Python itertools.chain() Examples

The following are 30 code examples of itertools.chain(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module itertools , or try the search function

Example #1

Source File: DDPAE.py From DDPAE-video-prediction with MIT License

6 votes

def setup_training(self):
    '''
    Setup Pyro SVI, optimizers.
    '''
    if not self.is_train:
      return

    self.pyro_optimizer = optim.Adam({'lr': self.lr_init})
    self.svis = {'elbo': SVI(self.model, self.guide, self.pyro_optimizer, loss=Trace_ELBO())}

    # Separate pose_model parameters and other networks' parameters
    params = []
    for name, net in self.nets.items():
      if name != 'pose_model':
        params.append(net.parameters())
    self.optimizer = torch.optim.Adam(\
                     [{'params': self.pose_model.parameters(), 'lr': self.lr_init},
                      {'params': itertools.chain(*params), 'lr': self.lr_init}
                     ], betas=(0.5, 0.999))

Example #2

Source File: block_base.py From DOTA_models with Apache License 2.0

6 votes

def CreateWeightLoss(self):
    """Returns L2 loss list of (almost) all variables used inside this block.

    When this method needs to be overridden, there are two choices.

    1. Override CreateWeightLoss() to change the weight loss of all variables
       that belong to this block, both directly and indirectly.
    2. Override _CreateWeightLoss() to change the weight loss of all
       variables that directly belong to this block but not to the sub-blocks.

    Returns:
      A Tensor object or None.
    """
    losses = list(itertools.chain(
        itertools.chain.from_iterable(
            t.CreateWeightLoss() for t in self._subblocks),
        self._CreateWeightLoss()))
    return losses

Example #3

Source File: single_path_functions.py From apted with MIT License

6 votes

def sub_spf1(ni, subtree1, subtree2, op, calculate):
    """Implements spf1 single path function for the case when the
    other subtree is a single node

    Params:
      ni -- node indexer for the subtree that has more than one element
      subtree1 -- subtree that has a single element
      subtree2 -- subtree that has more than one element
      op -- cost of deleting/inserting node
      calculate -- function(node, other) that returns the cost of
        renaming nodes
    """
    # pylint: disable=invalid-name
    # pylint: disable=too-many-arguments
    cost = subtree2.sum_cost
    max_cost = cost + op
    min_ren_minus_op = min(chain([cost], [
        calculate(subtree1, info)
        for _, info in ni.preorder_ltr(subtree2)
    ]))
    return min(min_ren_minus_op + cost, max_cost)

Example #4

Source File: EncodingDataParallel.py From torch-toolbox with BSD 3-Clause "New" or "Revised" License

6 votes

def forward(self, *inputs, **kwargs):
        if not self.device_ids:
            return self.module(*inputs, **kwargs)

        for t in chain(self.module.parameters(), self.module.buffers()):
            if t.device != self.src_device_obj:
                raise RuntimeError(
                    "module must have its parameters and buffers "
                    "on device {} (device_ids[0]) but found one of "
                    "them on device: {}".format(
                        self.src_device_obj, t.device))
        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
        if len(self.device_ids) == 1:
            return self.module(*inputs, **kwargs)
        replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
        outputs = self.parallel_apply(replicas, inputs, kwargs)
        return outputs

Example #5