Error when load state_dict to finetune on ucf101 from pretrained model r2p1d34_K_200ep.pth : Missing key(s) in state_dict

[nl8590687]

I am grateful for your works first. I get some problems when I load pretrained model file to R(2+1)D model. I used this command:

python main.py --root_path ../ --video_path ucf101_videos/jpg --annotation_path ucf101_01.json --result_path results --dataset ucf101 --n_classes 101 --n_pretrain_classes 700 --pretrain_path r2p1d34_K_200ep.pth --ft_begin_module fc --model resnet --model_depth 34 --batch_size 128 --n_threads 4 --checkpoint 5

You mentioned the same problem before in issue #15 and #32
But I have installed pytorch 1.8.1+cu102 and run it by GPU. I had downloaded pretrained model r2p1d34_K_200ep.pth
When I run it, I got:

Namespace(accimage=False, annotation_path=WindowsPath('../ucf101_01.json'), arch='resnet-34', batch_size=128, batchnorm_sync=False, begin_epoch=1, checkpoint=5, colorjitter=False, conv1_t_size=7, conv1_t_stride=1, dampening=0.0, dataset='ucf101', dist_url='tcp://127.0.0.1:23456', distributed=False, file_type='jpg', ft_begin_module='fc', inference=False, inference_batch_size=128, inference_crop='center', inference_no_average=False, inference_stride=16, inference_subset='val', input_type='rgb', learning_rate=0.1, lr_scheduler='multistep', manual_seed=1, mean=[0.4345, 0.4051, 0.3775], mean_dataset='kinetics', model='resnet', model_depth=34, momentum=0.9, multistep_milestones=[50, 100, 150], n_classes=700, n_epochs=200, n_finetune_classes=101, n_input_channels=3, n_pretrain_classes=700, n_threads=4, n_val_samples=3, nesterov=False, no_cuda=False, no_hflip=False, no_max_pool=False, no_mean_norm=False, no_std_norm=False, no_train=False, no_val=False, optimizer='sgd', output_topk=5, overwrite_milestones=False, plateau_patience=10, pretrain_path=WindowsPath('../r2p1d34_K_200ep.pth'), resnet_shortcut='B', resnet_widen_factor=1.0, resnext_cardinality=32, result_path=WindowsPath('../results'), resume_path=None, root_path=WindowsPath('..'), sample_duration=16, sample_size=112, sample_t_stride=1, std=[0.2768, 0.2713, 0.2737], tensorboard=False, train_crop='random', train_crop_min_ratio=0.75, train_crop_min_scale=0.25, train_t_crop='random', value_scale=1, video_path=WindowsPath('../ucf101_videos/jpg'), weight_decay=0.001, wide_resnet_k=2, world_size=-1)
loading pretrained model ..\r2p1d34_K_200ep.pth
Traceback (most recent call last):
  File "main.py", line 428, in <module>
    main_worker(-1, opt)
  File "main.py", line 343, in main_worker
    opt.n_finetune_classes)
  File "C:\Users\<username>\Desktop\3D-ResNets-PyTorch\model.py", line 102, in load_pretrained_model
    model.load_state_dict(pretrain['state_dict'])
  File "C:\Users\<username>\anaconda3\envs\py37_pytorch\lib\site-packages\torch\nn\modules\module.py", line 1224, in load_state_dict
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for ResNet:
        Missing key(s) in state_dict: "conv1.weight", "bn1.weight", "bn1.bias", "bn1.running_mean", "bn1.running_var", "layer1.0.conv1.weight", "layer1.0.bn1.weight", "layer1.0.bn1.bias", "layer1.0.bn1.running_mean", "layer1.0.bn1.running_var", "layer1.0.conv2.weight", "layer1.0.bn2.weight", "layer1.0.bn2.bias", "layer1.0.bn2.running_mean", "layer1.0.bn2.running_var", "layer1.1.conv1.weight", "layer1.1.bn1.weight", "layer1.1.bn1.bias", "layer1.1.bn1.running_mean", "layer1.1.bn1.running_var", "layer1.1.conv2.weight", "layer1.1.bn2.weight", "layer1.1.bn2.bias", "layer1.1.bn2.running_mean", "layer1.1.bn2.running_var", "layer1.2.conv1.weight", "layer1.2.bn1.weight", "layer1.2.bn1.bias", "layer1.2.bn1.running_mean", "layer1.2.bn1.running_var", "layer1.2.conv2.weight", "layer1.2.bn2.weight", "layer1.2.bn2.bias", "layer1.2.bn2.running_mean", "layer1.2.bn2.running_var", "layer2.0.conv1.weight" 
...

Can you tell me what should I do? Thanks!

[NeighborhoodCoding]

How can I load_state_dict?

[XiangL-Xr]

model.load_state_dict(pretrain['state_dict'], strict=False)