Unlock 30% off on Manual Testing Annual Plans this Holiday Season.

Copied to Clipboard!

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:...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.