data['category_id'] = self.cat_ids[label] IndexError: list index out of range #4243
Comments
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我之前也碰到了这个问题，查了源码之后发现了问题，看看你适用嘛：
class CocoDataset(CustomDataset):
coco数据集的标签格式： 希望能够解决你的问题。 |
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谢谢兄弟帮忙
…------------------ Original ------------------
From: LuckyStar <notifications@github.com>
Date: Tue,Dec 8,2020 6:24 PM
To: open-mmlab/mmdetection <mmdetection@noreply.github.com>
Cc: Demo Xu <531232693@qq.com>, Author <author@noreply.github.com>
Subject: Re: [open-mmlab/mmdetection] data['category_id'] = self.cat_ids[label] IndexError: list index out of range (#4243)
我之前也碰到了这个问题，查了源码之后发现了问题，看看你适用嘛：
修改 mmdetection/mmdet/datasets/coco.py 将类别改为一致
class CocoDataset(CustomDataset):
CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',
'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush')
CLASSES = ('shop_sign') # 此处改为你自己的186类，注意顺序，需要和接下来的2对齐
查看自己的数据集【train-val-test都要修改】，比如 mmdetection/data/coco/annotations/instances_train2017.json
看该文件中的 "categories" 标签中的 “ id” 和 “ name”
需要与1中你写在coco.py中的对齐。
coco数据集的标签格式：
{
"images":[],
"annotations":[],
"categories":[] # 就是这个地方，要改好，与1对齐，有186类，每一类中的“ id” 和 “ name”都要和1顺序一致
}
希望能够解决你的问题。
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楼主您好，我的问题和您的一模一样，请问您的问题怎么解决的呢？我的就只有两类，上一个答主说的对齐是 "categories": [ |
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Hi @Hexuanfang, I guess you also need to explicitly add your own classes name to the fields data.train (and data.val, and data.test) in the config. See ...
# dataset settings
dataset_type = 'CocoDataset'
classes = ('car', 'truck')
...
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
classes=classes,
ann_file='path/to/your/train/data',
...),
val=dict(
type=dataset_type,
classes=classes,
ann_file='path/to/your/val/data',
...),
test=dict(
type=dataset_type,
classes=classes,
ann_file='path/to/your/test/data',
...))
... |
我也是，我就一类，但是依然报错。 |
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Hi @secortot, there are two steps to train your own customized dataset with COCO format:
See |
数据集更改为了coco的json格式，单一类别的检测，这个错误是运行不定次数epoch之后会出现（看代码是验证时出的问题，训练时无问题），仔细check了几遍ground truth，确实没有问题，在这一句加了抛出异常问题解决。 |
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Hi @secortot, could you provide detailed error log during the inference mode? |
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Dear you, when modifying the category, please modify the parameter “num_classes“ in the model, otherwise this problem will occur. |
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请问你怎么解决的？我也是运行了不定数的epoch，然后报了这个错 |
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I encountered similar problems, and got exception in the middle of triaining. I fixed this problem by adding a comma "," at the end of class names in config file, like change "classes = ('person', 'car')" to "classes = ('person', 'car',)", but still confused why it works... some detailed gramma of python tuple might be the underlying reason. |
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I encountered this problem during the test. It's useless to try the above methods.What should I do? |
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In my situation, I added classes=('a', 'b', 'c', 'd', 'e') field to |
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Check:
dataset settingsdataset_type = 'CocoDataset' I know very few people can encounter the issue I had and this is not the solution to the original question here but still, it could be helpful for someone so I am writing. I was training on one dataset and testing on a different dataset, the original dataset had class names all having the first letter as Capital but testing dataset class names were in small letters which caused me this issue. So I made testing class names the same spelling, first Capital letter to make it work. Printing categories and their Ids inside |
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The categories in the train.json, val.json, and test.json files must be aligned. For example: In the test.json file, categories are: |
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我在使用HTC模型（htc_r50_fpn_1x_coco.py）训练自定义数据集时，参考用户指南对num_classes和数据集信息进行了修改后同样出现了上述报错。 我最终的解决方法如下：
不确定是否符合你的情况，希望可以带来帮助。 I have the same error reported above when training a custom dataset using the HTC model (htc_r50_fpn_1x_coco.py) after referring to the user's guide to make changes to the num_classes and dataset information. My final solution is as follows:
Not sure if this fits your situation, hope this helps. Translated with DeepL.com (free version) |
I am training detectors_htc_r50, I have 186 classes, and I set the num_classes to 187 in the box and mask. Then, I start to train my model on my own dataset, and test it symptomatically after every epoch. The test of the first epoch is normal and the test of the second epoch is error, the error is below:
writing results to ./test.pkl
Traceback (most recent call last):
File "tools/test.py", line 212, in
main()
File "tools/test.py", line 208, in main
print(dataset.evaluate(outputs, **eval_kwargs))
File "/home/mmdetection/mmdet/datasets/coco.py", line 588, in evaluate
result_files, tmp_dir = self.format_results(results, jsonfile_prefix)
File "/home/mmdetection/mmdet/datasets/coco.py", line 533, in format_results
result_files = self.results2json(results, jsonfile_prefix)
File "/home/mmdetection/mmdet/datasets/coco.py", line 470, in results2json
json_results = self._segm2json(results)
File "/home/mmdetection/mmdet/datasets/coco.py", line 421, in _segm2json
data['category_id'] = self.cat_ids[label]
The config is below:
base = [
'../base/datasets/coco_instance.py',
'../base/schedules/schedule_1x.py', '../base/default_runtime.py'
]
model settings
model = dict(
type='HybridTaskCascade',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
roi_head=dict(
type='HybridTaskCascadeRoIHead',
interleaved=True,
mask_info_flow=True,
num_stages=3,
stage_loss_weights=[1, 0.5, 0.25],
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=[
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=187,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=187,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=187,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
],
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
mask_head=[
dict(
type='HTCMaskHead',
with_conv_res=False,
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=187,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
dict(
type='HTCMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=187,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
dict(
type='HTCMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=187,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))
]))
model training and testing settings
train_cfg = dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=2000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=[
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.6,
neg_iou_thr=0.6,
min_pos_iou=0.6,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.7,
min_pos_iou=0.7,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False)
])
test_cfg = dict(
rpn=dict(
nms_across_levels=False,
nms_pre=1000,
nms_post=1000,
max_num=1000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
score_thr=0.001,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100,
mask_thr_binary=0.5))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline))
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