Python itertools.chain() Examples
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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
Source File: app.py From quart with MIT License | 6 votes |
def update_template_context(self, context: dict) -> None: """Update the provided template context. This adds additional context from the various template context processors. Arguments: context: The context to update (mutate). """ processors = self.template_context_processors[None] if has_request_context(): blueprint = _request_ctx_stack.top.request.blueprint if blueprint is not None and blueprint in self.template_context_processors: processors = chain( # type: ignore processors, self.template_context_processors[blueprint] ) extra_context: dict = {} for processor in processors: extra_context.update(await processor()) original = context.copy() context.update(extra_context) context.update(original)
Example #6
Source File: app.py From quart with MIT License | 6 votes |
def do_teardown_request( self, exc: Optional[BaseException], request_context: Optional[RequestContext] = None ) -> None: """Teardown the request, calling the teardown functions. Arguments: exc: Any exception not handled that has caused the request to teardown. request_context: The request context, optional as Flask omits this argument. """ request_ = (request_context or _request_ctx_stack.top).request functions = self.teardown_request_funcs[None] blueprint = request_.blueprint if blueprint is not None: functions = chain(functions, self.teardown_request_funcs[blueprint]) # type: ignore for function in functions: await function(exc) await request_tearing_down.send(self, exc=exc)
Example #7
Source File: app.py From quart with MIT License | 6 votes |
def do_teardown_websocket( self, exc: Optional[BaseException], websocket_context: Optional[WebsocketContext] = None ) -> None: """Teardown the websocket, calling the teardown functions. Arguments: exc: Any exception not handled that has caused the websocket to teardown. websocket_context: The websocket context, optional as Flask omits this argument. """ websocket_ = (websocket_context or _websocket_ctx_stack.top).websocket functions = self.teardown_websocket_funcs[None] blueprint = websocket_.blueprint if blueprint is not None: functions = chain(functions, self.teardown_websocket_funcs[blueprint]) # type: ignore for function in functions: await function(exc) await websocket_tearing_down.send(self, exc=exc)
Example #8
Source File: app.py From quart with MIT License | 6 votes |
def preprocess_request( self, request_context: Optional[RequestContext] = None ) -> Optional[ResponseReturnValue]: """Preprocess the request i.e. call before_request functions. Arguments: request_context: The request context, optional as Flask omits this argument. """ request_ = (request_context or _request_ctx_stack.top).request blueprint = request_.blueprint processors = self.url_value_preprocessors[None] if blueprint is not None: processors = chain(processors, self.url_value_preprocessors[blueprint]) # type: ignore for processor in processors: processor(request.endpoint, request.view_args) functions = self.before_request_funcs[None] if blueprint is not None: functions = chain(functions, self.before_request_funcs[blueprint]) # type: ignore for function in functions: result = await function() if result is not None: return result return None
Example #9
Source File: app.py From quart with MIT License | 6 votes |
def preprocess_websocket( self, websocket_context: Optional[WebsocketContext] = None ) -> Optional[ResponseReturnValue]: """Preprocess the websocket i.e. call before_websocket functions. Arguments: websocket_context: The websocket context, optional as Flask omits this argument. """ websocket_ = (websocket_context or _websocket_ctx_stack.top).websocket blueprint = websocket_.blueprint processors = self.url_value_preprocessors[None] if blueprint is not None: processors = chain(processors, self.url_value_preprocessors[blueprint]) # type: ignore for processor in processors: processor(websocket_.endpoint, websocket_.view_args) functions = self.before_websocket_funcs[None] if blueprint is not None: functions = chain(functions, self.before_websocket_funcs[blueprint]) # type: ignore for function in functions: result = await function() if result is not None: return result return None
Example #10
Source File: hmm.py From razzy-spinner with GNU General Public License v3.0 | 6 votes |
def point_entropy(self, unlabeled_sequence): """ Returns the pointwise entropy over the possible states at each position in the chain, given the observation sequence. """ unlabeled_sequence = self._transform(unlabeled_sequence) T = len(unlabeled_sequence) N = len(self._states) alpha = self._forward_probability(unlabeled_sequence) beta = self._backward_probability(unlabeled_sequence) normalisation = logsumexp2(alpha[T-1]) entropies = np.zeros(T, np.float64) probs = np.zeros(N, np.float64) for t in range(T): for s in range(N): probs[s] = alpha[t, s] + beta[t, s] - normalisation for s in range(N): entropies[t] -= 2**(probs[s]) * probs[s] return entropies
Example #11
Source File: berny.py From pyberny with Mozilla Public License 2.0 | 6 votes |
def __init__( self, geom, debug=False, restart=None, maxsteps=100, logger=None, **params ): self._debug = debug self._maxsteps = maxsteps self._converged = False self._n = 0 self._log = BernyAdapter(logger or log, {'step': self._n}) s = self._state = Berny.State() if restart: vars(s).update(restart) return s.geom = geom s.params = dict(chain(defaults.items(), params.items())) s.trust = s.params['trust'] s.coords = InternalCoords( s.geom, dihedral=s.params['dihedral'], superweakdih=s.params['superweakdih'] ) s.H = s.coords.hessian_guess(s.geom) s.weights = s.coords.weights(s.geom) s.future = Berny.Point(s.coords.eval_geom(s.geom), None, None) s.first = True for line in str(s.coords).split('\n'): self._log.info(line)
Example #12
Source File: didyoumean_internal.py From DidYouMean-Python with MIT License | 6 votes |
def get_attribute_suggestions(type_str, attribute, frame): """Get the suggestions closest to the attribute name for a given type.""" types = get_types_for_str(type_str, frame) attributes = set(a for t in types for a in dir(t)) if type_str == 'module': # For module, we manage to get the corresponding 'module' type # but the type doesn't bring much information about its content. # A hacky way to do so is to assume that the exception was something # like 'module_name.attribute' so that we can actually find the module # based on the name. Eventually, we check that the found object is a # module indeed. This is not failproof but it brings a whole lot of # interesting suggestions and the (minimal) risk is to have invalid # suggestions. module_name = frame.f_code.co_names[0] objs = get_objects_in_frame(frame) mod = objs[module_name][0].obj if inspect.ismodule(mod): attributes = set(dir(mod)) return itertools.chain( suggest_attribute_as_builtin(attribute, type_str, frame), suggest_attribute_alternative(attribute, type_str, attributes), suggest_attribute_as_typo(attribute, attributes), suggest_attribute_as_special_case(attribute))
Example #13
Source File: argparse_to_json.py From me-ica with GNU Lesser General Public License v2.1 | 6 votes |
def process(parser, widget_dict): mutually_exclusive_groups = [ [mutex_action for mutex_action in group_actions._group_actions] for group_actions in parser._mutually_exclusive_groups] group_options = list(chain(*mutually_exclusive_groups)) base_actions = [action for action in parser._actions if action not in group_options and action.dest != 'help'] required_actions = filter(is_required, base_actions) optional_actions = filter(is_optional, base_actions) return list(categorize(required_actions, widget_dict, required=True)) + \ list(categorize(optional_actions, widget_dict)) + \ map(build_radio_group, mutually_exclusive_groups)
Example #14
Source File: manager.py From zun with Apache License 2.0 | 6 votes |
def _wait_for_volumes_deleted(self, context, volmaps, container, timeout=60, poll_interval=1): start_time = time.time() try: volmaps = itertools.chain(volmaps) volmap = next(volmaps) while time.time() - start_time < timeout: if not volmap.auto_remove: volmap = next(volmaps) driver = self._get_driver(container) is_deleted, is_error = driver.is_volume_deleted( context, volmap) if is_deleted: volmap = next(volmaps) if is_error: break time.sleep(poll_interval) except StopIteration: return msg = _("Volumes cannot be successfully deleted after " "%d seconds") % (timeout) self._fail_container(context, container, msg, unset_host=True) raise exception.Conflict(msg)
Example #15
Source File: agent_pop.py From indras_net with GNU General Public License v3.0 | 5 votes |
def __iter__(self): alists = [] for var in self.varieties_iter(): alists.append(self.vars[var]["agents"]) # create an iterator that chains the lists together as if one: return itertools.chain(*alists)
Example #16
Source File: grid_env.py From indras_net with GNU General Public License v3.0 | 5 votes |
def __iter__(self): """ Iterate over all our cells: note, right now, this return the center cell twice. """ return itertools.chain(self.views)
Example #17
Source File: grid_env.py From indras_net with GNU General Public License v3.0 | 5 votes |
def __iter__(self): # create an iterator that chains the # rows of grid together as if one list: return itertools.chain(*self.grid)
Example #18
Source File: pipes.py From pypette with MIT License | 5 votes |
def report(self): """Method to pretty print the report.""" print("") print(crayons.green(self.name, bold=True)) if not self.thread_map: print(crayons.red("No jobs run in pipeline yet !")) return joblen = len(self.thread_map) for i, jobs in enumerate(self.thread_map.values()): print(crayons.blue(u"| ")) if len(jobs) == 1: print(crayons.blue(u"\u21E8 ") + Pipe._cstate(jobs[0])) else: if i == joblen - 1: pre = u" " else: pre = u"| " l1 = [u"-" * 10 for j in jobs] l1 = u"".join(l1) l1 = l1[:-1] print(crayons.blue(u"\u21E8 ") + crayons.blue(l1)) fmt = u"{0:^{wid}}" l2 = [fmt.format(u"\u21E9", wid=12) for j in jobs] print(crayons.blue(pre) + crayons.blue(u"".join(l2))) l3 = [ Pipe._cstate(fmt.format(j.state.name, wid=12)) for j in jobs ] print(crayons.blue(pre) + u"".join(l3)) pipes = filter( lambda x: isinstance(x.job, Pipe), chain(*self.thread_map.values()) ) for item in pipes: item.job.report()
Example #19
Source File: pipes.py From pypette with MIT License | 5 votes |
def _pretty_print(self): """Method to pretty print the pipeline.""" print("") print(crayons.green(self.name, bold=True)) if not self.job_map: print(crayons.red("No jobs added to the pipeline yet !")) return joblen = len(self.job_map) for i, jobs in enumerate(self.job_map.values()): print(crayons.blue(u"| ")) if len(jobs) == 1: print(crayons.blue(u"\u21E8 ") + crayons.white(jobs[0].name)) else: if i == joblen - 1: pre = u" " else: pre = u"| " l1 = [u"-" * (len(j.name) + 2) for j in jobs] l1 = u"".join(l1) l1 = l1[: -len(jobs[-1].name) // 2 + 1] print(crayons.blue(u"\u21E8 ") + crayons.blue(l1)) fmt = u"{0:^{wid}}" l2 = [fmt.format(u"\u21E9", wid=len(j.name) + 2) for j in jobs] print(crayons.blue(pre) + crayons.blue(u"".join(l2))) l3 = [fmt.format(j.name, wid=len(j.name) + 2) for j in jobs] print(crayons.blue(pre) + crayons.white(u"".join(l3))) pipes = filter( lambda x: isinstance(x, Pipe), chain(*self.job_map.values()) ) for item in pipes: item._pretty_print()
Example #20
Source File: ggtnn_graph_parse.py From gated-graph-transformer-network with MIT License | 5 votes |
def get_wordlist(stories): words = [PAD_WORD] + sorted(list(set((word for (sents_graphs, query, answer) in stories for wordbag in itertools.chain((s for s,g in sents_graphs), [query]) for word in wordbag )))) wordmap = list_to_map(words) return words, wordmap
Example #21
Source File: model.py From gated-graph-transformer-network with MIT License | 5 votes |
def params(self): return list(itertools.chain(*(l.params for l in self.parameterized)))
Example #22
Source File: input_embedding.py From icme2019 with MIT License | 5 votes |
def get_inputs_list(inputs): return list(chain(*list(map(lambda x: x.values(), filter(lambda x: x is not None, inputs)))))
Example #23
Source File: managers.py From pinax-documents with MIT License | 5 votes |
def members(self, folder, **kwargs): direct = kwargs.get("direct", True) user = kwargs.get("user") Document = apps.get_model("documents", "Document") folders = self.filter(parent=folder) documents = Document.objects.filter(folder=folder) if user: folders = folders.for_user(user) documents = documents.for_user(user) M = sorted(itertools.chain(folders, documents), key=operator.attrgetter("name")) if direct: return M for child in folders: M.extend(self.members(child, **kwargs)) return M
Example #24
Source File: db.py From query-exporter with GNU General Public License v3.0 | 5 votes |
def labels(self) -> FrozenSet[str]: """Resturn all labels for metrics in the query.""" return frozenset(chain(*(metric.labels for metric in self.metrics)))
Example #25
Source File: bio_utils.py From models with MIT License | 5 votes |
def sequence_to_int(sequences, max_len): if type(sequences) is list: seqs_enc = np.asarray([nucleotide_to_int(read, max_len) for read in sequences], 'uint8') else: seqs_enc = np.asarray([nucleotide_to_int(read, max_len) for read in sequences], 'uint8') seqs_enc = list(itertools.chain(*seqs_enc)) seqs_enc = np.asarray(seqs_enc) return seqs_enc
Example #26
Source File: model.py From VSE-C with MIT License | 5 votes |
def init_weights(self): for name, parameter in itertools.chain(self.gru_f.named_parameters(), self.gru_b.named_parameters()): if name.startswith('weight'): init.orthogonal(parameter.data) elif name.startswith('bias'): parameter.data.zero_() else: raise ValueError('Unknown parameter type: {}'.format(name))
Example #27
Source File: data_producer.py From neural-pipeline with MIT License | 5 votes |
def __init__(self, dataset:AbstractDataset, indices: list): super().__init__(dataset) self.indices = list(itertools.chain(*indices))
Example #28
Source File: ner.py From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 | 5 votes |
def build_vocab(nested_list): """ :param nested_list: list of list of string :return: dictionary mapping from string to int, inverse of that dictionary """ # Build vocabulary word_counts = Counter(itertools.chain(*nested_list)) # Mapping from index to label vocabulary_inv = [x[0] for x in word_counts.most_common()] # Mapping from label to index vocabulary = {x: i for i, x in enumerate(vocabulary_inv)} return vocabulary, vocabulary_inv
Example #29
Source File: data_helpers.py From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 | 5 votes |
def build_vocab(sentences): """ Builds a vocabulary mapping from word to index based on the sentences. Returns vocabulary mapping and inverse vocabulary mapping. """ # Build vocabulary word_counts = Counter(itertools.chain(*sentences)) # Mapping from index to word vocabulary_inv = [x[0] for x in word_counts.most_common()] # Mapping from word to index vocabulary = {x: i for i, x in enumerate(vocabulary_inv)} return [vocabulary, vocabulary_inv]
Example #30
Source File: data_helpers.py From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 | 5 votes |
def build_vocab(sentences): """ Builds a vocabulary mapping from word to index based on the sentences. Returns vocabulary mapping and inverse vocabulary mapping. """ # Build vocabulary word_counts = Counter(itertools.chain(*sentences)) # Mapping from index to word vocabulary_inv = [x[0] for x in word_counts.most_common()] # Mapping from word to index vocabulary = {x: i for i, x in enumerate(vocabulary_inv)} return [vocabulary, vocabulary_inv]