eval, f, cPickle.HIGHEST_PROTOCOL)
-        print('coco eval results saved to %s' % eval_file)
+        logger.info('eval results saved to %s' % eval_file)
 
     def _print_detection_metrics(self, coco_eval):
         IoU_lo_thresh = 0.5
@@ -214,15 +214,15 @@ def _get_thr_ind(coco_eval, thr):
         precision = \
             coco_eval.eval['precision'][ind_lo:(ind_hi + 1), :, :, 0, 2]
         ap_default = np.mean(precision[precision > -1])
-        print('~~~~ Mean and per-category AP @ IoU=%.2f,%.2f] ~~~~' % (IoU_lo_thresh, IoU_hi_thresh))
-        print('%-15s %5.1f' % ('all', 100 * ap_default))
+        logger.info('~~~~ Mean and per-category AP @ IoU=%.2f,%.2f] ~~~~' % (IoU_lo_thresh, IoU_hi_thresh))
+        logger.info('%-15s %5.1f' % ('all', 100 * ap_default))
         for cls_ind, cls in enumerate(self.classes):
             if cls == '__background__':
                 continue
             # minus 1 because of __background__
             precision = coco_eval.eval['precision'][ind_lo:(ind_hi + 1), :, cls_ind - 1, 0, 2]
             ap = np.mean(precision[precision > -1])
-            print('%-15s %5.1f' % (cls, 100 * ap))
+            logger.info('%-15s %5.1f' % (cls, 100 * ap))
 
-        print('~~~~ Summary metrics ~~~~')
+        logger.info('~~~~ Summary metrics ~~~~')
         coco_eval.summarize()

example/rcnn/rcnn/dataset/imdb.py

@@ -9,7 +9,7 @@
 'boxes', 'gt_classes', 'gt_overlaps', 'max_classes', 'max_overlaps', 'bbox_targets']
 """
 
-from __future__ import print_function
+from ..logger import logger
 import os
 import cPickle
 import numpy as np
@@ -70,8 +70,8 @@ def load_rpn_data(self, full=False):
             rpn_file = os.path.join(self.root_path, 'rpn_data', self.name + '_full_rpn.pkl')
         else:
             rpn_file = os.path.join(self.root_path, 'rpn_data', self.name + '_rpn.pkl')
-        print('loading {}'.format(rpn_file))
-        assert os.path.exists(rpn_file), 'rpn data not found at {}'.format(rpn_file)
+        assert os.path.exists(rpn_file), '%s rpn data not found at %s' % (self.name, rpn_file)
+        logger.info('%s loading rpn data from %s' % (self.name, rpn_file))
         with open(rpn_file, 'rb') as f:
             box_list = cPickle.load(f)
         return box_list
@@ -93,7 +93,7 @@ def rpn_roidb(self, gt_roidb, append_gt=False):
         :return: roidb of rpn
         """
         if append_gt:
-            print('appending ground truth annotations')
+            logger.info('%s appending ground truth annotations' % self.name)
             rpn_roidb = self.load_rpn_roidb(gt_roidb)
             roidb = IMDB.merge_roidbs(gt_roidb, rpn_roidb)
         else:
@@ -156,7 +156,7 @@ def append_flipped_images(self, roidb):
         :param roidb: [image_index]['boxes', 'gt_classes', 'gt_overlaps', 'flipped']
         :return: roidb: [image_index]['boxes', 'gt_classes', 'gt_overlaps', 'flipped']
         """
-        print('append flipped images to roidb')
+        logger.info('%s append flipped images to roidb' % self.name)
         assert self.num_images == len(roidb)
         for i in range(self.num_images):
             roi_rec = roidb[i]
@@ -211,8 +211,8 @@ def evaluate_recall(self, roidb, candidate_boxes=None, thresholds=None):
             area_counts.append(area_count)
         total_counts = float(sum(area_counts))
         for area_name, area_count in zip(area_names[1:], area_counts):
-            print('percentage of', area_name, area_count / total_counts)
-        print('average number of proposal', total_counts / self.num_images)
+            logger.info('percentage of %s is %f' % (area_name, area_count / total_counts))
+        logger.info('average number of proposal is %f' % (total_counts / self.num_images))
         for area_name, area_range in zip(area_names, area_ranges):
             gt_overlaps = np.zeros(0)
             num_pos = 0