Python collections.Sequence() Examples
The following are 30 code examples for showing how to use collections.Sequence(). These examples are extracted from open source projects. 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 check out the related API usage on the sidebar.
You may also want to check out all available functions/classes of the module
collections
, or try the search function
.
Example 1
Project: Attention-Gated-Networks Author: ozan-oktay File: aggregated_classifier.py License: MIT License | 6 votes |
def aggregate_output(self): """Given a list of predictions from net, make a decision based on aggreagation rule""" if isinstance(self.predictions, collections.Sequence): logits = [] for pred in self.predictions: logit = self.net.apply_argmax_softmax(pred).unsqueeze(0) logits.append(logit) logits = torch.cat(logits, 0) if self.aggregation == 'max': self.pred = logits.data.max(0)[0].max(1) elif self.aggregation == 'mean': self.pred = logits.data.mean(0).max(1) elif self.aggregation == 'weighted_mean': self.pred = (self.aggregation_weight.expand_as(logits) * logits).data.mean(0).max(1) elif self.aggregation == 'idx': self.pred = logits[self.aggregation_param].data.max(1) else: # Apply a softmax and return a segmentation map self.logits = self.net.apply_argmax_softmax(self.predictions) self.pred = self.logits.data.max(1)
Example 2
Project: Attention-Gated-Networks Author: ozan-oktay File: myImageTransformations.py License: MIT License | 6 votes |
def __call__(self, image): if isinstance(self.sigma, collections.Sequence): sigma = random_num_generator( self.sigma, random_state=self.random_state) else: sigma = self.sigma if isinstance(self.mean, collections.Sequence): mean = random_num_generator( self.mean, random_state=self.random_state) else: mean = self.mean row, col, ch = image.shape gauss = self.random_state.normal(mean, sigma, (row, col, ch)) gauss = gauss.reshape(row, col, ch) image += image * gauss return image
Example 3
Project: Attention-Gated-Networks Author: ozan-oktay File: myImageTransformations.py License: MIT License | 6 votes |
def __call__(self, img): for t in self.transforms: if isinstance(t, collections.Sequence): assert isinstance(img, collections.Sequence) and len(img) == len( t), "size of image group and transform group does not fit" tmp_ = [] for i, im_ in enumerate(img): if callable(t[i]): tmp_.append(t[i](im_)) else: tmp_.append(im_) img = tmp_ elif callable(t): img = t(img) elif t is None: continue else: raise Exception('unexpected type') return img
Example 4
Project: AerialDetection Author: dingjiansw101 File: utils.py License: Apache License 2.0 | 6 votes |
def to_tensor(data): """Convert objects of various python types to :obj:`torch.Tensor`. Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`, :class:`Sequence`, :class:`int` and :class:`float`. """ if isinstance(data, torch.Tensor): return data elif isinstance(data, np.ndarray): return torch.from_numpy(data) elif isinstance(data, Sequence) and not mmcv.is_str(data): return torch.tensor(data) elif isinstance(data, int): return torch.LongTensor([data]) elif isinstance(data, float): return torch.FloatTensor([data]) else: raise TypeError('type {} cannot be converted to tensor.'.format( type(data)))
Example 5
Project: opencv_transforms Author: jbohnslav File: functional.py License: MIT License | 6 votes |
def normalize(tensor, mean, std): """Normalize a tensor image with mean and standard deviation. .. note:: This transform acts in-place, i.e., it mutates the input tensor. See :class:`~torchvision.transforms.Normalize` for more details. Args: tensor (Tensor): Tensor image of size (C, H, W) to be normalized. mean (sequence): Sequence of means for each channel. std (sequence): Sequence of standard deviations for each channely. Returns: Tensor: Normalized Tensor image. """ if not _is_tensor_image(tensor): raise TypeError('tensor is not a torch image.') # This is faster than using broadcasting, don't change without benchmarking for t, m, s in zip(tensor, mean, std): t.sub_(m).div_(s) return tensor
Example 6
Project: vulscan Author: vulscanteam File: pyparsing.py License: MIT License | 6 votes |
def __init__( self, exprs, savelist = False ): super(ParseExpression,self).__init__(savelist) if isinstance( exprs, _generatorType ): exprs = list(exprs) if isinstance( exprs, basestring ): self.exprs = [ Literal( exprs ) ] elif isinstance( exprs, collections.Sequence ): # if sequence of strings provided, wrap with Literal if all(isinstance(expr, basestring) for expr in exprs): exprs = map(Literal, exprs) self.exprs = list(exprs) else: try: self.exprs = list( exprs ) except TypeError: self.exprs = [ exprs ] self.callPreparse = False
Example 7
Project: Counterfactual-StoryRW Author: qkaren File: hierarchical_encoders.py License: MIT License | 6 votes |
def flatten(x): """Flattens a cell state by concatenating a sequence of cell states along the last dimension. If the cell states are :tf_main:`LSTMStateTuple <contrib/rnn/LSTMStateTuple>`, only the hidden `LSTMStateTuple.h` is used. This process is used by default if :attr:`medium` is not provided to :meth:`_build`. """ if isinstance(x, LSTMStateTuple): return x.h if isinstance(x, collections.Sequence): return tf.concat( [HierarchicalRNNEncoder.flatten(v) for v in x], -1) else: return x
Example 8
Project: speaksee Author: aimagelab File: dataset.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
def collate_fn(self): def collate(batch): if len(self.fields) == 1: batch = [batch, ] else: batch = list(zip(*batch)) tensors = [] for field, data in zip(self.fields.values(), batch): tensor = field.process(data) if isinstance(tensor, collections.Sequence) and any(isinstance(t, torch.Tensor) for t in tensor): tensors.extend(tensor) else: tensors.append(tensor) if len(tensors) > 1: return tensors else: return tensors[0] return collate
Example 9
Project: EMANet Author: XiaLiPKU File: data_parallel.py License: GNU General Public License v3.0 | 6 votes |
def dict_gather(outputs, target_device, dim=0): """ Gathers variables from different GPUs on a specified device (-1 means the CPU), with dictionary support. """ def gather_map(outputs): out = outputs[0] if isinstance(out, Variable): # MJY(20180330) HACK:: force nr_dims > 0 if out.dim() == 0: outputs = [o.unsqueeze(0) for o in outputs] return Gather.apply(target_device, dim, *outputs) elif out is None: return None elif isinstance(out, collections.Mapping): return {k: gather_map([o[k] for o in outputs]) for k in out} elif isinstance(out, collections.Sequence): return type(out)(map(gather_map, zip(*outputs))) return gather_map(outputs)
Example 10
Project: deepchem Author: deepchem File: layers.py License: MIT License | 6 votes |
def build(self, input_shape): if isinstance(input_shape, collections.Sequence): input_shape = input_shape[0] out_channels = input_shape[1] if self.weights_initializer is None: weights_initializer = tf.keras.initializers.VarianceScaling else: weights_initializer = self.weights_initializer self.dense_H = tf.keras.layers.Dense( out_channels, activation=self.activation_fn, bias_initializer=self.biases_initializer, kernel_initializer=weights_initializer) self.dense_T = tf.keras.layers.Dense( out_channels, activation=tf.nn.sigmoid, bias_initializer=tf.constant_initializer(-1), kernel_initializer=weights_initializer) self.built = True
Example 11
Project: deep-smoke-machine Author: CMU-CREATE-Lab File: opencv_functional.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
def normalize(tensor, mean, std): """Normalize a tensor image with mean and standard deviation. .. note:: This transform acts in-place, i.e., it mutates the input tensor. See :class:`~torchvision.transforms.Normalize` for more details. Args: tensor (Tensor): Tensor image of size (C, H, W) to be normalized. mean (sequence): Sequence of means for each channel. std (sequence): Sequence of standard deviations for each channely. Returns: Tensor: Normalized Tensor image. """ if not _is_tensor_image(tensor): raise TypeError('tensor is not a torch image.') # This is faster than using broadcasting, don't change without benchmarking for t, m, s in zip(tensor, mean, std): t.sub_(m).div_(s) return tensor
Example 12
Project: jbox Author: jpush File: pyparsing.py License: MIT License | 6 votes |
def __init__( self, exprs, savelist = False ): super(ParseExpression,self).__init__(savelist) if isinstance( exprs, _generatorType ): exprs = list(exprs) if isinstance( exprs, basestring ): self.exprs = [ Literal( exprs ) ] elif isinstance( exprs, collections.Sequence ): # if sequence of strings provided, wrap with Literal if all(isinstance(expr, basestring) for expr in exprs): exprs = map(Literal, exprs) self.exprs = list(exprs) else: try: self.exprs = list( exprs ) except TypeError: self.exprs = [ exprs ] self.callPreparse = False
Example 13
Project: jbox Author: jpush File: pyparsing.py License: MIT License | 6 votes |
def __init__( self, exprs, savelist = False ): super(ParseExpression,self).__init__(savelist) if isinstance( exprs, _generatorType ): exprs = list(exprs) if isinstance( exprs, basestring ): self.exprs = [ Literal( exprs ) ] elif isinstance( exprs, collections.Sequence ): # if sequence of strings provided, wrap with Literal if all(isinstance(expr, basestring) for expr in exprs): exprs = map(Literal, exprs) self.exprs = list(exprs) else: try: self.exprs = list( exprs ) except TypeError: self.exprs = [ exprs ] self.callPreparse = False
Example 14
Project: tattle Author: kippandrew File: messages.py License: Mozilla Public License 2.0 | 6 votes |
def _serialize_internal(cls, msg): # insert the name of the class data = [msg.__class__.__name__] # get list of fields fields = msg.__class__.get_fields() for field_name, field_type in fields: attr = getattr(msg, field_name) if field_type is not None and attr is not None: # if attr has a field type defined deserialize that field data.extend(cls._serialize_internal(attr)) else: if isinstance(attr, str) or isinstance(attr, bytes): data.append(attr) elif isinstance(attr, collections.Sequence): data.append([cls._serialize_internal(i) for i in attr]) elif isinstance(attr, collections.Mapping): data.append({k: cls._serialize_internal(v) for k, v in attr.items()}) else: data.append(attr) return data
Example 15
Project: tattle Author: kippandrew File: state.py License: Mozilla Public License 2.0 | 6 votes |
def __init__(self, config, queue, events, loop=None): """ Initialize instance of the NodeManager class :param config: config object :param queue: broadcast queue :type config: tattle.config.Configuration :type events: tattle.event.EventManager :type queue: tattle.queue.BroadcastQueue """ self.config = config self._queue = queue self._events = events self._loop = loop or asyncio.get_event_loop() self._leaving = False self._nodes = list() self._nodes_map = dict() self._nodes_lock = asyncio.Lock() self._suspect_nodes = dict() self._local_node_name = None self._local_node_seq = sequence.Sequence()
Example 16
Project: python-netsurv Author: sofia-netsurv File: brain_fstrings.py License: MIT License | 6 votes |
def _clone_node_with_lineno(node, parent, lineno): cls = node.__class__ other_fields = node._other_fields _astroid_fields = node._astroid_fields init_params = {"lineno": lineno, "col_offset": node.col_offset, "parent": parent} postinit_params = {param: getattr(node, param) for param in _astroid_fields} if other_fields: init_params.update({param: getattr(node, param) for param in other_fields}) new_node = cls(**init_params) if hasattr(node, "postinit") and _astroid_fields: for param, child in postinit_params.items(): if child and not isinstance(child, collections.Sequence): cloned_child = _clone_node_with_lineno( node=child, lineno=new_node.lineno, parent=new_node ) postinit_params[param] = cloned_child new_node.postinit(**postinit_params) return new_node
Example 17
Project: python-netsurv Author: sofia-netsurv File: brain_fstrings.py License: MIT License | 6 votes |
def _clone_node_with_lineno(node, parent, lineno): cls = node.__class__ other_fields = node._other_fields _astroid_fields = node._astroid_fields init_params = {"lineno": lineno, "col_offset": node.col_offset, "parent": parent} postinit_params = {param: getattr(node, param) for param in _astroid_fields} if other_fields: init_params.update({param: getattr(node, param) for param in other_fields}) new_node = cls(**init_params) if hasattr(node, "postinit") and _astroid_fields: for param, child in postinit_params.items(): if child and not isinstance(child, collections.Sequence): cloned_child = _clone_node_with_lineno( node=child, lineno=new_node.lineno, parent=new_node ) postinit_params[param] = cloned_child new_node.postinit(**postinit_params) return new_node
Example 18
Project: lambda-packs Author: ryfeus File: nest.py License: MIT License | 6 votes |
def _sequence_like(instance, args): """Converts the sequence `args` to the same type as `instance`. Args: instance: an instance of `tuple`, `list`, or a `namedtuple` class. args: elements to be converted to a sequence. Returns: `args` with the type of `instance`. """ if (isinstance(instance, tuple) and hasattr(instance, "_fields") and isinstance(instance._fields, collections.Sequence) and all(isinstance(f, six.string_types) for f in instance._fields)): # This is a namedtuple return type(instance)(*args) else: # Not a namedtuple return type(instance)(args)
Example 19
Project: pyshgp Author: erp12 File: config.py License: MIT License | 5 votes |
def constrain_collection(config: PushConfig, coll: Sequence) -> Sequence: """Constrains the collection to a size that is safe for Push program execution.""" if len(coll) > config.collection_size_cap: return coll[:config.collection_size_cap] return coll
Example 20
Project: uplink Author: prkumar File: typing_.py License: MIT License | 5 votes |
def convert(self, value): if isinstance(value, collections.Sequence): return list(map(self._elem_converter, value)) else: # TODO: Handle the case where the value is not an sequence. return [self._elem_converter(value)]
Example 21
Project: uplink Author: prkumar File: typing_.py License: MIT License | 5 votes |
def _base_converter(self, type_): if isinstance(type_, BaseTypeConverter.Builder): return type_.build() elif self._check_typing(type_): if issubclass(type_.__origin__, self.typing.Sequence): return ListConverter(*type_.__args__) elif issubclass(type_.__origin__, self.typing.Mapping): return DictConverter(*type_.__args__)
Example 22
Project: py-solc Author: ethereum File: types.py License: MIT License | 5 votes |
def is_list_like(obj): return not is_string(obj) and isinstance(obj, collections.Sequence)
Example 23
Project: misp42splunk Author: remg427 File: exceptions.py License: GNU Lesser General Public License v3.0 | 5 votes |
def _to_primitive(cls, obj): """ recursive to_primitive for basic data types. """ if isinstance(obj, string_type): return obj if isinstance(obj, Sequence): return [cls._to_primitive(e) for e in obj] elif isinstance(obj, Mapping): return dict( (k, cls._to_primitive(v)) for k, v in obj.items() ) else: return str(obj)
Example 24
Project: misp42splunk Author: remg427 File: util.py License: GNU Lesser General Public License v3.0 | 5 votes |
def listify(value): if isinstance(value, list): return value elif value is None: return [] elif isinstance(value, string_type): return [value] elif isinstance(value, collections.Sequence): return list(value) else: return [value]
Example 25
Project: misp42splunk Author: remg427 File: compound.py License: GNU Lesser General Public License v3.0 | 5 votes |
def _coerce(self, value): if isinstance(value, list): return value elif isinstance(value, (string_type, Mapping)): # unacceptable iterables pass elif isinstance(value, Sequence): return value elif isinstance(value, Iterable): return value raise ConversionError(_('Could not interpret the value as a list'))
Example 26
Project: misp42splunk Author: remg427 File: sortedlist.py License: GNU Lesser General Public License v3.0 | 5 votes |
def __make_cmp(seq_op, symbol, doc): "Make comparator method." def comparer(self, other): "Compare method for sorted list and sequence." if not isinstance(other, Sequence): return NotImplemented self_len = self._len len_other = len(other) if self_len != len_other: if seq_op is eq: return False if seq_op is ne: return True for alpha, beta in zip(self, other): if alpha != beta: return seq_op(alpha, beta) return seq_op(self_len, len_other) seq_op_name = seq_op.__name__ comparer.__name__ = '__{0}__'.format(seq_op_name) doc_str = """Return true if and only if sorted list is {0} `other`. ``sl.__{1}__(other)`` <==> ``sl {2} other`` Comparisons use lexicographical order as with sequences. Runtime complexity: `O(n)` :param other: `other` sequence :return: true if sorted list is {0} `other` """ comparer.__doc__ = dedent(doc_str.format(doc, seq_op_name, symbol)) return comparer
Example 27
Project: misp42splunk Author: remg427 File: sortedlist.py License: GNU Lesser General Public License v3.0 | 5 votes |
def __make_cmp(seq_op, symbol, doc): "Make comparator method." def comparer(self, other): "Compare method for sorted list and sequence." if not isinstance(other, Sequence): return NotImplemented self_len = self._len len_other = len(other) if self_len != len_other: if seq_op is eq: return False if seq_op is ne: return True for alpha, beta in zip(self, other): if alpha != beta: return seq_op(alpha, beta) return seq_op(self_len, len_other) seq_op_name = seq_op.__name__ comparer.__name__ = '__{0}__'.format(seq_op_name) doc_str = """Return true if and only if sorted list is {0} `other`. ``sl.__{1}__(other)`` <==> ``sl {2} other`` Comparisons use lexicographical order as with sequences. Runtime complexity: `O(n)` :param other: `other` sequence :return: true if sorted list is {0} `other` """ comparer.__doc__ = dedent(doc_str.format(doc, seq_op_name, symbol)) return comparer
Example 28
Project: misp42splunk Author: remg427 File: util.py License: GNU Lesser General Public License v3.0 | 5 votes |
def listify(value): if isinstance(value, list): return value elif value is None: return [] elif isinstance(value, string_type): return [value] elif isinstance(value, collections.Sequence): return list(value) else: return [value]
Example 29
Project: misp42splunk Author: remg427 File: compound.py License: GNU Lesser General Public License v3.0 | 5 votes |
def _coerce(self, value): if isinstance(value, list): return value elif isinstance(value, (string_type, Mapping)): # unacceptable iterables pass elif isinstance(value, Sequence): return value elif isinstance(value, Iterable): return value raise ConversionError(_('Could not interpret the value as a list'))
Example 30
Project: misp42splunk Author: remg427 File: sortedlist.py License: GNU Lesser General Public License v3.0 | 5 votes |
def __make_cmp(seq_op, symbol, doc): "Make comparator method." def comparer(self, other): "Compare method for sorted list and sequence." if not isinstance(other, Sequence): return NotImplemented self_len = self._len len_other = len(other) if self_len != len_other: if seq_op is eq: return False if seq_op is ne: return True for alpha, beta in zip(self, other): if alpha != beta: return seq_op(alpha, beta) return seq_op(self_len, len_other) seq_op_name = seq_op.__name__ comparer.__name__ = '__{0}__'.format(seq_op_name) doc_str = """Return true if and only if sorted list is {0} `other`. ``sl.__{1}__(other)`` <==> ``sl {2} other`` Comparisons use lexicographical order as with sequences. Runtime complexity: `O(n)` :param other: `other` sequence :return: true if sorted list is {0} `other` """ comparer.__doc__ = dedent(doc_str.format(doc, seq_op_name, symbol)) return comparer