def load(self): self.class_mapping = load_dict(self.path_to_class_mapping) self.opt = load_dict(self.path_to_pipeline_opt) self.classifier = joblib.load(os.path.join(self.model_path, "classifier.pkl")) if self.class_mapping is not None: self.class_mapping_inv = {v: k for k, v in self.class_mapping.items()} print("Classifier was loaded!")
def load(self): self.class_mapping = load_dict(self.path_to_class_mapping) self.class_mapping_inv = {v: k for k, v in self.class_mapping.items()} self.feature_extractor = joblib.load(self.feature_extractor_path) self.classifier = joblib.load(self.classifier_path) self.aggregator = joblib.load(self.aggregator_path) self._pipeline_opt = load_dict(self.path_to_opt)
def run_test(df, mf, us): color_coding = load_dict(os.path.join(mf, "color_coding.json")) model = Model(mf) model.load(mf) res_fol = Folder(os.path.join(mf, "segmentations")) res_fol.check_n_make_dir(clean=True) vis_fol = Folder(os.path.join(mf, "overlays")) vis_fol.check_n_make_dir(clean=True) d_set = SegmentationDataSet(df, color_coding) t_set = d_set.load() sh = StatsHandler(color_coding) print("Processing Images...") for tid in tqdm(t_set): cls_map = model.predict(t_set[tid].load_x()) color_map = convert_cls_to_color(cls_map, color_coding, unsupervised=us) t_set[tid].write_result(res_fol.path(), color_map) if not us: t_set[tid].eval(color_map, sh) t_set[tid].visualize_result(vis_fol.path(), color_map) sh.eval() sh.show() sh.write_report(os.path.join(mf, "report.txt"))
def main(args_): df = args_.dataset_folder mf = args_.model_folder color_coding = load_dict(os.path.join(mf, "color_coding.json")) model = Model(mf) model.load(mf) res_fol = Folder(os.path.join(mf, "segmentations")) res_fol.check_n_make_dir(clean=True) vis_fol = Folder(os.path.join(mf, "overlays")) vis_fol.check_n_make_dir(clean=True) d_set = VideoSet(df, color_coding) t_set = d_set.load() sh = StatsHandler(color_coding) print("Processing Images...") for tid in tqdm(t_set): cls_map = model.predict(t_set[tid]) color_map = convert_cls_to_color(cls_map, color_coding) t_set[tid].write_result(res_fol.path(), color_map) t_set[tid].eval(color_map, sh) t_set[tid].visualize_result(vis_fol.path(), color_map) sh.eval() sh.show() sh.write_report(os.path.join(mf, "report.txt"))
def main(args_): df = args_.dataset_folder mf = args_.model_folder color_coding = load_dict(os.path.join(mf, "color_coding.json")) model = Model(mf) model.load(mf) res_fol = Folder(os.path.join(df, "inference")) res_fol.check_n_make_dir(clean=True) d_set = SegmentationDataSet(df, color_coding) t_set = d_set.load() sh = StatsHandler(color_coding) print("Processing Images...") for tid in tqdm(t_set): cls_map = model.predict(t_set[tid].load_x()) color_map = convert_cls_to_color(cls_map, color_coding) im_id = os.path.basename(t_set[tid].path_to_image_file) cv2.imwrite(os.path.join(str(res_fol), im_id[:-4] + ".png"), color_map) sh.eval() sh.show() sh.write_report(os.path.join(mf, "report.txt"))
def main(args_): cfg = Config(args_.model_folder) cfg.class_mapping = load_dict(args_.class_mapping) f_1 = start_training(args_, cfg)
def load(self, model_path, name="clf"): self.opt = load_dict( os.path.join(model_path, "{}_opt.json".format(name))) self.regressor = joblib.load( os.path.join(model_path, "{}.pkl".format(name)))
graph = state['graph'] text_f = state['text_f'] mappings = state['mappings'] num_ent = state['num_ent'] num_rel = state['num_rel'] id2ent = state['id2ent'] print("finish load") else: embedding_dim = args.nb_heads * args.hidden num_rel = int( open(os.path.join(args.data_dir, 'relation2id.txt')).readline()) text_file = os.path.join(args.data_dir, 'entity_text_title_tokenized.json') mappings, text_f = load_text(text_file, args.freq, args.max_len) ent_f = os.path.join(args.data_dir, 'entity2id.txt') id2ent, num_ent = load_dict(ent_f) # Load Graph Data graph, _ = load_graph(os.path.join(args.data_dir, 'train2id.txt'), num_ent) # Parse parameters parameters = OrderedDict() parameters['emb_dim'] = embedding_dim parameters['hid_dim'] = args.hidden parameters['out_dim'] = args.hidden * args.nb_heads parameters['num_voc'] = len(mappings['idx2word']) parameters['num_heads'] = args.nb_heads parameters['num_ent'] = num_ent parameters['num_rel'] = num_rel parameters['dropout'] = args.dropout parameters['alpha'] = args.alpha parameters['margin'] = args.margin
# Initialize # load previously saved data state = pickle.load(open('dataset.pth', 'rb')) parameters = state['parameters'] graph = state['graph'] text_f = state['text_f'] mappings = state['mappings'] num_ent = state['num_ent'] num_rel = state['num_rel'] id2ent = state['id2ent'] num_ent = state['num_ent'] print("finish load") rel_f = os.path.join(args.data_dir, 'relation2id.txt') id2rel, _ = load_dict(rel_f) name_f = os.path.join(args.data_dir, 'term.pth') ent2name = pickle.load(open(name_f, "rb")) # Load Positive and Negative Examples params = { 'batch_size': args.batch_size, 'shuffle': True, 'collate_fn': adjust_sent_order } train_set = LinkPredictionDataset(os.path.join(args.data_dir, 'train2id.txt'), text_f, id2ent, num_ent) train_triple_dict = train_set.get_triple_dict() train_generator = data.DataLoader(train_set, **params) print('Finish loading train')
def load(self, model_path): self.config = load_dict(os.path.join(model_path, "config.json")) self.pipeline = Pipeline(self.config, selected_layer=self.opt["selected_layer"])
def load_layer(self, model_folder): opt = load_dict(os.path.join(model_folder, "opt.json")) if "layer_type" not in opt: raise ValueError("No LayerType Option is defined!") if opt["layer_type"] == "GRAPH_LAYER": prev_layer = self.load_previous_layers(model_folder) layer = PixelLayer(prev_layer, opt["name"], opt["kernel"], opt["strides"], opt["kernel_shape"], opt["down_scale"]) layer.set_index(int(opt["index"])) layer.load(model_folder) return layer if opt["layer_type"] == "GRAPH3D_LAYER": prev_layer = self.load_previous_layers(model_folder) layer = Graph3DLayer(prev_layer, opt["name"], opt["kernel"], opt["kernel_shape"], opt["down_scale"]) layer.set_index(int(opt["index"])) layer.load(model_folder) return layer if opt["layer_type"] == "INPUT_LAYER": layer = InputLayer(opt["name"], opt["features_to_use"], height=opt["height"], width=opt["width"], initial_down_scale=opt["down_scale"]) layer.set_index(int(opt["index"])) layer.load(model_folder) return layer if opt["layer_type"] == "INPUT3D_LAYER": layer = Input3DLayer(opt["name"], opt["features_to_use"], height=opt["height"], width=opt["width"], initial_down_scale=opt["down_scale"]) layer.set_index(int(opt["index"])) layer.load(model_folder) return layer if opt["layer_type"] == "GLOBAL_CONTEXT_LAYER": prev_layer = self.load_previous_layers(model_folder) layer = GlobalContextLayer(prev_layer, opt["name"], opt["down_scale"]) layer.set_index(int(opt["index"])) return layer if opt["layer_type"] == "NORMALIZATION_LAYER": prev_layer = self.load_previous_layers(model_folder) layer = NormalizationLayer(prev_layer, opt["name"], norm_option=opt["norm_option"]) layer.set_index(int(opt["index"])) return layer if opt["layer_type"] == "SHAPE_REFINEMENT_LAYER": prev_layer = self.load_previous_layers(model_folder) layer = ShapeRefinementLayer(prev_layer, opt["name"], shape=opt["shape"], global_kernel=opt["global_kernel"]) layer.load(model_folder) layer.set_index(int(opt["index"])) return layer if opt["layer_type"] == "SHAPE_REFINEMENT_3D_LAYER": prev_layer = self.load_previous_layers(model_folder) layer = ShapeRefinement3DLayer(prev_layer, opt["name"], shape=opt["shape"], global_kernel=opt["global_kernel"]) layer.load(model_folder) layer.set_index(int(opt["index"])) return layer if opt["layer_type"] == "BOTTLE_NECK_LAYER": prev_layer = self.load_previous_layers(model_folder) layer = BottleNeckLayer(prev_layer, opt["name"]) layer.set_index(int(opt["index"])) return layer if opt["layer_type"] == "BOTTLE_NECK3D_LAYER": prev_layer = self.load_previous_layers(model_folder) layer = BottleNeck3DLayer(prev_layer, opt["name"]) layer.set_index(int(opt["index"])) return layer if opt["layer_type"] == "VOTING3D_Layer": prev_layer = self.load_previous_layers(model_folder) layer = Voting3DLayer(prev_layer, opt["name"]) layer.set_index(int(opt["index"])) return layer if opt["layer_type"] == "VOTING_Layer": prev_layer = self.load_previous_layers(model_folder) layer = VotingLayer(prev_layer, opt["name"]) layer.set_index(int(opt["index"])) return layer if opt["layer_type"] == "SUPER_PIXEL_LAYER": prev_layer = self.load_previous_layers(model_folder) layer = SuperPixelLayer( prev_layer, opt["name"], super_pixel_method=opt["super_pixel_method"], down_scale=opt["down_scale"], feature_aggregation=opt["feature_aggregation"]) layer.set_index(int(opt["index"])) layer.load(model_folder) return layer if opt["layer_type"] == "SIMPLE_LAYER": prev_layer = self.load_previous_layers(model_folder) if "selected_layer" not in opt: opt["selected_layer"] = -1 layer = SimpleLayer(prev_layer, opt["name"], None, selected_layer=opt["selected_layer"]) layer.set_index(int(opt["index"])) layer.load(model_folder) return layer if opt["layer_type"] == "SUPER_PIXEL_3D_LAYER": prev_layer = self.load_previous_layers(model_folder) layer = SuperPixel3DLayer( prev_layer, opt["name"], time_range=opt["time_range"], super_pixel_method=opt["super_pixel_method"], down_scale=opt["down_scale"], feature_aggregation=opt["feature_aggregation"], ) layer.set_index(int(opt["index"])) layer.load(model_folder) return layer if opt["layer_type"] == "FEATURE_EXTRACTION_LAYER": prev_layer = self.load_previous_layers(model_folder) layer = FeatureExtractionLayer(prev_layer, opt["name"], down_scale=opt["down_scale"], kernel=opt["kernel"], kernel_shape=opt["kernel_shape"]) layer.set_index(int(opt["index"])) layer.load(model_folder) return layer if opt["layer_type"] == "OBJECT_SELECTION_LAYER": prev_layer = self.load_previous_layers(model_folder) layer = ObjectSelectionLayer(prev_layer, opt["name"]) layer.set_index(int(opt["index"])) layer.load(model_folder) return layer raise ValueError("Layer: {} not recognised!".format(opt["layer_type"]))