evaluate cambridge error

[luocha0107]

(gs_model) root@autodl-container-f34d45a126-5b9fd9ad:~/data_user/ysl/DeViLoc# python evaluate.py configs/cambridge.yml --ckpt_path pretrained/deviloc_weights.ckpt ERROR:albumentations.check_version:Error fetching version info Traceback (most recent call last): File "/root/miniconda3/envs/gs_model/lib/python3.9/site-packages/albumentations/check_version.py", line 29, in fetch_version_info with opener.open(url, timeout=2) as response: File "/root/miniconda3/envs/gs_model/lib/python3.9/urllib/request.py", line 517, in open response = self._open(req, data) File "/root/miniconda3/envs/gs_model/lib/python3.9/urllib/request.py", line 534, in _open result = self._call_chain(self.handle_open, protocol, protocol + File "/root/miniconda3/envs/gs_model/lib/python3.9/urllib/request.py", line 494, in _call_chain result = func(*args) File "/root/miniconda3/envs/gs_model/lib/python3.9/urllib/request.py", line 1389, in https_open return self.do_open(http.client.HTTPSConnection, req, File "/root/miniconda3/envs/gs_model/lib/python3.9/urllib/request.py", line 1350, in do_open r = h.getresponse() File "/root/miniconda3/envs/gs_model/lib/python3.9/http/client.py", line 1377, in getresponse response.begin() File "/root/miniconda3/envs/gs_model/lib/python3.9/http/client.py", line 339, in begin self.headers = self.msg = parse_headers(self.fp) File "/root/miniconda3/envs/gs_model/lib/python3.9/http/client.py", line 236, in parse_headers headers = _read_headers(fp) File "/root/miniconda3/envs/gs_model/lib/python3.9/http/client.py", line 216, in _read_headers line = fp.readline(_MAXLINE + 1) File "/root/miniconda3/envs/gs_model/lib/python3.9/socket.py", line 704, in readinto return self._sock.recv_into(b) File "/root/miniconda3/envs/gs_model/lib/python3.9/ssl.py", line 1275, in recv_into return self.read(nbytes, buffer) File "/root/miniconda3/envs/gs_model/lib/python3.9/ssl.py", line 1133, in read return self._sslobj.read(len, buffer) socket.timeout: The read operation timed out {'batch_size': 1, 'ckpt_path': 'pretrained/deviloc_weights.ckpt', 'covis_clustering': False, 'dataset': 'aachen', 'main_cfg_path': 'configs/cambridge.yml', 'num_workers': 4, 'out_dir': 'deviloc_outputs', 'out_file': 'aachen_v11_eval.txt'} 2024-08-17 16:31:26.794 | INFO | main::220 - Args and config initialized! 2024-08-17 16:31:26.794 | INFO | main::221 - Do covisibily clustering: False Traceback (most recent call last): File "/root/data_user/ysl/DeViLoc/evaluate.py", line 227, in model = Dense2D3DMatcher(config=config["model"]) File "/root/data_user/ysl/DeViLoc/deviloc/models/model.py", line 26, in init self.matcher.load_state_dict(pretrained_matcher["state_dict"]) File "/root/data_user/ysl/DeViLoc/third_party/feat_matcher/TopicFM/src/models/topic_fm.py", line 90, in load_state_dict return super().load_state_dict(state_dict, *args, **kwargs) File "/root/miniconda3/envs/gs_model/lib/python3.9/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 TopicFM: Missing key(s) in state_dict: "backbone.conv1.weight", "backbone.layer1.dwconv.weight", "backbone.layer1.dwconv.bias", "backbone.layer1.pwconv1.weight", "backbone.layer1.pwconv1.bias", "backbone.layer1.norm2.gamma", "backbone.layer1.norm2.beta", "backbone.layer1.pwconv2.weight", "backbone.layer1.pwconv2.bias", "backbone.layer2.0.0.weight", "backbone.layer2.1.dwconv.weight", "backbone.layer2.1.dwconv.bias", "backbone.layer2.1.pwconv1.weight", "backbone.layer2.1.pwconv1.bias", "backbone.layer2.1.norm2.gamma", "backbone.layer2.1.norm2.beta", "backbone.layer2.1.pwconv2.weight", "backbone.layer2.1.pwconv2.bias", "backbone.layer3.0.0.weight", "backbone.layer3.1.dwconv.weight", "backbone.layer3.1.dwconv.bias", "backbone.layer3.1.pwconv1.weight", "backbone.layer3.1.pwconv1.bias", "backbone.layer3.1.norm2.gamma", "backbone.layer3.1.norm2.beta", "backbone.layer3.1.pwconv2.weight", "backbone.layer3.1.pwconv2.bias", "backbone.layer4.0.0.weight", "backbone.layer4.1.dwconv.weight", "backbone.layer4.1.dwconv.bias", "backbone.layer4.1.pwconv1.weight", "backbone.layer4.1.pwconv1.bias", "backbone.layer4.1.norm2.gamma", "backbone.layer4.1.norm2.beta", "backbone.layer4.1.pwconv2.weight", "backbone.layer4.1.pwconv2.bias", "backbone.layer3_outconv2.0.dwconv.weight", "backbone.layer3_outconv2.0.dwconv.bias", "backbone.layer3_outconv2.0.pwconv1.weight", "backbone.layer3_outconv2.0.pwconv1.bias", "backbone.layer3_outconv2.0.norm2.gamma", "backbone.layer3_outconv2.0.norm2.beta", "backbone.layer3_outconv2.0.pwconv2.weight", "backbone.layer3_outconv2.0.pwconv2.bias", "backbone.layer3_outconv2.1.dwconv.weight", "backbone.layer3_outconv2.1.dwconv.bias", "backbone.layer3_outconv2.1.pwconv1.weight", "backbone.layer3_outconv2.1.pwconv1.bias", "backbone.layer3_outconv2.1.norm2.gamma", "backbone.layer3_outconv2.1.norm2.beta", "backbone.layer3_outconv2.1.pwconv2.weight", "backbone.layer3_outconv2.1.pwconv2.bias", "backbone.layer2_outconv2.0.dwconv.weight", "backbone.layer2_outconv2.0.dwconv.bias", "backbone.layer2_outconv2.0.pwconv1.weight", "backbone.layer2_outconv2.0.pwconv1.bias", "backbone.layer2_outconv2.0.norm2.gamma", "backbone.layer2_outconv2.0.norm2.beta", "backbone.layer2_outconv2.0.pwconv2.weight", "backbone.layer2_outconv2.0.pwconv2.bias", "backbone.layer2_outconv2.1.dwconv.weight", "backbone.layer2_outconv2.1.dwconv.bias", "backbone.layer2_outconv2.1.pwconv1.weight", "backbone.layer2_outconv2.1.pwconv1.bias", "backbone.layer2_outconv2.1.norm2.gamma", "backbone.layer2_outconv2.1.norm2.beta", "backbone.layer2_outconv2.1.pwconv2.weight", "backbone.layer2_outconv2.1.pwconv2.bias", "backbone.layer1_outconv2.0.dwconv.weight", "backbone.layer1_outconv2.0.dwconv.bias", "backbone.layer1_outconv2.0.pwconv1.weight", "backbone.layer1_outconv2.0.pwconv1.bias", "backbone.layer1_outconv2.0.norm2.gamma", "backbone.layer1_outconv2.0.norm2.beta", "backbone.layer1_outconv2.0.pwconv2.weight", "backbone.layer1_outconv2.0.pwconv2.bias", "backbone.layer1_outconv2.1.dwconv.weight", "backbone.layer1_outconv2.1.dwconv.bias", "backbone.layer1_outconv2.1.pwconv1.weight", "backbone.layer1_outconv2.1.pwconv1.bias", "backbone.layer1_outconv2.1.norm2.gamma", "backbone.layer1_outconv2.1.norm2.beta", "backbone.layer1_outconv2.1.pwconv2.weight", "backbone.layer1_outconv2.1.pwconv2.bias", "fine_net.encoder_layers.2.mlp1.0.weight", "fine_net.encoder_layers.2.mlp1.0.bias", "fine_net.encoder_layers.2.mlp1.2.weight", "fine_net.encoder_layers.2.mlp1.2.bias", "fine_net.encoder_layers.2.mlp2.0.weight", "fine_net.encoder_layers.2.mlp2.0.bias", "fine_net.encoder_layers.2.mlp2.2.weight", "fine_net.encoder_layers.2.mlp2.2.bias", "fine_net.encoder_layers.2.norm1.weight", "fine_net.encoder_layers.2.norm1.bias", "fine_net.encoder_layers.2.norm2.weight", "fine_net.encoder_layers.2.norm2.bias", "fine_net.encoder_layers.3.mlp1.0.weight", "fine_net.encoder_layers.3.mlp1.0.bias", "fine_net.encoder_layers.3.mlp1.2.weight", "fine_net.encoder_layers.3.mlp1.2.bias", "fine_net.encoder_layers.3.mlp2.0.weight", "fine_net.encoder_layers.3.mlp2.0.bias", "fine_net.encoder_layers.3.mlp2.2.weight", "fine_net.encoder_layers.3.mlp2.2.bias", "fine_net.encoder_layers.3.norm1.weight", "fine_net.encoder_layers.3.norm1.bias", "fine_net.encoder_layers.3.norm2.weight", "fine_net.encoder_layers.3.norm2.bias". Unexpected key(s) in state_dict: "backbone.layer0.conv.weight", "backbone.layer0.norm.weight", "backbone.layer0.norm.bias", "backbone.layer0.norm.running_mean", "backbone.layer0.norm.running_var", "backbone.layer0.norm.num_batches_tracked", "backbone.layer1.0.conv.weight", "backbone.layer1.0.norm.weight", "backbone.layer1.0.norm.bias", "backbone.layer1.0.norm.running_mean", "backbone.layer1.0.norm.running_var", "backbone.layer1.0.norm.num_batches_tracked", "backbone.layer1.1.conv.weight", "backbone.layer1.1.norm.weight", "backbone.layer1.1.norm.bias", "backbone.layer1.1.norm.running_mean", "backbone.layer1.1.norm.running_var", "backbone.layer1.1.norm.num_batches_tracked", "backbone.layer2.0.conv.weight", "backbone.layer2.0.norm.weight", "backbone.layer2.0.norm.bias", "backbone.layer2.0.norm.running_mean", "backbone.layer2.0.norm.running_var", "backbone.layer2.0.norm.num_batches_tracked", "backbone.layer2.1.conv.weight", "backbone.layer2.1.norm.weight", "backbone.layer2.1.norm.bias", "backbone.layer2.1.norm.running_mean", "backbone.layer2.1.norm.running_var", "backbone.layer2.1.norm.num_batches_tracked", "backbone.layer3.0.conv.weight", "backbone.layer3.0.norm.weight", "backbone.layer3.0.norm.bias", "backbone.layer3.0.norm.running_mean", "backbone.layer3.0.norm.running_var", "backbone.layer3.0.norm.num_batches_tracked", "backbone.layer3.1.conv.weight", "backbone.layer3.1.norm.weight", "backbone.layer3.1.norm.bias", "backbone.layer3.1.norm.running_mean", "backbone.layer3.1.norm.running_var", "backbone.layer3.1.norm.num_batches_tracked", "backbone.layer4.0.conv.weight", "backbone.layer4.0.norm.weight", "backbone.layer4.0.norm.bias", "backbone.layer4.0.norm.running_mean", "backbone.layer4.0.norm.running_var", "backbone.layer4.0.norm.num_batches_tracked", "backbone.layer4.1.conv.weight", "backbone.layer4.1.norm.weight", "backbone.layer4.1.norm.bias", "backbone.layer4.1.norm.running_mean", "backbone.layer4.1.norm.running_var", "backbone.layer4.1.norm.num_batches_tracked", "backbone.layer3_outconv2.0.conv.weight", "backbone.layer3_outconv2.0.norm.weight", "backbone.layer3_outconv2.0.norm.bias", "backbone.layer3_outconv2.0.norm.running_mean", "backbone.layer3_outconv2.0.norm.running_var", "backbone.layer3_outconv2.0.norm.num_batches_tracked", "backbone.layer3_outconv2.1.weight", "backbone.layer2_outconv2.0.conv.weight", "backbone.layer2_outconv2.0.norm.weight", "backbone.layer2_outconv2.0.norm.bias", "backbone.layer2_outconv2.0.norm.running_mean", "backbone.layer2_outconv2.0.norm.running_var", "backbone.layer2_outconv2.0.norm.num_batches_tracked", "backbone.layer2_outconv2.1.weight", "backbone.layer1_outconv2.0.conv.weight", "backbone.layer1_outconv2.0.norm.weight", "backbone.layer1_outconv2.0.norm.bias", "backbone.layer1_outconv2.0.norm.running_mean", "backbone.layer1_outconv2.0.norm.running_var", "backbone.layer1_outconv2.0.norm.num_batches_tracked", "backbone.layer1_outconv2.1.weight". size mismatch for backbone.layer3_outconv.weight: copying a param with shape torch.Size([384, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 384, 1, 1]). size mismatch for backbone.layer2_outconv.weight: copying a param with shape torch.Size([256, 192, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]). size mismatch for backbone.layer1_outconv.weight: copying a param with shape torch.Size([192, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([96, 128, 1, 1]). size mismatch for backbone.norm_outlayer1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for backbone.norm_outlayer1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.encoder_layers.0.mlp2.0.weight: copying a param with shape torch.Size([128, 128]) from checkpoint, the shape in current model is torch.Size([96, 96]). size mismatch for fine_net.encoder_layers.0.mlp2.0.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.encoder_layers.0.mlp2.2.weight: copying a param with shape torch.Size([128, 128]) from checkpoint, the shape in current model is torch.Size([96, 96]). size mismatch for fine_net.encoder_layers.0.mlp2.2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.encoder_layers.0.norm1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.encoder_layers.0.norm1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.encoder_layers.0.norm2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.encoder_layers.0.norm2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.encoder_layers.1.mlp2.0.weight: copying a param with shape torch.Size([128, 128]) from checkpoint, the shape in current model is torch.Size([96, 96]). size mismatch for fine_net.encoder_layers.1.mlp2.0.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.encoder_layers.1.mlp2.2.weight: copying a param with shape torch.Size([128, 128]) from checkpoint, the shape in current model is torch.Size([96, 96]). size mismatch for fine_net.encoder_layers.1.mlp2.2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.encoder_layers.1.norm1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.encoder_layers.1.norm1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.encoder_layers.1.norm2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.encoder_layers.1.norm2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.detector.0.mlp2.0.weight: copying a param with shape torch.Size([128, 128]) from checkpoint, the shape in current model is torch.Size([96, 96]). size mismatch for fine_net.detector.0.mlp2.0.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.detector.0.mlp2.2.weight: copying a param with shape torch.Size([128, 128]) from checkpoint, the shape in current model is torch.Size([96, 96]). size mismatch for fine_net.detector.0.mlp2.2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.detector.0.norm1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.detector.0.norm1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.detector.0.norm2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.detector.0.norm2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]). size mismatch for fine_net.detector.1.weight: copying a param with shape torch.Size([1, 128]) from checkpoint, the shape in current model is torch.Size([1, 96]).

i test several times,but the problem seems to arise in the weight, network size mismatch