def train_batch(self,
inputs,
targets,
weights=None,
update=True,
logname="train"):
if update:
self.set_training(True)
self.optimizer.zero_grad()
else:
self.set_training(False)
self.set_inputs(inputs)
self.forward()
if weights is not None:
self.cuweights = autograd.Variable(torch.randn(1, 1).cuda())
dlh.assign(self.cuweights, weights, False)
self.cuoutput = self.weighted(self.cuoutput, self.cuweights)
culoss = self.compute_loss(targets, weights=weights)
if update:
culoss.backward()
self.optimizer.step()
ploss = dlh.novar(culoss)[0]
self.ntrain += dlh.size(inputs, 0)
add_log(self.log,
logname,
loss=ploss,
ntrain=self.ntrain,
lr=self.current_lr)
return self.get_outputs(), ploss
def set_targets(self, targets, weights=None):
b, d, h, w = tuple(self.cuoutput.size())
targets = dlh.as_nda(targets, (0, 2, 3, 1))
targets = zoom_like(targets, (b, h, w, d))
dlh.assign(self.cutarget, targets, (0, 3, 1, 2))
assert self.cutarget.size() == self.cuoutput.size()
if weights is not None:
weights = dlh.as_nda(weights, (0, 2, 3, 1))
weights = zoom_like(weights, (b, h, w, d))
dlh.assign(self.cuweights, weights, (0, 3, 1, 2))
def set_targets(self, targets, weights=None):
"""Sets the cutarget variable from the given tensor.
"""
dlh.assign(self.cutarget, targets, False)
assert self.cuoutput.size() == self.cutargets.size()
if weights is not None:
dlh.assign(self.cuweights, weights, False)
assert self.cuoutput.size() == self.cuweights.size()
else:
self.cuweights = None
def set_targets(self, targets, weights=None):
assert weights is None, "weights not implemented"
if dlh.rank(targets) == 1:
targets = dlh.as_torch(targets)
targets = targets.unsqueeze(1)
b, c = dlh.shp(self.cuoutput)
onehot = torch.zeros(b, c)
onehot.scatter_(1, targets, 1)
dlh.assign(self.cutarget, onehot)
else:
assert dlh.shp(targets) == dlh.shp(self.cuoutput)
dlh.assign(self.cutarget, targets)
def set_inputs(self, images):
dlh.assign(self.cuinput, images, (0, 3, 1, 2))
def set_inputs(self, images, depth1=False):
dlh.assign(self.cuinput, images, transpose_on_convert=(0, 3, 1, 2))
def set_inputs(self, batch):
"""Sets the cuinput variable from the input data.
"""
assert isinstance(batch, torch.Tensor)
dlh.assign(self.cuinput, batch)
