PaddleClas/docs/en/PULC /PULC_person_attribute python tools/infer.py error

[VisImage]

Thank you for sharing the code.

When trying to train the model using our customised dataset, following instruction as indicated in PaddleClas/docs/en/PULC /PULC_person_attribute_en.md. I got the error as below,. lease let me know what else I need to modify. Than you

3.3 Training succeeds.
python -m paddle.distributed.launch
--gpus="0"
tools/train.py
-c ./ppcls/configs/PULC/person_attribute/PPLCNet_x1_0_myData.yaml

3.4 Evaluation succeeds.
python tools/eval.py
-c ./ppcls/configs/PULC/person_attribute/PPLCNet_x1_0_myData.yaml
-o Global.pretrained_model="output/best_model/model.pdparams"

3.5 Inference failed.
python tools/infer.py
-c ./ppcls/configs/PULC/person_attribute/PPLCNet_x1_0_myData.yaml
-o Global.pretrained_model="output/best_model/model.pdparams"

ppcls ERROR: Exception occured when parse line: deploy/images/PULC/person_attribute/090007.jpg with msg: list index out of range

The file PPLCNet_x1_0_myData.yaml is shown below, which is based ./ppcls/configs/PULC/person_attribute/PPLCNet_x1_0.yaml with a few modifications:

------------- ./ppcls/configs/PULC/person_attribute/PPLCNet_x1_0_myData.yaml -------------

global configs

Global:
checkpoints: null
pretrained_model: null
output_dir: "./output/"
device: "gpu"
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 10
print_batch_step: 10
use_visualdl: False

used for static mode and model export

image_shape: [3, 256, 192]
save_inference_dir: "./inference"
use_multilabel: True

model architecture

Arch:
name: "PPLCNet_x1_0"
pretrained: True
use_ssld: True
class_num: 9
#class_num: 26

loss function config for traing/eval process

Loss:
Train:
- MultiLabelLoss:
weight: 1.0
weight_ratio: True
size_sum: True
Eval:
- MultiLabelLoss:
weight: 1.0
weight_ratio: True
size_sum: True

Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Cosine
learning_rate: 0.01
warmup_epoch: 5
regularizer:
name: 'L2'
coeff: 0.0005

data loader for train and eval

DataLoader:
Train:
dataset:
name: MultiLabelDataset
image_root: "dataset/myData/"
cls_label_path: "dataset/myData/train_list.txt"
#image_root: "dataset/pa100k/"
#cls_label_path: "dataset/pa100k/train_list.txt"
label_ratio: True
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
size: [192, 256]
- TimmAutoAugment:
prob: 0.8
config_str: rand-m9-mstd0.5-inc1
interpolation: bicubic
img_size: [192, 256]
- Padv2:
size: [212, 276]
pad_mode: 1
fill_value: 0
- RandomCropImage:
size: [192, 256]
- RandFlipImage:
flip_code: 1
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- RandomErasing:
EPSILON: 0.4
sl: 0.02
sh: 1.0/3.0
r1: 0.3
attempt: 10
use_log_aspect: True
mode: pixel
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: True
shuffle: True
loader:
num_workers: 4
use_shared_memory: True
Eval:
dataset:
name: MultiLabelDataset
image_root: "dataset/myData/"
cls_label_path: "dataset/myData/val_list.txt"
#image_root: "dataset/pa100k/"
#cls_label_path: "dataset/pa100k/val_list.txt"
label_ratio: True
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
size: [192, 256]
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True

Infer:
infer_imgs: deploy/images/PULC/person_attribute/
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
size: [192, 256]
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
PostProcess:
name: PersonAttribute
threshold: 0.5 #default threshold
#glasses_threshold: 0.3 #threshold only for glasses
#hold_threshold: 0.6 #threshold only for hold

Metric:
Eval:
- ATTRMetric:

[cuicheng01]

Hi，may I ask, what are the details of your yaml changes? Can you list them?