code:
{ parser.add_argument('--train_lib', type=str, default='', help='path to train MIL library binary')
}
code:
{
class MILdataset(data.Dataset):
def init(self, libraryfile='', transform=None):
lib = torch.load(libraryfile)
slides = []
for i,name in enumerate(lib['slides']):
sys.stdout.write('Opening SVS headers: [{}/{}]\r'.format(i+1, len(lib['slides'])))
sys.stdout.flush()
slides.append(openslide.OpenSlide(name))
print('')
}
question: the parameter of "train_lib" is tranfered to libraryfile, it is confused about the meaning of "train_lib" . By my understanding, it should be the path of data , but it looks like the path of trained model, if it is ,where is the model ?
code: { parser.add_argument('--train_lib', type=str, default='', help='path to train MIL library binary') }
code: { class MILdataset(data.Dataset): def init(self, libraryfile='', transform=None): lib = torch.load(libraryfile) slides = [] for i,name in enumerate(lib['slides']): sys.stdout.write('Opening SVS headers: [{}/{}]\r'.format(i+1, len(lib['slides']))) sys.stdout.flush() slides.append(openslide.OpenSlide(name)) print('') } question: the parameter of "train_lib" is tranfered to libraryfile, it is confused about the meaning of "train_lib" . By my understanding, it should be the path of data , but it looks like the path of trained model, if it is ,where is the model ?