RuntimeError: The size of tensor a (125) must match the size of tensor b (128) at non-singleton dimension 2

[Hr-Song]

A RuntimeError happened when I tried using newest fast_scnn to infer on my own dataset.
The error has never happened when I was using other models in this repository on the same images.

Here is the Traceback:
Traceback (most recent call last):
File "image_inference_box.py", line 116, in
main()
File "image_inference_box.py", line 42, in main
result = inference_segmentor(model, img)
File "/home/lzhpc/mmsegmentation-master/mmseg/apis/inference.py", line 95, in inference_segmentor
result = model(return_loss=False, rescale=True, **data)
File "/home/lzhpc/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/nn/modules/module.py", line 550, in call
result = self.forward(*input, **kwargs)
File "/home/lzhpc/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/fp16_utils.py", line 84, in new_func
return old_func(*args, **kwargs)
File "/home/lzhpc/mmsegmentation-master/mmseg/models/segmentors/base.py", line 124, in forward
return self.forward_test(img, img_metas, **kwargs)
File "/home/lzhpc/mmsegmentation-master/mmseg/models/segmentors/base.py", line 106, in forward_test
return self.simple_test(imgs[0], img_metas[0], **kwargs)
File "/home/lzhpc/mmsegmentation-master/mmseg/models/segmentors/encoder_decoder.py", line 261, in simple_test
seg_logit = self.inference(img, img_meta, rescale)
File "/home/lzhpc/mmsegmentation-master/mmseg/models/segmentors/encoder_decoder.py", line 246, in inference
seg_logit = self.whole_inference(img, img_meta, rescale)
File "/home/lzhpc/mmsegmentation-master/mmseg/models/segmentors/encoder_decoder.py", line 213, in whole_inference
seg_logit = self.encode_decode(img, img_meta)
File "/home/lzhpc/mmsegmentation-master/mmseg/models/segmentors/encoder_decoder.py", line 87, in encode_decode
x = self.extract_feat(img)
File "/home/lzhpc/mmsegmentation-master/mmseg/models/segmentors/encoder_decoder.py", line 79, in extract_feat
x = self.backbone(img)
File "/home/lzhpc/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/nn/modules/module.py", line 550, in call
result = self.forward(*input, **kwargs)
File "/home/lzhpc/mmsegmentation-master/mmseg/models/backbones/fast_scnn.py", line 381, in forward
lower_res_features)
File "/home/lzhpc/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/nn/modules/module.py", line 550, in call
result = self.forward(*input, **kwargs)
File "/home/lzhpc/mmsegmentation-master/mmseg/models/backbones/fast_scnn.py", line 249, in forward
out = higher_res_feature + lower_res_feature
RuntimeError: The size of tensor a (125) must match the size of tensor b (128) at non-singleton dimension 2

[johnzja]

Could you please show us your config file && the size of input images?
Tensor size @ dimension 2 is the picture height. If it mismatches, it is possible that the (H,W) of the input picture is not a multiple of 32. In our tests, we use size=(1024, 2048), and it seems to work.

[Hr-Song]

The size of input images was (2000, 2048). But in my config file, the test image scale was set to be resized to (1024,1024) .
Here is my config file.

norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='FastSCNN',
downsample_dw_channels=(32, 48),
global_in_channels=64,
global_block_channels=(64, 96, 128),
global_block_strides=(2, 2, 1),
global_out_channels=128,
higher_in_channels=64,
lower_in_channels=128,
fusion_out_channels=128,
out_indices=(0, 1, 2),
norm_cfg=dict(type='SyncBN', requires_grad=True, momentum=0.01),
align_corners=False),
decode_head=dict(
type='DepthwiseSeparableFCNHead',
in_channels=128,
channels=128,
concat_input=False,
num_classes=2,
in_index=-1,
norm_cfg=dict(type='SyncBN', requires_grad=True, momentum=0.01),
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
auxiliary_head=[
dict(
type='FCNHead',
in_channels=128,
channels=32,
num_convs=1,
num_classes=2,
in_index=-2,
norm_cfg=dict(type='SyncBN', requires_grad=True, momentum=0.01),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='FCNHead',
in_channels=64,
channels=32,
num_convs=1,
num_classes=2,
in_index=-3,
norm_cfg=dict(type='SyncBN', requires_grad=True, momentum=0.01),
concat_input=False,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4))
])
train_cfg = dict()
test_cfg = dict(mode='whole')
dataset_type = 'PascalVOCDataset'
data_root = 'data/VOCdevkit/VOC2012'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=False)
crop_size = (512, 512)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2048, 2048), ratio_range=(0.5, 0.8)),
dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='PhotoMetricDistortion'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=False),
dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1024, 1024),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=False),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
train=dict(
type='PascalVOCDataset',
data_root='data/VOCdevkit/VOC2012',
img_dir='JPEGImages',
ann_dir='SegmentationClassPNG',
split='ImageSets/Segmentation/trainval.txt',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
type='Resize', img_scale=(2048, 2048), ratio_range=(0.5, 0.8)),
dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='PhotoMetricDistortion'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=False),
dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]),
val=dict(
type='PascalVOCDataset',
data_root='data/VOCdevkit/VOC2012',
img_dir='JPEGImages',
ann_dir='SegmentationClassPNG',
split='ImageSets/Segmentation/val.txt',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1024, 1024),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=False),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]),
test=dict(
type='PascalVOCDataset',
data_root='data/VOCdevkit/VOC2012',
img_dir='JPEGImages',
ann_dir='SegmentationClassPNG',
split='ImageSets/Segmentation/test.txt',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1024, 1024),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=False),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]))
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook', by_epoch=False),
dict(type='TensorboardLoggerHook')
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudnn_benchmark = True
optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-05)
optimizer_config = dict()
lr_config = dict(policy='poly', power=0.9, min_lr=0.0001, by_epoch=False)
total_iters = 20000
checkpoint_config = dict(by_epoch=False, interval=2000)
evaluation = dict(interval=2000, metric='mIoU')
work_dir = './work_dirs/fast_scnn_4x8_80k_lr0.12_yiwu'
gpu_ids = range(0, 1)

[xvjiarui]

Fixed by #82