Training on ADE20K
See original GitHub issueHello! I try to train default models from train.py on ADE20K, but I see such error:
Input arguments:
weights_decoder
batch_size_per_gpu 2
weights_encoder
workers 16
lr_pow 0.9
seed 304
epoch_iters 5000
weight_decay 0.0001
root_dataset ./data/
list_train ./data/train.odgt
optim SGD
num_epoch 20
ckpt ./ckpt
list_val ./data/validation.odgt
id baseline
imgMaxSize 1000
deep_sup_scale 0.4
fix_bn False
lr_encoder 0.02
gpus 0
beta1 0.9
random_flip True
num_class 150
start_epoch 1
imgSize [300, 375, 450, 525, 600]
arch_decoder ppm_deepsup
disp_iter 20
padding_constant 8
segm_downsampling_rate 8
lr_decoder 0.02
fc_dim 2048
arch_encoder resnet50dilated
Model ID: baseline-resnet50dilated-ppm_deepsup-ngpus1-batchSize2-imgMaxSize1000-paddingConst8-segmDownsampleRate8-LR_encoder0.02-LR_decoder0.02-epoch20
# samples: 20210
1 Epoch = 5000 iters
Traceback (most recent call last):
File "train.py", line 325, in <module>
main(args)
File "train.py", line 200, in main
train(segmentation_module, iterator_train, optimizers, history, epoch, args)
File "train.py", line 37, in train
loss, acc = segmentation_module(batch_data)
File "/home/ponomareva/.local/lib/python3.5/site-packages/torch/nn/modules/module.py", line 477, in __call__
result = self.forward(*input, **kwargs)
File "/home/ponomareva/semantic-segmentation-pytorch/models/models.py", line 34, in forward
(pred, pred_deepsup) = self.decoder(self.encoder(feed_dict['img_data'], return_feature_maps=True))
TypeError: list indices must be integers or slices, not str
Issue Analytics
- State:
- Created 4 years ago
- Reactions:2
- Comments:5 (1 by maintainers)
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@hangzhaomit thanks your help and now this project supports single gpu?
@loveis98 You might want to try this: https://github.com/CSAILVision/semantic-segmentation-pytorch/issues/203#issuecomment-562524601