How to use prepare_metadata method in avocado

Best Python code snippet using avocado_python

dso_map.py

Source:dso_map.py

...68write_case_config = True  # Set true when wanting to update the QMax values in the system_case_config FNCS array69find_county = False  # Set True if you need to use API to find county as a function of latitude and longitude70write_industrials = False  # Set True if you want to write out the industrial load tape.71# ========   END INPUT SETTINGS  ========================72def prepare_metadata(node, end_row, feeder_mode, high_renewables_case, DSO_Load_Threshold):73    sheet_name = node + 'BusValues'74    if high_renewables_case:75        case_file = node + "-hi-metadata-" + feeder_mode76        config_file = node + "_hi_system_case_config"77    else:78        case_file = node + "-metadata-" + feeder_mode79        config_file = node + "_system_case_config"80    if find_county:81        out = csv.writer(open("county.csv", "w"), delimiter=',', lineterminator='\n')82    if write_industrials:83        dso_list = []84        indust_load_list = []85    use_dso_list = []86    book = xlrd.open_workbook(data_path + 'bus_mapping.xlsx')87    sheet = book.sheet_by_name(sheet_name)88    # os.rename(case_path + case_file + '.json', case_path + case_file + '_old.json')89    with open(data_path + 'metadata-general.json') as json_file:90        data = json.load(json_file)91        # # Clear out all existing DSOs in the file92        # for key in list(data.keys()):93        #     if 'DSO' in key:94        #         del data[key]95        # Get values from spreadsheet96        for irow in range(2, end_row):97            busid = int(sheet.cell(irow, 4).value)98            busname = sheet.cell(irow, 6).value99            climatezone = int(sheet.cell(irow, 7).value)100            ashrae_zone = sheet.cell(irow, 10).value101            blm_zone = int(sheet.cell(irow, 11).value)102            latitude = sheet.cell(irow, 0).value103            longitude = sheet.cell(irow, 1).value104            utiltype = sheet.cell(irow, 8).value105            if utiltype == 'Town':106                utiltype = 'Suburban'107            ownership_type = sheet.cell(irow, 9).value108            peakseason = sheet.cell(irow, 14).value109            county = sheet.cell(irow, 34).value110            roof_top_PV_MW = sheet.cell(irow, 38).value111            if high_renewables_case:112                max_load = sheet.cell(irow, 40).value113            else:114                max_load = sheet.cell(irow, 39).value115            congestion_factor = sheet.cell(irow, 41).value116            bus_simulated = sheet.cell(irow, 42).value117            total_average_load = sheet.cell(irow, 16).value118            if total_average_load == 0:119                total_average_load = 0.001120            res_average_load = sheet.cell(irow, 19).value121            comm_average_load = sheet.cell(irow, 20).value122            indust_average_load = sheet.cell(irow, 21).value123            res_customers = int(sheet.cell(irow, 25).value)124            comm_customers = int(sheet.cell(irow, 26).value)125            indust_customers = int(sheet.cell(irow, 27).value)126            total_customers = res_customers + comm_customers + indust_customers127            # Need to have non-zero residential customers for prepare case to run128            if total_customers == 0:129                total_customers = 1130                res_customers = 1131            if write_industrials:132                dso_list.append("Bus" + str(busid))133                indust_load_list.append(round(indust_average_load, 1))134            if find_county:135                # Find county by lat and longitude136                # From https://geo.fcc.gov/api/census/137                response = requests.get("https://geo.fcc.gov/api/census/area?lat=" + str(latitude) + "&lon=" + str(longitude) + "&format=json")138                test = json.loads(response.content.decode("utf-8"))139                county = test['results'][0]['county_name']140                out.writerow([latitude, longitude, county])141            # Values for lean version142            if feeder_mode == 'lean':143            #     if utiltype == "Rural":144            #         feeders = {"feeder1": {"name": "R4-25.00-1", "ercot": False},145            #                    "feeder2": {"name": "R5-12.47-3", "ercot": False}}146            #         num_gld_homes = 2192147            #     elif utiltype == "Suburban":148            #         if climatezone == 3:149            #             feeders = {"feeder1": {"name": "R3-12.47-3", "ercot": False}}150            #             num_gld_homes = 1326151            #         if climatezone == 4:152            #             feeders = {"feeder1": {"name": "R3-12.47-3", "ercot": False},153            #                        "feeder2": {"name": "R5-12.47-3", "ercot": False}}154            #             num_gld_homes = 2865155            #         if climatezone == 5:156            #             feeders = {"feeder1": {"name": "R5-12.47-1", "ercot": False},157            #                        "feeder2": {"name": "R5-12.47-5", "ercot": False}}158            #             num_gld_homes = 2541159            #     elif utiltype == "Urban":160            #         if climatezone == 3:161            #             # Omitted for now as feeder R3-12.47-1 has large static load162            #             # feeders = {"feeder1": {"name": "R3-12.47-1", "ercot": False},163            #             #            "feeder2": {"name": "R4-12.47-1", "ercot": False}}164            #             # num_gld_homes = 980165            #             feeders = {"feeder1": {"name": "R4-12.47-1", "ercot": False},166            #                        "feeder2": {"name": "R5-12.47-1", "ercot": False}}167            #             num_gld_homes = 1525168            #         if climatezone == 4:169            #             feeders = {"feeder1": {"name": "R4-12.47-1", "ercot": False},170            #                        "feeder2": {"name": "R4-12.47-2", "ercot": False}}171            #             num_gld_homes = 893172            #         if climatezone == 5:173            #             # Omitted for now as feeder R5-12.47-4 has large static load174            #             # feeders = {"feeder1": {"name": "R5-12.47-1", "ercot": False},175            #             #            "feeder2": {"name": "R5-12.47-2", "ercot": False},176            #             #            "feeder3": {"name": "R5-12.47-4", "ercot": False}}177            #             # num_gld_homes = 2234178            #             feeders = {"feeder1": {"name": "R5-12.47-1", "ercot": False},179            #                        "feeder2": {"name": "R5-12.47-2", "ercot": False},180            #                        "feeder3": {"name": "R5-12.47-5", "ercot": False}}181            #             num_gld_homes = 2847182            #183            # # Values for lean version184            # if feeder_mode == 'test':185                if utiltype == "Rural":186                    # feeders = {"feeder1": {"name": "R4-25.00-1", "ercot": False},187                    #            "feeder2": {"name": "R5-12.47-5", "ercot": False}}188                    feeders = {"feeder1": {"name": "R5-12.47-5", "ercot": False}}189                    num_gld_homes = 1539190                elif utiltype == "Suburban":191                    feeders = {"feeder1": {"name": "R5-12.47-5", "ercot": False}}192                    num_gld_homes = 1539193                    # if climatezone == 3:194                    #     feeders = {"feeder1": {"name": "R3-12.47-3", "ercot": False}}195                    #     num_gld_homes = 1326196                    # if climatezone == 4:197                    #     feeders = {"feeder1": {"name": "R3-12.47-3", "ercot": False}}198                    #     num_gld_homes = 1326199                    # if climatezone == 5:200                    #     feeders = {"feeder1": {"name": "R5-12.47-5", "ercot": False}}201                    #     num_gld_homes = 1539202                elif utiltype == "Urban":203                    if climatezone == 3:204                        feeders = {"feeder1": {"name": "R4-12.47-1", "ercot": False},205                                   "feeder2": {"name": "R5-12.47-1", "ercot": False}}206                        num_gld_homes = 1525207                    if climatezone == 4:208                        feeders = {"feeder1": {"name": "R4-12.47-1", "ercot": False},209                                   "feeder2": {"name": "R4-12.47-2", "ercot": False}}210                        num_gld_homes = 893211                    if climatezone == 5:212                        feeders = {"feeder1": {"name": "R5-12.47-1", "ercot": False},213                                   "feeder2": {"name": "R5-12.47-2", "ercot": False}}214                        num_gld_homes = 1308215            elif feeder_mode == 'stub':216                feeders = {"feeder1": {217                    "name": "GC-12.47-1",218                    "ercot": False219                }}220                num_gld_homes = 0.1221            elif feeder_mode == 'skinny':222                feeders = {223                    "feeder1": {224                        "name": "R4-25.00-1",225                        "ercot": False226                    }}227                num_gld_homes = 168228            elif feeder_mode == 'slim':229                if busid == 2:230                    feeders = {231                        "feeder1": {232                            "name": "R4-12.47-1",233                            "ercot": False234                        }}235                    num_gld_homes = 523236                else:237                    feeders = {"feeder1": {238                        "name": "GC-12.47-1",239                        "ercot": False240                    }}241                    num_gld_homes = 0.1242            if write_case_config:243                # [bus id, name, gld_scale, Pnom, Qnom, curve_scale, curve_skew, Pinit, Qinit]244                if bus_simulated:245                    use_dso_list.append([busid, "Substation_" + str(busid),246                                         (res_customers / num_gld_homes),247                                         max_load * congestion_factor, 0, 0.5, 0, total_average_load, 0])248                else:249                    use_dso_list.append([busid, "Substation_" + str(busid),250                                         (res_customers / num_gld_homes),251                                         0, 0, 0.5, 0, total_average_load, 0])252            if DSO_Load_Threshold == 'File':253                if bus_simulated:254                    dso_simulate = True255                else:256                    dso_simulate = False257            else:258                if total_average_load >= DSO_Load_Threshold:259                    dso_simulate = True260                else:261                    dso_simulate = False262            data["DSO_" + str(busid)] = {263                "bus_number": busid,264                "used": dso_simulate,  # If true the DSO will be instantiated in prepare case and simulated265                "name": busname,266                "climate_zone": climatezone,267                "county": county,268                "latitude": latitude,269                "longitude": longitude,270                "time_zone_offset": -6,271                "utility_type": utiltype,272                "ownership_type": ownership_type,273                "ashrae_zone": ashrae_zone,274                "blm_zone": blm_zone,275                "peak_season": peakseason,276                "substation": "Substation_" + str(busid),277                "random_seed": busid,278                "feeders": feeders,279                "RCI energy mix": {280                    "residential": round(res_average_load / total_average_load, 4),281                    "commercial": round(comm_average_load / total_average_load, 4),282                    "industrial": round(indust_average_load / total_average_load, 4)283                },284                "number_of_customers": total_customers,285                "number_of_gld_homes": num_gld_homes,286                "comm_customers_per_bldg": 2.09,287                "number_of_substations": 1,288                "MVA_growth_rate": 0.01,289                "weather_file": "weather_Bus_" + str(busid) + "_" + str(latitude) + "_" + str(longitude) + ".dat",290                "RCI customer count mix": {291                    "residential": round(res_customers / total_customers, 4),292                    "commercial": round(comm_customers / total_customers, 4),293                    "industrial": round(indust_customers / total_customers, 4)294                },295                "average_load_MW": total_average_load,296                "rooftop_pv_rating_MW": roof_top_PV_MW,297                "winter_peak_MW": 0,298                "summer_peak_MW": 0,299                "capacity_limit_MW": 0,300                "scaling_factor": res_customers / num_gld_homes,301                "total_other_O&M": 0,302                "bilaterals": {303                    "price": 11.11,304                    "load_fraction": 0.11305                },306                "DSO_system_energy_fraction": 0.11307            }308        print("\n=== {0:d} DSOs Defined in Metadata File =====".format(len(data) - 1))309    json_file.close()310    # write it in the original data file311    with open(data_path + case_file + '.json', 'w') as outfile:312        json.dump(data, outfile, indent=2)313    # write out FNCS array in system_config314    if write_case_config:315        with open(case_path + config_file + '.json') as caseconfig_file:316            case_data = json.load(caseconfig_file)317            case_data['DSO'] = use_dso_list318            for i in range(len(case_data['bus'])):319                if len(case_data['bus'][i]) == 13:320                    case_data['bus'][i].append(0)321                    case_data['bus'][i].append(0)322        caseconfig_file.close()323        with open(case_path + config_file + '.json', 'w') as caseoutfile:324            json.dump(case_data, caseoutfile, indent=2)325    # write out industrial load file326    if write_industrials:327        days = 35328        num_stamps = 35 * 24 * 12329        time_stamps = [n * 300 for n in range(num_stamps)]330        array = [indust_load_list for i in range(len(time_stamps))]331        indust_df = pd.DataFrame(array,332                                 index=time_stamps,333                                 columns=dso_list)334        indust_df.index.name = 'seconds'335        indust_df.to_csv(data_path + '/200_indust_p.csv')336# def prepare_metadata(node, end_row, feeder_mode, high_renewables_case, DSO_Load_Threshold):337prepare_metadata('8', 10, 'lean', True, 0)338# prepare_metadata('8', 10, 'skinny', True, 0)339# prepare_metadata('8', 10, 'stub', True, 0)340prepare_metadata('8', 10, 'lean', False, 0)341# prepare_metadata('8', 10, 'skinny', False, 0)342# prepare_metadata('8', 10, 'test', False, 0)343# prepare_metadata('8', 10, 'stub', False, 0)344# prepare_metadata('200', 202, 'lean', True, 1130)345# prepare_metadata('200', 202, 'skinny', True, 300)346# prepare_metadata('200', 202, 'stub', True, 300)347# prepare_metadata('200', 202, 'lean', False, 1130)348# prepare_metadata('200', 202, 'test', False, 1130)349prepare_metadata('200', 202, 'lean', False, 'File')350prepare_metadata('200', 202, 'lean', True, 'File')351# prepare_metadata('200', 202, 'skinny', False, 300)...

pipeline_manager.py

Source:pipeline_manager.py

...30		train_d(self.config_file_d, self.profiler)31	def train_r(self):32		self.profiler = Profiler(self.config_file_r)33		train_r(self.config_file_r, self.profiler)34	def prepare_metadata(self):35		self.profiler = Profiler(self.config_file_d)36		prepare_metadata(self.config_dataset, self.profiler)37	def test_d(self):38		self.profiler = Profiler(self.config_file_d)39		test_d(self.config_file_d, self.profiler)40	def test_r(self):41		self.profiler = Profiler(self.config_file_r)42		test_r(self.config_file_r, self.profiler)43	def test_one_d(self, path=None, out_path=None):44		self.profiler = Profiler(self.config_file_d)45		test_one_d(self.config_file_d, path, out_path, self.profiler)46	def test_one_r(self, path, out_path):47		self.profiler = Profiler(self.config_file_r)48		test_one_r(self.config_file_r, path, out_path, self.profiler)49	def test_one_rd(self, path=None, out_path=None):50		self.profiler = Profiler(self.config_file_d)51		test_one_rd(self.config_file_r, self.config_file_d, path, out_path, self.profiler)52	def test_entire_folder_d(self, path, out_path):53		self.profiler = Profiler(self.config_file_d)54		test_entire_folder_d(self.config_file_d, path, out_path, self.profiler)55	def test_entire_folder_r(self, path, out_path):56		self.profiler = Profiler(self.config_file_r)57		test_entire_folder_r(self.config_file_r, path, out_path, self.profiler)58	def test_entire_folder_rd(self, path, out_path):59		self.profiler = Profiler(self.config_file_d)60		test_entire_folder_rd(self.config_file_r, self.config_file_d, path, out_path, self.profiler)61def train_d(config, profiler):62	"""63	Have a look at main.py for description64	:param config: dynamic variables taken from the folder configs65	:param profiler: A class to generate statistics of time usage of each function66	"""67	from .Dlmodel.TrainTestD import TrainTestD68	driver = profiler(TrainTestD, config, 'train', profiler, profiler_type='once')69	success = profiler(driver.train_d, profiler_type='once')70	log.info(success)71	profiler.dump()72	profiler.plot()73def train_r(config, profiler):74	"""75	Have a look at main.py for description76	:param config: dynamic variables taken from the folder configs77	:param profiler: A class to generate statistics of time usage of each function78	"""79	from .Dlmodel.TrainTestR import TrainTestR80	driver = profiler(TrainTestR, config, 'train', profiler, profiler_type='once')81	success = profiler(driver.train_r, profiler_type='once')82	log.info(success)83	profiler.dump()84	profiler.plot()85def test_d(config, profiler):86	"""87	Have a look at main.py for description88	:param config: dynamic variables taken from the folder configs89	:param profiler: A class to generate statistics of time usage of each function90	"""91	from .Dlmodel.TrainTestD import TrainTestD92	driver = profiler(TrainTestD, config, 'test', profiler, profiler_type='once')  # , profiler_type='single'93	profiler(driver.start_testing, profiler_type='once')94	profiler(driver.test_d, profiler_type='once')95def test_r(config, profiler):96	"""97	Have a look at main.py for description98	:param config: dynamic variables taken from the folder configs99	:param profiler: A class to generate statistics of time usage of each function100	"""101	from .Dlmodel.TrainTestR import TrainTestR102	driver = profiler(TrainTestR, config, 'test', profiler, profiler_type='once')  # , profiler_type='single'103	profiler(driver.start_testing, profiler_type='once')104	profiler(driver.test_r, profiler_type='once')105def test_one_r(config, path, out_path, profiler):106	"""107	Have a look at main.py for description108	:param config: dynamic variables taken from the folder configs109	:param path: Input Image path110	:param out_path: Output Image Path with filename as text_predicted111	:param profiler: A class to generate statistics of time usage of each function112	"""113	from .Dlmodel.TestOneImageR import TestOneImageRClass114	driver = profiler(TestOneImageRClass, config, 'test_one', profiler, profiler_type='once')115	profiler(driver.test_one_image_r, path, out_path, profiler_type='once')116def test_entire_folder_r(config_file_r, path, out_path, profiler):117	"""118	Have a look at main.py for description119	:param config_file_r: dynamic variables taken from the folder configs120	:param path: Input Folder path121	:param out_path: Output Folder Path with filename as text_predicted122	:param profiler: A class to generate statistics of time usage of each function123	"""124	from .Dlmodel.TestOneImageR import TestOneImageRClass125	driver = profiler(TestOneImageRClass, config_file_r, 'test_one', profiler, profiler_type='once')126	images = [i for i in sorted(os.listdir(path)) if '.' != i[0]]127	for image in images:128		print("Recognising text in image:", image)129		profiler(driver.test_one_image_r, path + '/' + image, out_path, profiler_type='once')130def test_one_rd(config_r, config_d, path, out_path, profiler):131	"""132	Have a look at main.py for description133	:param config_r: dynamic variables taken from the folder configs(recognition)134	:param config_d: dynamic variables taken from the folder configs(detection)135	:param path: Input Image path136	:param out_path: Output Image Path with filename as text_predicted137	:param profiler: A class to generate statistics of time usage of each function138	"""139	from .Dlmodel.TestOneImageRD import TestOneImageRDClass140	driver = profiler(TestOneImageRDClass, config_r, config_d, 'test_one', profiler, profiler_type='once')141	contours, text = profiler(driver.test_one_image_rd, path, out_path, profiler_type='once')142	import pickle143	pickle.dump([contours, text], open(out_path + '/output.pkl', 'wb'))144	return contours, text145def test_entire_folder_rd(config_r, config_d, ipath, opath, profiler):146	"""147	Have a look at main.py for description148	:param config_r: dynamic variables taken from the folder configs(recognition)149	:param config_d: dynamic variables taken from the folder configs(detection)150	:param ipath: Input Image path151	:param opath: Output Image Path with filename as text_predicted152	:param profiler: A class to generate statistics of time usage of each function153	"""154	def gen_rd(model, path, out, out_label):155		"""156		A Recursive function to be called to generate output which is described in main.py157		:param model: model object which contains functions to detect and recognise158		:param path: Current Folder path159		:param out: Current Output path160		:param out_label: Current Label path161		"""162		all_files = os.listdir(path)163		# all_files is a list with all the files(could be a directory) in the directory 'path'164		# creates input and output directories, and checks that image format is correct165		for file_i in all_files:166			if file_i.split('.')[-1].lower() in ['jpeg', 'png', 'jpg']:167				log.info(path + '/' + file_i)168				if not (os.path.exists(out + '/' + file_i) and os.path.exists(169						out_label + '/' + '.'.join(file_i.split('.')[:-1]) + '.pkl')):170					contours, text = model.test_one_image_rd(path + '/' + file_i, out + '/' + file_i)171					with open(out_label + '/' + '.'.join(file_i.split('.')[:-1]) + '.pkl', 'wb') as f:172						pickle.dump([contours, text], f)173			elif os.path.isdir(path + '/' + file_i):174				if not os.path.exists(out + '/' + file_i):175					os.mkdir(out + '/' + file_i)176				if not os.path.exists(out_label + '/' + file_i):177					os.mkdir(out_label + '/' + file_i)178				gen_rd(model, path + '/' + file_i, out + '/' + file_i, out_label + '/' + file_i)179			# if the file in the list all_files is a directory, call the function again180	# creates output directory if it doesn't exist, calls gen_rd181	first_out_label = opath + '_label'182	log.info(ipath, opath, first_out_label)183	from .Dlmodel.TestOneImageRD import TestOneImageRDClass184	if not os.path.exists(opath):185		os.mkdir(opath)186	if not os.path.exists(first_out_label):187		os.mkdir(first_out_label)188	driver = profiler(TestOneImageRDClass, config_r, config_d, 'test_one', profiler, profiler_type='once')189	profiler(gen_rd, driver, ipath, opath, first_out_label, profiler_type='once')190def test_one_d(config, path, out_path, profiler):191	"""192	Have a look at main.py for description193	:param config: dynamic variables taken from the folder configs194	:param path: Input Image path195	:param out_path: Output Image Path with filename as text_predicted196	:param profiler: A class to generate statistics of time usage of each function197	"""198	from .Dlmodel.TestOneImageD import TestOneImageDClass199	driver = profiler(TestOneImageDClass, config, 'test_one', profiler, profiler_type='once')200	profiler(driver.test_one_image_d, path, out_path, profiler_type='once')201def test_entire_folder_d(config, ipath, opath, profiler):202	"""203	Have a look at main.py for description204	:param config: dynamic variables taken from the folder configs205	:param ipath: Input Image path206	:param opath: Output Image Path with filename as text_predicted207	:param profiler: A class to generate statistics of time usage of each function208	"""209	def gen_d(model, path, out):210		"""211		A Recursive function to be called to generate output which is described in main.py212		:param model: model object which contains functions to detect and recognise213		:param path: Current Folder path214		:param out: Current Output path215		"""216		all_files = os.listdir(path)217		# all_files is a list with all the files(could be a directory) in the directory 'path'218		# creates input and output directories, and checks that image format is correct219		for file_i in all_files:220			if file_i.split('.')[-1].lower() in ['jpeg', 'png', 'jpg', 'gif']:221				log.info(path + '/' + file_i)222				if not os.path.exists(out + '/' + file_i):223					model.test_one_image_d(path + '/' + file_i, out + '/' + file_i)224			elif os.path.isdir(path + '/' + file_i):225				if not os.path.exists(out + '/' + file_i):226					os.mkdir(out + '/' + file_i)227				gen_d(model, path + '/' + file_i, out + '/' + file_i)228			# if the file in the list all_files is a directory, call the function again229	# creates output directory if it doesn't exist, calls gen230	print(ipath, opath)231	from .Dlmodel.TestOneImageD import TestOneImageDClass232	if not os.path.exists(opath):233		os.mkdir(opath)234	driver = profiler(TestOneImageDClass, config, 'test_one', profiler=profiler, profiler_type='once')235	profiler(gen_d, driver, ipath, opath, profiler_type='once')236def prepare_metadata(config, profiler):237	"""238	Have a look at main.py for description239	:param config: dynamic variables taken from the folder configs240	:param profiler: A class to generate statistics of time usage of each function241	"""242	datasets = set(config['dataset_train'] + config['dataset_test'])243	for d_name in datasets:244		if d_name == 'COCO':245			from .prepare_metadata.prepare_metadata import MetaCoco as Meta246		elif d_name == 'IC13':247			from .prepare_metadata.prepare_metadata import MetaIC13 as Meta248		elif d_name == 'IC15':249			from .prepare_metadata.prepare_metadata import MetaIC15 as Meta250		elif d_name == 'SYNTH':...

prepare_kinetics_metadata.py

Source:prepare_kinetics_metadata.py

1import os2import argparse3import pickle4from torchvision.datasets.kinetics import Kinetics5def prepare_metadata(split='train', force=False):6    print(f'preparing kinetics split {split}...')7    metadata_filename = f'./data/kinetics400_{split}_metadata.pkl'8    9    if not force and os.path.exists(metadata_filename):10        with open(metadata_filename, 'rb') as f:11            metadata = pickle.load(f)12            return metadata13        14    dataset_root = '~/workspace/dataset/kinetics/k400'15    dataset_root = os.path.expanduser(dataset_root)16    kinetics400_ds = Kinetics(17        root=dataset_root,18        frames_per_clip=16,19        split=split,20        num_workers=16,21        frame_rate=2,22    )23    24    with open(metadata_filename, 'wb') as f:25        pickle.dump(kinetics400_ds.metadata, f)26if __name__ == "__main__":27    parser = argparse.ArgumentParser()28    parser.add_argument('--split', choices=['train', 'val', 'all'])29    parser.add_argument('--force', action='store_true')30    args = parser.parse_args()31    force = args.force32    33    if args.split == 'all':34        prepare_metadata(split='train', force=force)35        prepare_metadata(split='val', force=force)36    else:...

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