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How to use get_hook_params method in Lemoncheesecake

Best Python code snippet using lemoncheesecake

script_generator_discreteMI.py

Source:script_generator_discreteMI.py

...21                         model_checkpoint=model_checkpoint)22        self.hook_config = yaml_load(PROJECT_PATH + "/" + opt_hook_path[self.get_hook_name()])23    def get_hook_name(self):24        return "udaiic"25    def get_hook_params(self, feature_names, mi_weights, consistency_weight, two_stage, dense_paddings):26        return {"DiscreteMIConsistencyParams":27                    {"feature_names": feature_names, "mi_weights": mi_weights,28                     "consistency_weight": consistency_weight,29                     "dense_paddings": dense_paddings},30                "Trainer": {"two_stage": two_stage}31                }32    def generate_single_script(self, save_dir, labeled_scan_num, seed, hook_path):33        from semi_seg import ft_lr_zooms34        ft_lr = ft_lr_zooms[self._data_name]35        return f"python main.py Trainer.name=semi  Trainer.save_dir={save_dir} " \36               f" Optim.lr={ft_lr:.7f} RandomSeed={str(seed)} Data.labeled_scan_num={int(labeled_scan_num)} " \37               f" {' '.join(self.conditions)} " \38               f" --opt-path {hook_path}"39    def grid_search_on(self, *, seed, **kwargs):40        jobs = []41        labeled_scan_list = ratio_zoo[self._data_name][:-1] if len(ratio_zoo[self._data_name]) > 1 else ratio_zoo[42            self._data_name]43        for param in grid_search(**{**kwargs, **{"seed": seed}}):44            random_seed = param.pop("seed")45            hook_params = self.get_hook_params(**param)46            sub_save_dir = self._get_hyper_param_string(**param)47            merged_config = dictionary_merge_by_hierachy(self.hook_config, hook_params)48            config_path = write_yaml(merged_config, save_dir=TEMP_DIR, save_name=utils.random_string() + ".yaml")49            true_save_dir = os.path.join(self._save_dir, "Seed_" + str(random_seed), sub_save_dir)50            job = " && ".join(51                [self.generate_single_script(save_dir=os.path.join(true_save_dir, "tra", f"labeled_scan_{l:02d}"),52                                             seed=random_seed, hook_path=config_path, labeled_scan_num=l)53                 for l in labeled_scan_list])54            jobs.append(job)55        return jobs56if __name__ == '__main__':57    parser = argparse.ArgumentParser("udaiic method")58    parser.add_argument("--data-name", required=True, type=str, help="dataset_name",59                        choices=["acdc", "prostate", "mmwhsct"])...

script_generator_pretrain.py

Source:script_generator_pretrain.py

...15                         pre_max_epoch=pre_max_epoch, ft_max_epoch=ft_max_epoch)16        self.hook_config = yaml_load(PROJECT_PATH + "/" + opt_hook_path[self.get_hook_name()])17    def get_hook_name(self):18        return "infonce"19    def get_hook_params(self, weight, contrast_on):20        return {"InfonceParams": {"weights": weight,21                                  "contrast_ons": contrast_on}}22    def grid_search_on(self, *, seed, **kwargs):23        jobs = []24        for param in grid_search(**{**kwargs, **{"seed": seed}}):25            random_seed = param.pop("seed")26            hook_params = self.get_hook_params(**param)27            sub_save_dir = self._get_hyper_param_string(**param)28            merged_config = dictionary_merge_by_hierachy(self.hook_config, hook_params)29            config_path = write_yaml(merged_config, save_dir=TEMP_DIR, save_name=utils.random_string() + ".yaml")30            true_save_dir = os.path.join(self._save_dir, "Seed_" + str(random_seed), sub_save_dir)31            job = self.generate_single_script(save_dir=true_save_dir,32                                              seed=random_seed, hook_path=config_path)33            jobs.append(job)34        return jobs35class PretrainSPInfoNCEScriptGenerator(PretrainInfoNCEScriptGenerator):36    def get_hook_name(self):37        return "spinfonce"38    def get_hook_params(self, weight, contrast_on, begin_values, end_values, mode, correct_grad):39        return {"SPInfonceParams": {"weights": weight,40                                    "contrast_ons": contrast_on,41                                    "begin_values": begin_values,42                                    "end_values": end_values,43                                    "mode": mode,44                                    "correct_grad": correct_grad45                                    }}46if __name__ == '__main__':47    submittor = JobSubmiter(on_local=True, project_path="../", time=4)48    submittor.prepare_env([49        "module load python/3.8.2 ",50        f"source ~/venv/bin/activate ",51        'if [ $(which python) == "/usr/bin/python" ]',52        "then",...

script_generator_adv.py

Source:script_generator_adv.py

...18                         model_checkpoint=model_checkpoint)19        self.hook_config = yaml_load(PROJECT_PATH + "/" + opt_hook_path[self.get_hook_name()])20    def get_hook_name(self):21        return "adv"22    def get_hook_params(self, reg_weight, dis_consider_image):23        return {"Trainer": {"reg_weight": reg_weight, "dis_consider_image": dis_consider_image}}24    def generate_single_script(self, save_dir, labeled_scan_num, seed, hook_path):25        from semi_seg import ft_lr_zooms26        ft_lr = ft_lr_zooms[self._data_name]27        return f"python main_adv.py  Trainer.save_dir={save_dir} " \28               f" Optim.lr={ft_lr:.7f} RandomSeed={str(seed)} Data.labeled_scan_num={int(labeled_scan_num)} " \29               f" {' '.join(self.conditions)} " \30               f" --opt-path {hook_path}"31    def grid_search_on(self, *, seed, **kwargs):32        jobs = []33        labeled_scan_list = ratio_zoo[self._data_name][:-1] if len(ratio_zoo[self._data_name]) > 1 else ratio_zoo[34            self._data_name]35        for param in grid_search(**{**kwargs, **{"seed": seed}}):36            random_seed = param.pop("seed")37            hook_params = self.get_hook_params(**param)38            sub_save_dir = self._get_hyper_param_string(**param)39            merged_config = dictionary_merge_by_hierachy(self.hook_config, hook_params)40            config_path = write_yaml(merged_config, save_dir=TEMP_DIR, save_name=utils.random_string() + ".yaml")41            true_save_dir = os.path.join(self._save_dir, "Seed_" + str(random_seed), sub_save_dir)42            job = " && ".join(43                [self.generate_single_script(save_dir=os.path.join(true_save_dir, "tra", f"labeled_scan_{l:02d}"),44                                             seed=random_seed, hook_path=config_path, labeled_scan_num=l)45                 for l in labeled_scan_list])46            jobs.append(job)47        return jobs48if __name__ == '__main__':49    parser = argparse.ArgumentParser("adv method")50    parser.add_argument("--data-name", required=True, type=str, help="dataset_name",51                        choices=["acdc", "prostate", "mmwhsct"])...

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