FileNotFoundError: [Errno 2] No such file or directory: '/home/sroot/work/nncf_pytorch-master/nncf/install_type' about nncf

[vshampor]

@sroot0, no problem - in the future you may also post the solution here so that others may use it if the stumble upon this thread 😉

[vshampor]

@sroot0, could you resolve the issue on your own? This kind of error is most likely to happen if you did not install NNCF according to the instructions.

[sroot0]

@ sroot0，您可以自己解决问题吗？如果您没有按照说明安装NNCF，则很可能发生这种错误。

I solved this problem. I wanted to withdraw it, but I didn’t succeed. I’m sorry to bother you.

[sroot0]

@ sroot0，您可以自己解决问题吗？如果您没有按照说明安装NNCF，则很可能发生这种错误

@ sroot0，没问题-将来您也可以在此处发布解决方案，以便其他人可以在遇到此线程时使用它😉

“add the repository root folder to the PYTHONPATH environment variable ”This is how I solved it, but I have already installed the package and still need to add it manually. there is a new problem:
python main.py -m train --config configs/quantization/test.json --data /home/sroot/work/nncf_pytorch-master/examples/classification/cifar10 --log-dir=../../results/quantizat/ion/resnet18/ --cpu-only
batch_size : 64
checkpoint_save_dir : ../../results/quantizat/ion/resnet18/resnet18_imagenet_int8/2020-08-27__19-36-57
compression : {'algorithm': 'quantization', 'initializer': {'range': {'num_init_steps': 10}}}
config : configs/quantization/test.json
cpu_only : True
current_gpu : None
dataset : None
dataset_dir : /home/sroot/work/nncf_pytorch-master/examples/classification/cifar10
device : cpu
dist_backend : nccl
dist_url : tcp://127.0.0.1:8899
distributed : False
epochs : 2
execution_mode : cpu_only
gpu_id : None
hw_config_type : None
imshow_batch : False
input_info : {'sample_size': [1, 3, 32, 32]}
intermediate_checkpoints_path: ../../results/quantizat/ion/resnet18/resnet18_imagenet_int8/2020-08-27__19-36-57/intermediate_checkpoints
log_dir : ../../results/quantizat/ion/resnet18/resnet18_imagenet_int8/2020-08-27__19-36-57
metrics_dump : None
mode : train
model : resnet18
multiprocessing_distributed: False
name : resnet18_imagenet_int8
nncf_config : {'model': 'resnet18', 'pretrained': True, 'input_info': {'sample_size': [1, 3, 32, 32]}, 'num_classes': 10, 'batch_size': 64, 'epochs': 2, 'optimizer': {'type': 'Adam', 'base_lr': 1e-05, 'schedule_type': 'multistep', 'steps': [5]}, 'compression': {'algorithm': 'quantization', 'initializer': {'range': {'num_init_steps': 10}}}, 'log_dir': '../../results/quantizat/ion/resnet18/resnet18_imagenet_int8/2020-08-27__19-36-57'}
num_classes : 10
optimizer : {'type': 'Adam', 'base_lr': 1e-05, 'schedule_type': 'multistep', 'steps': [5]}
pretrained : True
print_freq : 10
print_step : False
rank : 0
resuming_checkpoint_path : None
save_freq : 5
seed : None
start_epoch : 0
tb : <tensorboardX.writer.SummaryWriter object at 0x7f895bdbcf10>
test_every_n_epochs : 1
to_onnx : None
weights : None
workers : 4
world_size : 1
Loading model: resnet18
Downloading: "https://download.pytorch.org/models/resnet18-5c106cde.pth" to /home/sroot/.cache/torch/hub/checkpoints/resnet18-5c106cde.pth
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 44.7M/44.7M [00:26<00:00, 1.79MB/s]

Traceback (most recent call last):
File "main.py", line 539, in
main(sys.argv[1:])
File "main.py", line 97, in main
start_worker(main_worker, config)
File "/home/sroot/work/nncf_pytorch-master/examples/common/execution.py", line 95, in start_worker
main_worker(current_gpu=None, config=config)
File "main.py", line 141, in main_worker
model = load_model(model_name,
File "/home/sroot/work/nncf_pytorch-master/examples/common/model_loader.py", line 36, in load_model
loaded_model = safe_thread_call(load_model_fn)
File "/home/sroot/work/nncf_pytorch-master/nncf/utils.py", line 282, in safe_thread_call
result = main_call_fn()
File "/home/sroot/anaconda3/envs/NNCF/lib/python3.8/site-packages/torchvision/models/resnet.py", line 240, in resnet18
return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
File "/home/sroot/anaconda3/envs/NNCF/lib/python3.8/site-packages/torchvision/models/resnet.py", line 228, in _resnet
model.load_state_dict(state_dict)
File "/home/sroot/anaconda3/envs/NNCF/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1044, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for ResNet:
size mismatch for fc.weight: copying a param with shape torch.Size([1000, 512]) from checkpoint, the shape in current model is torch.Size([10, 512]).
size mismatch for fc.bias: copying a param with shape torch.Size([1000]) from checkpoint, the shape in current model is torch.Size([10]).

[vshampor]

@sroot0 you are trying to load an ImageNet pre-trained checkpoint (1000 classes) into a CIFAR10-model (10 classes), that's why you are getting troubles with the last fully connected layer. Download the .pth manually, set "pretrained": False in the NNCF config .json file and pass an additional --weights *path_to_resnet18_imagenet_pth* to the main.py launch command line, if you want to fine-tune for CIFAR10 starting with ImageNet weights.

[sroot0]

@ sroot0，您正在尝试将ImageNet预先训练的检查点（1000个类）加载到CIFAR10模型（10个类）中，这就是为什么在最后一个完全连接的层上遇到麻烦的原因。如果要从ImageNet权重开始对CIFAR10进行微调，请手动下载.pth，"pretrained": False在NNCF config .json文件中进行设置，然后将其他内容传递--weights *path_to_resnet18_imagenet_pth*给main.py启动命令行。

thank you very much

[sroot0]

@ sroot0，您正在尝试将ImageNet预先训练的检查点（1000个类）加载到CIFAR10模型（10个类）中，这就是为什么在最后一个完全连接的层上遇到麻烦的原因。如果要从ImageNet权重开始对CIFAR10进行微调，请手动下载.pth，"pretrained": False在NNCF config .json文件中进行设置，然后将其他内容传递--weights *path_to_resnet18_imagenet_pth*给main.py启动命令行。

Sorry, I have been thinking about it for two days, but this problem has not been solved. Why do I load the imagenet checkpoint? How can the model load the cifar10 checkpoint? Or is it not possible to train from scratch without loading the checkpoint file?

[vshampor]

The ImageNet checkpoint is being loaded because this is what is available pretrained in Torchvision. I'm fairly sure that the CIFAR-10 pretrained checkpoint is not available there. ImageNet-pretrained feature extractors have a better chance to be fine-tuned to CIFAR-10 and keep the accuracy on the target dataset than vice versa. It is also possible to train from scratch without loading the checkpoint file - remove the "pretrained": true entry from the NNCF .json config file, and omit any --weights or --resume parameters from the command line.

[sroot0]

正在加载ImageNet检查点，因为这是Torchvision中预先训练的可用内容。我相当确定CIFAR-10预训练的检查点在那里不可用。反之，ImageNet预先训练的特征提取器有更好的机会被微调到CIFAR-10，并保持目标数据集的准确性。也可以从零开始训练而无需加载检查点文件- "pretrained": true从NNCF .json配置文件中删除条目，并从命令行省略任何--weights或--resume参数。

Thank you very much, I’m sorry to trouble you so much, I’ll give it a try,