predict.py size mismatch for scoring_list.0.weight and scoring_list.0.bias error

[akkefa]

@namisan Please help.

When I am running predict.py on test set for evaluation score using my mt-dnn trained model. I am facing this error. The reason I think they model architecture and checkpoints architecture are not the same.

Command:
python predict.py --task="mdd" --task_id=0 --prep_input="data//mdd_test.json" --with_label --score="mdd_score.txt"

Error:

missing_keys, unexpected_keys = self.network.load_state_dict(state_dict['state'], strict=False)
  File "/Users/ikramali/.pyenv/versions/3.8.6/envs/ml/lib/python3.8/site-packages/torch/nn/modules/module.py", line 846, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for SANBertNetwork:
        size mismatch for scoring_list.0.weight: copying a param with shape torch.Size([2, 1024]) from checkpoint, the shape in current model is torch.Size([26, 1024]).
        size mismatch for scoring_list.0.bias: copying a param with shape torch.Size([2]) from checkpoint, the shape in current model is torch.Size([26]).

tasks.yml file:

fid:
data_format: PremiseOnly
n_class: 2
labels:
- irony
- not_irony
task_type: Classification
enable_san: False
metric_meta:
- F1
loss: CeCriterion

xnli:
data_format: PremiseAndOneHypothesis
enable_san: false
labels:
- contradiction
- neutral
- entailment
metric_meta:
- ACC
loss: CeCriterion
n_class: 3
task_type: Classification

mdd:
data_format: PremiseOnly
n_class: 26
labels:
- RAB
- DAM
- BEI
- DOH
- JED
- SFX
- BAS
- MOS
- FES
- BAG
- ALG
- ALX
- JER
- TRI
- RIY
- MSA
- SAN
- ALE
- MUS
- CAI
- AMM
- ASW
- TUN
- BEN
- SAL
- KHA
task_type: Classification
enable_san: False
metric_meta:
- F1MAC
loss: CeCriterion

[namisan]

Make sure you use the same number of categories.