def __init__(self, word_vec_list, args, input_dimension=1500, hidden_dimensions=None): self.session = load_session() self.args = args self.weights, self.biases = {}, {} self.input_dimension = input_dimension if hidden_dimensions is None: hidden_dimensions = [1024, 512, self.args.dim] self.hidden_dimensions = hidden_dimensions self.layer_num = len(self.hidden_dimensions) self.encoder_output = None self.decoder_output = None self.decoder_op = None self.word_vec_list = np.reshape(word_vec_list, [len(word_vec_list), input_dimension]) if self.args.encoder_normalize: self.word_vec_list = preprocessing.normalize(self.word_vec_list) self._init_graph() self._loss_optimizer() tf.global_variables_initializer().run(session=self.session)
async def load_session(ctx, *, arg): global sess if sess: utils.save_session(sess, f'Sessions/{sess.name}.json') session_name = arg sess = utils.load_session(f'Sessions/{session_name}.json') await ctx.send(f'Loaded session: {session_name}.')
def play(self, f=None, dev=0): cap = cv.VideoCapture(dev) pausa = False img_index = 0 roi_index = 0 #creating the time name value of the session time_session = strftime('%d_%m', gmtime()) path = 'images/' + time_session + '/' if not os.path.exists(path + 'save.pkl'): img_index = 0 roi_index = 0 else: img_list,img_index = utils.load_session(path) while True: key = cv.waitKey(1) & 0xFF ret, self.frame = cap.read() #set the function mark_corner to left click cv.setMouseCallback('frame', self.mark_corner) if key == 27: cap.release() break if key == 32: pausa = not pausa #creates a capture if key == ord('c'): if not os.path.exists(path): os.makedirs(path) cv.imwrite(path + '/img' + str(img_index) + '.png', self.frame) print('#Capture: img' + str(img_index)) img_index+=1 if key == ord('t') and self.roi_capt: cv.imwrite(path + '/roi' + '.png', self.roi) #saves the current session if key == ord('s'): utils.save_session(path) if pausa: continue if self.roi_capt: self.roi = self.roi_capture(self.frame, (self.ix, self.iy), (self.jx, self.jy), f, (255, 255, 255)) cv.imshow('frame',self.frame) cv.destroyAllWindows()
def __init__(self, args): self.args = args self.session = load_session() self.kgs = read_kgs_from_folder(args.training_data, args.dataset_division, args.alignment_module, False) self.entities = self.kgs.kg1.entities_set | self.kgs.kg2.entities_set self.word2vec_path = args.word2vec_path if os.path.exists( os.path.join(args.training_data, 'entity_local_name_1')) and os.path.exists( os.path.join(args.training_data, 'entity_local_name_2')): self.entity_local_name_dict = read_local_name( args.training_data, set(self.kgs.kg1.entities_id_dict.keys()), set(self.kgs.kg2.entities_id_dict.keys())) else: self.entity_local_name_dict = self._get_local_name_by_name_triple() self._generate_literal_vectors() self._generate_name_vectors_mat() self._generate_attribute_value_vectors()
def __init__(self, data, args, attr_align_model): super().__init__(data, args, attr_align_model) self.flag1 = -1 self.flag2 = -1 self.early_stop = False self._define_variables() self._define_name_view_graph() self._define_relation_view_graph() self._define_attribute_view_graph() self._define_cross_kg_entity_reference_relation_view_graph() self._define_cross_kg_entity_reference_attribute_view_graph() self._define_cross_kg_relation_reference_graph() self._define_cross_kg_attribute_reference_graph() self._define_common_space_learning_graph() self._define_space_mapping_graph() self.session = load_session() tf.global_variables_initializer().run(session=self.session)
def __init__(self, data, args, predicate_align_model): super().__init__(data, args, predicate_align_model) self.out_folder = generate_out_folder(self.args.output, self.args.training_data, '', self.__class__.__name__) self.flag1 = -1 self.flag2 = -1 self.early_stop = False self._define_variables() self._define_name_view_graph() self._define_relation_view_graph() self._define_attribute_view_graph() self._define_cross_kg_entity_reference_relation_view_graph() self._define_cross_kg_entity_reference_attribute_view_graph() self._define_cross_kg_attribute_reference_graph() self._define_cross_kg_relation_reference_graph() self._define_common_space_learning_graph() self.session = load_session() tf.global_variables_initializer().run(session=self.session)
experiment.set_name(args.namestr) args.experiment = experiment # Because we all like reproducibility (...and also know where we keep our towels) # ------------------------------------------------------------------------------ np.random.seed(42) torch.manual_seed(42) torch.cuda.manual_seed_all(42) # Obtain and train our model here: # ------------------------------------------------------------------------------ model, optim = get_model() if use_cuda: model.cuda() training_loader, validation_loader = _dataloader(args) # load trained model if necessary if args.load_dir is not None: model, optim, start_epoch = load_session(model, optim, args) else: start_epoch = 0 fit(model, training_loader, validation_loader, optim, start_epoch, args) args.experiment.end() # ------------------------------------------------------------------------------ # So Long, and Thanks for All the Fish! >< ((('> >< ((('> >< ((('> # ------------------------------------------------------------------------------
# load ONE and mice import numpy as np import utils from oneibl.one import ONE one = ONE() mice_names, ins, ins_id, sess_id, _ = utils.get_bwm_ins_alyx(one) stimuli_arr, actions_arr, stim_sides_arr, session_uuids = [], [], [], [] # select particular mice mouse_name = 'KS016' for i in range(len(sess_id)): if mice_names[i] == mouse_name: # take only sessions of first mice data = utils.load_session(sess_id[i]) if data['choice'] is not None and data['probabilityLeft'][0] == 0.5: stim_side, stimuli, actions, pLeft_oracle = utils.format_data(data) stimuli_arr.append(stimuli) actions_arr.append(actions) stim_sides_arr.append(stim_side) session_uuids.append(sess_id[i]) # format data stimuli, actions, stim_side = utils.format_input(stimuli_arr, actions_arr, stim_sides_arr) session_uuids = np.array(session_uuids) # import models from models.expSmoothing_stimside import expSmoothing_stimside as exp_stimside from models.expSmoothing_prevAction import expSmoothing_prevAction as exp_prevAction from models.optimalBayesian import optimal_Bayesian as optBay from models.biasedApproxBayesian import biased_ApproxBayesian as baisedApproxBay from models.biasedBayesian import biased_Bayesian