def forward(self,
action_label,
stop_label,
action_pred,
stop_pred,
mask,
reduce=False,
metadata=None):
fwd_pred = action_pred[:, 0] # fwd velocity
ang_pred = action_pred[:, 2] # angular velocity
fwd_label = action_label[:, 0]
ang_label = action_label[:, 2]
if mask is not None:
mask = mask.unsqueeze(1).byte()
(fwd_pred, ang_pred, stop_pred, fwd_label, ang_label, stop_label) = \
mask_tensors((fwd_pred, ang_pred, stop_pred, fwd_label, ang_label, stop_label), mask)
fwd_loss = self.act_loss(fwd_pred, fwd_label)
ang_loss = self.act_loss(ang_pred, ang_label)
stop_loss = self.stoploss(stop_pred, stop_label)
if reduce:
loss = 0.2 * fwd_loss + 0.6 * ang_loss + 0.2 * stop_loss
else:
loss = torch.cat([fwd_loss, ang_loss, stop_loss])
nans = loss != loss
if torch.sum(nans.long()).data.item() > 0:
raise ValueError("Nan's encountered in loss calculation")
return loss
def forward(self, action_label, stop_label, action_pred, stop_pred, mask, reduce=False, metadata=None):
fwd_pred = action_pred[:, 0] # fwd velocity
ang_pred = action_pred[:, 2] # angular velocity
fwd_label = action_label[:, 0]
ang_label = action_label[:, 2]
if mask is not None:
mask = mask.unsqueeze(1).byte()
(fwd_pred, ang_pred, stop_pred, fwd_label, ang_label, stop_label) = \
mask_tensors((fwd_pred, ang_pred, stop_pred, fwd_label, ang_label, stop_label), mask)
fwd_loss = self.act_loss(fwd_pred, fwd_label)
ang_loss = self.act_loss(ang_pred, ang_label)
stop_loss = self.stoploss(stop_pred, stop_label)
if reduce:
loss = 0.2 * fwd_loss + 0.6 * ang_loss + 0.2 * stop_loss
else:
loss = torch.cat([fwd_loss, ang_loss, stop_loss])
nans = loss != loss
if torch.sum(nans.long()).data[0] > 0:
print ("WARNING: Nan's encountered in loss calculation")
print(loss)
loss[nans] = 0
return Variable(empty_float_tensor(list[loss.size()]), self.is_cuda, self.cuda_device)
return loss
def forward(self, action_label, action_pred, mask=None, reduce=False, flags=None, batchreduce=True):
fwd_pred = action_pred[:, 0] # fwd velocity
ang_pred = action_pred[:, 2] # angular velocity
stop_pred = action_pred[:, 3]
fwd_label = action_label[:, 0]
ang_label = action_label[:, 2]
stop_label = action_label[:, 3]
if mask is not None:
mask = mask.unsqueeze(1).byte()
(fwd_pred, ang_pred, stop_pred, fwd_label, ang_label, stop_label) = \
mask_tensors((fwd_pred, ang_pred, stop_pred, fwd_label, ang_label, stop_label), mask)
# Compute loss for each element in the batch
fwd_loss = self.act_loss(fwd_pred, fwd_label)
ang_loss = self.act_loss(ang_pred, ang_label)
# Aggregate
flagged_losses = {}
"""
if flags is not None and None not in flags:
batch_size = fwd_pred.size(0)
seq_len = int(batch_size / len(flags))
real_batch_size = int(batch_size / seq_len)
for b in range(real_batch_size):
for s in range(seq_len):
flag_loss = (0.2 * fwd_loss[b * seq_len + s] + 0.6 * ang_loss[b * seq_len + s]).data
flagged_losses[flags[b]] = flag_loss.cpu().numpy()[0]
"""
if batchreduce:
# Reduce the losses manually
fwd_loss = torch.sum(fwd_loss)
ang_loss = torch.sum(ang_loss)
# Stop loss is already reduced, because PyTorch at the time of writing didn't have a reduce arg for it.
stop_loss = self.stoploss(stop_pred, stop_label)
loss = torch.cat([fwd_loss, ang_loss, stop_loss])
else:
stop_loss = torch.zeros_like(stop_pred)
for i in range(len(stop_pred)):
stop_loss[i:i+1] = self.stoploss(stop_pred[i:i+1], stop_label[i:i+1])
loss = torch.stack([fwd_loss, ang_loss, stop_loss], dim=1)
if reduce:
loss = self.reduce_loss(loss)
nans = loss != loss
# import pdb; pdb.set_trace()
if torch.sum(nans.long()).data[0].item() > 0:
print ("WARNING: Nan's encountered in loss calculation")
print(loss)
loss[nans] = 0
return Variable(empty_float_tensor(list[loss.size()]), self.is_cuda, self.cuda_device)
return loss, flagged_losses