whatashot / danet Goto Github PK

Hi,

May I know how to get the running example in paper such as health assessment for illustrating our insights.

Hi I have a error when inference. May you help check how to solve it? Thank you

I use the best model
Traceback (most recent call last):
File "predict.py", line 58, in
clf.load_model(filepath, input_dim=X_test.shape[1], output_dim=output_dim, n_gpu=n_gpu)
File "/home/DANet/abstract_model.py", line 223, in load_model
self.layer, self.virtual_batch_size = load_model['layer_num'], load_model['virtual_batch_size']
TypeError: 'DANet' object is not subscriptable

I use the checkpoint model:
Traceback (most recent call last):
File "predict.py", line 58, in
clf.load_model(filepath, input_dim=X_test.shape[1], output_dim=output_dim, n_gpu=n_gpu)
File "/home/gdu/DANet/abstract_model.py", line 223, in load_model
self.layer, self.virtual_batch_size = load_model['layer_num'], load_model['virtual_batch_size']
KeyError: 'layer_num'

I download the code+date from your github, but get mse=0.91, not 0.55.
There is some codes I debug:
(1) in data_util.py, add '.txt'
def svm2pkl(source, save_path):
before:
X_train, y_train = load_svmlight_file(os.path.join(source, 'train'))
X_valid, y_valid = load_svmlight_file(os.path.join(source, 'vali'))
X_test, y_test = load_svmlight_file(os.path.join(source, 'test'))
after:
def svm2pkl(source, save_path):
X_train, y_train = load_svmlight_file(os.path.join(source, 'train.txt'))
X_valid, y_valid = load_svmlight_file(os.path.join(source, 'vali.txt'))
X_test, y_test = load_svmlight_file(os.path.join(source, 'test.txt'))
(2)in deault.py, add 'cfg.fit.weight_decay = 1e-5','cfg.fit.schedule_step = 20'.
As I do regression task, when I run main.py, the Keyerror: weight_decay ,Keyerror: schedule_step .I can't find these two parameters define in deault.py ,MSLR.yaml, main.py, so I add 'cfg.fit.weight_decay = 1e-5','cfg.fit.schedule_step = 20' in deault.py.
(3)Run codes by using python predict.py -d [dataset_name] -m [model_file_path] -g [gpu_id] where the best.pth path is already specified. I don't get the mean: Replace the resume_dir path with the file path containing your trained model/weight. Does it mean set the resume_dir path to the best.pth path?