Is this the expected performance?

[KawaiiNotHawaii]

bash codes to start inference:
`# Input arguments
INPUT_PATH="demo02.jpg"
OUTPUT_BASE="outputs"

Extract the filename without extension

FILENAME=$(basename "$INPUT_PATH" | sed 's/.[^.]*$//')

Construct the output folder path

OUTPUT_PATH="$OUTPUT_BASE/$FILENAME"

Run the command

CUDA_VISIBLE_DEVICES=1 python inference_on_a_image.py
-c config_model/UniPose_SwinT.py
-p weights/unipose_swint.pth
-i "$INPUT_PATH"
-o "$OUTPUT_PATH"
-t "car"`

logs:
********* sub_sentence_present True
_IncompatibleKeys(missing_keys=['clip_model.positional_embedding', 'clip_model.text_projection', 'clip_model.logit_scale', 'clip_model.transformer.resblocks.0.attn.in_proj_weight', 'clip_model.transformer.resblocks.0.attn.in_proj_bias', 'clip_model.transformer.resblocks.0.attn.out_proj.weight', 'clip_model.transformer.resblocks.0.attn.out_proj.bias', 'clip_model.transformer.resblocks.0.ln_1.weight', 'clip_model.transformer.resblocks.0.ln_1.bias', 'clip_model.transformer.resblocks.0.mlp.c_fc.weight', 'clip_model.transformer.resblocks.0.mlp.c_fc.bias', 'clip_model.transformer.resblocks.0.mlp.c_proj.weight', 'clip_model.transformer.resblocks.0.mlp.c_proj.bias', 'clip_model.transformer.resblocks.0.ln_2.weight', 'clip_model.transformer.resblocks.0.ln_2.bias', 'clip_model.transformer.resblocks.1.attn.in_proj_weight', 'clip_model.transformer.resblocks.1.attn.in_proj_bias', 'clip_model.transformer.resblocks.1.attn.out_proj.weight', 'clip_model.transformer.resblocks.1.attn.out_proj.bias', 'clip_model.transformer.resblocks.1.ln_1.weight', 'clip_model.transformer.resblocks.1.ln_1.bias', 'clip_model.transformer.resblocks.1.mlp.c_fc.weight', 'clip_model.transformer.resblocks.1.mlp.c_fc.bias', 'clip_model.transformer.resblocks.1.mlp.c_proj.weight', 'clip_model.transformer.resblocks.1.mlp.c_proj.bias', 'clip_model.transformer.resblocks.1.ln_2.weight', 'clip_model.transformer.resblocks.1.ln_2.bias', 'clip_model.transformer.resblocks.2.attn.in_proj_weight', 'clip_model.transformer.resblocks.2.attn.in_proj_bias', 'clip_model.transformer.resblocks.2.attn.out_proj.weight', 'clip_model.transformer.resblocks.2.attn.out_proj.bias', 'clip_model.transformer.resblocks.2.ln_1.weight', 'clip_model.transformer.resblocks.2.ln_1.bias', 'clip_model.transformer.resblocks.2.mlp.c_fc.weight', 'clip_model.transformer.resblocks.2.mlp.c_fc.bias', 'clip_model.transformer.resblocks.2.mlp.c_proj.weight', 'clip_model.transformer.resblocks.2.mlp.c_proj.bias', 'clip_model.transformer.resblocks.2.ln_2.weight', 'clip_model.transformer.resblocks.2.ln_2.bias', 'clip_model.transformer.resblocks.3.attn.in_proj_weight', 'clip_model.transformer.resblocks.3.attn.in_proj_bias', 'clip_model.transformer.resblocks.3.attn.out_proj.weight', 'clip_model.transformer.resblocks.3.attn.out_proj.bias', 'clip_model.transformer.resblocks.3.ln_1.weight', 'clip_model.transformer.resblocks.3.ln_1.bias', 'clip_model.transformer.resblocks.3.mlp.c_fc.weight', 'clip_model.transformer.resblocks.3.mlp.c_fc.bias', 'clip_model.transformer.resblocks.3.mlp.c_proj.weight', 'clip_model.transformer.resblocks.3.mlp.c_proj.bias', 'clip_model.transformer.resblocks.3.ln_2.weight', 'clip_model.transformer.resblocks.3.ln_2.bias', 'clip_model.transformer.resblocks.4.attn.in_proj_weight', 'clip_model.transformer.resblocks.4.attn.in_proj_bias', 'clip_model.transformer.resblocks.4.attn.out_proj.weight', 'clip_model.transformer.resblocks.4.attn.out_proj.bias', 'clip_model.transformer.resblocks.4.ln_1.weight', 'clip_model.transformer.resblocks.4.ln_1.bias', 'clip_model.transformer.resblocks.4.mlp.c_fc.weight', 'clip_model.transformer.resblocks.4.mlp.c_fc.bias', 'clip_model.transformer.resblocks.4.mlp.c_proj.weight', 'clip_model.transformer.resblocks.4.mlp.c_proj.bias', 'clip_model.transformer.resblocks.4.ln_2.weight', 'clip_model.transformer.resblocks.4.ln_2.bias', 'clip_model.transformer.resblocks.5.attn.in_proj_weight', 'clip_model.transformer.resblocks.5.attn.in_proj_bias', 'clip_model.transformer.resblocks.5.attn.out_proj.weight', 'clip_model.transformer.resblocks.5.attn.out_proj.bias', 'clip_model.transformer.resblocks.5.ln_1.weight', 'clip_model.transformer.resblocks.5.ln_1.bias', 'clip_model.transformer.resblocks.5.mlp.c_fc.weight', 'clip_model.transformer.resblocks.5.mlp.c_fc.bias', 'clip_model.transformer.resblocks.5.mlp.c_proj.weight', 'clip_model.transformer.resblocks.5.mlp.c_proj.bias', 'clip_model.transformer.resblocks.5.ln_2.weight', 'clip_model.transformer.resblocks.5.ln_2.bias', 'clip_model.transformer.resblocks.6.attn.in_proj_weight', 'clip_model.transformer.resblocks.6.attn.in_proj_bias', 'clip_model.transformer.resblocks.6.attn.out_proj.weight', 'clip_model.transformer.resblocks.6.attn.out_proj.bias', 'clip_model.transformer.resblocks.6.ln_1.weight', 'clip_model.transformer.resblocks.6.ln_1.bias', 'clip_model.transformer.resblocks.6.mlp.c_fc.weight', 'clip_model.transformer.resblocks.6.mlp.c_fc.bias', 'clip_model.transformer.resblocks.6.mlp.c_proj.weight', 'clip_model.transformer.resblocks.6.mlp.c_proj.bias', 'clip_model.transformer.resblocks.6.ln_2.weight', 'clip_model.transformer.resblocks.6.ln_2.bias', 'clip_model.transformer.resblocks.7.attn.in_proj_weight', 'clip_model.transformer.resblocks.7.attn.in_proj_bias', 'clip_model.transformer.resblocks.7.attn.out_proj.weight', 'clip_model.transformer.resblocks.7.attn.out_proj.bias', 'clip_model.transformer.resblocks.7.ln_1.weight', 'clip_model.transformer.resblocks.7.ln_1.bias', 'clip_model.transformer.resblocks.7.mlp.c_fc.weight', 'clip_model.transformer.resblocks.7.mlp.c_fc.bias', 'clip_model.transformer.resblocks.7.mlp.c_proj.weight', 'clip_model.transformer.resblocks.7.mlp.c_proj.bias', 'clip_model.transformer.resblocks.7.ln_2.weight', 'clip_model.transformer.resblocks.7.ln_2.bias', 'clip_model.transformer.resblocks.8.attn.in_proj_weight', 'clip_model.transformer.resblocks.8.attn.in_proj_bias', 'clip_model.transformer.resblocks.8.attn.out_proj.weight', 'clip_model.transformer.resblocks.8.attn.out_proj.bias', 'clip_model.transformer.resblocks.8.ln_1.weight', 'clip_model.transformer.resblocks.8.ln_1.bias', 'clip_model.transformer.resblocks.8.mlp.c_fc.weight', 'clip_model.transformer.resblocks.8.mlp.c_fc.bias', 'clip_model.transformer.resblocks.8.mlp.c_proj.weight', 'clip_model.transformer.resblocks.8.mlp.c_proj.bias', 'clip_model.transformer.resblocks.8.ln_2.weight', 'clip_model.transformer.resblocks.8.ln_2.bias', 'clip_model.transformer.resblocks.9.attn.in_proj_weight', 'clip_model.transformer.resblocks.9.attn.in_proj_bias', 'clip_model.transformer.resblocks.9.attn.out_proj.weight', 'clip_model.transformer.resblocks.9.attn.out_proj.bias', 'clip_model.transformer.resblocks.9.ln_1.weight', 'clip_model.transformer.resblocks.9.ln_1.bias', 'clip_model.transformer.resblocks.9.mlp.c_fc.weight', 'clip_model.transformer.resblocks.9.mlp.c_fc.bias', 'clip_model.transformer.resblocks.9.mlp.c_proj.weight', 'clip_model.transformer.resblocks.9.mlp.c_proj.bias', 'clip_model.transformer.resblocks.9.ln_2.weight', 'clip_model.transformer.resblocks.9.ln_2.bias', 'clip_model.transformer.resblocks.10.attn.in_proj_weight', 'clip_model.transformer.resblocks.10.attn.in_proj_bias', 'clip_model.transformer.resblocks.10.attn.out_proj.weight', 'clip_model.transformer.resblocks.10.attn.out_proj.bias', 'clip_model.transformer.resblocks.10.ln_1.weight', 'clip_model.transformer.resblocks.10.ln_1.bias', 'clip_model.transformer.resblocks.10.mlp.c_fc.weight', 'clip_model.transformer.resblocks.10.mlp.c_fc.bias', 'clip_model.transformer.resblocks.10.mlp.c_proj.weight', 'clip_model.transformer.resblocks.10.mlp.c_proj.bias', 'clip_model.transformer.resblocks.10.ln_2.weight', 'clip_model.transformer.resblocks.10.ln_2.bias', 'clip_model.transformer.resblocks.11.attn.in_proj_weight', 'clip_model.transformer.resblocks.11.attn.in_proj_bias', 'clip_model.transformer.resblocks.11.attn.out_proj.weight', 'clip_model.transformer.resblocks.11.attn.out_proj.bias', 'clip_model.transformer.resblocks.11.ln_1.weight', 'clip_model.transformer.resblocks.11.ln_1.bias', 'clip_model.transformer.resblocks.11.mlp.c_fc.weight', 'clip_model.transformer.resblocks.11.mlp.c_fc.bias', 'clip_model.transformer.resblocks.11.mlp.c_proj.weight', 'clip_model.transformer.resblocks.11.mlp.c_proj.bias', 'clip_model.transformer.resblocks.11.ln_2.weight', 'clip_model.transformer.resblocks.11.ln_2.bias', 'clip_model.token_embedding.weight', 'clip_model.ln_final.weight', 'clip_model.ln_final.bias'], unexpected_keys=[])
/nlp_group/xxx/anaconda3/envs/unipose/lib/python3.10/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
warnings.warn(
/nlp/xxx/anaconda3/envs/unipose/lib/python3.10/site-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
warnings.warn(
Inference succeeds.
savename: /nlp/xxx/X-Pose/demo05/pred.jpg

predictions: