def prepare_resume(self): """Tries to resume the experiment by using the defined resume path or PytorchExperiment.""" checkpoint_file = "" base_dir = "" reset_epochs = self._resume_reset_epochs if self._resume_path is not None: if isinstance(self._resume_path, str): if self._resume_path.endswith(".pth.tar"): checkpoint_file = self._resume_path base_dir = os.path.dirname( os.path.dirname(checkpoint_file)) elif self._resume_path.endswith( "checkpoint") or self._resume_path.endswith( "checkpoint/"): checkpoint_file = get_last_file(self._resume_path) base_dir = os.path.dirname( os.path.dirname(checkpoint_file)) elif "checkpoint" in os.listdir( self._resume_path) and "config" in os.listdir( self._resume_path): checkpoint_file = get_last_file(self._resume_path) base_dir = self._resume_path else: warnings.warn( "You have not selected a valid experiment folder, will search all sub folders", UserWarning) if self.elog is not None: self.elog.text_logger.log_to( "You have not selected a valid experiment folder, will search all " "sub folders", "warnings") checkpoint_file = get_last_file(self._resume_path) base_dir = os.path.dirname( os.path.dirname(checkpoint_file)) if base_dir: if not self._ignore_resume_config: load_config = Config() load_config.load(os.path.join(base_dir, "config/config.json")) self._config_raw = load_config self.config = Config.init_objects(self._config_raw) self.print("Loaded existing config from:", base_dir) if self.n_epochs is None: self.n_epochs = self._config_raw.get("n_epochs") if checkpoint_file: self.load_checkpoint(name="", path=checkpoint_file, save_types=self._resume_save_types) self._resume_path = checkpoint_file shutil.copyfile( checkpoint_file, os.path.join(self.elog.checkpoint_dir, "0_checkpoint.pth.tar")) self.print("Loaded existing checkpoint from:", checkpoint_file) self._resume_reset_epochs = reset_epochs if self._resume_reset_epochs: self._epoch_idx = 0
def __init__(self, base_dir, exp_dir="", name=None, decode_config_clean_str=True): super(ExperimentReader, self).__init__() self.base_dir = base_dir self.exp_dir = exp_dir self.work_dir = os.path.abspath( os.path.join(self.base_dir, self.exp_dir)) self.config_dir = os.path.join(self.work_dir, "config") self.log_dir = os.path.join(self.work_dir, "log") self.checkpoint_dir = os.path.join(self.work_dir, "checkpoint") self.img_dir = os.path.join(self.work_dir, "img") self.plot_dir = os.path.join(self.work_dir, "plot") self.save_dir = os.path.join(self.work_dir, "save") self.result_dir = os.path.join(self.work_dir, "result") self.config = Config() if decode_config_clean_str: self.config.load(os.path.join(self.config_dir, "config.json"), decoder_cls_=StringMultiTypeDecoder) else: self.config.load(os.path.join(self.config_dir, "config.json"), decoder_cls_=None) self.exp_info = Config() exp_info_file = os.path.join(self.config_dir, "exp.json") if os.path.exists(exp_info_file): self.exp_info.load(exp_info_file) self.__results_dict = None self.meta_name = None self.meta_star = False self.meta_ignore = False self.read_meta_info() if name is not None: self.exp_name = name elif self.meta_name is not None: self.exp_name = self.meta_name elif "name" in self.exp_info: self.exp_name = self.exp_info['name'] elif "exp_name" in self.config: self.exp_name = self.config['exp_name'] else: self.exp_name = "experiments" self.ignore = self.meta_ignore self.star = self.meta_star
def get_config(): # Set your own path, if needed. data_root_dir = os.path.abspath( 'data') # The path where the downloaded dataset is stored. c = Config( update_from_argv= True, # If set 'True', it allows to update each configuration by a cmd/terminal parameter. # Train parameters num_classes=2, in_channels=1, batch_size=3, # works with 6 on GB GPU patch_size=512, n_epochs=1, learning_rate=0.0002, fold= 0, # The 'splits.pkl' may contain multiple folds. Here we choose which one we want to use. device= "cuda", # 'cuda' is the default CUDA device, you can use also 'cpu'. For more information, see https://pytorch.org/docs/stable/notes/cuda.html # Logging parameters name='Basic_Unet', author='kleina', # Author of this project plot_freq=10, # How often should stuff be shown in visdom append_rnd_string= False, # Appends a random string to the experiment name to make it unique. start_visdom= True, # You can either start a visom server manually or have trixi start it for you. do_instancenorm= True, # Defines whether or not the UNet does a instance normalization in the contracting path do_load_checkpoint=False, checkpoint_dir='', # Adapt to your own path, if needed. google_drive_id='1jzeNU1EKnK81PyTsrx0ujfNl-t0Jo8uE', #spleen dataset_name='Task09_Spleen', base_dir=os.path.abspath( 'output_experiment'), # Where to log the output of the experiment. data_root_dir= data_root_dir, # The path where the downloaded dataset is stored. data_dir=os.path.join( data_root_dir, 'Task09_Spleen/preprocessed' ), # This is where your training and validation data is stored data_test_dir=os.path.join(data_root_dir, 'Task09_Spleen/preprocessed' ), # This is where your test data is stored split_dir=os.path.join( data_root_dir, 'Task09_Spleen' ), # This is where the 'splits.pkl' file is located, that holds your splits. # execute a segmentation process on a specific image using the model model_dir=os.path.join( os.path.abspath('output_experiment'), '20200108-035420_Basic_Unet/checkpoint/checkpoint_current' ), # the model being used for segmentation ) print(c) return c
def group_experiments_by(exps, group_by_list): configs_flat = [e.config.flat() for e in exps] ### Exclude already combined experiments config_diff = Config.difference_config_static(*configs_flat) group_diff_key_list = [] group_diff_val_list = [] for diff_key in group_by_list: if diff_key in config_diff: group_diff_key_list.append(diff_key) group_diff_val_list.append(set(config_diff[diff_key])) val_combis = itertools.product(*group_diff_val_list) group_dict = defaultdict(list) for val_combi in val_combis: for e in exps: e_config = e.config.flat() is_match = True for key, val in zip(group_diff_key_list, val_combi): if e_config[key] != val: is_match = False if is_match: group_dict[val_combi].append(e) return list(group_dict.values())
def get_config(self): combi_config = copy.deepcopy(self.experiments[0].config) config_diff = Config.difference_config_static( *[e.config for e in self.experiments], only_set=True) combi_config.update(config_diff) return combi_config
def gen_config(parser): args = parser.parse_args() # prepare config dictionary, add all arguments from args c = Config() for arg in vars(args): c[arg] = getattr(args, arg) return c
def load_config(self, name, **kwargs): """ Loads a config from a json file from the experiment config dir Args: name: the name of the config file Returns: A Config/ dict filled with the json file content """ if not name.endswith(".json"): name += ".json" c = Config() c.load(os.path.join(self.config_dir, name), **kwargs) return c
def get_config(): # Set your own path, if needed. data_root_dir = os.path.abspath( 'data') # The path where the downloaded dataset is stored. c = Config( update_from_argv= True, # If set 'True', it allows to update each configuration by a cmd/terminal parameter. # Train parameters num_classes=3, in_channels=1, batch_size=8, patch_size=64, n_epochs=10, learning_rate=0.0002, fold= 0, # The 'splits.pkl' may contain multiple folds. Here we choose which one we want to use. device= "cuda", # 'cuda' is the default CUDA device, you can use also 'cpu'. For more information, see https://pytorch.org/docs/stable/notes/cuda.html # Logging parameters name='Basic_Unet', author='kleina', # Author of this project plot_freq=10, # How often should stuff be shown in visdom append_rnd_string= False, # Appends a random string to the experiment name to make it unique. start_visdom= True, # You can either start a visom server manually or have trixi start it for you. do_instancenorm= True, # Defines whether or not the UNet does a instance normalization in the contracting path do_load_checkpoint=False, checkpoint_dir='', # Adapt to your own path, if needed. google_drive_id= '1RzPB1_bqzQhlWvU-YGvZzhx2omcDh38C', # This id is used to download the example dataset. dataset_name='Task04_Hippocampus', base_dir=os.path.abspath( 'output_experiment'), # Where to log the output of the experiment. data_root_dir= data_root_dir, # The path where the downloaded dataset is stored. data_dir=os.path.join( data_root_dir, 'Task04_Hippocampus/preprocessed' ), # This is where your training and validation data is stored data_test_dir=os.path.join(data_root_dir, 'Task04_Hippocampus/preprocessed' ), # This is where your test data is stored split_dir=os.path.join( data_root_dir, 'Task04_Hippocampus' ), # This is where the 'splits.pkl' file is located, that holds your splits. ) print(c) return c
def _save_exp_config(self): if self.elog is not None and not isinstance(self.elog, Mock): cur_time = time.strftime("%y-%m-%d_%H:%M:%S", time.localtime(time.time())) self.elog.save_config(Config(**{'name': self.name, 'time': cur_time, 'state': "Stub", 'current_time': cur_time, 'epoch': 0 }), "exp")
def __init__(self, base_dir, exp_dirs=(), name=None, decode_config_clean_str=True): self.base_dir = base_dir if name is None or name == "": self.exp_name = "combi-experiments" else: self.exp_name = name self.experiments = [] for exp_dir in exp_dirs: self.experiments.append( ExperimentReader( base_dir=base_dir, exp_dir=exp_dir, decode_config_clean_str=decode_config_clean_str)) exp_base_dirs = os.path.commonpath( [os.path.dirname(e.work_dir) for e in self.experiments]) if exp_base_dirs != "": self.base_dir = exp_base_dirs self.exp_info = Config() self.exp_info["epoch"] = -1 self.exp_info["name"] = self.exp_name self.exp_info["state"] = "Combined" self.exp_info["time"] = time.strftime("%y-%m-%d_%H:%M:%S", time.localtime(time.time())) self.__results_dict = None self.work_dir = None self.config_dir = "not_saved_yet" self.log_dir = "not_saved_yet" self.checkpoint_dir = "not_saved_yet" self.img_dir = "not_saved_yet" self.plot_dir = "not_saved_yet" self.save_dir = "not_saved_yet" self.result_dir = "not_saved_yet" self.exp_dir = "not_saved_yet" self.meta_name = None self.meta_star = False self.meta_ignore = False self.config = self.get_config() self.elog = None
def get_config(): # Set your own path, if needed. #data_root_dir = os.path.abspath('data') # The path where the downloaded dataset is stored. #data_root_dir = "/home/ramesh/Desktop/WS/Implementation/experiment/Data/Filtereddataset" #data_root_dir ="/home/ramesh/Desktop/IIITB/experiment/data/FilteredDataSet" data_root_dir = "/home/ramesh/Desktop/IIITB/experiment/data/NucliiData" taskName = "Nuclii Segmentation" #taskName = "Task09_Spleen" c = Config( update_from_argv=True, # Train parameters num_classes=1, in_channels=1, batch_size=6, patch_size=64, n_epochs=50, learning_rate=0.00001, fold= 0, # The 'splits.pkl' may contain multiple folds. Here we choose which one we want to use. device= "cuda", # 'cuda' is the default CUDA device, you can use also 'cpu'. For more information, see https://pytorch.org/docs/stable/notes/cuda.html # Logging parameters name='Segmentation_Experiment_Unet_Nuclii', plot_freq=10, # How often should stuff be shown in visdom append_rnd_string=False, start_visdom=True, do_instancenorm= True, # Defines whether or not the UNet does a instance normalization in the contracting path do_load_checkpoint=False, checkpoint_dir=' ', # Adapt to your own path, if needed. #google_drive_id='1RzPB1_bqzQhlWvU-YGvZzhx2omcDh38C', dataset_name=taskName, base_dir=os.path.abspath( 'output_experiment'), # Where to log the output of the experiment. data_root_dir= data_root_dir, # The path where the downloaded dataset is stored. data_dir=os.path.join( data_root_dir, taskName, 'imagesTr' ), # This is where your training and validation data is stored #data_test_dir=os.path.join(data_root_dir, 'Task04_Hippocampus/preprocessed'), # This is where your test data is stored split_dir=os.path.join( data_root_dir, taskName, 'preprocessed' ), # This is where the 'splits.pkl' file is located, that holds your splits. ) print(c) return c
def get_config(): # Set your own path, if needed. #data_root_dir = os.path.abspath('data') # The path where the downloaded dataset is stored. #data_root_dir = "/home/ramesh/Desktop/WS/Implementation/data/MedSegDecathlon" data_root_dir = "/home/ramesh/Desktop/WS/Implementation/data/Brats17" c = Config( update_from_argv=True, # Train parameters num_classes=4, in_channels=1, batch_size=8, patch_size=160, n_epochs=1, learning_rate=0.0002, fold= 0, # The 'splits.pkl' may contain multiple folds. Here we choose which one we want to use. device= "cuda", # 'cuda' is the default CUDA device, you can use also 'cpu'. For more information, see https://pytorch.org/docs/stable/notes/cuda.html # Logging parameters name='Basic_Unet', plot_freq=1, # How often should stuff be shown in visdom append_rnd_string=False, start_visdom=True, do_instancenorm= True, # Defines whether or not the UNet does a instance normalization in the contracting path do_load_checkpoint=False, checkpoint_dir= '/home/ramesh/Desktop/WS/Implementation/experiment/basic_unet_example/output_experiment/20190504-180510_Basic_Unet', # Adapt to your own path, if needed. google_drive_id='1RzPB1_bqzQhlWvU-YGvZzhx2omcDh38C', dataset_name='Brats17', base_dir=os.path.abspath( 'output_experiment'), # Where to log the output of the experiment. data_root_dir= data_root_dir, # The path where the downloaded dataset is stored. data_dir=os.path.join( data_root_dir, 'Brats17_preprocessed' ), # This is where your training and validation data is stored data_test_dir=os.path.join( data_root_dir, 'Brats17_preprocessed'), # This is where your test data is stored split_dir=os.path.join( data_root_dir, 'Brats17_preprocessed' ), # This is where the 'splits.pkl' file is located, that holds your splits. ) print(c) return c
def _save_exp_config(self): if self.elog is not None: cur_time = time.strftime("%y-%m-%d_%H:%M:%S", time.localtime(time.time())) self.elog.save_config( Config( **{ 'name': self.exp_name, 'time': self._time_start, 'state': self._exp_state, 'current_time': cur_time, 'epoch': self._epoch_idx }), "exp")
def get_add_config(): # Set your own path, if needed. data_root_dir = os.path.abspath( 'data') # The path where the downloaded dataset is stored. c = Config( do_instancenorm= True, # Defines whether or not the UNet does a instance normalization in the contracting path do_load_checkpoint=False, checkpoint_dir='', # Adapt to your own path, if needed. google_drive_id='1RzPB1_bqzQhlWvU-YGvZzhx2omcDh38C', dataset_name='CHD_segmentation_dataset', base_dir=os.path.abspath( 'output_experiment'), # Where to log the output of the experiment. data_root_dir= data_root_dir, # The path where the downloaded dataset is stored. data_dir=os.path.join( data_root_dir, 'CHD_interpolation_dataset/preprocessed' ), # This is where your training and validation data is stored data_test_dir=os.path.join(data_root_dir, 'CHD_interpolation_dataset/preprocessed' ), # This is where your test data is stored split_dir=os.path.join( data_root_dir, 'CHD_interpolation_dataset' ), # This is where the 'splits.pkl' file is located, that holds your splits. scaled_image_16_dir=os.path.join( data_root_dir, 'CHD_interpolation_dataset/scaled_to_16'), scaled_image_32_dir=os.path.join( data_root_dir, 'CHD_interpolation_dataset/scaled_to_32'), scaled_image_64_dir=os.path.join( data_root_dir, 'CHD_interpolation_dataset/scaled_to_64'), stage_1_dir=os.path.join(data_root_dir, 'CHD_interpolation_dataset/stage_1'), stage_1_dir_32=os.path.join(data_root_dir, 'CHD_interpolation_dataset/stage_1_32'), # stage_1_dir = os.path.join(data_root_dir, 'CHD_segmentation_dataset/stage_1') ) print(c) return c
def get_config(): c = Config() c.batch_size = 6 c.patch_size = 512 c.n_epochs = 20 c.learning_rate = 0.0002 c.do_ce_weighting = True c.do_batchnorm = True if torch.cuda.is_available(): c.use_cuda = True else: c.use_cuda = False c.rnd_seed = 1 c.log_interval = 200 c.base_dir='/media/kleina/Data2/output/meddec' c.data_dir='/media/kleina/Data2/Data/meddec/Task07_Pancreas_expert_preprocessed' c.split_dir='/media/kleina/Data2/Data/meddec/Task07_Pancreas_preprocessed' c.data_file = 'C:/dev/data/Endoviz2018/GIANA/polyp_detection_segmentation/image_gt_data_file_list_all_640x640.csv' c.additional_slices=5 c.name='' print(c) return c
def get_config_heart(fine_tune_type='None', exp_name='', checkpoint_filename='', checkpoint_dir='', nr_train_samples=0, download_data_from_drive=False): # Set your own path, if needed. data_root_dir = os.path.abspath( 'data') # The path where the downloaded dataset is stored. c = Config( update_from_argv=True, # Train parameters num_classes=2, in_channels=1, batch_size=8, patch_size=256, n_epochs=60, learning_rate=0.0002, fold= 0, # The 'splits.pkl' may contain multiple folds. Here we choose which one we want to use. device="cuda", # 'cuda' is the default CUDA device, you can use also 'cpu'. For more information, see https://pytorch.org/docs/stable/notes/cuda.html # Logging parameters name=exp_name, author='maxi', # Author of this project plot_freq=10, # How often should stuff be shown in visdom append_rnd_string=False, start_visdom=False, do_instancenorm= True, # Defines whether or not the UNet does a instance normalization in the contracting path do_load_checkpoint=True, checkpoint_filename=checkpoint_filename, checkpoint_dir=checkpoint_dir, fine_tune=fine_tune_type, # Adapt to your own path, if needed. download_data=download_data_from_drive, google_drive_id='1RzPB1_bqzQhlWvU-YGvZzhx2omcDh38C', dataset_name='Task02_Heart', base_dir=os.path.abspath( 'output_experiment'), # Where to log the output of the experiment. data_root_dir= data_root_dir, # The path where the downloaded dataset is stored. data_dir=os.path.join(data_root_dir, 'Task02_Heart/preprocessed'), # This is where your training and validation data is stored data_test_dir=os.path.join(data_root_dir, 'Task02_Heart/preprocessed'), # This is where your test data is stored split_dir=os.path.join(data_root_dir, 'Task02_Heart'), # This is where the 'splits.pkl' file is located, that holds your splits. train_samples=nr_train_samples, # This is the amount of samples used in the train set. Use 0 for original split (1/2 train, 1/4 val, 1/4 test) # The validation set will be the same size, and the test set is the rest of the images # Testing visualize_segm=True) print(c) return c
def run_experiment(experiment, configs, args, mods=None, **kwargs): # set a few defaults if "explogger_kwargs" not in kwargs: kwargs["explogger_kwargs"] = dict( folder_format="{experiment_name}_%Y%m%d-%H%M%S") if "explogger_freq" not in kwargs: kwargs["explogger_freq"] = 1 if "resume_save_types" not in kwargs: kwargs["resume_save_types"] = ("model", "simple", "th_vars", "results") config = Config(file_=args.config) if args.config is not None else Config() config.update_missing(configs[args.default_config].deepcopy()) if args.mods is not None and mods is not None: for mod in args.mods: config.update(mods[mod]) config = Config(config=config, update_from_argv=True) # GET EXISTING EXPERIMENTS TO BE ABLE TO SKIP CERTAIN CONFIGS if args.skip_existing: existing_configs = [] for exp in os.listdir(args.base_dir): try: existing_configs.append( Config(file_=os.path.join(args.base_dir, exp, "config", "config.json"))) except Exception as e: pass if args.grid is not None: grid = GridSearch().read(args.grid) else: grid = [{}] for combi in grid: config.update(combi) if args.skip_existing: skip_this = False for existing_config in existing_configs: if existing_config.contains(config): skip_this = True break if skip_this: continue if "backup_every" in config: kwargs["save_checkpoint_every_epoch"] = config["backup_every"] loggers = {} if args.visdomlogger: loggers["v"] = ("visdom", {}, 1) if args.tensorboardxlogger is not None: if args.tensorboardxlogger == "same": loggers["tx"] = ("tensorboard", {}, 1) else: loggers["tx"] = ("tensorboard", { "target_dir": args.tensorboardxlogger }, 1) if args.telegramlogger: kwargs["use_telegram"] = True if args.automatic_description: difference_to_default = Config.difference_config_static( config, configs["DEFAULTS"]).flat(keep_lists=True, max_split_size=0, flatten_int=True) description_str = "" for key, val in difference_to_default.items(): val = val[0] description_str = "{} = {}\n{}".format(key, val, description_str) config.description = description_str exp = experiment(config=config, base_dir=args.base_dir, resume=args.resume, ignore_resume_config=args.ignore_resume_config, loggers=loggers, **kwargs) trained = False if args.resume is None or args.test is False: exp.run() trained = True if args.test: exp.run_test(setup=not trained) if isinstance(args.resume, str) and exp.elog is not None and args.copy_test: for f in glob.glob(os.path.join(exp.elog.save_dir, "test*")): if os.path.isdir(f): shutil.copytree( f, os.path.join(args.resume, "save", os.path.basename(f))) else: shutil.copy(f, os.path.join(args.resume, "save"))
def experiment(base_dir): experiment_paths = request.args.getlist('exp') name = request.args.get('name', "") do_save = request.args.get('save', "") combi = request.args.get('combi', 'false') experiments = [] if combi == "true": combi_exp = CombiExperimentReader(base_dir, experiment_paths, name=name) if do_save == "true": combi_exp.save() experiments = [combi_exp] else: # Get all Experiments for experiment_path in sorted(experiment_paths): exp = ExperimentReader(base_dir, experiment_path) experiments.append(exp) # Assign unique names exp_names = [exp.exp_name for exp in experiments] if len(exp_names) > len(set(exp_names)): for i, exp in enumerate(experiments): exp.exp_name += str(i) exp_names = [exp.exp_name for exp in experiments] # Site Content content = {} # Get config default_val = "-" combi_config = {} exp_configs = [exp.config.flat(False) for exp in experiments] diff_config_keys = list( Config.difference_config_static(*exp_configs).keys()) config_keys = set([k for c in exp_configs for k in c.keys()]) for k in sorted(config_keys): combi_config[k] = [] for conf in exp_configs: combi_config[k].append(conf.get(k, default_val)) config_keys = list(sorted(list(config_keys))) # Get results default_val = "-" combi_results = {} exp_results = [exp.get_results() for exp in experiments] result_keys = set([k for r in exp_results for k in r.keys()]) for k in sorted(result_keys): combi_results[k] = [] for res in exp_results: combi_results[k].append(res.get(k, default_val)) result_keys = list(sorted(list(result_keys))) # Get images images = OrderedDict({}) image_keys = set() image_path = {} for exp in experiments: exp_images = exp.get_images() img_groups = group_images(exp_images) images[exp.exp_name] = img_groups image_path[exp.exp_name] = exp.img_dir image_keys.update(list(img_groups.keys())) image_keys = list(image_keys) image_keys.sort() # Get Plots plots = OrderedDict({}) for exp in experiments: exp_plots = exp.get_plots() plots[exp.exp_name] = exp_plots # Get logs logs_dict = OrderedDict({}) for exp in experiments: exp_logs = [(os.path.basename(l), exp.exp_dir) for l in exp.get_logs()] logs_dict[exp.exp_name] = exp_logs content["title"] = experiments content["images"] = { "img_path": image_path, "imgs": images, "img_keys": image_keys } content["plots"] = {"plots": plots} content["config"] = { "exps": experiments, "configs": combi_config, "keys": config_keys, "diff_keys": diff_config_keys } content["results"] = { "exps": exp_names, "results": combi_results, "keys": result_keys } content["logs"] = {"logs_dict": logs_dict} return render_template('experiment.html', **content)
def make_defaults(patch_size=112, in_channels=4, latent_size=3, labels=[0, 1, 2, 3]): if hasattr(patch_size, "__iter__"): if len(patch_size) > 1: patch_size = tuple(patch_size) else: patch_size = patch_size[0] if not hasattr(patch_size, "__iter__"): patch_size = tuple([ patch_size, ] * 3) DEFAULTS = Config( # Base name=os.path.basename(__file__).split(".")[0], description=DESCRIPTION, n_epochs=50000, batch_size=2, batch_size_val=1, patch_size=patch_size, in_channels=in_channels, out_channels=len(labels), latent_size=latent_size, seed=1, device="cuda", # Data split_val=3, split_test=4, data_module=data, data_dir=None, # we're setting data_module.data_dir if this is given mmap_mode="r", npz=False, debug=0, # 1 selects (10, 5, 5) patients, 2 a single batch train_on_all=False, # adds val and test to training set generator_train=data.RandomBatchGenerator, generator_val=data.LinearBatchGenerator, transforms_train={ 0: { "type": SpatialTransform, "kwargs": { "patch_size": patch_size, "patch_center_dist_from_border": patch_size[0] // 2, "do_elastic_deform": False, "p_el_per_sample": 0.2, "p_rot_per_sample": 0.3, "p_scale_per_sample": 0.3 }, "active": True }, 1: { "type": MirrorTransform, "kwargs": { "axes": (0, 1, 2) }, "active": True }, 2: { "type": SegLabelSelectionBinarizeTransform, "kwargs": { "label": [1, 2, 3] }, "active": False } }, transforms_val={ 0: { "type": CenterCropTransform, "kwargs": { "crop_size": patch_size }, "active": False }, 1: { "type": SegLabelSelectionBinarizeTransform, "kwargs": { "label": [1, 2, 3] }, "active": False }, 2: { "type": SpatialTransform, "kwargs": { "patch_size": patch_size, "patch_center_dist_from_border": patch_size[0] // 2, "do_elastic_deform": False, "do_rotation": False, "do_scale": True, "p_scale_per_sample": 1, "scale": (1.25, 1.25) }, "active": False } }, augmenter_train=MultiThreadedAugmenter, augmenter_train_kwargs={ "num_processes": 11, "num_cached_per_queue": 6, "pin_memory": True }, augmenter_val=MultiThreadedAugmenter, augmenter_val_kwargs={ "num_processes": 2, "pin_memory": True }, # Model model=ProbabilisticSegmentationNet, model_kwargs={ "in_channels": in_channels, "out_channels": len(labels), "num_feature_maps": 24, "latent_size": latent_size, "depth": 5, "latent_distribution": distributions.Normal, "task_op": InjectionUNet3D, "task_kwargs": { "output_activation_op": nn.LogSoftmax, "output_activation_kwargs": { "dim": 1 }, "activation_kwargs": { "inplace": True } }, "prior_op": InjectionConvEncoder3D, "prior_kwargs": { "in_channels": in_channels, "out_channels": latent_size * 2, "depth": 5, "block_depth": 2, "num_feature_maps": 24, "feature_map_multiplier": 2, "activation_kwargs": { "inplace": True }, "norm_depth": 2, }, "posterior_op": InjectionConvEncoder3D, "posterior_kwargs": { "in_channels": in_channels + len(labels), "out_channels": latent_size * 2, "depth": 5, "block_depth": 2, "num_feature_maps": 24, "feature_map_multiplier": 2, "activation_kwargs": { "inplace": True }, "norm_depth": 2, }, }, model_init_weights_args=[nn.init.kaiming_uniform_, 0], model_init_bias_args=[nn.init.constant_, 0], # Learning optimizer=optim.Adam, optimizer_kwargs={"lr": 1e-4}, scheduler=optim.lr_scheduler.StepLR, scheduler_kwargs={ "step_size": 200, "gamma": 0.985 }, criterion_segmentation=nn.NLLLoss, criterion_segmentation_kwargs={"reduction": "sum"}, criterion_latent=distributions.kl_divergence, criterion_latent_kwargs={}, criterion_latent_init=False, criterion_segmentation_seg_onehot=False, criterion_segmentation_weight=1.0, criterion_latent_weight=1.0, criterion_segmentation_seg_dtype=torch.long, # Logging backup_every=1000, validate_every=1000, validate_subset=0.1, # validate only this percentage randomly show_every=10, validate_metrics=["Dice"], labels=labels, evaluator=Evaluator, evaluator_kwargs={ "label_values": list(labels) + [tuple(labels[1:])], "label_names": { 0: "Background", 1: "Edema", 2: "Enhancing", 3: "Necrosis", tuple(labels[1:]): "Whole Tumor" }, "nan_for_nonexisting": True }, val_save_output=False, val_example_samples=10, val_save_images=False, latent_plot_range=[-5, 5], test_on_val=True, test_save_output=False, test_future=True, test_std_factor=3, test_std_scale=1.) TASKMEAN = Config( criterion_segmentation_kwargs={"reduction": "elementwise_mean"}) ELASTIC = Config( transforms_train={0: { "kwargs": { "do_elastic_deform": True } }}) NONORM = Config(model_kwargs={ "prior_kwargs": { "norm_depth": 0 }, "posterior_kwargs": { "norm_depth": 0 } }) FULLNORM = Config( model_kwargs={ "prior_kwargs": { "norm_depth": "full" }, "posterior_kwargs": { "norm_depth": "full" } }) BATCHNORM = Config( model_kwargs={ "prior_kwargs": { "norm_op": nn.BatchNorm3d }, "posterior_kwargs": { "norm_op": nn.BatchNorm3d }, "task_kwargs": { "norm_op": nn.BatchNorm3d } }) WHOLETUMOR = Config(transforms_train={2: { "active": True }}, transforms_val={1: { "active": True }}, out_channels=2, labels=[0, 1], model_kwargs={ "out_channels": 2, "posterior_kwargs": { "in_channels": in_channels + 2 } }, evaluator_kwargs={ "label_values": [0, 1], "label_names": { 0: "Background", 1: "Whole Tumor" } }) ENHANCING = Config( transforms_train={2: { "kwargs": { "label": 2 }, "active": True }}, transforms_val={1: { "kwargs": { "label": 2 }, "active": True }}, out_channels=2, labels=[0, 1], model_kwargs={ "out_channels": 2, "posterior_kwargs": { "in_channels": in_channels + 2 } }, evaluator_kwargs={ "label_values": [0, 1], "label_names": { 0: "Background", 1: "Whole Tumor" } }) NOAUGMENT = Config( transforms_train={ 0: { "kwargs": { "p_el_per_sample": 0, "p_rot_per_sample": 0, "p_scale_per_sample": 0 } }, 1: { "active": False } }) LOWAUGMENT = Config( transforms_train={ 0: { "kwargs": { "p_el_per_sample": 0., "p_rot_per_sample": 0.15, "p_scale_per_sample": 0.15 } } }) NOBG = Config(criterion_segmentation_kwargs={"ignore_index": 0}) VALIDATEPATCHED = Config(transforms_val={2: {"active": True}}) MODS = { "TASKMEAN": TASKMEAN, "ELASTIC": ELASTIC, "NONORM": NONORM, "FULLNORM": FULLNORM, "BATCHNORM": BATCHNORM, "WHOLETUMOR": WHOLETUMOR, "ENHANCING": ENHANCING, "NOAUGMENT": NOAUGMENT, "LOWAUGMENT": LOWAUGMENT, "NOBG": NOBG, "VALIDATEPATCHED": VALIDATEPATCHED } return {"DEFAULTS": DEFAULTS}, MODS
def test_2Dexperiment(): c = Config() c.batch_size = 200 c.n_epochs = 40 c.learning_rate = 0.001 if torch.cuda.is_available(): c.use_cuda = True else: c.use_cuda = False c.rnd_seed = 1 c.log_interval = 200 # model-specific c.n_coupling = 8 c.prior = 'gauss' exp = SmileyExperiment( c, name='gauss', n_epochs=c.n_epochs, seed=42, base_dir='experiment_dir', loggers={'visdom': ['visdom', { "exp_name": "myenv" }]}) exp.run() # sampling samples = exp.model.sample(1000).cpu().numpy() sns.jointplot(samples[:, 0], samples[:, 1]) plt.show()
class ExperimentReader(object): """Reader class to read out experiments created by :class:`trixi.experimentlogger.ExperimentLogger`. Args: work_dir (str): Directory with the structure defined by :class:`trixi.experimentlogger.ExperimentLogger`. name (str): Optional name for the experiment. If None, will try to read name from experiment config. """ def __init__(self, base_dir, exp_dir="", name=None, decode_config_clean_str=True): super(ExperimentReader, self).__init__() self.base_dir = base_dir self.exp_dir = exp_dir self.work_dir = os.path.abspath( os.path.join(self.base_dir, self.exp_dir)) self.config_dir = os.path.join(self.work_dir, "config") self.log_dir = os.path.join(self.work_dir, "log") self.checkpoint_dir = os.path.join(self.work_dir, "checkpoint") self.img_dir = os.path.join(self.work_dir, "img") self.plot_dir = os.path.join(self.work_dir, "plot") self.save_dir = os.path.join(self.work_dir, "save") self.result_dir = os.path.join(self.work_dir, "result") self.config = Config() if decode_config_clean_str: self.config.load(os.path.join(self.config_dir, "config.json"), decoder_cls_=StringMultiTypeDecoder) else: self.config.load(os.path.join(self.config_dir, "config.json"), decoder_cls_=None) self.exp_info = Config() exp_info_file = os.path.join(self.config_dir, "exp.json") if os.path.exists(exp_info_file): self.exp_info.load(exp_info_file) self.__results_dict = None self.meta_name = None self.meta_star = False self.meta_ignore = False self.read_meta_info() if name is not None: self.exp_name = name elif self.meta_name is not None: self.exp_name = self.meta_name elif "name" in self.exp_info: self.exp_name = self.exp_info['name'] elif "exp_name" in self.config: self.exp_name = self.config['exp_name'] else: self.exp_name = "experiments" self.ignore = self.meta_ignore self.star = self.meta_star @staticmethod def get_file_contents(folder): """Get all files in a folder. Returns: list: All files joined with folder path. """ if os.path.isdir(folder): list_ = map(lambda x: os.path.join(folder, x), sorted(os.listdir(folder))) return list(filter(lambda x: os.path.isfile(x), list_)) else: return [] def get_images(self): imgs = [] imgs += ExperimentReader.get_file_contents(self.img_dir) if os.path.isdir(self.img_dir): for f in os.listdir(self.img_dir): f = os.path.join(self.img_dir, f) if os.path.isdir(f): imgs += ExperimentReader.get_file_contents(f) return imgs def get_plots(self): return ExperimentReader.get_file_contents(self.plot_dir) def get_checkpoints(self): return ExperimentReader.get_file_contents(self.checkpoint_dir) def get_logs(self): return ExperimentReader.get_file_contents(self.log_dir) def get_log_file_content(self, file_name): """Read out log file and HTMLify. Args: file_name (str): Name of the log file. Returns: str: Log file contents as HTML ready string. """ content = "" log_file = os.path.join(self.log_dir, file_name) if os.path.exists(log_file): with open(log_file, 'r') as f: content = f.read() content = content.replace("\n", "<br>") return content def get_results_log(self): """Build result dictionary. During the experiment result items are written out as a stream of quasi-atomic units. This reads the stream and builds arrays of corresponding items. The resulting dict looks like this:: { "result group": { "result": { "counter": x-array, "data": y-array } } } Returns: dict: Result dictionary. """ results_merged = {} results = [] try: with open(os.path.join(self.result_dir, "results-log.json"), "r") as results_file: results = json.load(results_file) except Exception as e: try: with open(os.path.join(self.result_dir, "results-log.json"), "r") as results_file: results_str = results_file.readlines() if "]" in "]" in "".join(results_str): results_str = [ rs.replace("]", ",") for rs in results_str ] results_str.append("{}]") results_json = "".join(results_str) results = json.loads(results_json) except Exception as ee: print("Could not load result log from", self.result_dir) print(ee) for result in results: for key in result.keys(): counter = result[key]["counter"] data = result[key]["data"] label = str(result[key]["label"]) if label not in results_merged: results_merged[label] = {} if key not in results_merged[label]: results_merged[label][key] = defaultdict(list) results_merged[label][key]["data"].append(data) results_merged[label][key]["counter"].append(counter) if "max" in result[key] and "min" in result[key]: results_merged[label][key]["max"].append( result[key]["max"]) results_merged[label][key]["min"].append( result[key]["min"]) return results_merged def get_results(self): """Get the last result item. Returns: dict: The last result item in the experiment. """ if self.__results_dict is None: self.__results_dict = {} results_file = os.path.join(self.result_dir, "results.json") if os.path.exists(results_file): try: with open(results_file, "r") as f: self.__results_dict = json.load(f) except Exception as e: pass return self.__results_dict def ignore_experiment(self): """Create a flag file, so the browser ignores this experiment.""" self.update_meta_info(ignore=True) def read_meta_info(self): """Reads the meta info of the experiment i.e. new name, stared or ignored""" meta_dict = {} meta_file = os.path.join(self.work_dir, ".exp_info") if os.path.exists(meta_file): with open(meta_file, "r") as mf: meta_dict = json.load(mf) self.meta_name = meta_dict.get("name") self.meta_star = meta_dict.get("star", False) self.meta_ignore = meta_dict.get("ignore", False) def update_meta_info(self, name=None, star=None, ignore=None): """ Updates the meta info i.e. new name, stared or ignored and saves it in the experiment folder Args: name (str): New name of the experiment star (bool): Flag, if experiment is starred/ favorited ignore (boll): Flag, if experiment should be ignored """ if name is not None: self.meta_name = name if star is not None: self.meta_star = star if ignore is not None: self.meta_ignore = ignore meta_dict = { "name": self.meta_name, "star": self.meta_star, "ignore": self.meta_ignore } meta_file = os.path.join(self.work_dir, ".exp_info") with open(meta_file, "w") as mf: json.dump(meta_dict, mf)
def __init__(self, config=None, name=None, n_epochs=None, seed=None, base_dir=None, globs=None, resume=None, ignore_resume_config=False, resume_save_types=("model", "optimizer", "simple", "th_vars", "results"), resume_reset_epochs=True, parse_sys_argv=False, parse_config_sys_argv=True, checkpoint_to_cpu=True, safe_checkpoint_every_epoch=1, use_visdomlogger=True, visdomlogger_kwargs=None, visdomlogger_c_freq=1, use_explogger=True, explogger_kwargs=None, explogger_c_freq=100, use_telegrammessagelogger=False, telegrammessagelogger_kwargs=None, telegrammessagelogger_c_freq=1000, append_rnd_to_name=False): # super(PytorchExperiment, self).__init__() Experiment.__init__(self) if parse_sys_argv: config_path, resume_path = get_vars_from_sys_argv() if config_path: config = config_path if resume_path: resume = resume_path self._config_raw = None if isinstance(config, str): self._config_raw = Config(file_=config, update_from_argv=parse_config_sys_argv) elif isinstance(config, Config): self._config_raw = Config(config=config, update_from_argv=parse_config_sys_argv) elif isinstance(config, dict): self._config_raw = Config(config=config, update_from_argv=parse_config_sys_argv) else: self._config_raw = Config(update_from_argv=parse_config_sys_argv) self.n_epochs = n_epochs if 'n_epochs' in self._config_raw: self.n_epochs = self._config_raw["n_epochs"] if self.n_epochs is None: self.n_epochs = 0 self._seed = seed if 'seed' in self._config_raw: self._seed = self._config_raw.seed if self._seed is None: random_data = os.urandom(4) seed = int.from_bytes(random_data, byteorder="big") self._config_raw.seed = seed self._seed = seed self.exp_name = name if 'name' in self._config_raw: self.exp_name = self._config_raw["name"] if append_rnd_to_name: rnd_str = ''.join( random.choice(string.ascii_letters + string.digits) for _ in range(5)) self.exp_name += "_" + rnd_str if 'base_dir' in self._config_raw: base_dir = self._config_raw["base_dir"] self._checkpoint_to_cpu = checkpoint_to_cpu self._safe_checkpoint_every_epoch = safe_checkpoint_every_epoch self.results = dict() # Init loggers logger_list = [] self.vlog = None if use_visdomlogger: if visdomlogger_kwargs is None: visdomlogger_kwargs = {} self.vlog = PytorchVisdomLogger(name=self.exp_name, **visdomlogger_kwargs) if visdomlogger_c_freq is not None and visdomlogger_c_freq > 0: logger_list.append((self.vlog, visdomlogger_c_freq)) self.elog = None if use_explogger: if explogger_kwargs is None: explogger_kwargs = {} self.elog = PytorchExperimentLogger(base_dir=base_dir, experiment_name=self.exp_name, **explogger_kwargs) if explogger_c_freq is not None and explogger_c_freq > 0: logger_list.append((self.elog, explogger_c_freq)) # Set results log dict to the right path self.results = ResultLogDict("results-log.json", base_dir=self.elog.result_dir) self.tlog = None if use_telegrammessagelogger: if telegrammessagelogger_kwargs is None: telegrammessagelogger_kwargs = {} self.tlog = TelegramMessageLogger(**telegrammessagelogger_kwargs, exp_name=self.exp_name) if telegrammessagelogger_c_freq is not None and telegrammessagelogger_c_freq > 0: logger_list.append((self.tlog, telegrammessagelogger_c_freq)) self.clog = CombinedLogger(*logger_list) set_seed(self._seed) # Do the resume stuff self._resume_path = None self._resume_save_types = resume_save_types self._ignore_resume_config = ignore_resume_config self._resume_reset_epochs = resume_reset_epochs if resume is not None: if isinstance(resume, str): if resume == "last": self._resume_path = os.path.join( base_dir, sorted(os.listdir(base_dir))[-1]) else: self._resume_path = resume elif isinstance(resume, PytorchExperiment): self._resume_path = resume.elog.base_dir if self._resume_path is not None and not self._ignore_resume_config: self._config_raw.update(Config(file_=os.path.join( self._resume_path, "config", "config.json")), ignore=list( map(lambda x: re.sub("^-+", "", x), sys.argv))) # self.elog.save_config(self.config, "config_pre") if globs is not None: zip_name = os.path.join(self.elog.save_dir, "sources.zip") SourcePacker.zip_sources(globs, zip_name) # Init objects in config self.config = Config.init_objects(self._config_raw) atexit.register(self.at_exit_func)
class PytorchExperiment(Experiment): """ A PytorchExperiment extends the basic functionality of the :class:`.Experiment` class with convenience features for PyTorch (and general logging) such as creating a folder structure, saving, plotting results and checkpointing your experiment. The basic life cycle of a PytorchExperiment is the same as :class:`.Experiment`:: setup() prepare() for epoch in n_epochs: train() validate() end() where the distinction between the first two is that between them PytorchExperiment will automatically restore checkpoints and save the :attr:`_config_raw` in :meth:`._setup_internal`. Please see below for more information on this. To get your own experiment simply inherit from the PytorchExperiment and overwrite the :meth:`.setup`, :meth:`.prepare`, :meth:`.train`, :meth:`.validate` method (or you can use the `very` experimental decorator :func:`.experimentify` to convert your class into a experiment). Then you can run your own experiment by calling the :meth:`.run` method. Internally PytorchExperiment will provide a number of member variables which you can access. - n_epochs Number of epochs. - exp_name Name of your experiment. - config The (initialized) :class:`.Config` of your experiment. You can access the uninitialized one via :attr:`_config_raw`. - result A dict in which you can store your result values. If a :class:`.PytorchExperimentLogger` is used, results will be a :class:`.ResultLogDict` that directly automatically writes to a file and also stores the N last entries for each key for quick access (e.g. to quickly get the running mean). - vlog (if use_visdomlogger is True) A :class:`.PytorchVisdomLogger` instance which can log your results to a running visdom server. Start the server via :code:`python -m visdom.server` or pass :data:`auto_start=True` in the :attr:`visdomlogger_kwargs`. - elog (if use_explogger is True) A :class:`.PytorchExperimentLogger` instance which can log your results to a given folder. - tlog (if use_telegrammessagelogger is True) A :class:`.TelegramMessageLogger` instance which can send the results to your telegram account - clog A :class:`.CombinedLogger` instance which logs to all loggers with different frequencies (specified with the :attr:`_c_freq` for each logger where 1 means every time and N means every Nth time, e.g. if you only want to send stuff to Visdom every 10th time). The most important attribute is certainly :attr:`.config`, which is the initialized :class:`.Config` for the experiment. To understand how it needs to be structured to allow for automatic instantiation of types, please refer to its documentation. If you decide not to use this functionality, :attr:`config` and :attr:`_config_raw` are identical. **Beware however that by default the Pytorchexperiment only saves the raw config** after :meth:`.setup`. If you modify :attr:`config` during setup, make sure to implement :meth:`._setup_internal` yourself should you want the modified config to be saved:: def _setup_internal(self): super(YourExperiment, self)._setup_internal() # calls .prepare_resume() self.elog.save_config(self.config, "config") Args: config (dict or Config): Configures your experiment. If :attr:`name`, :attr:`n_epochs`, :attr:`seed`, :attr:`base_dir` are given in the config, it will automatically overwrite the other args/kwargs with the values from the config. In addition (defined by :attr:`parse_config_sys_argv`) the config automatically parses the argv arguments and updates its values if a key matches a console argument. name (str): The name of the PytorchExperiment. n_epochs (int): The number of epochs (number of times the training cycle will be executed). seed (int): A random seed (which will set the random, numpy and torch seed). base_dir (str): A base directory in which the experiment result folder will be created. globs: The :func:`globals` of the script which is run. This is necessary to get and save the executed files in the experiment folder. resume (str or PytorchExperiment): Another PytorchExperiment or path to the result dir from another PytorchExperiment from which it will load the PyTorch modules and other member variables and resume the experiment. ignore_resume_config (bool): If :obj:`True` it will not resume with the config from the resume experiment but take the current/own config. resume_save_types (list or tuple): A list which can define which values to restore when resuming. Choices are: - "model" <-- Pytorch models - "optimizer" <-- Optimizers - "simple" <-- Simple python variables (basic types and lists/tuples - "th_vars" <-- torch tensors/variables - "results" <-- The result dict parse_sys_argv (bool): Parsing the console arguments (argv) to get a :attr:`config path` and/or :attr:`resume_path`. parse_config_sys_argv (bool): Parse argv to update the config (if the keys match). checkpoint_to_cpu (bool): When checkpointing, transfer all tensors to the CPU beforehand. safe_checkpoint_every_epoch (int): Determines after how many epochs a checkpoint is stored. use_visdomlogger (bool): Use a :class:`.PytorchVisdomLogger`. Is accessible via the :attr:`vlog` attribute. visdomlogger_kwargs (dict): Keyword arguments for :attr:`vlog` instantiation. visdomlogger_c_freq (int): The frequency x (meaning one in x) with which the :attr:`clog` will call the :attr:`vlog`. use_explogger (bool): Use a :class:`.PytorchExperimentLogger`. Is accessible via the :attr:`elog` attribute. This will create the experiment folder structure. explogger_kwargs (dict): Keyword arguments for :attr:`elog` instantiation. explogger_c_freq (int): The frequency x (meaning one in x) with which the :attr:`clog` will call the :attr:`elog`. use_telegrammessagelogger (bool): Use a :class:`.TelegramMessageLogger`. Is accessible via the :attr:`tlog` attribute. telegrammessagelogger_kwargs (dict): Keyword arguments for :attr:`tlog` instantiation. telegrammessagelogger_c_freq (int): The frequency x (meaning one in x) with which the :attr:`clog` will call the :attr:`tlog`. append_rnd_to_name (bool): If :obj:`True`, will append a random six digit string to the experiment name. """ def __init__(self, config=None, name=None, n_epochs=None, seed=None, base_dir=None, globs=None, resume=None, ignore_resume_config=False, resume_save_types=("model", "optimizer", "simple", "th_vars", "results"), resume_reset_epochs=True, parse_sys_argv=False, parse_config_sys_argv=True, checkpoint_to_cpu=True, safe_checkpoint_every_epoch=1, use_visdomlogger=True, visdomlogger_kwargs=None, visdomlogger_c_freq=1, use_explogger=True, explogger_kwargs=None, explogger_c_freq=100, use_telegrammessagelogger=False, telegrammessagelogger_kwargs=None, telegrammessagelogger_c_freq=1000, append_rnd_to_name=False): # super(PytorchExperiment, self).__init__() Experiment.__init__(self) if parse_sys_argv: config_path, resume_path = get_vars_from_sys_argv() if config_path: config = config_path if resume_path: resume = resume_path self._config_raw = None if isinstance(config, str): self._config_raw = Config(file_=config, update_from_argv=parse_config_sys_argv) elif isinstance(config, Config): self._config_raw = Config(config=config, update_from_argv=parse_config_sys_argv) elif isinstance(config, dict): self._config_raw = Config(config=config, update_from_argv=parse_config_sys_argv) else: self._config_raw = Config(update_from_argv=parse_config_sys_argv) self.n_epochs = n_epochs if 'n_epochs' in self._config_raw: self.n_epochs = self._config_raw["n_epochs"] if self.n_epochs is None: self.n_epochs = 0 self._seed = seed if 'seed' in self._config_raw: self._seed = self._config_raw.seed if self._seed is None: random_data = os.urandom(4) seed = int.from_bytes(random_data, byteorder="big") self._config_raw.seed = seed self._seed = seed self.exp_name = name if 'name' in self._config_raw: self.exp_name = self._config_raw["name"] if append_rnd_to_name: rnd_str = ''.join( random.choice(string.ascii_letters + string.digits) for _ in range(5)) self.exp_name += "_" + rnd_str if 'base_dir' in self._config_raw: base_dir = self._config_raw["base_dir"] self._checkpoint_to_cpu = checkpoint_to_cpu self._safe_checkpoint_every_epoch = safe_checkpoint_every_epoch self.results = dict() # Init loggers logger_list = [] self.vlog = None if use_visdomlogger: if visdomlogger_kwargs is None: visdomlogger_kwargs = {} self.vlog = PytorchVisdomLogger(name=self.exp_name, **visdomlogger_kwargs) if visdomlogger_c_freq is not None and visdomlogger_c_freq > 0: logger_list.append((self.vlog, visdomlogger_c_freq)) self.elog = None if use_explogger: if explogger_kwargs is None: explogger_kwargs = {} self.elog = PytorchExperimentLogger(base_dir=base_dir, experiment_name=self.exp_name, **explogger_kwargs) if explogger_c_freq is not None and explogger_c_freq > 0: logger_list.append((self.elog, explogger_c_freq)) # Set results log dict to the right path self.results = ResultLogDict("results-log.json", base_dir=self.elog.result_dir) self.tlog = None if use_telegrammessagelogger: if telegrammessagelogger_kwargs is None: telegrammessagelogger_kwargs = {} self.tlog = TelegramMessageLogger(**telegrammessagelogger_kwargs, exp_name=self.exp_name) if telegrammessagelogger_c_freq is not None and telegrammessagelogger_c_freq > 0: logger_list.append((self.tlog, telegrammessagelogger_c_freq)) self.clog = CombinedLogger(*logger_list) set_seed(self._seed) # Do the resume stuff self._resume_path = None self._resume_save_types = resume_save_types self._ignore_resume_config = ignore_resume_config self._resume_reset_epochs = resume_reset_epochs if resume is not None: if isinstance(resume, str): if resume == "last": self._resume_path = os.path.join( base_dir, sorted(os.listdir(base_dir))[-1]) else: self._resume_path = resume elif isinstance(resume, PytorchExperiment): self._resume_path = resume.elog.base_dir if self._resume_path is not None and not self._ignore_resume_config: self._config_raw.update(Config(file_=os.path.join( self._resume_path, "config", "config.json")), ignore=list( map(lambda x: re.sub("^-+", "", x), sys.argv))) # self.elog.save_config(self.config, "config_pre") if globs is not None: zip_name = os.path.join(self.elog.save_dir, "sources.zip") SourcePacker.zip_sources(globs, zip_name) # Init objects in config self.config = Config.init_objects(self._config_raw) atexit.register(self.at_exit_func) def process_err(self, e): if self.elog is not None: self.elog.text_logger.log_to( "\n".join(traceback.format_tb(e.__traceback__)), "err") def update_attributes(self, var_dict, ignore=()): """ Updates the member attributes with the attributes given in the var_dict Args: var_dict (dict): dict in which the update values stored. If a key matches a member attribute name the member attribute will be updated ignore (list or tuple): iterable of keys to ignore """ for key, val in var_dict.items(): if key == "results": self.results.load(val) continue if key in ignore: continue if hasattr(self, key): setattr(self, key, val) def get_pytorch_modules(self, from_config=True): """ Returns all torch.nn.Modules stored in the experiment in a dict. Args: from_config (bool): Also get modules that are stored in the :attr:`.config` attribute. Returns: dict: Dictionary of PyTorch modules """ pyth_modules = dict() for key, val in self.__dict__.items(): if isinstance(val, torch.nn.Module): pyth_modules[key] = val if from_config: for key, val in self.config.items(): if isinstance(val, torch.nn.Module): if type(key) == str: key = "config." + key pyth_modules[key] = val return pyth_modules def get_pytorch_optimizers(self, from_config=True): """ Returns all torch.optim.Optimizers stored in the experiment in a dict. Args: from_config (bool): Also get optimizers that are stored in the :attr:`.config` attribute. Returns: dict: Dictionary of PyTorch optimizers """ pyth_optimizers = dict() for key, val in self.__dict__.items(): if isinstance(val, torch.optim.Optimizer): pyth_optimizers[key] = val if from_config: for key, val in self.config.items(): if isinstance(val, torch.optim.Optimizer): if type(key) == str: key = "config." + key pyth_optimizers[key] = val return pyth_optimizers def get_simple_variables(self, ignore=()): """ Returns all standard variables in the experiment in a dict. Specifically, this looks for types :class:`int`, :class:`float`, :class:`bytes`, :class:`bool`, :class:`str`, :class:`set`, :class:`list`, :class:`tuple`. Args: ignore (list or tuple): Iterable of names which will be ignored Returns: dict: Dictionary of variables """ simple_vars = dict() for key, val in self.__dict__.items(): if key in ignore: continue if isinstance(val, (int, float, bytes, bool, str, set, list, tuple)): simple_vars[key] = val return simple_vars def get_pytorch_tensors(self, ignore=()): """ Returns all torch.tensors in the experiment in a dict. Args: ignore (list or tuple): Iterable of names which will be ignored Returns: dict: Dictionary of PyTorch tensor """ pytorch_vars = dict() for key, val in self.__dict__.items(): if key in ignore: continue if torch.is_tensor(val): pytorch_vars[key] = val return pytorch_vars def get_pytorch_variables(self, ignore=()): """Same as :meth:`.get_pytorch_tensors`.""" return self.get_pytorch_tensors(ignore) def save_results(self, name="results.json"): """ Saves the result dict as a json file in the result dir of the experiment logger. Args: name (str): The name of the json file in which the results are written. """ if self.elog is None: return with open(os.path.join(self.elog.result_dir, name), "w") as file_: json.dump(self.results, file_, indent=4) def save_pytorch_models(self): """Saves all torch.nn.Modules as model files in the experiment checkpoint folder.""" if self.elog is None: return pyth_modules = self.get_pytorch_modules() for key, val in pyth_modules.items(): self.elog.save_model(val, key) def load_pytorch_models(self): """Loads all model files from the experiment checkpoint folder.""" if self.elog is None: return pyth_modules = self.get_pytorch_modules() for key, val in pyth_modules.items(): self.elog.load_model(val, key) def log_simple_vars(self): """ Logs all simple python member variables as a json file in the experiment log folder. The file will be names 'simple_vars.json'. """ if self.elog is None: return simple_vars = self.get_simple_variables() with open(os.path.join(self.elog.log_dir, "simple_vars.json"), "w") as file_: json.dump(simple_vars, file_) def load_simple_vars(self): """ Restores all simple python member variables from the 'simple_vars.json' file in the log folder. """ if self.elog is None: return simple_vars = {} with open(os.path.join(self.elog.log_dir, "simple_vars.json"), "r") as file_: simple_vars = json.load(file_) self.update_attributes(simple_vars) def save_checkpoint(self, name="checkpoint", save_types=("model", "optimizer", "simple", "th_vars", "results"), n_iter=None, iter_format="{:05d}", prefix=False): """ Saves a current model checkpoint from the experiment. Args: name (str): The name of the checkpoint file save_types (list or tuple): What kind of member variables should be stored? Choices are: "model" <-- Pytorch models, "optimizer" <-- Optimizers, "simple" <-- Simple python variables (basic types and lists/tuples), "th_vars" <-- torch tensors, "results" <-- The result dict n_iter (int): Number of iterations. Together with the name, defined by the iter_format, a file name will be created. iter_format (str): Defines how the name and the n_iter will be combined. prefix (bool): If True, the formatted n_iter will be prepended, otherwise appended. """ if self.elog is None: return model_dict = {} optimizer_dict = {} simple_dict = {} th_vars_dict = {} results_dict = {} if "model" in save_types: model_dict = self.get_pytorch_modules() if "optimizer" in save_types: optimizer_dict = self.get_pytorch_optimizers() if "simple" in save_types: simple_dict = self.get_simple_variables() if "th_vars" in save_types: th_vars_dict = self.get_pytorch_variables() if "results" in save_types: results_dict = {"results": self.results} checkpoint_dict = { **model_dict, **optimizer_dict, **simple_dict, **th_vars_dict, **results_dict } self.elog.save_checkpoint(name=name, n_iter=n_iter, iter_format=iter_format, prefix=prefix, move_to_cpu=self._checkpoint_to_cpu, **checkpoint_dict) def load_checkpoint(self, name="checkpoint", save_types=("model", "optimizer", "simple", "th_vars", "results"), n_iter=None, iter_format="{:05d}", prefix=False, path=None): """ Loads a checkpoint and restores the experiment. Make sure you have your torch stuff already on the right devices beforehand, otherwise this could lead to errors e.g. when making a optimizer step (and for some reason the Adam states are not already on the GPU: https://discuss.pytorch.org/t/loading-a-saved-model-for-continue-training/17244/3 ) Args: name (str): The name of the checkpoint file save_types (list or tuple): What kind of member variables should be loaded? Choices are: "model" <-- Pytorch models, "optimizer" <-- Optimizers, "simple" <-- Simple python variables (basic types and lists/tuples), "th_vars" <-- torch tensors, "results" <-- The result dict n_iter (int): Number of iterations. Together with the name, defined by the iter_format, a file name will be created and searched for. iter_format (str): Defines how the name and the n_iter will be combined. prefix (bool): If True, the formatted n_iter will be prepended, otherwise appended. path (str): If no path is given then it will take the current experiment dir and formatted name, otherwise it will simply use the path and the formatted name to define the checkpoint file. """ if self.elog is None: return model_dict = {} optimizer_dict = {} simple_dict = {} th_vars_dict = {} results_dict = {} if "model" in save_types: model_dict = self.get_pytorch_modules() if "optimizer" in save_types: optimizer_dict = self.get_pytorch_optimizers() if "simple" in save_types: simple_dict = self.get_simple_variables() if "th_vars" in save_types: th_vars_dict = self.get_pytorch_variables() if "results" in save_types: results_dict = {"results": self.results} checkpoint_dict = { **model_dict, **optimizer_dict, **simple_dict, **th_vars_dict, **results_dict } if n_iter is not None: name = name_and_iter_to_filename(name, n_iter, ".pth.tar", iter_format=iter_format, prefix=prefix) if path is None: restore_dict = self.elog.load_checkpoint(name=name, **checkpoint_dict) else: checkpoint_path = os.path.join(path, name) if checkpoint_path.endswith("/"): checkpoint_path = checkpoint_path[:-1] restore_dict = self.elog.load_checkpoint_static( checkpoint_file=checkpoint_path, **checkpoint_dict) self.update_attributes(restore_dict) def _end_internal(self): """Ends the experiment and stores the final results/checkpoint""" if isinstance(self.results, ResultLogDict): self.results.close() self.save_results() self.save_end_checkpoint() self._save_exp_config() self.print("Experiment ended. Checkpoints stored =)") def _end_test_internal(self): """Ends the experiment after test and stores the final results and config""" self.save_results() self._save_exp_config() self.print("Testing ended. Results stored =)") def at_exit_func(self): """ Stores the results and checkpoint at the end (if not already stored). This method is also called if an error occurs. """ if self._exp_state not in ("Ended", "Tested"): if isinstance(self.results, ResultLogDict): self.results.print_to_file("]") self.save_checkpoint(name="checkpoint_exit-" + self._exp_state) self.save_results() self._save_exp_config() self.print("Experiment exited. Checkpoints stored =)") time.sleep( 2 ) # allow checkpoint saving to finish. We need a better solution for this :D def _setup_internal(self): self.prepare_resume() if self.elog is not None: self.elog.save_config(self._config_raw, "config") self._save_exp_config() def _start_internal(self): self._save_exp_config() def prepare_resume(self): """Tries to resume the experiment by using the defined resume path or PytorchExperiment.""" checkpoint_file = "" base_dir = "" reset_epochs = self._resume_reset_epochs if self._resume_path is not None: if isinstance(self._resume_path, str): if self._resume_path.endswith(".pth.tar"): checkpoint_file = self._resume_path base_dir = os.path.dirname( os.path.dirname(checkpoint_file)) elif self._resume_path.endswith( "checkpoint") or self._resume_path.endswith( "checkpoint/"): checkpoint_file = get_last_file(self._resume_path) base_dir = os.path.dirname( os.path.dirname(checkpoint_file)) elif "checkpoint" in os.listdir( self._resume_path) and "config" in os.listdir( self._resume_path): checkpoint_file = get_last_file(self._resume_path) base_dir = self._resume_path else: warnings.warn( "You have not selected a valid experiment folder, will search all sub folders", UserWarning) if self.elog is not None: self.elog.text_logger.log_to( "You have not selected a valid experiment folder, will search all " "sub folders", "warnings") checkpoint_file = get_last_file(self._resume_path) base_dir = os.path.dirname( os.path.dirname(checkpoint_file)) if base_dir: if not self._ignore_resume_config: load_config = Config() load_config.load(os.path.join(base_dir, "config/config.json")) self._config_raw = load_config self.config = Config.init_objects(self._config_raw) self.print("Loaded existing config from:", base_dir) if checkpoint_file: self.load_checkpoint(name="", path=checkpoint_file, save_types=self._resume_save_types) self._resume_path = checkpoint_file shutil.copyfile( checkpoint_file, os.path.join(self.elog.checkpoint_dir, "0_checkpoint.pth.tar")) self.print("Loaded existing checkpoint from:", checkpoint_file) self._resume_reset_epochs = reset_epochs if self._resume_reset_epochs: self._epoch_idx = 0 def _end_epoch_internal(self, epoch): self.save_results() if epoch % self._safe_checkpoint_every_epoch == 0: self.save_temp_checkpoint() self._save_exp_config() def _save_exp_config(self): if self.elog is not None: cur_time = time.strftime("%y-%m-%d_%H:%M:%S", time.localtime(time.time())) self.elog.save_config( Config( **{ 'name': self.exp_name, 'time': self._time_start, 'state': self._exp_state, 'current_time': cur_time, 'epoch': self._epoch_idx }), "exp") def save_temp_checkpoint(self): """Saves the current checkpoint as checkpoint_current.""" self.save_checkpoint(name="checkpoint_current") def save_end_checkpoint(self): """Saves the current checkpoint as checkpoint_last.""" self.save_checkpoint(name="checkpoint_last") def add_result(self, value, name, counter=None, tag=None, label=None, plot_result=True, plot_running_mean=False): """ Saves a results and add it to the result dict, this is similar to results[key] = val, but in addition also logs the value to the combined logger (it also stores in the results-logs file). **This should be your preferred method to log your numeric values** Args: value: The value of your variable name (str): The name/key of your variable counter (int or float): A counter which can be seen as the x-axis of your value. Normally you would just use the current epoch for this. tag (str): A label/tag which can group similar values and will plot values with the same label in the same plot label: deprecated label plot_result (bool): By default True, will also log all your values to the combined logger (with show_value). """ if label is not None: warnings.warn( "label in add_result is deprecated, please use tag instead") if tag is None: tag = label tag_name = tag if tag_name is None: tag_name = name r_elem = ResultElement(data=value, label=tag_name, epoch=self._epoch_idx, counter=counter) self.results[name] = r_elem if plot_result: if tag is None: legend = False else: legend = True if plot_running_mean: value = np.mean(self.results.running_mean_dict[name]) self.clog.show_value(value=value, name=name, tag=tag_name, counter=counter, show_legend=legend) def get_result(self, name): """ Similar to result[key] this will return the values in the results dictionary with the given name/key. Args: name (str): the name/key for which a value is stored. Returns: The value with the key 'name' in the results dict. """ return self.results.get(name) def add_result_without_epoch(self, val, name): """ A faster method to store your results, has less overhead and does not call the combined logger. Will only store to the results dictionary. Args: val: the value you want to add. name (str): the name/key of your value. """ self.results[name] = val def get_result_without_epoch(self, name): """ Similar to result[key] this will return the values in result with the given name/key. Args: name (str): the name/ key for which a value is stores. Returns: The value with the key 'name' in the results dict. """ return self.results.get(name) def print(self, *args): """ Calls 'print' on the experiment logger or uses builtin 'print' if former is not available. """ if self.elog is None: print(*args) else: self.elog.print(*args)
def test_MNIST_experiment(): c = Config() c.batch_size = 64 c.n_epochs = 50 c.learning_rate = 0.001 c.weight_decay = 5e-5 if torch.cuda.is_available(): c.use_cuda = True else: c.use_cuda = False c.rnd_seed = 1 c.log_interval = 100 c.subset_size = 10 # model-specific c.n_coupling = 8 c.n_filters = 64 exp = MNISTExperiment( c, name='mnist_test', n_epochs=c.n_epochs, seed=42, base_dir='experiment_dir', loggers={'visdom': ['visdom', { "exp_name": "myenv" }]}) exp.run() exp.model.eval() exp.model.to('cpu') with torch.no_grad(): samples = exp.model.sample(16, device='cpu') img_grid = make_grid(samples).permute((1, 2, 0)) plt.imshow(img_grid) plt.show() return exp.model
def run_experiment(experiment, configs, args, mods=None, **kwargs): config = Config(file_=args.config) if args.config is not None else Config() config.update_missing(configs[args.default_config]) if args.mods is not None: for mod in args.mods: config.update(mods[mod]) config = Config(config=config, update_from_argv=True) # GET EXISTING EXPERIMENTS TO BE ABLE TO SKIP CERTAIN CONFIGS if args.skip_existing: existing_configs = [] for exp in os.listdir(args.base_dir): try: existing_configs.append( Config(file_=os.path.join(args.base_dir, exp, "config", "config.json"))) except Exception as e: pass if args.grid is not None: grid = GridSearch().read(args.grid) else: grid = [{}] for combi in grid: config.update(combi) if args.skip_existing: skip_this = False for existing_config in existing_configs: if existing_config.contains(config): skip_this = True break if skip_this: continue loggers = {} if args.visdomlogger: loggers["visdom"] = ("visdom", {}, 1) exp = experiment(config=config, base_dir=args.base_dir, resume=args.resume, ignore_resume_config=args.ignore_resume_config, loggers=loggers, **kwargs) if not args.test: exp.run() else: exp.run_test()
import numpy as np import torch from trixi.util import Config from experiment import MNISTexperiment from util import plot_dependency_map import matplotlib.pyplot as plt c = Config() c.batch_size = 128 c.n_epochs = 10 c.learning_rate = 0.001 if torch.cuda.is_available(): c.use_cuda = True else: c.use_cuda = False c.rnd_seed = 1 c.log_interval = 100 exp = MNISTexperiment(config=c, name='test', n_epochs=c.n_epochs, seed=c.rnd_seed, base_dir='./experiment_dir', loggers={"visdom": ["visdom", { "exp_name": "myenv" }]}) # # run backpropagation for each dimension to compute what other # # dimensions it depends on. # exp.setup() # d = 28
def test_Resnet(): c = Config() c.batch_size = 64 c.batch_size_test = 1000 c.n_epochs = 10 c.learning_rate = 0.01 c.momentum = 0.9 if torch.cuda.is_available(): c.use_cuda = True else: c.use_cuda = False c.rnd_seed = 1 c.log_interval = 200 exp = MNIST_classification(config=c, name='experiment', n_epochs=c.n_epochs, seed=42, base_dir='./experiment_dir', loggers={"visdom": "visdom"}) exp.run()
def process_base_dir(base_dir, view_dir="", default_val="-", short_len=25, ignore_keys=IGNORE_KEYS): """Create an overview table of all experiments in the given directory. Args: directory (str): A directory containing experiment folders. default_val (str): Default value if an entry is missing. short_len (int): Cut strings to this length. Full string in alt-text. Returns: dict: {"ccols": Columns for config entries, "rcols": Columns for result entries, "rows": The actual data} """ full_dir = os.path.join(base_dir, view_dir) config_keys = set() result_keys = set() exps = [] non_exps = [] ### Load Experiments with keys / different param values for sub_dir in sorted(os.listdir(full_dir)): dir_path = os.path.join(full_dir, sub_dir) if os.path.isdir(dir_path): try: exp = ExperimentReader(full_dir, sub_dir) if exp.ignore: continue config_keys.update(list(exp.config.flat().keys())) result_keys.update(list(exp.get_results().keys())) exps.append(exp) except Exception as e: print("Could not load experiment: ", dir_path) print(e) print("-" * 20) non_exps.append(os.path.join(view_dir, sub_dir)) ### Get not common val keys diff_keys = list( Config.difference_config_static(*[xp.config for xp in exps]).flat()) ### Remove unwanted keys config_keys -= set(ignore_keys) result_keys -= set(ignore_keys) ### Generate table rows sorted_c_keys1 = sorted([c for c in config_keys if c in diff_keys], key=lambda x: str(x).lower()) sorted_c_keys2 = sorted([c for c in config_keys if c not in diff_keys], key=lambda x: str(x).lower()) sorted_r_keys = sorted(result_keys, key=lambda x: str(x).lower()) rows = [] for exp in exps: config_row = [] for key in sorted_c_keys1: attr_strng = str(exp.config.flat().get(key, default_val)) config_row.append((attr_strng, attr_strng[:short_len])) for key in sorted_c_keys2: attr_strng = str(exp.config.flat().get(key, default_val)) config_row.append((attr_strng, attr_strng[:short_len])) result_row = [] for key in sorted_r_keys: attr_strng = str(exp.get_results().get(key, default_val)) result_row.append((attr_strng, attr_strng[:short_len])) name = exp.exp_name time = exp.exp_info.get( "time", default_val) if "time" in exp.exp_info else exp.config.get( "time", default_val) state = exp.exp_info.get( "state", default_val) if "state" in exp.exp_info else exp.config.get( "state", default_val) epoch = exp.exp_info.get( "epoch", default_val) if "epoch" in exp.exp_info else exp.config.get( "epoch", default_val) rows.append((os.path.relpath(exp.work_dir, base_dir), exp.star, str(name), str(time), str(state), str(epoch), config_row, result_row)) return { "ccols1": sorted_c_keys1, "ccols2": sorted_c_keys2, "rcols": sorted_r_keys, "rows": rows, "noexp": non_exps }
def make_defaults(): DEFAULTS = Config( # Base name="gqn", description=DESCRIPTION, n_epochs=1000000, batch_size=36, batch_size_val=36, seed=1, device="cuda", # Data split_val=3, # index for set of 5 split_test=4, # index for set of 5 data_module=loader, dataset="shepard_metzler_5_parts", data_dir=None, # will be set for data_module if not None debug= 0, # 1 for single repeating batch, 2 for single viewpoint (i.e. reconstruct known images) generator_train=loader.RandomBatchGenerator, generator_val=loader.LinearBatchGenerator, num_viewpoints_val=8, # use this many viewpoints in validation shuffle_viewpoints_val=False, augmenter=MultiThreadedAugmenter, augmenter_kwargs={"num_processes": 8}, # Model model=GenerativeQueryNetwork, model_kwargs={ "in_channels": 3, "query_channels": 7, "r_channels": 256, "encoder_kwargs": { "activation_op": nn.ReLU }, "decoder_kwargs": { "z_channels": 64, "h_channels": 128, "scale": 4, "core_repeat": 12 } }, model_init_weights_args=None, # e.g. [nn.init.kaiming_normal_, 1e-2], model_init_bias_args=None, # e.g. [nn.init.constant_, 0], # Learning optimizer=optim.Adam, optimizer_kwargs={"weight_decay": 1e-5}, lr_initial=5e-4, lr_final=5e-5, lr_cutoff=16e4, # lr is increased linearly in cutoff epochs sigma_initial=2.0, sigma_final=0.7, sigma_cutoff=2e4, # sigma is increased linearly in cutoff epochs kl_weight_initial=0.05, kl_weight_final=1.0, kl_weight_cutoff=1e5, # kl_weight is increased linearly in cutoff epochs nll_weight=1.0, # Logging backup_every=10000, validate_every=1000, validate_subset=0.01, # validate only this percentage randomly show_every=100, val_example_samples= 10, # draw this many random samples for last validation item test_on_val=True, # test on the validation set ) SHAREDCORES = Config( model_kwargs={"decoder_kwargs": { "core_shared": True }}) MODS = {"SHAREDCORES": SHAREDCORES} return {"DEFAULTS": DEFAULTS}, MODS
def get_config(): # Set your own path, if needed. data_root_dir = os.path.abspath( 'data') # The path where the downloaded dataset is stored. c = Config( update_from_argv=True, # Train parameters num_classes=8, in_channels=1, batch_size=8, patch_size=64, n_epochs=50, learning_rate=0.0002, fold= 0, # The 'splits.pkl' may contain multiple folds. Here we choose which one we want to use. device= "cuda", # 'cuda' is the default CUDA device, you can use also 'cpu'. For more information, see https://pytorch.org/docs/stable/notes/cuda.html # Logging parameters name='Basic_Unet', plot_freq=10, # How often should stuff be shown in visdom append_rnd_string=False, start_visdom=True, do_instancenorm= True, # Defines whether or not the UNet does a instance normalization in the contracting path do_load_checkpoint=False, checkpoint_dir='', # Adapt to your own path, if needed. google_drive_id='1RzPB1_bqzQhlWvU-YGvZzhx2omcDh38C', dataset_name='CHD_segmentation_dataset', base_dir=os.path.abspath( 'output_experiment'), # Where to log the output of the experiment. data_root_dir= data_root_dir, # The path where the downloaded dataset is stored. data_dir=os.path.join( data_root_dir, 'CHD_segmentation_dataset/preprocessed' ), # This is where your training and validation data is stored data_test_dir=os.path.join(data_root_dir, 'CHD_segmentation_dataset/preprocessed' ), # This is where your test data is stored split_dir=os.path.join( data_root_dir, 'CHD_segmentation_dataset' ), # This is where the 'splits.pkl' file is located, that holds your splits. scaled_image_16_dir=os.path.join( data_root_dir, 'CHD_segmentation_dataset/scaled_to_16'), scaled_image_32_dir=os.path.join( data_root_dir, 'CHD_segmentation_dataset/scaled_to_32'), scaled_image_64_dir=os.path.join( data_root_dir, 'CHD_segmentation_dataset/scaled_to_64'), stage_1_dir=os.path.join(data_root_dir, 'CHD_segmentation_dataset/stage_1'), stage_1_dir_32=os.path.join(data_root_dir, 'CHD_segmentation_dataset/stage_1_32'), # stage_1_dir = os.path.join(data_root_dir, 'CHD_segmentation_dataset/stage_1') ) print(c) return c