issue with class weight and cross entropy loss

[JosephBChoi]

Describe the bug

Tried to train PSPNet with class weights [0.052, 9.23].
When calculating cross entropy loss, it tries to create a tensor from list of the tensor where PyTorch throws an error.

[/content/mmsegmentation/mmseg/models/losses/cross_entropy_loss.py](https://ra7s57vsu1g-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20231024-060124_RC00_576097381#) in cross_entropy(pred, label, weight, class_weight, reduction, avg_factor, ignore_index, avg_non_ignore)
     64         else:
     65             # the average factor should take the class weights into account
---> 66             label_weights = torch.tensor([class_weight[cls] for cls in label],
     67                                          device=class_weight.device)
     68             if avg_non_ignore:

ValueError: only one element tensors can be converted to Python scalars

If I remove the class weights, it doesn't produce the error.

[shinpaul14]

Have you solved this?

[giuseppemartino26]

I have the same issue too

[JosephBChoi]

Have you solved this?

Still having the same issue.

[yzb2]

Seems to be a duplicate of #3406

[LHamnett]

Same issue here, attaching current config and error trace:

Config:

checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pth'
crop_size = (
768,
768,
)
data_preprocessor = dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
512,
512,
),
std=[
58.395,
57.12,
57.375,
],
type='SegDataPreProcessor')
data_root = '/content/gdrive/MyDrive/BCSS_dataset'
dataset_type = 'CancerDataset'
default_hooks = dict(
checkpoint=dict(by_epoch=False, interval=0, type='CheckpointHook'),
logger=dict(interval=100, log_metric_by_epoch=False, type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
timer=dict(type='IterTimerHook'),
visualization=dict(type='SegVisualizationHook'))
default_scope = 'mmseg'
env_cfg = dict(
cudnn_benchmark=True,
dist_cfg=dict(backend='nccl'),
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
fp16 = dict(opt_level='O3')
img_ratios = [
0.5,
0.75,
1.0,
1.25,
1.5,
1.75,
]
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=False)
model = dict(
backbone=dict(
attn_drop_rate=0.0,
drop_path_rate=0.1,
drop_rate=0.0,
embed_dims=64,
in_channels=3,
init_cfg=dict(
checkpoint=
'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pth',
type='Pretrained'),
mlp_ratio=4,
num_heads=[
1,
2,
5,
8,
],
num_layers=[
3,
6,
40,
3,
],
num_stages=4,
out_indices=(
0,
1,
2,
3,
),
patch_sizes=[
7,
3,
3,
3,
],
qkv_bias=True,
sr_ratios=[
8,
4,
2,
1,
],
type='MixVisionTransformer'),
data_preprocessor=dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
512,
512,
),
std=[
58.395,
57.12,
57.375,
],
type='SegDataPreProcessor'),
decode_head=dict(
align_corners=False,
channels=256,
dropout_ratio=0.1,
ignore_index=0,
in_channels=[
64,
128,
320,
512,
],
in_index=[
0,
1,
2,
3,
],
loss_decode=[
dict(
class_weight=[
1,
1.1,
1,
1,
1,
1,
],
loss_name='loss_ce',
loss_weight=1.0,
type='CrossEntropyLoss'),
],
norm_cfg=dict(requires_grad=True, type='BN'),
num_classes=6,
type='SegformerHead'),
pretrained=None,
test_cfg=dict(mode='whole'),
train_cfg=dict(),
type='EncoderDecoder')
norm_cfg = dict(requires_grad=True, type='BN')
num_classes = 6
optim_wrapper = dict(
optimizer=dict(
betas=(
0.9,
0.999,
), lr=0.001, type='AdamW', weight_decay=0.01),
paramwise_cfg=dict(
custom_keys=dict(
head=dict(lr_mult=10.0),
norm=dict(decay_mult=0.0),
pos_block=dict(decay_mult=0.0))),
type='AmpOptimWrapper')
optimizer = dict(lr=0.005, momentum=0.9, type='AdamW', weight_decay=0.0005)
param_scheduler = [
dict(
by_epoch=False,
eta_max=0.001,
three_phase=True,
total_steps=500,
type='OneCycleLR'),
]
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
batch_size=1,
dataset=dict(
ann_file='splits/val.txt',
data_prefix=dict(
img_path=
'/content/gdrive/MyDrive/BCSS_dataset/patched_images_masks_2d_6_class/images',
seg_map_path=
'/content/gdrive/MyDrive/BCSS_dataset/patched_images_masks_2d_6_class/masks'
),
data_root='/content/gdrive/MyDrive/BCSS_dataset',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
type='CancerDataset'),
num_workers=4,
persistent_workers=False,
sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
iou_metrics=[
'mDice',
], type='IoUMetric')
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
]
train_cfg = dict(max_iters=500, type='IterBasedTrainLoop', val_interval=1000)
train_dataloader = dict(
batch_size=2,
dataset=dict(
dataset=dict(
ann_file='splits/train.txt',
data_prefix=dict(
img_path=
'/content/gdrive/MyDrive/BCSS_dataset/patched_images_masks_2d_6_class/images',
seg_map_path=
'/content/gdrive/MyDrive/BCSS_dataset/patched_images_masks_2d_6_class/masks'
),
data_root='/content/gdrive/MyDrive/BCSS_dataset',
pipeline=[
dict(type='LoadImageFromFile'),
dict(reduce_zero_label=False, type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
type='CancerDataset'),
times=5,
type='RepeatDataset'),
num_workers=1,
persistent_workers=False,
sampler=dict(shuffle=True, type='InfiniteSampler'))
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(reduce_zero_label=False, type='LoadAnnotations'),
dict(type='PackSegInputs'),
]
tta_model = None
tta_pipeline = [
dict(backend_args=None, type='LoadImageFromFile'),
dict(
transforms=[
[
dict(keep_ratio=True, scale_factor=0.5, type='Resize'),
dict(keep_ratio=True, scale_factor=0.75, type='Resize'),
dict(keep_ratio=True, scale_factor=1.0, type='Resize'),
dict(keep_ratio=True, scale_factor=1.25, type='Resize'),
dict(keep_ratio=True, scale_factor=1.5, type='Resize'),
],
[
dict(direction='vertical', prob=0.5, type='RandomFlip'),
dict(direction='horizontal', prob=0.5, type='RandomFlip'),
],
[
dict(type='LoadAnnotations'),
],
[
dict(type='PackSegInputs'),
],
],
type='TestTimeAug'),
]
val_cfg = dict(type='ValLoop')
val_dataloader = dict(
batch_size=1,
dataset=dict(
ann_file='splits/val.txt',
data_prefix=dict(
img_path=
'/content/gdrive/MyDrive/BCSS_dataset/patched_images_masks_2d_6_class/images',
seg_map_path=
'/content/gdrive/MyDrive/BCSS_dataset/patched_images_masks_2d_6_class/masks'
),
data_root='/content/gdrive/MyDrive/BCSS_dataset',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
type='CancerDataset'),
num_workers=4,
persistent_workers=False,
sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
iou_metrics=[
'mDice',
'mIoU',
], type='IoUMetric')
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
name='visualizer',
type='SegLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
])
work_dir = '/content/gdrive/MyDrive/BCSS_dataset/logs'

---

Error trace:

`ValueError Traceback (most recent call last)
in <cell line: 3>()
1 #test one model make sure one model can be trained correctly
2 runner = Runner.from_cfg(cfg)
----> 3 runner.train()

15 frames
/usr/local/lib/python3.10/dist-packages/mmengine/runner/runner.py in train(self)
1775 self._maybe_compile('train_step')
1776
-> 1777 model = self.train_loop.run() # type: ignore
1778 self.call_hook('after_run')
1779 return model

/usr/local/lib/python3.10/dist-packages/mmengine/runner/loops.py in run(self)
276
277 data_batch = next(self.dataloader_iterator)
--> 278 self.run_iter(data_batch)
279
280 self._decide_current_val_interval()

/usr/local/lib/python3.10/dist-packages/mmengine/runner/loops.py in run_iter(self, data_batch)
299 # synchronization during gradient accumulation process.
300 # outputs should be a dict of loss.
--> 301 outputs = self.runner.model.train_step(
302 data_batch, optim_wrapper=self.runner.optim_wrapper)
303

/usr/local/lib/python3.10/dist-packages/mmengine/model/base_model/base_model.py in train_step(self, data, optim_wrapper)
112 with optim_wrapper.optim_context(self):
113 data = self.data_preprocessor(data, True)
--> 114 losses = self._run_forward(data, mode='loss') # type: ignore
115 parsed_losses, log_vars = self.parse_losses(losses) # type: ignore
116 optim_wrapper.update_params(parsed_losses)

/usr/local/lib/python3.10/dist-packages/mmengine/model/base_model/base_model.py in _run_forward(self, data, mode)
344 """
345 if isinstance(data, dict):
--> 346 results = self(**data, mode=mode)
347 elif isinstance(data, (list, tuple)):
348 results = self(*data, mode=mode)

/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _wrapped_call_impl(self, *args, **kwargs)
1516 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1517 else:
-> 1518 return self._call_impl(*args, **kwargs)
1519
1520 def _call_impl(self, *args, **kwargs):

/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
1525 or _global_backward_pre_hooks or _global_backward_hooks
1526 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527 return forward_call(*args, **kwargs)
1528
1529 try:

/content/mmsegmentation/mmseg/models/segmentors/base.py in forward(self, inputs, data_samples, mode)
92 """
93 if mode == 'loss':
---> 94 return self.loss(inputs, data_samples)
95 elif mode == 'predict':
96 return self.predict(inputs, data_samples)

/content/mmsegmentation/mmseg/models/segmentors/encoder_decoder.py in loss(self, inputs, data_samples)
176 losses = dict()
177
--> 178 loss_decode = self._decode_head_forward_train(x, data_samples)
179 losses.update(loss_decode)
180

/content/mmsegmentation/mmseg/models/segmentors/encoder_decoder.py in _decode_head_forward_train(self, inputs, data_samples)
137 training."""
138 losses = dict()
--> 139 loss_decode = self.decode_head.loss(inputs, data_samples,
140 self.train_cfg)
141

/content/mmsegmentation/mmseg/models/decode_heads/decode_head.py in loss(self, inputs, batch_data_samples, train_cfg)
260 """
261 seg_logits = self.forward(inputs)
--> 262 losses = self.loss_by_feat(seg_logits, batch_data_samples)
263 return losses
264

/content/mmsegmentation/mmseg/models/decode_heads/decode_head.py in loss_by_feat(self, seg_logits, batch_data_samples)
322 for loss_decode in losses_decode:
323 if loss_decode.loss_name not in loss:
--> 324 loss[loss_decode.loss_name] = loss_decode(
325 seg_logits,
326 seg_label,

/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _wrapped_call_impl(self, *args, **kwargs)
1516 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1517 else:
-> 1518 return self._call_impl(*args, **kwargs)
1519
1520 def _call_impl(self, *args, **kwargs):

/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
1525 or _global_backward_pre_hooks or _global_backward_hooks
1526 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527 return forward_call(*args, **kwargs)
1528
1529 try:

/content/mmsegmentation/mmseg/models/losses/cross_entropy_loss.py in forward(self, cls_score, label, weight, avg_factor, reduction_override, ignore_index, **kwargs)
283 class_weight = None
284 # Note: for BCE loss, label < 0 is invalid.
--> 285 loss_cls = self.loss_weight * self.cls_criterion(
286 cls_score,
287 label,

/content/mmsegmentation/mmseg/models/losses/cross_entropy_loss.py in cross_entropy(pred, label, weight, class_weight, reduction, avg_factor, ignore_index, avg_non_ignore)
64 else:
65 # the average factor should take the class weights into account
---> 66 label_weights = torch.tensor([class_weight[cls] for cls in label],
67 device=class_weight.device)
68 if avg_non_ignore:

ValueError: only one element tensors can be converted to Python scalars`

[mmeendez8]

Same issue here. It seems a bug was introduced in this PR.
If you downgrade to version 1.1.2 everything should work as expected.
Will try to send a fix soon

[call560]

Same issue here, attaching current config and error trace:

Config:

checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pth'
crop_size = (
768,
768,
)
data_preprocessor = dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
512,
512,
),
std=[
58.395,
57.12,
57.375,
],
type='SegDataPreProcessor')
data_root = '/content/gdrive/MyDrive/BCSS_dataset'
dataset_type = 'CancerDataset'
default_hooks = dict(
checkpoint=dict(by_epoch=False, interval=0, type='CheckpointHook'),
logger=dict(interval=100, log_metric_by_epoch=False, type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
timer=dict(type='IterTimerHook'),
visualization=dict(type='SegVisualizationHook'))
default_scope = 'mmseg'
env_cfg = dict(
cudnn_benchmark=True,
dist_cfg=dict(backend='nccl'),
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
fp16 = dict(opt_level='O3')
img_ratios = [
0.5,
0.75,
1.0,
1.25,
1.5,
1.75,
]
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=False)
model = dict(
backbone=dict(
attn_drop_rate=0.0,
drop_path_rate=0.1,
drop_rate=0.0,
embed_dims=64,
in_channels=3,
init_cfg=dict(
checkpoint=
'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pth',
type='Pretrained'),
mlp_ratio=4,
num_heads=[
1,
2,
5,
8,
],
num_layers=[
3,
6,
40,
3,
],
num_stages=4,
out_indices=(
0,
1,
2,
3,
),
patch_sizes=[
7,
3,
3,
3,
],
qkv_bias=True,
sr_ratios=[
8,
4,
2,
1,
],
type='MixVisionTransformer'),
data_preprocessor=dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
512,
512,
),
std=[
58.395,
57.12,
57.375,
],
type='SegDataPreProcessor'),
decode_head=dict(
align_corners=False,
channels=256,
dropout_ratio=0.1,
ignore_index=0,
in_channels=[
64,
128,
320,
512,
],
in_index=[
0,
1,
2,
3,
],
loss_decode=[
dict(
class_weight=[
1,
1.1,
1,
1,
1,
1,
],
loss_name='loss_ce',
loss_weight=1.0,
type='CrossEntropyLoss'),
],
norm_cfg=dict(requires_grad=True, type='BN'),
num_classes=6,
type='SegformerHead'),
pretrained=None,
test_cfg=dict(mode='whole'),
train_cfg=dict(),
type='EncoderDecoder')
norm_cfg = dict(requires_grad=True, type='BN')
num_classes = 6
optim_wrapper = dict(
optimizer=dict(
betas=(
0.9,
0.999,
), lr=0.001, type='AdamW', weight_decay=0.01),
paramwise_cfg=dict(
custom_keys=dict(
head=dict(lr_mult=10.0),
norm=dict(decay_mult=0.0),
pos_block=dict(decay_mult=0.0))),
type='AmpOptimWrapper')
optimizer = dict(lr=0.005, momentum=0.9, type='AdamW', weight_decay=0.0005)
param_scheduler = [
dict(
by_epoch=False,
eta_max=0.001,
three_phase=True,
total_steps=500,
type='OneCycleLR'),
]
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
batch_size=1,
dataset=dict(
ann_file='splits/val.txt',
data_prefix=dict(
img_path=
'/content/gdrive/MyDrive/BCSS_dataset/patched_images_masks_2d_6_class/images',
seg_map_path=
'/content/gdrive/MyDrive/BCSS_dataset/patched_images_masks_2d_6_class/masks'
),
data_root='/content/gdrive/MyDrive/BCSS_dataset',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
type='CancerDataset'),
num_workers=4,
persistent_workers=False,
sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
iou_metrics=[
'mDice',
], type='IoUMetric')
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
]
train_cfg = dict(max_iters=500, type='IterBasedTrainLoop', val_interval=1000)
train_dataloader = dict(
batch_size=2,
dataset=dict(
dataset=dict(
ann_file='splits/train.txt',
data_prefix=dict(
img_path=
'/content/gdrive/MyDrive/BCSS_dataset/patched_images_masks_2d_6_class/images',
seg_map_path=
'/content/gdrive/MyDrive/BCSS_dataset/patched_images_masks_2d_6_class/masks'
),
data_root='/content/gdrive/MyDrive/BCSS_dataset',
pipeline=[
dict(type='LoadImageFromFile'),
dict(reduce_zero_label=False, type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
type='CancerDataset'),
times=5,
type='RepeatDataset'),
num_workers=1,
persistent_workers=False,
sampler=dict(shuffle=True, type='InfiniteSampler'))
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(reduce_zero_label=False, type='LoadAnnotations'),
dict(type='PackSegInputs'),
]
tta_model = None
tta_pipeline = [
dict(backend_args=None, type='LoadImageFromFile'),
dict(
transforms=[
[
dict(keep_ratio=True, scale_factor=0.5, type='Resize'),
dict(keep_ratio=True, scale_factor=0.75, type='Resize'),
dict(keep_ratio=True, scale_factor=1.0, type='Resize'),
dict(keep_ratio=True, scale_factor=1.25, type='Resize'),
dict(keep_ratio=True, scale_factor=1.5, type='Resize'),
],
[
dict(direction='vertical', prob=0.5, type='RandomFlip'),
dict(direction='horizontal', prob=0.5, type='RandomFlip'),
],
[
dict(type='LoadAnnotations'),
],
[
dict(type='PackSegInputs'),
],
],
type='TestTimeAug'),
]
val_cfg = dict(type='ValLoop')
val_dataloader = dict(
batch_size=1,
dataset=dict(
ann_file='splits/val.txt',
data_prefix=dict(
img_path=
'/content/gdrive/MyDrive/BCSS_dataset/patched_images_masks_2d_6_class/images',
seg_map_path=
'/content/gdrive/MyDrive/BCSS_dataset/patched_images_masks_2d_6_class/masks'
),
data_root='/content/gdrive/MyDrive/BCSS_dataset',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
type='CancerDataset'),
num_workers=4,
persistent_workers=False,
sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
iou_metrics=[
'mDice',
'mIoU',
], type='IoUMetric')
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
name='visualizer',
type='SegLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
])
work_dir = '/content/gdrive/MyDrive/BCSS_dataset/logs'

Error trace:

ValueError Traceback (most recent call last) in <cell line: 3>() 1 #test one model make sure one model can be trained correctly 2 runner = Runner.from_cfg(cfg) ----> 3 runner.train() 15 frames [/usr/local/lib/python3.10/dist-packages/mmengine/runner/runner.py](https://localhost:8080/#) in train(self) 1775 self._maybe_compile('train_step') 1776 -> 1777 model = self.train_loop.run() # type: ignore 1778 self.call_hook('after_run') 1779 return model [/usr/local/lib/python3.10/dist-packages/mmengine/runner/loops.py](https://localhost:8080/#) in run(self) 276 277 data_batch = next(self.dataloader_iterator) --> 278 self.run_iter(data_batch) 279 280 self._decide_current_val_interval() [/usr/local/lib/python3.10/dist-packages/mmengine/runner/loops.py](https://localhost:8080/#) in run_iter(self, data_batch) 299 # synchronization during gradient accumulation process. 300 # outputs should be a dict of loss. --> 301 outputs = self.runner.model.train_step( 302 data_batch, optim_wrapper=self.runner.optim_wrapper) 303 [/usr/local/lib/python3.10/dist-packages/mmengine/model/base_model/base_model.py](https://localhost:8080/#) in train_step(self, data, optim_wrapper) 112 with optim_wrapper.optim_context(self): 113 data = self.data_preprocessor(data, True) --> 114 losses = self._run_forward(data, mode='loss') # type: ignore 115 parsed_losses, log_vars = self.parse_losses(losses) # type: ignore 116 optim_wrapper.update_params(parsed_losses) [/usr/local/lib/python3.10/dist-packages/mmengine/model/base_model/base_model.py](https://localhost:8080/#) in _run_forward(self, data, mode) 344 """ 345 if isinstance(data, dict): --> 346 results = self(**data, mode=mode) 347 elif isinstance(data, (list, tuple)): 348 results = self(*data, mode=mode) [/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _wrapped_call_impl(self, *args, **kwargs) 1516 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc] 1517 else: -> 1518 return self._call_impl(*args, **kwargs) 1519 1520 def _call_impl(self, *args, **kwargs): [/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *args, **kwargs) 1525 or _global_backward_pre_hooks or _global_backward_hooks 1526 or _global_forward_hooks or _global_forward_pre_hooks): -> 1527 return forward_call(*args, **kwargs) 1528 1529 try: [/content/mmsegmentation/mmseg/models/segmentors/base.py](https://localhost:8080/#) in forward(self, inputs, data_samples, mode) 92 """ 93 if mode == 'loss': ---> 94 return self.loss(inputs, data_samples) 95 elif mode == 'predict': 96 return self.predict(inputs, data_samples) [/content/mmsegmentation/mmseg/models/segmentors/encoder_decoder.py](https://localhost:8080/#) in loss(self, inputs, data_samples) 176 losses = dict() 177 --> 178 loss_decode = self._decode_head_forward_train(x, data_samples) 179 losses.update(loss_decode) 180 [/content/mmsegmentation/mmseg/models/segmentors/encoder_decoder.py](https://localhost:8080/#) in _decode_head_forward_train(self, inputs, data_samples) 137 training.""" 138 losses = dict() --> 139 loss_decode = self.decode_head.loss(inputs, data_samples, 140 self.train_cfg) 141 [/content/mmsegmentation/mmseg/models/decode_heads/decode_head.py](https://localhost:8080/#) in loss(self, inputs, batch_data_samples, train_cfg) 260 """ 261 seg_logits = self.forward(inputs) --> 262 losses = self.loss_by_feat(seg_logits, batch_data_samples) 263 return losses 264 [/content/mmsegmentation/mmseg/models/decode_heads/decode_head.py](https://localhost:8080/#) in loss_by_feat(self, seg_logits, batch_data_samples) 322 for loss_decode in losses_decode: 323 if loss_decode.loss_name not in loss: --> 324 loss[loss_decode.loss_name] = loss_decode( 325 seg_logits, 326 seg_label, [/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _wrapped_call_impl(self, *args, **kwargs) 1516 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc] 1517 else: -> 1518 return self._call_impl(*args, **kwargs) 1519 1520 def _call_impl(self, *args, **kwargs): [/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *args, **kwargs) 1525 or _global_backward_pre_hooks or _global_backward_hooks 1526 or _global_forward_hooks or _global_forward_pre_hooks): -> 1527 return forward_call(*args, **kwargs) 1528 1529 try: [/content/mmsegmentation/mmseg/models/losses/cross_entropy_loss.py](https://localhost:8080/#) in forward(self, cls_score, label, weight, avg_factor, reduction_override, ignore_index, **kwargs) 283 class_weight = None 284 # Note: for BCE loss, label < 0 is invalid. --> 285 loss_cls = self.loss_weight * self.cls_criterion( 286 cls_score, 287 label, [/content/mmsegmentation/mmseg/models/losses/cross_entropy_loss.py](https://localhost:8080/#) in cross_entropy(pred, label, weight, class_weight, reduction, avg_factor, ignore_index, avg_non_ignore) 64 else: 65 # the average factor should take the class weights into account ---> 66 label_weights = torch.tensor([class_weight[cls] for cls in label], 67 device=class_weight.device) 68 if avg_non_ignore: ValueError: only one element tensors can be converted to Python scalars

I have the same error when I was training the "Knet" with WCE loss. May I ask how you solved it? Thank you!

[mmeendez8]

I sent a PR to fix the error and that was already merged:
#3457

[JosephBChoi]

@mmeendez8 I assume this issues has been resolved by the recent PR. If so, could I close this issue?

[mmeendez8]

Yes, I think so

[JosephBChoi]

closing this issue with the PR #3457

[JH95-ai]

I use new code ,and use class weights in cityscape dataset and pidnet-s models ,only have error
`

   else:
        # the average factor should take the class weights into account
        label_weights = torch.stack([class_weight[cls] for cls in label
                                     ]).to(device=class_weight.device)
        if avg_non_ignore:
            label_weights[label == ignore_index] = 0
        avg_factor = label_weights.sum()`

@mmeendez8 @JosephBChoi