Tensor size not matched during training

[cjwku1209]

After running the Training on Human3.6M steps in Experiments.md.

python -m torch.distributed.launch --nproc_per_node=1 \
       src/tools/run_fastmetro_bodymesh.py \
       --train_yaml Tax-H36m-coco40k-Muco-UP-Mpii/train.yaml \
       --val_yaml human3.6m/valid.protocol2.yaml \
       --arch hrnet-w64 \
       --model_name FastMETRO-L \
       --num_workers 1 \
       --per_gpu_train_batch_size 16 \
       --per_gpu_eval_batch_size 16 \
       --lr 1e-4 \
       --num_train_epochs 60 \
       --output_dir FastMETRO-L-H64_h36m/

I had encounter the following error

File "/hdd/input_pruning_exp/HMR_transformer/FastMETRO/src/modeling/_smpl.py", line 99, in forward
    v_posed = v_shaped + torch.matmul(posedirs, lrotmin[:, :, None]).view(-1, 6890, 3)
RuntimeError: The size of tensor a (480) must match the size of tensor b (16) at non-singleton dimension 0
Killing subprocess 5813

It seems like the v_shaped dimension does not match posedirs and lrotmin with a batch size 16. I had printed out the tensor size for v_shaped, posedirs & lrotmin for reference

v_shaped.shape = torch.Size([480, 6890, 3])
lrotmin[:, :, None].shape = torch.Size([16, 207, 1])
posedirs.shape = torch.Size([16, 20670, 207])

[cjwku1209]

The files on SMPLify had been updated to version 1.1.0. The problem had been solved by finding and replacing the basic model back to 1.0.0 version.