returntorch.tensor(batch, dtype=torch.float64) elifisinstance(elem, int_classes): returntorch.tensor(batch) elifisinstance(elem, string_classes): returnbatch elifisinstance(elem, container_abcs.Mapping): return{key: default_collate([d[key]fordinbatch])forkeyinelem} elifisinstance(elem,tuple)andha...
return torch.DoubleTensor(batch) elif isinstance(batch[0], string_classes): return batch elif isinstance(batch[0], collections.Mapping): return {key: default_collate([d[key] for d in batch]) for key in batch[0]} elif isinstance(batch[0], collections.Sequence): transposed = zip(*batch) ...
return default_restore_location(storage, location) elif isinstance(map_location, _string_classes): # map_location = 'cuda:0' : 不管location是什么,都放到'cuda:0'上 def restore_location(storage, location): return default_restore_location(storage, map_location) elif isinstance(map_location, torch....
// see the 20 semantic classes link in Introduction const int DOG = 12; const int PERSON = 15; const int SHEEP = 17; NSMutableData* data = [NSMutableData dataWithLength: sizeof(unsigned char) * 3 * WIDTH * HEIGHT]; unsigned char* buffer = (unsigned char*)[data mutableBytes]; // g...
elif not isinstance(name, torch._six.string_classes): raise TypeError("parameter name should be a string. " "Got {}".format(torch.typename(name))) elif '.' in name: raise KeyError("parameter name can't contain \".\"") elif name == '': ...
elif not isinstance(name, torch._six.string_classes): raise TypeError("module name should be a string. Got {}".format( torch.typename(name))) elif hasattr(self, name) and name not in self._modules: raise KeyError("attribute '{}' already exists".format(name)) ...
()}") import torch, torchtext from torchtext.models import RobertaClassificationHead from torchtext.functional import to_tensor xlmr_large = torchtext.models.XLMR_LARGE_ENCODER classifier_head = torchtext.models.RobertaClassificationHead(num_classes=2, input_dim = 1024) model = xlmr_large.get_...
//1\. the example deeplab.jpg sizeissize 400x400andthere are21semantic classes constintWIDTH =400; constintHEIGHT =400; constintCLASSNUM =21; at::Tensor tensor = torch::from_blob(imageBuffer, {1,3, WIDTH, HEIGHT}, at::kFloat); ...
def add_scalar(self,tag,scalar_value,global_step=None,walltime=None,new_style=False,double_precision=False,):"""Add scalar data to summary.Args:tag (string): Data identifierscalar_value (float or string/blobname): Value to saveglobal_step (int): Global step value to recordwalltime (float...
import xml.etree.ElementTree as ETfrom os import getcwdsets=['train','val','test','trainval']classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']def convert_annotation(image_id, list_file):in_file = open('Annotations/%s.xml'%(image...