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
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()
灵魂脱壳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_datat
datat, all_boxes, all_gms, all_keyps, output_dir
File "/home/learner/github/detectron/detectron/datats/task_evaluation.py", line 60, in evaluate_all
datat, all_boxes, output_dir, u_matlab=u_matlab
File "/home/learner/github/detectron/detectron/datats/task_evaluation.py", line 93, in evaluate_boxes
datat, all_boxes, output_dir, u_matlab=u_matlab
File "/home/learner/github/detectron/detectron/datats/voc_datat_evaluator.py", line 46, in evaluate_boxes
filenames = _write_voc_results_files(json_datat, all_boxes, salt)
File "/home/learner/github/detectron/detectron/datats/voc_datat_evaluator.py", line 69, in _write_voc_results_files
asrt index == image_index[i]
AsrtionError
报错⼤意为图⽚的标注⽂件的名称⽂件中的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=Fal, opts=['TEST.WEIGHTS', '/home/learner/github/detectron/experiments/output/train/voc_2007_train/generalized INFO test_net.py: 105: Testing with config:
INFO test_net.py: 106: {'BBOX_XFORM_CLIP': 4.135166556742356,
'CLUSTER': {'ON_CLUSTER': Fal},
'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,
艾滋传染
'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': Fal,
'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': Fal,
'ZERO_INIT_LATERAL': Fal},
'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': Fal,
'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': Fal,
'USE_DECONV_OUTPUT': Fal},
'MATLAB': 'matlab',
'MEMONGER': True,
'MEMONGER_SHARE_ACTIVATIONS': Fal,
'MODEL': {'BBOX_REG_WEIGHTS': (10.0, 10.0, 5.0, 5.0),
'CLS_AGNOSTIC_BBOX_REG': Fal,
'CONV_BODY': 'FPN.add_fpn_ResNet50_conv5_body',
'EXECUTION_TYPE': 'dag',
'FASTER_RCNN': True,
'KEYPOINTS_ON': Fal,
'MASK_ON': Fal,
福字倒贴的传说'NUM_CLASSES': 4,
'RPN_ONLY': Fal,
'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,
'USE_FC_OUTPUT': Fal,
'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': Fal,
'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': Fal,
'SCALES_PER_OCTAVE': 3,
'SHARE_CLS_BBOX_TOWER': Fal,
'SOFTMAX': Fal},
'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': Fal,
'COORD_HEUR': 'UNION',
'ENABLED': Fal,
'H_FLIP': Fal,
'MAX_SIZE': 4000,
'SCALES': (),
'SCALE_H_FLIP': Fal,
'SCALE_SIZE_DEP': Fal,
'SCORE_HEUR': 'UNION'},
'BBOX_REG': True,
'BBOX_VOTE': {'ENABLED': Fal,
'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': Fal,
'KPS_AUG': {'AREA_TH': 32400,
'ASPECT_RATIOS': (),
'ASPECT_RATIO_H_FLIP': Fal,
'ENABLED': Fal,
'HEUR': 'HM_AVG',
'H_FLIP': Fal,
'MAX_SIZE': 4000,
'SCALES': (),
'SCALE_H_FLIP': Fal,
'SCALE_SIZE_DEP': Fal},
'MASK_AUG': {'AREA_TH': 32400,
'ASPECT_RATIOS': (),
'ASPECT_RATIO_H_FLIP': Fal,
'ENABLED': Fal,
'HEUR': 'SOFT_AVG',
'H_FLIP': Fal,
'MAX_SIZE': 4000,
'SCALES': (),
'SCALE_H_FLIP': Fal,
'SCALE_SIZE_DEP': Fal},
'MAX_SIZE': 833,
'NMS': 0.5,
'PRECOMPUTED_PROPOSALS': Fal,
'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': Fal, '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': Fal,
'CROWD_FILTER_THRESH': 0.7,
'DATASETS': ('voc_2007_train',),
'FG_FRACTION': 0.25,
'FG_THRESH': 0.5,
'FREEZE_AT': 2,
'FREEZE_CONV_BODY': Fal,
'GT_MIN_AREA': -1,
'IMS_PER_BATCH': 2,
'MAX_SIZE': 833,
'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,),
洗手间漏水怎么办'SNAPSHOT_ITERS': 20000,
'USE_FLIPPED': True,
'WEIGHTS': '/home/learner/github/detectron/pretrained_model/R-50.pkl'},
'USE_NCCL': Fal,
'VIS': Fal,
'VIS_TH': 0.9}
loading annotations
Done (t=0.02s)
index created!
loading annotations
Done (t=0.02s)
index created!
WARNING cnn.py: 25: [====DEPRECATE WARNING====]: you are creating an object from CNNModelHelper class which will be deprecated soon. Plea u ModelHelper object with brew module. For more information, plea refer to caffe2. 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
草莓蛋糕的做法@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
ElementTree import ElementTree, Element
from collections import OrderedDict
XML_PATH = "/home/learner/datats/VOCdevkit2007/VOC2007/Annotations/test"
JSON_PATH = "./test.json"
json_obj = {}
images = []
annotations = []
categories = []
categories_list = []
annotation_id = 1
def read_xml(in_path):
消防培训主要内容'''读取并解析xml⽂件'''
tree = ElementTree()
tree.par(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:
(key) != (key):
return Fal
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):
京酱牛肉
#os.path.splitext(xml)
place('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
el:
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.tdefault(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 = {}
gmentation = []
bbox = []
g_coordinate = [] #坐标
g_coordinate.append(int(xmin_nodes[index].text))
g_coordinate.append(int(ymin_nodes[index].text))
g_coordinate.append(int(xmin_nodes[index].text))
g_coordinate.append(int(ymax_nodes[index].text))
g_coordinate.append(int(xmax_nodes[index].text))
g_coordinate.append(int(ymax_nodes[index].text))
g_coordinate.append(int(xmax_nodes[index].text))
g_coordinate.append(int(ymin_nodes[index].text))
gmentation.append(g_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["gmentation"] = gmentation
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=='no':
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
el:
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-------------------")
<⽣成（test.py）:
import os,sys
XML_PATH = "/home/learner/datats/VOCdevkit2007/VOC2007/Annotations/test"
final_path = "./"
xml_names = []
for xml in os.listdir(XML_PATH):
#os.path.splitext(xml)
place('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_datats.py中的类别信息：
def get_COCO_datat():
"""A dummy VOC datat"""
ds = AttrDict()
class = ['__background__',
'eye', 'no', 'head']
ds.class = {i:name for i, name in enumerate(class)}
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
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 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 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 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 /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_ba.py:3250: RuntimeWarning: Invalid val
ue 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" 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 = ain_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
rai 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:
其余参考博客：
os.path
源码解读
各种model拯救的反义词
