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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__":...
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