log.txt

python main.py --phase test --n_blocks 28 --block res  --pretrained_model ../../pretrained_models/modelnet_cls/ModelNet40-dense-edge-n14-C64-k9-drop0.5-lr0.001_B32_best_model.pth --data_dir /media/root/WDdata/dataset/modelnet40_dataset
/root2/anaconda3/envs/deepgcn/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/root2/anaconda3/envs/deepgcn/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/root2/anaconda3/envs/deepgcn/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/root2/anaconda3/envs/deepgcn/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/root2/anaconda3/envs/deepgcn/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/root2/anaconda3/envs/deepgcn/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
/root2/anaconda3/envs/deepgcn/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/root2/anaconda3/envs/deepgcn/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/root2/anaconda3/envs/deepgcn/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/root2/anaconda3/envs/deepgcn/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/root2/anaconda3/envs/deepgcn/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/root2/anaconda3/envs/deepgcn/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
2020-11-13 16:56:52,988 saving log, checkpoint and back up code in folder: log
2020-11-13 16:56:52,988 ==========       args      =============
2020-11-13 16:56:52,988 exp_name:DeepGCN
2020-11-13 16:56:52,988 root_dir:log
2020-11-13 16:56:52,989 data_dir:/media/root/WDdata/dataset/modelnet40_dataset
2020-11-13 16:56:52,989 num_points:1024
2020-11-13 16:56:52,989 augment:True
2020-11-13 16:56:52,989 phase:test
2020-11-13 16:56:52,989 use_cpu:False
2020-11-13 16:56:52,989 batch_size:8
2020-11-13 16:56:52,989 epochs:400
2020-11-13 16:56:52,989 use_sgd:True
2020-11-13 16:56:52,989 weight_decay:0.0001
2020-11-13 16:56:52,989 lr:0.001
2020-11-13 16:56:52,989 seed:1
2020-11-13 16:56:52,989 multi_gpus:False
2020-11-13 16:56:52,989 test_batch_size:8
2020-11-13 16:56:52,989 pretrained_model:../../pretrained_models/modelnet_cls/ModelNet40-dense-edge-n14-C64-k9-drop0.5-lr0.001_B32_best_model.pth
2020-11-13 16:56:52,989 k:9
2020-11-13 16:56:52,989 block:res
2020-11-13 16:56:52,989 conv:edge
2020-11-13 16:56:52,989 act:relu
2020-11-13 16:56:52,989 norm:batch
2020-11-13 16:56:52,989 bias:True
2020-11-13 16:56:52,989 n_blocks:28
2020-11-13 16:56:52,989 n_filters:64
2020-11-13 16:56:52,989 in_channels:3
2020-11-13 16:56:52,989 out_channels:40
2020-11-13 16:56:52,989 emb_dims:1024
2020-11-13 16:56:52,990 dropout:0.5
2020-11-13 16:56:52,990 use_dilation:True
2020-11-13 16:56:52,990 epsilon:0.2
2020-11-13 16:56:52,990 stochastic:True
2020-11-13 16:56:52,990 device:cuda
2020-11-13 16:56:52,990 exp_dir:log
2020-11-13 16:56:52,990 loglevel:info
2020-11-13 16:56:52,990 ==========     args END    =============
2020-11-13 16:56:52,990 

2020-11-13 16:56:52,990 ===> Phase is test.
2020-11-13 16:56:53,042 ===> Creating dataloader ...
2020-11-13 16:56:56,547 ===> Loading ModelNet40 from /media/root/WDdata/dataset/modelnet40_dataset. number of classes equal to 40
2020-11-13 16:56:56,547 ===> Loading the network ...
2020-11-13 16:57:06,063 ===> loading pre-trained ...
2020-11-13 16:57:06,913 ===> Loading checkpoint '../../pretrained_models/modelnet_cls/ModelNet40-dense-edge-n14-C64-k9-drop0.5-lr0.001_B32_best_model.pth'
Traceback (most recent call last):
  File "main.py", line 170, in <module>
    model, opt.best_value, opt.epoch = load_pretrained_models(model, opt.pretrained_model, opt.phase)
  File "/data/code13/deep_gcns_torch/examples/modelnet_cls/../../utils/ckpt_util.py", line 67, in load_pretrained_models
    model.load_state_dict(ckpt_model_state_dict)
  File "/root2/anaconda3/envs/deepgcn/lib/python3.7/site-packages/torch/nn/modules/module.py", line 830, in load_state_dict
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for DeepGCN:
	Missing key(s) in state_dict: "backbone.13.body.gconv.nn.0.weight", "backbone.13.body.gconv.nn.0.bias", "backbone.13.body.gconv.nn.2.weight", "backbone.13.body.gconv.nn.2.bias", "backbone.13.body.gconv.nn.2.running_mean", "backbone.13.body.gconv.nn.2.running_var", "backbone.14.body.gconv.nn.0.weight", "backbone.14.body.gconv.nn.0.bias", "backbone.14.body.gconv.nn.2.weight", "backbone.14.body.gconv.nn.2.bias", "backbone.14.body.gconv.nn.2.running_mean", "backbone.14.body.gconv.nn.2.running_var", "backbone.15.body.gconv.nn.0.weight", "backbone.15.body.gconv.nn.0.bias", "backbone.15.body.gconv.nn.2.weight", "backbone.15.body.gconv.nn.2.bias", "backbone.15.body.gconv.nn.2.running_mean", "backbone.15.body.gconv.nn.2.running_var", "backbone.16.body.gconv.nn.0.weight", "backbone.16.body.gconv.nn.0.bias", "backbone.16.body.gconv.nn.2.weight", "backbone.16.body.gconv.nn.2.bias", "backbone.16.body.gconv.nn.2.running_mean", "backbone.16.body.gconv.nn.2.running_var", "backbone.17.body.gconv.nn.0.weight", "backbone.17.body.gconv.nn.0.bias", "backbone.17.body.gconv.nn.2.weight", "backbone.17.body.gconv.nn.2.bias", "backbone.17.body.gconv.nn.2.running_mean", "backbone.17.body.gconv.nn.2.running_var", "backbone.18.body.gconv.nn.0.weight", "backbone.18.body.gconv.nn.0.bias", "backbone.18.body.gconv.nn.2.weight", "backbone.18.body.gconv.nn.2.bias", "backbone.18.body.gconv.nn.2.running_mean", "backbone.18.body.gconv.nn.2.running_var", "backbone.19.body.gconv.nn.0.weight", "backbone.19.body.gconv.nn.0.bias", "backbone.19.body.gconv.nn.2.weight", "backbone.19.body.gconv.nn.2.bias", "backbone.19.body.gconv.nn.2.running_mean", "backbone.19.body.gconv.nn.2.running_var", "backbone.20.body.gconv.nn.0.weight", "backbone.20.body.gconv.nn.0.bias", "backbone.20.body.gconv.nn.2.weight", "backbone.20.body.gconv.nn.2.bias", "backbone.20.body.gconv.nn.2.running_mean", "backbone.20.body.gconv.nn.2.running_var", "backbone.21.body.gconv.nn.0.weight", "backbone.21.body.gconv.nn.0.bias", "backbone.21.body.gconv.nn.2.weight", "backbone.21.body.gconv.nn.2.bias", "backbone.21.body.gconv.nn.2.running_mean", "backbone.21.body.gconv.nn.2.running_var", "backbone.22.body.gconv.nn.0.weight", "backbone.22.body.gconv.nn.0.bias", "backbone.22.body.gconv.nn.2.weight", "backbone.22.body.gconv.nn.2.bias", "backbone.22.body.gconv.nn.2.running_mean", "backbone.22.body.gconv.nn.2.running_var", "backbone.23.body.gconv.nn.0.weight", "backbone.23.body.gconv.nn.0.bias", "backbone.23.body.gconv.nn.2.weight", "backbone.23.body.gconv.nn.2.bias", "backbone.23.body.gconv.nn.2.running_mean", "backbone.23.body.gconv.nn.2.running_var", "backbone.24.body.gconv.nn.0.weight", "backbone.24.body.gconv.nn.0.bias", "backbone.24.body.gconv.nn.2.weight", "backbone.24.body.gconv.nn.2.bias", "backbone.24.body.gconv.nn.2.running_mean", "backbone.24.body.gconv.nn.2.running_var", "backbone.25.body.gconv.nn.0.weight", "backbone.25.body.gconv.nn.0.bias", "backbone.25.body.gconv.nn.2.weight", "backbone.25.body.gconv.nn.2.bias", "backbone.25.body.gconv.nn.2.running_mean", "backbone.25.body.gconv.nn.2.running_var", "backbone.26.body.gconv.nn.0.weight", "backbone.26.body.gconv.nn.0.bias", "backbone.26.body.gconv.nn.2.weight", "backbone.26.body.gconv.nn.2.bias", "backbone.26.body.gconv.nn.2.running_mean", "backbone.26.body.gconv.nn.2.running_var". 
	size mismatch for backbone.1.body.gconv.nn.0.weight: copying a param with shape torch.Size([64, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 128, 1, 1]).
	size mismatch for backbone.2.body.gconv.nn.0.weight: copying a param with shape torch.Size([64, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 128, 1, 1]).
	size mismatch for backbone.3.body.gconv.nn.0.weight: copying a param with shape torch.Size([64, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 128, 1, 1]).
	size mismatch for backbone.4.body.gconv.nn.0.weight: copying a param with shape torch.Size([64, 640, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 128, 1, 1]).
	size mismatch for backbone.5.body.gconv.nn.0.weight: copying a param with shape torch.Size([64, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 128, 1, 1]).
	size mismatch for backbone.6.body.gconv.nn.0.weight: copying a param with shape torch.Size([64, 896, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 128, 1, 1]).
	size mismatch for backbone.7.body.gconv.nn.0.weight: copying a param with shape torch.Size([64, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 128, 1, 1]).
	size mismatch for backbone.8.body.gconv.nn.0.weight: copying a param with shape torch.Size([64, 1152, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 128, 1, 1]).
	size mismatch for backbone.9.body.gconv.nn.0.weight: copying a param with shape torch.Size([64, 1280, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 128, 1, 1]).
	size mismatch for backbone.10.body.gconv.nn.0.weight: copying a param with shape torch.Size([64, 1408, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 128, 1, 1]).
	size mismatch for backbone.11.body.gconv.nn.0.weight: copying a param with shape torch.Size([64, 1536, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 128, 1, 1]).
	size mismatch for backbone.12.body.gconv.nn.0.weight: copying a param with shape torch.Size([64, 1664, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 128, 1, 1]).
	size mismatch for fusion_block.0.weight: copying a param with shape torch.Size([1024, 6720, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 1792, 1, 1]).
(deepgcn) root@milton-ThinkCentre-M93p:/data/code13/deep_gcns_torch/examples/modelnet_cls#