i got an error Line2D.set()

[yvr354]

i am using old anomalib in ubunto 22.04 i got an error , AttributeError: Line2D.set() got an unexpected keyword argument 'figsize', can anyone help me how to solve this .

[samet-akcay]

Hi @yvr354, can you provide more information, like what was the command you ran, what was you config file etc.

[yvr354]

dataset:
name: custom
format: folder
root: datasets/bumpers_data
normal_dir: GOOD # name of the folder containing normal images.
abnormal_dir: test_NEW #broken_val #Reject_1 #Reject # name of the folder containing abnormal images.
task: segmentation # classification or segmentation
mask_dir: ground_truth_NEW #optional
normal_test_dir: null # optional
extensions: null
split_ratio: 0.2 # normal images ratio to create a test split
seed: 0
image_size: 800
train_batch_size: 8
eval_batch_size: 8
num_workers: 8
normalization: imagenet # data distribution to which the images will be normalized
test_split_mode: from_dir # options [from_dir, synthetic]
val_split_ratio: 0.4 # fraction of train/test images held out for validation (usage depends on val_split_mode)
transform_config:
train: null
eval: null
val_split_mode: from_test # determines how the validation set is created, options [same_as_test, from_test]
tiling:
apply: false
tile_size: null
stride: null
remove_border_count: 0
use_random_tiling: False
random_tile_count: 16

model:
name: padim
backbone: resnet18
pre_trained: true
layers:
- layer1
- layer2
- layer3
normalization_method: min_max # options: [none, min_max, cdf]

metrics:
image:
- F1Score
- AUROC
pixel:
- F1Score
- AUROC
threshold:
method: adaptive #options: [adaptive, manual]
manual_image: null
manual_pixel: null

visualization:
show_images: False # show images on the screen
save_images: True # save images to the file system
log_images: True # log images to the available loggers (if any)
image_save_path: null # path to which images will be saved
mode: full # options: ["full", "simple"]

project:
seed: 42
path: ./results_fuck

logging:
logger: [ csv] # options: [comet, tensorboard, wandb, csv] or combinations.
log_graph: true # Logs the model graph to respective logger.

optimization:
export_mode: onnx # options: torch, onnx, openvino null

PL Trainer Args. Don't add extra parameter here.

trainer:
enable_checkpointing: true
default_root_dir: null
gradient_clip_val: 0
gradient_clip_algorithm: norm
num_nodes: 1
devices: 1
enable_progress_bar: true
overfit_batches: 0.0
track_grad_norm: -1
check_val_every_n_epoch: 1 # Don't validate before extracting features.
fast_dev_run: false
accumulate_grad_batches: 1
max_epochs: 1
min_epochs: null
max_steps: -1
min_steps: null
max_time: null
limit_train_batches: 1.0
limit_val_batches: 1.0
limit_test_batches: 1.0
limit_predict_batches: 1.0
val_check_interval: 1.0 # Don't validate before extracting features.
log_every_n_steps: 5
accelerator: auto # <"cpu", "gpu", "tpu", "ipu", "hpu", "auto">
strategy: null
sync_batchnorm: false
precision: 32
enable_model_summary: true
num_sanity_val_steps: 0
profiler: null
benchmark: false
deterministic: false
reload_dataloaders_every_n_epochs: 0
auto_lr_find: false
replace_sampler_ddp: true
detect_anomaly: false
auto_scale_batch_size: false
plugins: null
move_metrics_to_cpu: false
multiple_trainloader_mode: max_size_cycle

#python tools/train.py --config src/anomalib/models/padim/padim.yaml

[yvr354]

python tools/train.py --config src/anomalib/models/padim/padim.yaml