Test your AI Agents with the all-new Agent to Agent Testing Platform.Learn More
How to use run_config method in avocado
Best Python code snippet using avocado_python
trainer.py
Source:trainer.py 
...93 2. a model_fn takes following args:94 1. features95 2. param96 3. mode97 4. run_config(optional)98 and returns a `propeller.ModelSpec`99 params: any python object, will pass to your `model_fn` or `propeller.train.Model`100 run_config (propeller.RunConfig): run_config.max_steps should not be None.101 warm_start_setting (propeller.WarmStartSetting): Optional. warm start variable will overwrite model variable.102 """103 if run_config.model_dir is None:104 raise ValueError('model_dir should specified in run_config')105 if inspect.isfunction(model_class_or_model_fn):106 _model_fn = model_class_or_model_fn107 elif issubclass(model_class_or_model_fn, Model):108 _model_fn = _build_model_fn(model_class_or_model_fn)109 else:110 raise ValueError('unknown model %s' % model_class_or_model_fn)111 self.model_fn = _model_fn112 self.params = params113 self.run_config = run_config114 self.warm_start_setting = warm_start_setting115 def _build_for_train(self, train_dataset):116 train_dataset.name = 'train'117 train_program = F.Program()118 startup_prog = F.Program()119 with F.program_guard(train_program, startup_prog):120 with collection.Collections() as collections:121 log.info('Building Train Graph...')122 fea = train_dataset.features()123 model_spec = _build_net(self.model_fn, fea, RunMode.TRAIN, self.params, self.run_config)124 log.info('Building Train Graph: Done')125 scalars = collections.get(collection.Key.SUMMARY_SCALAR)126 histograms = collections.get(collection.Key.SUMMARY_HISTOGRAM)127 skip_optimize_ops = collections.get(collection.Key.SKIP_OPTIMIZE)128 skip_opt = set()129 if skip_optimize_ops is not None:130 skip_opt |= set(skip_optimize_ops)131 if scalars is not None:132 skip_opt |= {t for _, t in scalars}133 if histograms is not None:134 skip_opt |= {t for _, t in histograms}135 skip_opt = list(skip_opt)136 log.info('Train with: \n> Run_config: %s\n> Params: %s\n> Train_model_spec: %s\n' % (repr(137 self.run_config), repr(self.params), repr(model_spec)))138 summary_record = SummaryRecord(139 scalar=collections.get(collection.Key.SUMMARY_SCALAR),140 histogram=collections.get(collection.Key.SUMMARY_HISTOGRAM),141 )142 return ProgramPair(train_program=train_program, startup_program=startup_prog), model_spec, summary_record143 def _build_for_eval(self, ds):144 ds.name = 'eval'145 program = F.Program()146 startup_prog = F.Program()147 with F.program_guard(program, startup_prog):148 #share var with Train net149 log.info('Building Eval Graph')150 fea = ds.features()151 model_spec = _build_net(self.model_fn, fea, RunMode.EVAL, self.params, self.run_config)152 log.info('Done')153 #program = program.clone(for_test=True)154 log.info('Eval with: \n> Run_config: %s\n> Params: %s\n> Train_model_spec: %s\n' % (repr(155 self.run_config), repr(self.params), repr(model_spec)))156 return ProgramPair(train_program=program, startup_program=startup_prog), model_spec157 def _build_for_predict(self, ds):158 ds.name = 'predict'159 program = F.Program()160 startup_prog = F.Program()161 with F.program_guard(program, startup_prog):162 #share var with Train net163 log.info('Building Predict Graph')164 fea = ds.features()165 model_spec = _build_net(self.model_fn, fea, RunMode.PREDICT, self.params, self.run_config)166 log.info('Done')167 #program = program.clone(for_test=True)168 log.info('Predict with: \n> Run_config: %s\n> Params: %s\n> Train_model_spec: %s\n' % (repr(169 self.run_config), repr(self.params), repr(model_spec)))170 return ProgramPair(train_program=program, startup_program=startup_prog), model_spec171 def train(self, train_ds, train_hooks=[]):172 """train on a `Dataset`"""173 if not isinstance(train_ds, Dataset):174 raise ValueError('expect dataset to be instance of Dataset, got %s' % repr(train_ds))175 train_program, model_spec, summary_record = self._build_for_train(train_ds)176 train_run_hooks = [177 hooks.StopAtStepHook(self.run_config.max_steps, self.run_config.run_steps),178 hooks.LoggingHook(179 model_spec.loss,180 summary_record=summary_record,181 summary_writer=_get_summary_writer(os.path.join(self.run_config.model_dir, 'train_history')),182 per_step=self.run_config.log_steps,183 skip_step=self.run_config.skip_steps),184 ]185 if model_spec.train_hooks is not None:186 train_run_hooks.extend(model_spec.train_hooks)187 train_run_hooks.extend(train_hooks)188 train_executor = F.Executor(_get_one_place())189 mon_exe = MonitoredExecutor(190 train_executor,191 train_program,192 loss=model_spec.loss,193 run_config=self.run_config,194 run_hooks=train_run_hooks,195 warm_start_setting=self.warm_start_setting)196 distribution.init_distribuition_env(train_program) #only initialize distribute training with197 mon_exe.init_or_restore_variables()198 if distribution.status.is_master:199 mon_exe._hooks.append(200 hooks.CheckpointSaverHook(mon_exe._saver, per_step=mon_exe._save_steps, skip_step=mon_exe._skip_steps))201 try:202 with mon_exe:203 for data in train_ds.start():204 mon_exe.run(feed=data)205 except (StopException, F.core.EOFException) as e:206 pass207 return mon_exe.result208 def evaluate(self, eval_dataset, eval_hooks=[]):209 """eval on a `Dataset`"""210 if not isinstance(eval_dataset, Dataset):211 raise ValueError('expect dataset to be instance of Dataset, got %s' % repr(eval_dataset))212 program, model_spec = self._build_for_eval(eval_dataset)213 single_card_place = _get_one_place()214 eval_executor = F.Executor(single_card_place)215 eval_run_hooks = [216 hooks.StopAtStepHook(self.run_config.eval_max_steps, self.run_config.eval_max_steps),217 hooks.EvalHook(model_spec.metrics, )218 ]219 if model_spec.eval_hooks is not None:220 eval_run_hooks.extend(model_spec.eval_hooks)221 eval_run_hooks.extend(eval_hooks)222 mon_exe = MonitoredExecutor(eval_executor, program, run_config=self.run_config, run_hooks=eval_run_hooks)223 mon_exe.init_or_restore_variables()224 try:225 with mon_exe:226 for data in eval_dataset.start(places=[single_card_place]):227 mon_exe.run(feed=data)228 except (StopException, F.core.EOFException) as e:229 pass230 _, eval_result = mon_exe.result231 summary_writer = _get_summary_writer(os.path.join(self.run_config.model_dir, 'eval_history'))232 _log_eval_result('eval', eval_result, summary_writer, mon_exe.state)233 return mon_exe.result234 def predict(self, predict_dataset, ckpt=-1, ckpt_path=None, steps=-1, split_batch=True):235 """236 Perform predictoin237 will call `model_fn` and initiate user-specifed model in `propeller.RunMode.PREDICT` mode238 Args:239 infer_dataset (propeller.data.Dataset): should not `shuffle` or `repeat`240 steps (int): steps to predict, if None is specifed,241 will stop when `StopException` is raised in `infer_dataset`242 ckpt_path (None|str): Path of a specific checkpoint to predict.243 If None, the latest checkpoint in model_dir is used.244 If there are no checkpoints in model_dir,245 prediction is run with newly initialized Variables instead of ones restored from checkpoint.246 ckpt (int): deprecated args247 split_batch (bool): if True, prediction of each example in a batch is returned.248 Yields:249 Evaluated values of predictions tensors.250 """251 if not isinstance(predict_dataset, Dataset):252 raise ValueError('expect dataset to be instance of Dataset, got %s' % repr(predict_dataset))253 program, model_spec = self._build_for_predict(predict_dataset)254 single_card_place = _get_one_place()255 executor = F.Executor(single_card_place)256 pred_run_config = RunConfig(run_steps=steps if steps == -1 else None, model_dir=self.run_config.model_dir)257 mon_exe = MonitoredExecutor(258 executor,259 program,260 run_config=pred_run_config,261 warm_start_setting=self.warm_start_setting,262 )263 mon_exe.init_or_restore_variables(ckpt if ckpt_path is None else ckpt_path)264 try:265 with mon_exe:266 log.info('Runining predict from dir: %s' % repr(mon_exe.state))267 single_card_place = _get_one_place()268 for data in predict_dataset.start(places=[single_card_place]):269 res = mon_exe.run(fetch_list=model_spec.predictions, feed=data)270 if split_batch:271 res = map(lambda i: i.tolist(), res)272 res = zip(*res) # transpose273 for r in res:274 yield r275 else:276 yield list(map(lambda i: i.tolist(), res))277 except (StopException, F.core.EOFException) as e:278 pass279def train_and_eval(_placeholder=None,280 model_class_or_model_fn=None,281 params=None,282 run_config=None,283 train_dataset=None,284 eval_dataset=None,285 warm_start_setting=None,286 train_hooks=[],287 eval_hooks=[],288 exporters=[]):289 """290 Perform train and evaluate procesure.291 will call `model_fn` and initiate user-specifed model in `propeller.RunMode.PREDICT` mode292 Args:293 model_class_or_model_fn(callable|propeller.train.Model): `model_class_or_model_fn` be specified in 2 ways:294 1. subclass of propeller.train.Model295 2. a model_fn takes following args: 1. features; 2. param; 3. mode; 4. run_config(optional)296 and returns a `propeller.ModelSpec`297 params: any python object, will pass to your `model_fn` or `propeller.train.Model`298 run_config (propeller.RunConfig): run_config.max_steps should not be None.299 train_dataset (propeller.paddle.data.Dataset): training will stop if global_step > run_config.max_steps.300 eval_dataset (propeller.paddle.data.Dataset|dict): Optional, if Dict of propeller.data.Dataset were specified,301 will perform evluatation on every evaluation sets and report results.302 warm_start_setting (propeller.WarmStartSetting): Optional. warm start variable will overwrite model variable.303 train_hooks (list of propeller.paddle.train.RunHook): Optional.304 eval_hooks (list of propeller.paddle.train.RunHook): Optional.305 exporters (list of propeller.paddle.train.Exporter): Optional.306 """307 if _placeholder is not None:308 raise ValueError('specify keyword args to this function')309 if model_class_or_model_fn is None or params is None or run_config is None or train_dataset is None:...
hcsearchSegRunner.py
Source:hcsearchSegRunner.py
1#!/usr/bin/env python2import argparse3import os4import subprocess5import platform6import time7import traceback8import shutil9# paths to MATLAB10MAC_MATLAB_PATH = "/Applications/MATLAB_R2015b.app/bin/matlab"11WIN_MATLAB_PATH = "C:\\Program Files\\MATLAB\\R2015b\\bin\\matlab.exe"12# training mode13# 0 = use shipped models, no training14# 1 = use shipped training images for training (images not yet uploaded)15# 2 = use provided training images for training (not yet implemented)16TRAIN_OPTION = 017# time bound for HC-Search18TIME_BOUND = 1019# whether to log MATLAB runs (for debugging)20LOG_MATLAB_RUNS = True21def run_matlab_function(func_string, func_name, logs_dir):22 '''Wraps function with try-catch to exit MATLAB on errors'''23 wrapped_func_string = "try;{0};catch exception;disp(getReport(exception));exit(1);end;exit".format(func_string)24 print "executing: {0}".format(wrapped_func_string)25 print26 # check OS and use appropriate command/arguments27 logfile = os.path.join(logs_dir,28 'matlab_run_{0}_{1}.txt'.format(29 int(time.time()),30 func_name))31 application = []32 if platform.system() == 'Darwin': # Mac33 application = [34 MAC_MATLAB_PATH,35 "-nodisplay",36 '-r "{0}"'.format(wrapped_func_string)]37 # for Mac, have to separate arguments like this38 if LOG_MATLAB_RUNS:39 application.append('-logfile "{0}"'.format(logfile))40 elif platform.system() == 'Windows': # Windows41 application = [42 WIN_MATLAB_PATH,43 "-nosplash",44 "-wait",45 "-nodesktop",46 "-minimize",47 "-r",48 '"{0}"'.format(wrapped_func_string)]49 # for Windows, have to separate arguments like this50 if LOG_MATLAB_RUNS:51 application.append("-logfile")52 application.append('"{0}"'.format(logfile))53 elif platform.system() == 'Linux': # Linux54 print "Linux unsupported at this time."55 print56 print "running step Error"57 exit(1)58 else:59 print "Unrecognized OS/Platform: {0}".format(platform.system())60 print61 print "running step Error"62 exit(1)63 # execute MATLAB64 try:65 exit_status = subprocess.call(application)66 except:67 print "Could not successfully open MATLAB. Is this the correct path: "68 if platform.system() == 'Windows':69 print WIN_MATLAB_PATH70 elif platform.system() == 'Darwin':71 print MAC_MATLAB_PATH72 else:73 print "(unknown os)"74 traceback.print_exc()75 exit(1)76 if exit_status != 0:77 print "MATLAB exited with error. ({0})".format(func_name)78 print79 print "running step Error"80 exit(1)81 print "MATLAB exited successfully. ({0})".format(func_name)82def run_hc_search(input_dir, output_dir, time_bound, logs_dir, base_dir, infer_only):83 '''Calls HC-Search command line.'''84 # check OS and use appropriate command/arguments85 stdout_logfile = os.path.join(logs_dir,86 'hcsearch_run_stdout_{0}.txt'.format(int(time.time())))87 stderr_logfile = os.path.join(logs_dir,88 'hcsearch_run_stderr_{0}.txt'.format(int(time.time())))89 application = []90 HC_SEARCH_BASE_PATH = 'nematocyst/'91 if platform.system() == 'Darwin': # Mac92 application = [93 os.path.join(HC_SEARCH_BASE_PATH, 'HCSearch'),94 '{}'.format(input_dir),95 '{}'.format(output_dir),96 str(time_bound)]97 if not infer_only:98 application.append("--learn")99 application.extend(["--infer",100 "--prune",101 "none",102 "--ranker",103 "vw",104 "--successor",105 "flipbit-neighbors",106 "--base-path",107 '{}'.format(base_dir)])108 # for Mac, have to separate arguments like this109 elif platform.system() == 'Windows': # Windows110 application = [111 os.path.join(HC_SEARCH_BASE_PATH, 'HCSearch.exe'),112 '{}'.format(input_dir),113 '{}'.format(output_dir),114 str(time_bound)]115 if not infer_only:116 application.append("--learn")117 application.extend(["--infer",118 "--prune",119 "none",120 "--ranker",121 "vw",122 "--successor",123 "flipbit-neighbors",124 "--base-path",125 '{}'.format(base_dir)])126 # for Windows, have to separate arguments like this127 elif platform.system() == 'Linux': # Linux128 print "Linux unsupported at this time."129 print130 print "running step Error"131 exit(1)132 else:133 print "Unrecognized OS/Platform: {0}".format(platform.system())134 print135 print "running step Error"136 exit(1)137 # application = [HC_SEARCH_PATH, '--help'] # DEBUG TODO138 print "Calling HC-Search command line: {}".format(application)139 print140 # execute HC-Search141 try:142 with open(stdout_logfile, 'w') as stdout:143 with open(stderr_logfile, 'w') as stderr:144 exit_status = subprocess.call(application, stdout=stdout, stderr=stderr)145 except:146 print "Could not successfully call HC-Search command."147 traceback.print_exc()148 exit(1)149 if exit_status != 0:150 print "HC-Search exited with error."151 print152 print "running step Error"153 exit(1)154 print "HC-Search ran without error."155def main():156 '''Main loop'''157 # parse arguments158 parser = argparse.ArgumentParser(description="Launch HC-Search segmentation algorithm")159 parser.add_argument("runConfigFileName", help="path to the runConfig_segmentation.txt file that is generated from the avatol_cv program.")160 args = parser.parse_args()161 run_config_file_name = args.runConfigFileName162 # constants: paths163 THIS_DIR = os.path.dirname(os.path.realpath(__file__))164 THIRD_PARTY_DIR = os.path.join(THIS_DIR, '..', '..', '3rdParty')165 # constants: keys in run_config_file_name166 TEST_IMAGES_FILE = "testImagesFile"167 SEGMENTATION_OUTPUT_DIR = "segmentationOutputDir"168 TRAIN_IMAGES_FILE = "userProvidedGroundTruthImagesFile"169 GT_IMAGES_FILE = "userProvidedTrainImagesFile"170 #171 # parse run config file172 #173 print "run_config_file_name is {0}".format(run_config_file_name)174 print175 # loop to parse run config file for key-value pairs176 run_config = {}177 with open(run_config_file_name, "r") as f:178 for line in f:179 key, value = line.partition("=")[::2]180 run_config[key.strip()] = value.strip()181 # check that run config file has all the expected key-value pairs182 # expecting: testImagesFile, segmentationOutputDir183 if TEST_IMAGES_FILE not in run_config:184 print "segmentationRunConfig file missing entry for {0}".format(TEST_IMAGES_FILE)185 exit(1)186 if SEGMENTATION_OUTPUT_DIR not in run_config:187 print "segmentationRunConfig file missing entry for {0}".format(SEGMENTATION_OUTPUT_DIR)188 exit(1)189 if TRAIN_IMAGES_FILE not in run_config:190 print "segmentationRunConfig file missing optional entry for {0}".format(TRAIN_IMAGES_FILE)191 if GT_IMAGES_FILE not in run_config:192 print "segmentationRunConfig file missing optional entry for {0}".format(GT_IMAGES_FILE)193 print "run_config['{1}'] is {0}".format(194 run_config[TEST_IMAGES_FILE],195 TEST_IMAGES_FILE)196 print "run_config['{1}'] is {0}".format(197 run_config[SEGMENTATION_OUTPUT_DIR],198 SEGMENTATION_OUTPUT_DIR)199 if TRAIN_IMAGES_FILE in run_config:200 print "run_config['{1}'] is {0}".format(201 run_config[TRAIN_IMAGES_FILE],202 TRAIN_IMAGES_FILE)203 else:204 run_config[TRAIN_IMAGES_FILE] = ""205 if GT_IMAGES_FILE in run_config:206 print "run_config['{1}'] is {0}".format(207 run_config[GT_IMAGES_FILE],208 GT_IMAGES_FILE)209 else:210 run_config[GT_IMAGES_FILE] = ""211 print212 # make logs directory213 logs_dir = os.path.dirname(run_config[TEST_IMAGES_FILE])214 logs_dir = os.path.join(logs_dir, 'logs')215 logs_dir = os.path.join(logs_dir, 'segmentation')216 if not os.path.exists(logs_dir):217 os.makedirs(logs_dir)218 # make temp directory219 print "creating temp directory"220 temp_dir = os.path.dirname(run_config[TEST_IMAGES_FILE])221 temp_dir = os.path.join(temp_dir, 'segmentationTemp')222 images_processed_dir = os.path.join(temp_dir, 'imagesPreprocessed')223 hcsearch_output_dir = os.path.join(temp_dir, 'hcSearchOutput')224 if not os.path.exists(images_processed_dir):225 os.makedirs(images_processed_dir)226 if not os.path.exists(hcsearch_output_dir):227 os.makedirs(hcsearch_output_dir)228 # copy test images to temp folder229 print "copying test images to temp"230 test_temp_dir = os.path.dirname(run_config[TEST_IMAGES_FILE])231 test_temp_dir = os.path.join(test_temp_dir, 'segmentationTemp')232 test_temp_dir = os.path.join(test_temp_dir, 'images')233 if not os.path.exists(test_temp_dir):234 os.makedirs(test_temp_dir)235 with open(run_config[TEST_IMAGES_FILE], 'r') as f:236 for line in f:237 file_name_base = os.path.basename(line.strip())238 shutil.copyfile(line.strip(), os.path.join(test_temp_dir, file_name_base))239 if TRAIN_OPTION == 0:240 # copy shipped model files241 print "copying shipped models to temp"242 shipped_models_dir = os.path.join(THIS_DIR, 'models')243 shipped_models = ["codebook.txt", "edgeclassifier_model.txt",244 "edgeclassifier_training.txt", "initfunc_model.txt",245 "initfunc_training.txt"]246 for m in shipped_models:247 shutil.copyfile(os.path.join(shipped_models_dir, m),248 os.path.join(images_processed_dir, m))249 hcsearch_models_dir = os.path.join(hcsearch_output_dir, 'models')250 if not os.path.exists(hcsearch_models_dir):251 os.makedirs(hcsearch_models_dir)252 shipped_models = ["model_cost.txt", "model_cost.txt.model",253 "model_heuristic.txt", "model_heuristic.txt.model"]254 for m in shipped_models:255 shutil.copyfile(os.path.join(shipped_models_dir, m),256 os.path.join(hcsearch_models_dir, m))257 # set to not train images258 run_config[TRAIN_IMAGES_FILE] = ""259 run_config[GT_IMAGES_FILE] = ""260 shipped_training_images_file = ""261 elif TRAIN_OPTION == 1:262 # copy shipped training images263 print "copying shipped training images to temp"264 train_temp_dir = os.path.dirname(run_config[TEST_IMAGES_FILE])265 train_temp_dir = os.path.join(train_temp_dir, 'segmentationTemp')266 train_temp_dir = os.path.join(train_temp_dir, 'images')267 if not os.path.exists(train_temp_dir):268 os.makedirs(train_temp_dir)269 shipped_training_images_file = os.path.join(THIS_DIR, 'models/', 'training/', 'training_images_list.txt')270 training_images_dir = os.path.join(THIS_DIR, 'models/', 'training/', 'images/')271 gt_images_dir = os.path.join(THIS_DIR, 'models/', 'training/', 'groundtruth/')272 with open(shipped_training_images_file, 'r') as f:273 for line in f:274 file_name_base = line.strip()275 shutil.copyfile(os.path.join(training_images_dir, '{0}.jpg'.format(file_name_base)),276 os.path.join(train_temp_dir, '{0}.jpg'.format(file_name_base)))277 shutil.copyfile(os.path.join(gt_images_dir, '{0}.jpg'.format(file_name_base)),278 os.path.join(train_temp_dir, '{0}.jpg'.format(file_name_base)))279 else:280 print "Unknown/unimplemented TRAIN_OPTION: {0}".format(TRAIN_OPTION)281 print282 print "running step Error"283 exit(1)284 #285 # call matlab...286 #287 matlab_func1 = "preprocess_for_hcsearch('{0}', '{1}', '{2}', '{3}', '{4}', '{5}')".format(288 run_config[SEGMENTATION_OUTPUT_DIR],289 run_config[TEST_IMAGES_FILE],290 run_config[TRAIN_IMAGES_FILE],291 run_config[GT_IMAGES_FILE],292 shipped_training_images_file,293 THIRD_PARTY_DIR)294 print 'running step Preprocessing'295 os.chdir(THIS_DIR)296 run_matlab_function(matlab_func1, "preprocess_for_hcsearch", logs_dir)297 #298 # call HC-Search...299 #300 base_dir = os.path.join(THIS_DIR, 'nematocyst/')301 infer_only = TRAIN_OPTION == 0302 print 'running step Segmenting'303 os.chdir(THIS_DIR)304 run_hc_search(images_processed_dir, hcsearch_output_dir, TIME_BOUND, logs_dir, base_dir, infer_only)305 #306 # call matlab...307 #308 matlab_func2 = "postprocess_for_hcsearch('{0}', '{1}', '{2}', '{3}', '{4}')".format(309 run_config[SEGMENTATION_OUTPUT_DIR],310 run_config[TEST_IMAGES_FILE],311 run_config[TRAIN_IMAGES_FILE],312 run_config[GT_IMAGES_FILE],313 TIME_BOUND)314 print 'running step Postprocessing'315 os.chdir(THIS_DIR)316 run_matlab_function(matlab_func2, "postprocess_for_hcsearch", logs_dir)317 #318 # copy original images319 #320 # copy original images to segmentedData/*_orig.jpg321 print "copying original images"322 with open(run_config[TEST_IMAGES_FILE], 'r') as f:323 for line in f:324 orig_image_path = line.strip()325 old_file_name_base = os.path.splitext(os.path.basename(orig_image_path))[0]326 new_file_name = '{}_orig.jpg'.format(old_file_name_base)327 dest = os.path.join(run_config[SEGMENTATION_OUTPUT_DIR], new_file_name)328 shutil.copyfile(orig_image_path, dest)329 print 'run completed'330# run script (or do nothing on import)331if __name__ == "__main__":...
batSkullScore.py
Source:batSkullScore.py
1#!/usr/bin/env python2import argparse3import os4import subprocess5import platform6import time7import traceback8# paths to MaATLAB9MAC_MATLAB_PATH = "/Applications/MATLAB_R2015b.app/bin/matlab"10WIN_MATLAB_PATH = "C:\\Program Files\\MATLAB\\R2015b\\bin\\matlab.exe"11# whether to log MATLAB runs (for debugging)12LOG_MATLAB_RUNS = True13def remove_cache_directory(cache_dir):14 '''Delete the cache directory'''15 print "cache_dir is {0}".format(cache_dir)16 print17 os.remove(cache_dir)18def run_matlab_function(func_string, func_name, logs_dir):19 '''Wraps function with try-catch to exit MATLAB on errors'''20 wrapped_func_string = "try;{0};catch exception;disp(getReport(exception));exit(1);end;exit".format(func_string)21 print "executing: {0}".format(wrapped_func_string)22 print23 # check OS and use appropriate command/arguments24 logfile = os.path.join(logs_dir,25 'matlab_run_{0}_{1}.txt'.format(26 int(time.time()),27 func_name))28 application = []29 if platform.system() == 'Darwin': # Mac30 application = [31 MAC_MATLAB_PATH,32 "-nodisplay",33 '-r "{0}"'.format(wrapped_func_string)]34 # for Mac, have to separate arguments like this35 if LOG_MATLAB_RUNS:36 application.append('-logfile "{0}"'.format(logfile))37 elif platform.system() == 'Windows': # Windows38 application = [39 WIN_MATLAB_PATH,40 "-nosplash",41 "-wait",42 "-nodesktop",43 "-minimize",44 "-r",45 '"{0}"'.format(wrapped_func_string)]46 # for Windows, have to separate arguments like this47 if LOG_MATLAB_RUNS:48 application.append("-logfile")49 application.append('"{0}"'.format(logfile))50 elif platform.system() == 'Linux': # Linux51 print "Linux unsupported at this time."52 print53 print "running step Error"54 exit(1)55 else:56 print "Unrecognized OS/Platform: {0}".format(platform.system())57 print58 print "running step Error"59 exit(1)60 # execute MATLAB61 try:62 exit_status = subprocess.call(application)63 except:64 print "Could not successfully open MATLAB. Is this the correct path: "65 if platform.system() == 'Windows':66 print WIN_MATLAB_PATH67 elif platform.system() == 'Darwin':68 print MAC_MATLAB_PATH69 else:70 print "(unknown os)"71 traceback.print_exc()72 exit(1)73 if exit_status != 0:74 print "MATLAB exited with error. ({0})".format(func_name)75 print76 print "running step Error"77 exit(1)78 print "MATLAB exited successfully. ({0})".format(func_name)79def main():80 '''Main loop'''81 # parse arguments82 parser = argparse.ArgumentParser(description="Launch bat scoring algorithm")83 parser.add_argument("runConfigFileName", help="path to the runConfig_scoring.txt file that is generated from the avatol_cv program.")84 args = parser.parse_args()85 run_config_file_name = args.runConfigFileName86 # constants: paths87 THIS_DIR = os.path.dirname(os.path.realpath(__file__))88 # THIRD_PARTY_DIR = os.path.join(THIS_DIR, '..', '..', '3rdParty')89 # constants: keys in run_config_file_name90 TEST_IMAGES_FILE = "testImagesFile"91 TRAINING_DATA_DIR = "trainingDataDir"92 SCORING_OUTPUT_DIR = "scoringOutputDir"93 #94 # parse run config file95 #96 print "run_config_file_name is {0}".format(run_config_file_name)97 print98 # loop to parse run config file for key-value pairs99 run_config = {}100 with open(run_config_file_name, "r") as f:101 for line in f:102 key, value = line.partition("=")[::2]103 run_config[key.strip()] = value.strip()104 # check that run config file has all the expected key-value pairs105 # expecting: testImagesFile, trainingDataDir, scoringOutputDir106 if TEST_IMAGES_FILE not in run_config:107 print "scoringRunConfig file missing entry for {0}".format(TEST_IMAGES_FILE)108 exit(1)109 if TRAINING_DATA_DIR not in run_config:110 print "scoringRunConfig file missing entry for {0}".format(TRAINING_DATA_DIR)111 exit(1)112 if SCORING_OUTPUT_DIR not in run_config:113 print "scoringRunConfig file missing entry for {0}".format(SCORING_OUTPUT_DIR)114 exit(1)115 print "run_config['{1}'] is {0}".format(116 run_config[TEST_IMAGES_FILE],117 TEST_IMAGES_FILE)118 print "run_config['{1}'] is {0}".format(119 run_config[TRAINING_DATA_DIR],120 TRAINING_DATA_DIR)121 print "run_config['{1}'] is {0}".format(122 run_config[SCORING_OUTPUT_DIR],123 SCORING_OUTPUT_DIR)124 print125 # remove cache directory126 cache_dir = os.path.dirname(run_config[TEST_IMAGES_FILE])127 cache_dir = os.path.join(cache_dir, 'legacy_format', 'cache')128 # remove_cache_directory(cache_dir)129 # logs directory130 logs_dir = os.path.dirname(run_config[TEST_IMAGES_FILE])131 logs_dir = os.path.join(logs_dir, 'logs')132 logs_dir = os.path.join(logs_dir, 'scoring')133 if not os.path.exists(logs_dir):134 os.makedirs(logs_dir)135 #136 # call matlab to translate input137 #138 matlab_func1 = "translate_input('{0}', '{1}', '{2}')".format(139 run_config[SCORING_OUTPUT_DIR],140 run_config[TRAINING_DATA_DIR],141 run_config[TEST_IMAGES_FILE])142 print 'running step Processing Inputs'143 os.chdir(THIS_DIR)144 run_matlab_function(matlab_func1, "translate_input", logs_dir)145 #146 # call matlab to score147 #148 summary_file = os.path.dirname(run_config[TEST_IMAGES_FILE])149 summary_file = os.path.join(summary_file, 'legacy_format', 'input', 'summary.txt')150 print "summary_file is {0}".format(summary_file)151 matlab_func2 = "invoke_batskull_system('{0}','{1}')".format(152 summary_file,153 "regime2")154 print 'running step Training and Scoring'155 os.chdir(os.path.join('bat','chain_rpm'))156 run_matlab_function(matlab_func2, "invoke_batskull_system", logs_dir)157 os.chdir(THIS_DIR)158 #159 # call matlab to translate output160 #161 matlab_func3 = "translate_output('{0}', '{1}', '{2}')".format(162 run_config[SCORING_OUTPUT_DIR],163 run_config[TRAINING_DATA_DIR],164 run_config[TEST_IMAGES_FILE])165 print 'running step Processing Outputs'166 os.chdir(THIS_DIR)167 run_matlab_function(matlab_func3, "translate_output", logs_dir)168 print 'run completed'169# run script (or do nothing on import)170if __name__ == "__main__":...
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.
LambdaTest Learning Hubs:
- JUnit Tutorial
- TestNG Tutorial
- Webdriver Tutorial
- WebDriverIO Tutorial
- Protractor Tutorial
- Selenium 4 Tutorial
- Jenkins Tutorial
- NUnit Tutorial
- Jest Tutorial
- Playwright Tutorial
- Cypress Tutorial
- PyTest Tutorial
YouTube
You could also refer to video tutorials over LambdaTest YouTube channel to get step by step demonstration from industry experts.
Run avocado automation tests on LambdaTest cloud grid
Perform automation testing on 3000+ real desktop and mobile devices online.
Try LambdaTest Now !!
Get 100 minutes of automation test minutes FREE!!
Was this article helpful?
