Comments (7)
xksteven
commented on August 23, 2024
I will double check tonight or tomorrow and get back to you.
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edwardcho
commented on August 23, 2024
Yes.. Thanks..
If you have some opinions for me, please tell me...
Thanks.
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xksteven
commented on August 23, 2024
Could you maybe provide some more context to your setup?
I downloaded the code and data from scratch. Then followed the instructions on the README. Had to install yacs which as a dependency I forgot to mention, but I believe is mentioned in the submodule requirements then started the training.
python3 train.py --gpus 0-1
[2022-01-06 23:14:56,706 INFO train.py line 240 1846380] Loaded configuration file config/ade20k-resnet50dilated-ppm_deepsup.yaml
[2022-01-06 23:14:56,706 INFO train.py line 241 1846380] Running with config:
DATASET:
imgMaxSize: 1000
imgSizes: (300, 375, 450, 525, 600)
list_train: ./data/training.odgt
list_val: ./data/validation.odgt
num_class: 150
padding_constant: 8
random_flip: True
root_dataset: ./data/
segm_downsampling_rate: 8
DIR: ckpt/ade20k-resnet50dilated-ppm_deepsup
MODEL:
arch_decoder: ppm_deepsup
arch_encoder: resnet50dilated
fc_dim: 2048
weights_decoder:
weights_encoder:
OOD:
exclude_back: False
ood: msp
out_labels: (13,)
TEST:
batch_size: 1
checkpoint: epoch_20.pth
result: ./
TRAIN:
batch_size_per_gpu: 2
beta1: 0.9
deep_sup_scale: 0.4
disp_iter: 20
epoch_iters: 5000
fix_bn: False
lr_decoder: 0.02
lr_encoder: 0.02
lr_pow: 0.9
num_epoch: 20
optim: SGD
seed: 304
start_epoch: 0
weight_decay: 0.0001
workers: 16
VAL:
batch_size: 1
checkpoint: epoch_20.pth
visualize: False
[2022-01-06 23:14:56,706 INFO train.py line 246 1846380] Outputing checkpoints to: ckpt/ade20k-resnet50dilated-ppm_deepsup
# samples: 5125
1 Epoch = 5000 iters
Epoch: [1][0/5000], Time: 9.62, Data: 2.50, lr_encoder: 0.020000, lr_decoder: 0.020000, Accuracy: 0.66, Loss: 7.690948
Epoch: [1][20/5000], Time: 1.21, Data: 0.16, lr_encoder: 0.019996, lr_decoder: 0.019996, Accuracy: 70.52, Loss: 2.431588
Epoch: [1][40/5000], Time: 0.96, Data: 0.10, lr_encoder: 0.019993, lr_decoder: 0.019993, Accuracy: 76.97, Loss: 1.634942It seems to be training successfully.
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xksteven
commented on August 23, 2024
Will close soon unless I get updated with more information. Otherwise I cannot reproduce your issue.
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LT1st
commented on August 23, 2024
Facing the same issue, have u BEBUG it yet?
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xksteven
commented on August 23, 2024
@LT1st can you describe your steps or what you did?
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xksteven
commented on August 23, 2024
It seems the issue is with training with one GPU. I'll update the Readme and previous issue. The solution is something along these lines CSAILVision/semantic-segmentation-pytorch#58
But I haven't tested single GPU support and not sure when I'll be able to test it. Maybe sometime next week.
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Related Issues (20)
- Is there any restriction regarding classes definition? HOT 5
- Question: Paper Section 3 HOT 4
- pretrained model HOT 4
- Issues with evaluate the model on OOD test set HOT 1
- Couldn't match the BDD100K file paths HOT 3
- StreetHazards color encoding for visualization HOT 6
- Some questions about the CAOS benchmark HOT 2
- Some points to confirm about the CAOS benchmark HOT 4
- Two more stages in resnet HOT 2
- how about the class define? HOT 2
- how to show the anomaly scores? HOT 1
- Evaluation based on each image vs entire dataset HOT 1
- What is the true label of -1 in seg_label HOT 1
- Anomaly detecting and visualization HOT 2
- About metrics AUPR, AUROC, and FPR95 HOT 2
- About ground truth Semantic Segmentation images HOT 4
- Broken link to BDD-Anomaly dataset HOT 1
- Updating the google drive link of the Species OOD dataset HOT 4
- Question about CAOS dataset training HOT 13
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