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--FPN_pascal2007.yaml TEST.WEIGHTS /home/learner/github/detectron/experiments/output/train/voc_2007_train/generalized_rcnn/model_final.pkl NUM_GPUS
报错如下：
INFO test_engine.py: : Wrote detections to: /home/learner/github/detectron/test/voc_2007_test/generalized_rcnn/detections.pkl
INFO test_engine.py: : Total inference time: .493s
INFO task_evaluation.py: : Evaluating detections
Traceback (most recent call last):
File "tools/test_net.py", line , in <module>
check_expected_results=True,
File "/home/learner/github/detectron/detectron/core/test_engine.py", line , in run_inference
all_results = result_getter()
File "/home/learner/github/detectron/detectron/core/test_engine.py", line , in result_getter
multi_gpu=multi_gpu_testing
File "/home/learner/github/detectron/detectron/core/test_engine.py", line , 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 , in evaluate_all
dataset, all_boxes, output_dir, use_matlab=use_matlab
File "/home/learner/github/detectron/detectron/datasets/task_evaluation.py", line , in evaluate_boxes
dataset, all_boxes, output_dir, use_matlab=use_matlab
File "/home/learner/github/detectron/detectron/datasets/voc_dataset_evaluator.py", line , 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 , 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', ''], 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--64x4d-FPN_1x.yaml OUTPUT_DIR experiments/outputx
报错如下：
INFO net.py: : End of model: generalized_rcnn
json_stats: {"accuracy_cls": 0.228516, "eta": "17 days, 2:30:10", "iter": , "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": , "mem": , "time": 8.210058}
json_stats: {"accuracy_cls": 0.961914, "eta": "2 days, 17:47:12", "iter": , "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": , "mem": , "time": 1.315882}
json_stats: {"accuracy_cls": 0.941406, "eta": "1 day, 22:37:55", "iter": , "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": , "mem": , "time": 0.932848}
json_stats: {"accuracy_cls": 0.947266, "eta": "1 day, 23:10:17", "iter": , "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": , "mem": , "time": 0.943747}
/home/learner/github/detectron/detectron/utils/boxes.py:: RuntimeWarning: overflow encountered in multiply
pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis]
/home/learner/github/detectron/detectron/utils/boxes.py:: RuntimeWarning: overflow encountered in multiply
pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis]
/usr/local/lib/python2./dist-packages/numpy/lib/function_base.py:: RuntimeWarning: Invalid value encountered in median
r = func(a, **kwargs)
json_stats: {"accuracy_cls": 0.974609, "eta": "1 day, 23:20:54", "iter": , "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": , "mem": , "time": 0.947391}
CRITICAL train.py: : Loss is NaN
INFO loader.py: : Stopping enqueue thread
INFO loader.py: : Stopping mini-batch loading thread
INFO loader.py: : Stopping mini-batch loading thread
INFO loader.py: : Stopping mini-batch loading thread
INFO loader.py: : Stopping mini-batch loading thread
Traceback (most recent call last):
File "tools/train_net.py", line , in <module>
main()
File "tools/train_net.py", line , in main
checkpoints = detectron.utils.train.train_model()
File "/home/learner/github/detectron/detectron/utils/train.py", line , in train_model
handle_critical_error(model, 'Loss is NaN')
File "/home/learner/github/detectron/detectron/utils/train.py", line , 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
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