detectron——test 错误集锦

一、测试错误,运行如下代码

python2 tools/test_net.py     --cfg experiments/e2e_faster_rcnn_resnet-50-FPN_pascal2007.yaml     TEST.WEIGHTS /home/learner/github/detectron/experiments/output/train/voc_2007_train/generalized_rcnn/model_final.pkl NUM_GPUS 1

报错如下:

INFO test_engine.py: 320: Wrote detections to: /home/learner/github/detectron/test/voc_2007_test/generalized_rcnn/detections.pkl
INFO test_engine.py: 162: Total inference time: 241.493s
INFO task_evaluation.py:  76: Evaluating detections
Traceback (most recent call last):
  File "tools/test_net.py", line 116, in <module>
    check_expected_results=True,
  File "/home/learner/github/detectron/detectron/core/test_engine.py", line 128, in run_inference
    all_results = result_getter()
  File "/home/learner/github/detectron/detectron/core/test_engine.py", line 108, in result_getter
    multi_gpu=multi_gpu_testing
  File "/home/learner/github/detectron/detectron/core/test_engine.py", line 164, in test_net_on_dataset
    dataset, all_boxes, all_segms, all_keyps, output_dir
  File "/home/learner/github/detectron/detectron/datasets/task_evaluation.py", line 60, in evaluate_all
    dataset, all_boxes, output_dir, use_matlab=use_matlab
  File "/home/learner/github/detectron/detectron/datasets/task_evaluation.py", line 93, in evaluate_boxes
    dataset, all_boxes, output_dir, use_matlab=use_matlab
  File "/home/learner/github/detectron/detectron/datasets/voc_dataset_evaluator.py", line 46, in evaluate_boxes
    filenames = _write_voc_results_files(json_dataset, all_boxes, salt)
  File "/home/learner/github/detectron/detectron/datasets/voc_dataset_evaluator.py", line 69, in _write_voc_results_files
    assert index == image_index[i]
AssertionError

报错大意为图片的标注文件的名称test.txt文件中的xml文件名称与test.json中xml文件顺序不相符。运行以下两个代码,将结果替换原先文件,可实现均一致,编号从小到大。

附配置文件:

Found Detectron ops lib: /usr/local/lib/libcaffe2_detectron_ops_gpu.so
INFO test_net.py:  98: Called with args:
INFO test_net.py:  99: Namespace(cfg_file='experiments/e2e_faster_rcnn_resnet-50-FPN_pascal2007.yaml', multi_gpu_testing=False, opts=['TEST.WEIGHTS', '/home/learner/github/detectron/experiments/output/train/voc_2007_train/generalized_rcnn/model_final.pkl', 'NUM_GPUS', '1'], range=None, vis=False, wait=True)
INFO test_net.py: 105: Testing with config:
INFO test_net.py: 106: {'BBOX_XFORM_CLIP': 4.135166556742356,
 'CLUSTER': {'ON_CLUSTER': False},
 'DATA_LOADER': {'BLOBS_QUEUE_CAPACITY': 8,
                 'MINIBATCH_QUEUE_SIZE': 64,
                 'NUM_THREADS': 4},
 'DEDUP_BOXES': 0.0625,
 'DOWNLOAD_CACHE': '/tmp/detectron-download-cache',
 'EPS': 1e-14,
 'EXPECTED_RESULTS': [],
 'EXPECTED_RESULTS_ATOL': 0.005,
 'EXPECTED_RESULTS_EMAIL': '',
 'EXPECTED_RESULTS_RTOL': 0.1,
 'EXPECTED_RESULTS_SIGMA_TOL': 4,
 'FAST_RCNN': {'CONV_HEAD_DIM': 256,
               'MLP_HEAD_DIM': 1024,
               'NUM_STACKED_CONVS': 4,
               'ROI_BOX_HEAD': 'fast_rcnn_heads.add_roi_2mlp_head',
               'ROI_XFORM_METHOD': 'RoIAlign',
               'ROI_XFORM_RESOLUTION': 7,
               'ROI_XFORM_SAMPLING_RATIO': 2},
 'FPN': {'COARSEST_STRIDE': 32,
         'DIM': 256,
         'EXTRA_CONV_LEVELS': False,
         'FPN_ON': True,
         'MULTILEVEL_ROIS': True,
         'MULTILEVEL_RPN': True,
         'ROI_CANONICAL_LEVEL': 4,
         'ROI_CANONICAL_SCALE': 224,
         'ROI_MAX_LEVEL': 5,
         'ROI_MIN_LEVEL': 2,
         'RPN_ANCHOR_START_SIZE': 32,
         'RPN_ASPECT_RATIOS': (0.5, 1, 2),
         'RPN_MAX_LEVEL': 6,
         'RPN_MIN_LEVEL': 2,
         'USE_GN': False,
         'ZERO_INIT_LATERAL': False},
 'GROUP_NORM': {'DIM_PER_GP': -1, 'EPSILON': 1e-05, 'NUM_GROUPS': 32},
 'KRCNN': {'CONV_HEAD_DIM': 256,
           'CONV_HEAD_KERNEL': 3,
           'CONV_INIT': 'GaussianFill',
           'DECONV_DIM': 256,
           'DECONV_KERNEL': 4,
           'DILATION': 1,
           'HEATMAP_SIZE': -1,
           'INFERENCE_MIN_SIZE': 0,
           'KEYPOINT_CONFIDENCE': 'bbox',
           'LOSS_WEIGHT': 1.0,
           'MIN_KEYPOINT_COUNT_FOR_VALID_MINIBATCH': 20,
           'NMS_OKS': False,
           'NORMALIZE_BY_VISIBLE_KEYPOINTS': True,
           'NUM_KEYPOINTS': -1,
           'NUM_STACKED_CONVS': 8,
           'ROI_KEYPOINTS_HEAD': '',
           'ROI_XFORM_METHOD': 'RoIAlign',
           'ROI_XFORM_RESOLUTION': 7,
           'ROI_XFORM_SAMPLING_RATIO': 0,
           'UP_SCALE': -1,
           'USE_DECONV': False,
           'USE_DECONV_OUTPUT': False},
 'MATLAB': 'matlab',
 'MEMONGER': True,
 'MEMONGER_SHARE_ACTIVATIONS': False,
 'MODEL': {'BBOX_REG_WEIGHTS': (10.0, 10.0, 5.0, 5.0),
           'CLS_AGNOSTIC_BBOX_REG': False,
           'CONV_BODY': 'FPN.add_fpn_ResNet50_conv5_body',
           'EXECUTION_TYPE': 'dag',
           'FASTER_RCNN': True,
           'KEYPOINTS_ON': False,
           'MASK_ON': False,
           'NUM_CLASSES': 4,
           'RPN_ONLY': False,
           'TYPE': 'generalized_rcnn'},
 'MRCNN': {'CLS_SPECIFIC_MASK': True,
           'CONV_INIT': 'GaussianFill',
           'DILATION': 2,
           'DIM_REDUCED': 256,
           'RESOLUTION': 14,
           'ROI_MASK_HEAD': '',
           'ROI_XFORM_METHOD': 'RoIAlign',
           'ROI_XFORM_RESOLUTION': 7,
           'ROI_XFORM_SAMPLING_RATIO': 0,
           'THRESH_BINARIZE': 0.5,
           'UPSAMPLE_RATIO': 1,
           'USE_FC_OUTPUT': False,
           'WEIGHT_LOSS_MASK': 1.0},
 'NUM_GPUS': 1,
 'OUTPUT_DIR': '.',
 'PIXEL_MEANS': array([[[102.9801, 115.9465, 122.7717]]]),
 'RESNETS': {'NUM_GROUPS': 1,
             'RES5_DILATION': 1,
             'SHORTCUT_FUNC': 'basic_bn_shortcut',
             'STEM_FUNC': 'basic_bn_stem',
             'STRIDE_1X1': True,
             'TRANS_FUNC': 'bottleneck_transformation',
             'WIDTH_PER_GROUP': 64},
 'RETINANET': {'ANCHOR_SCALE': 4,
               'ASPECT_RATIOS': (0.5, 1.0, 2.0),
               'BBOX_REG_BETA': 0.11,
               'BBOX_REG_WEIGHT': 1.0,
               'CLASS_SPECIFIC_BBOX': False,
               'INFERENCE_TH': 0.05,
               'LOSS_ALPHA': 0.25,
               'LOSS_GAMMA': 2.0,
               'NEGATIVE_OVERLAP': 0.4,
               'NUM_CONVS': 4,
               'POSITIVE_OVERLAP': 0.5,
               'PRE_NMS_TOP_N': 1000,
               'PRIOR_PROB': 0.01,
               'RETINANET_ON': False,
               'SCALES_PER_OCTAVE': 3,
               'SHARE_CLS_BBOX_TOWER': False,
               'SOFTMAX': False},
 'RFCN': {'PS_GRID_SIZE': 3},
 'RNG_SEED': 3,
 'ROOT_DIR': '/home/learner/github/detectron',
 'RPN': {'ASPECT_RATIOS': (0.5, 1, 2),
         'RPN_ON': True,
         'SIZES': (64, 128, 256, 512),
         'STRIDE': 16},
 'SOLVER': {'BASE_LR': 0.0025,
            'GAMMA': 0.1,
            'LOG_LR_CHANGE_THRESHOLD': 1.1,
            'LRS': [],
            'LR_POLICY': 'steps_with_decay',
            'MAX_ITER': 60000,
            'MOMENTUM': 0.9,
            'SCALE_MOMENTUM': True,
            'SCALE_MOMENTUM_THRESHOLD': 1.1,
            'STEPS': [0, 30000, 40000],
            'STEP_SIZE': 30000,
            'WARM_UP_FACTOR': 0.3333333333333333,
            'WARM_UP_ITERS': 500,
            'WARM_UP_METHOD': u'linear',
            'WEIGHT_DECAY': 0.0001,
            'WEIGHT_DECAY_GN': 0.0},
 'TEST': {'BBOX_AUG': {'AREA_TH_HI': 32400,
                       'AREA_TH_LO': 2500,
                       'ASPECT_RATIOS': (),
                       'ASPECT_RATIO_H_FLIP': False,
                       'COORD_HEUR': 'UNION',
                       'ENABLED': False,
                       'H_FLIP': False,
                       'MAX_SIZE': 4000,
                       'SCALES': (),
                       'SCALE_H_FLIP': False,
                       'SCALE_SIZE_DEP': False,
                       'SCORE_HEUR': 'UNION'},
          'BBOX_REG': True,
          'BBOX_VOTE': {'ENABLED': False,
                        'SCORING_METHOD': 'ID',
                        'SCORING_METHOD_BETA': 1.0,
                        'VOTE_TH': 0.8},
          'COMPETITION_MODE': True,
          'DATASETS': ('voc_2007_test',),
          'DETECTIONS_PER_IM': 100,
          'FORCE_JSON_DATASET_EVAL': False,
          'KPS_AUG': {'AREA_TH': 32400,
                      'ASPECT_RATIOS': (),
                      'ASPECT_RATIO_H_FLIP': False,
                      'ENABLED': False,
                      'HEUR': 'HM_AVG',
                      'H_FLIP': False,
                      'MAX_SIZE': 4000,
                      'SCALES': (),
                      'SCALE_H_FLIP': False,
                      'SCALE_SIZE_DEP': False},
          'MASK_AUG': {'AREA_TH': 32400,
                       'ASPECT_RATIOS': (),
                       'ASPECT_RATIO_H_FLIP': False,
                       'ENABLED': False,
                       'HEUR': 'SOFT_AVG',
                       'H_FLIP': False,
                       'MAX_SIZE': 4000,
                       'SCALES': (),
                       'SCALE_H_FLIP': False,
                       'SCALE_SIZE_DEP': False},
          'MAX_SIZE': 833,
          'NMS': 0.5,
          'PRECOMPUTED_PROPOSALS': False,
          'PROPOSAL_FILES': (),
          'PROPOSAL_LIMIT': 2000,
          'RPN_MIN_SIZE': 0,
          'RPN_NMS_THRESH': 0.7,
          'RPN_POST_NMS_TOP_N': 1000,
          'RPN_PRE_NMS_TOP_N': 1000,
          'SCALE': 500,
          'SCORE_THRESH': 0.05,
          'SOFT_NMS': {'ENABLED': False, 'METHOD': 'linear', 'SIGMA': 0.5},
          'WEIGHTS': '/home/learner/github/detectron/experiments/output/train/voc_2007_train/generalized_rcnn/model_final.pkl'},
 'TRAIN': {'ASPECT_GROUPING': True,
           'AUTO_RESUME': True,
           'BATCH_SIZE_PER_IM': 512,
           'BBOX_THRESH': 0.5,
           'BG_THRESH_HI': 0.5,
           'BG_THRESH_LO': 0.0,
           'COPY_WEIGHTS': False,
           'CROWD_FILTER_THRESH': 0.7,
           'DATASETS': ('voc_2007_train',),
           'FG_FRACTION': 0.25,
           'FG_THRESH': 0.5,
           'FREEZE_AT': 2,
           'FREEZE_CONV_BODY': False,
           'GT_MIN_AREA': -1,
           'IMS_PER_BATCH': 2,
           'MAX_SIZE': 833,
           'PROPOSAL_FILES': (),
           'RPN_BATCH_SIZE_PER_IM': 256,
           'RPN_FG_FRACTION': 0.5,
           'RPN_MIN_SIZE': 0,
           'RPN_NEGATIVE_OVERLAP': 0.3,
           'RPN_NMS_THRESH': 0.7,
           'RPN_POSITIVE_OVERLAP': 0.7,
           'RPN_POST_NMS_TOP_N': 2000,
           'RPN_PRE_NMS_TOP_N': 2000,
           'RPN_STRADDLE_THRESH': 0,
           'SCALES': (500,),
           'SNAPSHOT_ITERS': 20000,
           'USE_FLIPPED': True,
           'WEIGHTS': '/home/learner/github/detectron/pretrained_model/R-50.pkl'},
 'USE_NCCL': False,
 'VIS': False,
 'VIS_TH': 0.9}
loading annotations into memory...
Done (t=0.02s)
creating index...
index created!
loading annotations into memory...
Done (t=0.02s)
creating index...
index created!
WARNING cnn.py:  25: [====DEPRECATE WARNING====]: you are creating an object from CNNModelHelper class which will be deprecated soon. Please use ModelHelper object with brew module. For more information, please refer to caffe2.ai and python/brew.py, python/brew_test.py for more information.
INFO net.py:  60: Loading weights from: /home/learner/github/detectron/experiments/output/train/voc_2007_train/generalized_rcnn/model_final.pkl

 

test.json文件生成(xmltojson.py):

# -*- coding: utf-8 -*-
"""
Created on Tue Aug 28 15:01:03 2018

@author: Administrator
"""
#!/usr/bin/python
# -*- coding:utf-8 -*-
# @Author: hbchen
# @Time: 2018-01-29
# @Description: xml转换到coco数据集json格式
 
import os, sys, json,xmltodict
 
from xml.etree.ElementTree import ElementTree, Element
from collections import OrderedDict
 
XML_PATH = "/home/learner/datasets/VOCdevkit2007/VOC2007/Annotations/test"
JSON_PATH = "./test.json"
json_obj = {}
images = []
annotations = []
categories = []
categories_list = []
annotation_id = 1
 
def read_xml(in_path):
    '''读取并解析xml文件'''
    tree = ElementTree()
    tree.parse(in_path)
    return tree
 
def if_match(node, kv_map):
    '''判断某个节点是否包含所有传入参数属性
      node: 节点
      kv_map: 属性及属性值组成的map'''
    for key in kv_map:
        if node.get(key) != kv_map.get(key):
            return False
    return True
 
def get_node_by_keyvalue(nodelist, kv_map):
    '''根据属性及属性值定位符合的节点,返回节点
      nodelist: 节点列表
      kv_map: 匹配属性及属性值map'''
    result_nodes = []
    for node in nodelist:
        if if_match(node, kv_map):
            result_nodes.append(node)
    return result_nodes
 
def find_nodes(tree, path):
    '''查找某个路径匹配的所有节点
      tree: xml树
      path: 节点路径'''
    return tree.findall(path)
 
print ("-----------------Start------------------")
xml_names = []
for xml in os.listdir(XML_PATH):
    #os.path.splitext(xml)
    xml=xml.replace('Cow_','')
    xml_names.append(xml)
    

'''xml_path_list=os.listdir(XML_PATH)
os.path.split
xml_path_list.sort(key=len)'''
xml_names.sort(key=lambda x:int(x[:-4]))
new_xml_names = []
for i in xml_names:
    j = 'Cow_' + i
    new_xml_names.append(j)

#print xml_names
#print new_xml_names
for xml in new_xml_names:
    tree = read_xml(XML_PATH + "/" + xml)
    object_nodes = get_node_by_keyvalue(find_nodes(tree, "object"), {})
    if len(object_nodes) == 0:
        print (xml, "no object")
        continue
    else:
        image = OrderedDict()
        file_name = os.path.splitext(xml)[0];  # 文件名
                #print os.path.splitext(xml)
        para1 = file_name + ".jpg"
               

                height_nodes = get_node_by_keyvalue(find_nodes(tree, "size/height"), {})
        para2 = int(height_nodes[0].text)
                   
                width_nodes = get_node_by_keyvalue(find_nodes(tree, "size/width"), {})
        para3 = int(width_nodes[0].text)
        
        fname=file_name[4:]
        para4 = int(fname)
        
        
                
                for f,i in [("file_name",para1),("height",para2),("width",para3),("id",para4)]:
                    image.setdefault(f,i)

                #print(image)
                images.append(image)    #构建images
          
     
        name_nodes = get_node_by_keyvalue(find_nodes(tree, "object/name"), {})
        xmin_nodes = get_node_by_keyvalue(find_nodes(tree, "object/bndbox/xmin"), {})
        ymin_nodes = get_node_by_keyvalue(find_nodes(tree, "object/bndbox/ymin"), {})
        xmax_nodes = get_node_by_keyvalue(find_nodes(tree, "object/bndbox/xmax"), {})
        ymax_nodes = get_node_by_keyvalue(find_nodes(tree, "object/bndbox/ymax"), {})
           # print ymax_nodes
        for index, node in enumerate(object_nodes):
            annotation = {}
            segmentation = []
            bbox = []
            seg_coordinate = []     #坐标
            seg_coordinate.append(int(xmin_nodes[index].text))
            seg_coordinate.append(int(ymin_nodes[index].text))
            seg_coordinate.append(int(xmin_nodes[index].text))
            seg_coordinate.append(int(ymax_nodes[index].text))
            seg_coordinate.append(int(xmax_nodes[index].text))
            seg_coordinate.append(int(ymax_nodes[index].text))
            seg_coordinate.append(int(xmax_nodes[index].text))
            seg_coordinate.append(int(ymin_nodes[index].text))
            segmentation.append(seg_coordinate)
            width = int(xmax_nodes[index].text) - int(xmin_nodes[index].text)
            height = int(ymax_nodes[index].text) - int(ymin_nodes[index].text)
            area = width * height
            bbox.append(int(xmin_nodes[index].text))
            bbox.append(int(ymin_nodes[index].text))
            bbox.append(width)
            bbox.append(height)
     
            annotation["segmentation"] = segmentation
            annotation["area"] = area
            annotation["iscrowd"] = 0
            fname=file_name[4:]
            annotation["image_id"] = int(fname)
            annotation["bbox"] = bbox
            cate=name_nodes[index].text
            if cate=='head':
                category_id=1
            elif cate=='eye':
                category_id=2
            elif cate=='nose':
                category_id=3
            annotation["category_id"] = category_id
            annotation["id"] = annotation_id
            annotation_id += 1
            annotation["ignore"] = 0
            annotations.append(annotation)
     
            if category_id in categories_list:
                pass
            else:
                categories_list.append(category_id)
                categorie = {}
                categorie["supercategory"] = "none"
                categorie["id"] = category_id
                categorie["name"] = name_nodes[index].text
                categories.append(categorie)
     
json_obj["images"] = images
json_obj["type"] = "instances"
json_obj["annotations"] = annotations
json_obj["categories"] = categories
 
f = open(JSON_PATH, "w")
#json.dump(json_obj, f)
json_str = json.dumps(json_obj)
f.write(json_str)
print ("------------------End-------------------")

test.txt生成(test.py):

import os,sys
XML_PATH = "/home/learner/datasets/VOCdevkit2007/VOC2007/Annotations/test"
final_path = "./test.txt"
xml_names = []
for xml in os.listdir(XML_PATH):
    #os.path.splitext(xml)
    xml=xml.replace('Cow_','')
    xml_names.append(xml)


'''xml_path_list=os.listdir(XML_PATH)
os.path.split
xml_path_list.sort(key=len)'''
xml_names.sort(key=lambda x:int(x[:-4]))
new_xml_names = []
for i in xml_names:
    j = 'Cow_' + i
    new_xml_names.append(j)

#print xml_names
#print new_xml_names
f = open(final_path,"w")
for xml in new_xml_names:
    file_name = os.path.splitext(xml)[0];
    f.write(file_name)
    f.write('\n')

二、计算inference类别标的有问题,比如本应该是head,框显示car:

commend如下:

 python2 tools/infer_simple.py --cfg experiments/e2e_faster_rcnn_resnet-50-FPN_pascal2007.yaml --output-dir experiments/test_out/ --wts experiments/output/train/voc_2007_train/generalized_rcnn/model_final.pkl test_demo_cow 

修改dummy_datasets.py中的类别信息:

def get_COCO_dataset():
    """A dummy VOC dataset"""
    ds = AttrDict()
    classes = ['__background__', 
               'eye', 'nose', 'head']
    ds.classes = {i:name for i, name in enumerate(classes)}
    return ds

三、train的代码

python2 tools/train_net.py --cfg experiments/e2e_faster_rcnn_resnet-50-FPN_pascal2007.yaml  OUTPUT_DIR experiments/output

会报一些错,需要将起初权重改为R-50.pkl,参考http://www.yueye.org/2018/train-object-detection-model-using-detectron.html。

四、更换model

运行代码

python2 tools/train_net.py --cfg experiments/e2e_faster_rcnn_X-101-64x4d-FPN_1x.yaml  OUTPUT_DIR experiments/outputx

报错如下:

INFO net.py: 254: End of model: generalized_rcnn
json_stats: {"accuracy_cls": 0.228516, "eta": "17 days, 2:30:10", "iter": 0, "loss": 2.118356, "loss_bbox": 0.047415, "loss_cls": 1.357394, "loss_rpn_bbox_fpn2": 0.000000, "loss_rpn_bbox_fpn3": 0.000000, "loss_rpn_bbox_fpn4": 0.017527, "loss_rpn_bbox_fpn5": 0.000000, "loss_rpn_bbox_fpn6": 0.002472, "loss_rpn_cls_fpn2": 0.510349, "loss_rpn_cls_fpn3": 0.129855, "loss_rpn_cls_fpn4": 0.039958, "loss_rpn_cls_fpn5": 0.005469, "loss_rpn_cls_fpn6": 0.007917, "lr": 0.003333, "mb_qsize": 64, "mem": 6441, "time": 8.210058}
json_stats: {"accuracy_cls": 0.961914, "eta": "2 days, 17:47:12", "iter": 20, "loss": 1.014767, "loss_bbox": 0.064824, "loss_cls": 0.274080, "loss_rpn_bbox_fpn2": 0.011057, "loss_rpn_bbox_fpn3": 0.005120, "loss_rpn_bbox_fpn4": 0.004911, "loss_rpn_bbox_fpn5": 0.000987, "loss_rpn_bbox_fpn6": 0.002837, "loss_rpn_cls_fpn2": 0.424010, "loss_rpn_cls_fpn3": 0.099319, "loss_rpn_cls_fpn4": 0.031872, "loss_rpn_cls_fpn5": 0.007901, "loss_rpn_cls_fpn6": 0.009875, "lr": 0.003600, "mb_qsize": 64, "mem": 6453, "time": 1.315882}
json_stats: {"accuracy_cls": 0.941406, "eta": "1 day, 22:37:55", "iter": 40, "loss": 0.620170, "loss_bbox": 0.130436, "loss_cls": 0.284250, "loss_rpn_bbox_fpn2": 0.025175, "loss_rpn_bbox_fpn3": 0.006801, "loss_rpn_bbox_fpn4": 0.000663, "loss_rpn_bbox_fpn5": 0.001616, "loss_rpn_bbox_fpn6": 0.000000, "loss_rpn_cls_fpn2": 0.087775, "loss_rpn_cls_fpn3": 0.043141, "loss_rpn_cls_fpn4": 0.019907, "loss_rpn_cls_fpn5": 0.008191, "loss_rpn_cls_fpn6": 0.002730, "lr": 0.003867, "mb_qsize": 64, "mem": 6469, "time": 0.932848}
json_stats: {"accuracy_cls": 0.947266, "eta": "1 day, 23:10:17", "iter": 60, "loss": 0.508122, "loss_bbox": 0.112828, "loss_cls": 0.233173, "loss_rpn_bbox_fpn2": 0.003497, "loss_rpn_bbox_fpn3": 0.003379, "loss_rpn_bbox_fpn4": 0.002439, "loss_rpn_bbox_fpn5": 0.000000, "loss_rpn_bbox_fpn6": 0.002276, "loss_rpn_cls_fpn2": 0.051088, "loss_rpn_cls_fpn3": 0.028643, "loss_rpn_cls_fpn4": 0.017298, "loss_rpn_cls_fpn5": 0.006002, "loss_rpn_cls_fpn6": 0.006945, "lr": 0.004133, "mb_qsize": 64, "mem": 6469, "time": 0.943747}
/home/learner/github/detectron/detectron/utils/boxes.py:175: RuntimeWarning: overflow encountered in multiply
  pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis]
/home/learner/github/detectron/detectron/utils/boxes.py:176: RuntimeWarning: overflow encountered in multiply
  pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis]
/usr/local/lib/python2.7/dist-packages/numpy/lib/function_base.py:3250: RuntimeWarning: Invalid value encountered in median
  r = func(a, **kwargs)
json_stats: {"accuracy_cls": 0.974609, "eta": "1 day, 23:20:54", "iter": 80, "loss": NaN, "loss_bbox": NaN, "loss_cls": NaN, "loss_rpn_bbox_fpn2": NaN, "loss_rpn_bbox_fpn3": NaN, "loss_rpn_bbox_fpn4": NaN, "loss_rpn_bbox_fpn5": NaN, "loss_rpn_bbox_fpn6": NaN, "loss_rpn_cls_fpn2": NaN, "loss_rpn_cls_fpn3": NaN, "loss_rpn_cls_fpn4": NaN, "loss_rpn_cls_fpn5": NaN, "loss_rpn_cls_fpn6": 0.004436, "lr": 0.004400, "mb_qsize": 64, "mem": 6473, "time": 0.947391}
CRITICAL train.py:  98: Loss is NaN
INFO loader.py: 126: Stopping enqueue thread
INFO loader.py: 113: Stopping mini-batch loading thread
INFO loader.py: 113: Stopping mini-batch loading thread
INFO loader.py: 113: Stopping mini-batch loading thread
INFO loader.py: 113: Stopping mini-batch loading thread
Traceback (most recent call last):
  File "tools/train_net.py", line 132, in <module>
    main()
  File "tools/train_net.py", line 114, in main
    checkpoints = detectron.utils.train.train_model()
  File "/home/learner/github/detectron/detectron/utils/train.py", line 86, in train_model
    handle_critical_error(model, 'Loss is NaN')
  File "/home/learner/github/detectron/detectron/utils/train.py", line 100, in handle_critical_error
    raise Exception(msg)
Exception: Loss is NaN

修改BASE_LR的值从0.01改为0.001(在此e2e_faster_rcnn_X-101-64x4d-FPN_1x.yaml文件中

X参考博客:

Inference:

https://blog.csdn.net/blateyang/article/details/79815490

https://blog.csdn.net/Blateyang/article/details/80655802

https://github.com/facebookresearch/Detectron/issues/485

https://github.com/royhuang9/Detectron/blob/master/README.md

其余参考博客:

http://www.yueye.org/2018/train-object-detection-model-using-detectron.html

os.path

https://blog.csdn.net/T1243_3/article/details/80170006

源码解读

https://blog.csdn.net/zziahgf/article/details/79652946

各种model

https://github.com/royhuang9/Detectron/blob/master/MODEL_ZOO.md

 

posted @ 2018-09-13 22:25  Sailon  阅读(1830)  评论(0编辑  收藏  举报