hi
I tried to predict complex structure, and I met this problem
ValueError: Could not find path to the "hhblits" binary. Make sure it is installed on your system. Starting prediction... start to load params multimer.unifold.pt Traceback (most recent call last): File "/zhangyudi/Uni-Fold/unifold/inference.py", line 266, in <module> main(args) File "/zhangyudi/Uni-Fold/unifold/inference.py", line 91, in main model.load_state_dict(state_dict) File "/opt/anaconda3/envs/unifold/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for AlphaFold: Missing key(s) in state_dict: "template_pair_embedder.linear.weight", "template_pair_embedder.linear.bias", "template_pointwise_att.mha.linear_q.weight", "template_pointwise_att.mha.linear_k.weight", "template_pointwise_att.mha.linear_v.weight", "template_pointwise_att.mha.linear_o.weight", "template_pointwise_att.mha.linear_o.bias", "structure_module.ipa.linear_q.bias", "structure_module.ipa.linear_kv.weight", "structure_module.ipa.linear_kv.bias", "structure_module.ipa.linear_kv_points.weight", "structure_module.ipa.linear_kv_points.bias". Unexpected key(s) in state_dict: "template_proj.output_linear.weight", "template_proj.output_linear.bias", "template_pair_embedder.z_layer_norm.weight", "template_pair_embedder.z_layer_norm.bias", "template_pair_embedder.z_linear.weight", "template_pair_embedder.z_linear.bias", "template_pair_embedder.linear.0.weight", "template_pair_embedder.linear.0.bias", "template_pair_embedder.linear.1.weight", "template_pair_embedder.linear.1.bias", "template_pair_embedder.linear.2.weight", "template_pair_embedder.linear.2.bias", "template_pair_embedder.linear.3.weight", "template_pair_embedder.linear.3.bias", "template_pair_embedder.linear.4.weight", "template_pair_embedder.linear.4.bias", "template_pair_embedder.linear.5.weight", "template_pair_embedder.linear.5.bias", "template_pair_embedder.linear.6.weight", "template_pair_embedder.linear.6.bias", "template_pair_embedder.linear.7.weight", "template_pair_embedder.linear.7.bias", "structure_module.ipa.linear_k.weight", "structure_module.ipa.linear_v.weight", "structure_module.ipa.linear_k_points.weight", "structure_module.ipa.linear_k_points.bias", "structure_module.ipa.linear_v_points.weight", "structure_module.ipa.linear_v_points.bias", "aux_heads.pae.linear.weight", "aux_heads.pae.linear.bias". size mismatch for input_embedder.linear_tf_z_i.weight: copying a param with shape torch.Size([128, 21]) from checkpoint, the shape in current model is torch.Size([128, 22]). size mismatch for input_embedder.linear_tf_z_j.weight: copying a param with shape torch.Size([128, 21]) from checkpoint, the shape in current model is torch.Size([128, 22]). size mismatch for input_embedder.linear_tf_m.weight: copying a param with shape torch.Size([256, 21]) from checkpoint, the shape in current model is torch.Size([256, 22]). size mismatch for input_embedder.linear_relpos.weight: copying a param with shape torch.Size([128, 73]) from checkpoint, the shape in current model is torch.Size([128, 65]). size mismatch for template_angle_embedder.linear_1.weight: copying a param with shape torch.Size([256, 34]) from checkpoint, the shape in current model is torch.Size([256, 57]). size mismatch for aux_heads.masked_msa.linear.weight: copying a param with shape torch.Size([22, 256]) from checkpoint, the shape in current model is torch.Size([23, 256]). size mismatch for aux_heads.masked_msa.linear.bias: copying a param with shape torch.Size([22]) from checkpoint, the shape in current model is torch.Size([23]).
hi I tried to predict complex structure, and I met this problem
ValueError: Could not find path to the "hhblits" binary. Make sure it is installed on your system. Starting prediction... start to load params multimer.unifold.pt Traceback (most recent call last): File "/zhangyudi/Uni-Fold/unifold/inference.py", line 266, in <module> main(args) File "/zhangyudi/Uni-Fold/unifold/inference.py", line 91, in main model.load_state_dict(state_dict) File "/opt/anaconda3/envs/unifold/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for AlphaFold: Missing key(s) in state_dict: "template_pair_embedder.linear.weight", "template_pair_embedder.linear.bias", "template_pointwise_att.mha.linear_q.weight", "template_pointwise_att.mha.linear_k.weight", "template_pointwise_att.mha.linear_v.weight", "template_pointwise_att.mha.linear_o.weight", "template_pointwise_att.mha.linear_o.bias", "structure_module.ipa.linear_q.bias", "structure_module.ipa.linear_kv.weight", "structure_module.ipa.linear_kv.bias", "structure_module.ipa.linear_kv_points.weight", "structure_module.ipa.linear_kv_points.bias". Unexpected key(s) in state_dict: "template_proj.output_linear.weight", "template_proj.output_linear.bias", "template_pair_embedder.z_layer_norm.weight", "template_pair_embedder.z_layer_norm.bias", "template_pair_embedder.z_linear.weight", "template_pair_embedder.z_linear.bias", "template_pair_embedder.linear.0.weight", "template_pair_embedder.linear.0.bias", "template_pair_embedder.linear.1.weight", "template_pair_embedder.linear.1.bias", "template_pair_embedder.linear.2.weight", "template_pair_embedder.linear.2.bias", "template_pair_embedder.linear.3.weight", "template_pair_embedder.linear.3.bias", "template_pair_embedder.linear.4.weight", "template_pair_embedder.linear.4.bias", "template_pair_embedder.linear.5.weight", "template_pair_embedder.linear.5.bias", "template_pair_embedder.linear.6.weight", "template_pair_embedder.linear.6.bias", "template_pair_embedder.linear.7.weight", "template_pair_embedder.linear.7.bias", "structure_module.ipa.linear_k.weight", "structure_module.ipa.linear_v.weight", "structure_module.ipa.linear_k_points.weight", "structure_module.ipa.linear_k_points.bias", "structure_module.ipa.linear_v_points.weight", "structure_module.ipa.linear_v_points.bias", "aux_heads.pae.linear.weight", "aux_heads.pae.linear.bias". size mismatch for input_embedder.linear_tf_z_i.weight: copying a param with shape torch.Size([128, 21]) from checkpoint, the shape in current model is torch.Size([128, 22]). size mismatch for input_embedder.linear_tf_z_j.weight: copying a param with shape torch.Size([128, 21]) from checkpoint, the shape in current model is torch.Size([128, 22]). size mismatch for input_embedder.linear_tf_m.weight: copying a param with shape torch.Size([256, 21]) from checkpoint, the shape in current model is torch.Size([256, 22]). size mismatch for input_embedder.linear_relpos.weight: copying a param with shape torch.Size([128, 73]) from checkpoint, the shape in current model is torch.Size([128, 65]). size mismatch for template_angle_embedder.linear_1.weight: copying a param with shape torch.Size([256, 34]) from checkpoint, the shape in current model is torch.Size([256, 57]). size mismatch for aux_heads.masked_msa.linear.weight: copying a param with shape torch.Size([22, 256]) from checkpoint, the shape in current model is torch.Size([23, 256]). size mismatch for aux_heads.masked_msa.linear.bias: copying a param with shape torch.Size([22]) from checkpoint, the shape in current model is torch.Size([23]).can you give me some advice?