RuntimeError: mat1 and mat2 shapes cannot be multiplied (6144x1456 and 2720x85)

[smallzhu]

When I select efficentnet for training I get the following error, only swin-transformer does not report it
Can you help me?

Start Training 1 EpochTraceback (most recent call last):
File "D:/hxy/FGVC-PIM-master/main.py", line 301, in
main(args, tlogger)
File "D:/hxy/FGVC-PIM-master/main.py", line 253, in main
train(args, epoch, model, scaler, amp_context, optimizer, schedule, train_loader)
File "D:/hxy/FGVC-PIM-master/main.py", line 140, in train
outs = model(datas)
File "C:\Users\mj\anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "C:\Users\mj\anaconda3\envs\pytorch\lib\site-packages\torch\nn\parallel\data_parallel.py", line 168, in forward
outputs = self.parallel_apply(replicas, inputs, kwargs)
File "C:\Users\mj\anaconda3\envs\pytorch\lib\site-packages\torch\nn\parallel\data_parallel.py", line 178, in parallel_apply
return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
File "C:\Users\mj\anaconda3\envs\pytorch\lib\site-packages\torch\nn\parallel\parallel_apply.py", line 86, in parallel_apply
output.reraise()
File "C:\Users\mj\anaconda3\envs\pytorch\lib\site-packages\torch_utils.py", line 457, in reraise
raise exception
RuntimeError: Caught RuntimeError in replica 0 on device 0.
Original Traceback (most recent call last):
File "C:\Users\mj\anaconda3\envs\pytorch\lib\site-packages\torch\nn\parallel\parallel_apply.py", line 61, in _worker
output = module(*input, **kwargs)
File "C:\Users\mj\anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "D:\hxy\FGVC-PIM-master\models\pim_module\pim_module.py", line 414, in forward
comb_outs = self.combiner(selects)
File "C:\Users\mj\anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "D:\hxy\FGVC-PIM-master\models\pim_module\pim_module.py", line 81, in forward
hs = self.param_pool0(hs)
File "C:\Users\mj\anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "C:\Users\mj\anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\linear.py", line 103, in forward
return F.linear(input, self.weight, self.bias)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (6144x1456 and 2720x85)

[chou141253]

How is your config file set?

[smallzhu]

How is your config file set?

project_name: tea_swin_t
exp_name: T3000
use_wandb: True
wandb_entity: dechenzhu
train_root: D:\hxy\FGVC-PIM-master\dataset\data3
val_root: D:\hxy\FGVC-PIM-master\dataset\test1
data_size: 448
num_workers: 2
batch_size: 8
model_name: efficient
pretrained: ~
optimizer: SGD
max_lr: 0.0005
wdecay: 0.0005
max_epochs: 300
warmup_batchs: 800
use_amp: True
use_fpn: True
fpn_size: 1536
use_selection: True
num_classes: 25
num_selects:
layer1: 2048
layer2: 512
layer3: 128
layer4: 32
use_combiner: False
lambda_b: 0.5
lambda_s: 0.0
lambda_n: 5.0
lambda_c: 1.0
update_freq: 2
log_freq: 100
eval_freq: 2

[Chaoran-F]

same question in here, backbone is EfficientNet

[Chaoran-F]

How is your config file set?

project_name: tea_swin_t exp_name: T3000 use_wandb: True wandb_entity: dechenzhu train_root: D:\hxy\FGVC-PIM-master\dataset\data3 val_root: D:\hxy\FGVC-PIM-master\dataset\test1 data_size: 448 num_workers: 2 batch_size: 8 model_name: efficient pretrained: ~ optimizer: SGD max_lr: 0.0005 wdecay: 0.0005 max_epochs: 300 warmup_batchs: 800 use_amp: True use_fpn: True fpn_size: 1536 use_selection: True num_classes: 25 num_selects: layer1: 2048 layer2: 512 layer3: 128 layer4: 32 use_combiner: False lambda_b: 0.5 lambda_s: 0.0 lambda_n: 5.0 lambda_c: 1.0 update_freq: 2 log_freq: 100 eval_freq: 2

did you solve this issue