def resize_as(self, tensor):
if has_torch_function_variadic(self, tensor):
return handle_torch_function(Tensor.resize_as, (self, tensor),
self, tensor)
warnings.warn("non-inplace resize_as is deprecated")
from torch.autograd._functions import Resize
return Resize.apply(self, tensor.size())
def epoch_eval(self, eval_loader) -> Tuple[float, float]:
self._model.eval()
epoch_loss: float = 0.
iou_list: List[float] = []
for inputs, targets in eval_loader:
inputs = inputs.to(self.device)
targets = targets.to(self.device)
outputs = self._model(inputs)
b, _, h, w = outputs.size()
outputs = outputs.permute(0, 2, 3, 1)
outputs = Resize.apply(outputs, (b * h * w, self.num_classes))
targets = targets.reshape(-1)
m_iou = mean_intersection_over_union(y_true=targets,
y_pred=outputs,
num_classes=self.num_classes)
iou_list.append(m_iou)
loss = self.criterion(outputs, targets)
epoch_loss += loss.item()
mean_loss: float = epoch_loss / len(eval_loader)
mean_iou: float = sum(iou_list) / len(eval_loader)
return mean_loss, mean_iou
def resize_as(self, tensor):
relevant_args = (self, tensor)
from torch.overrides import has_torch_function, handle_torch_function
if type(self) is not Tensor and type(tensor) is not Tensor and has_torch_function(relevant_args):
return handle_torch_function(Tensor.resize_as, relevant_args, self, tensor)
warnings.warn("non-inplace resize_as is deprecated")
from torch.autograd._functions import Resize
return Resize.apply(self, tensor.size())
def resize_as(self, tensor):
warnings.warn("non-inplace resize_as is deprecated")
from torch.autograd._functions import Resize
return Resize.apply(self, tensor.size())
def resize(self, *sizes):
warnings.warn("non-inplace resize is deprecated")
from torch.autograd._functions import Resize
return Resize.apply(self, sizes)
def resize(self, *sizes):
if has_torch_function_unary(self):
return handle_torch_function(Tensor.resize, (self, ), self, *sizes)
warnings.warn("non-inplace resize is deprecated")
from torch.autograd._functions import Resize
return Resize.apply(self, sizes)
def resize_as(self, tensor):
warnings.warn("non-inplace resize_as is deprecated")
from torch.autograd._functions import Resize
return Resize.apply(self, tensor.size())
def resize(self, *sizes):
warnings.warn("non-inplace resize is deprecated")
from torch.autograd._functions import Resize
return Resize.apply(self, sizes)
