def process_queue(source_directory, destination_directory, queue_directory, encoding_profile): queue = listdir(queue_directory) for queued_name in queue: source_file = join(source_directory, queued_name) if exists(source_file): destination_file = generate_output_name(source_file, destination_directory) generate_output(source_file, destination_file, encoding_profile) queued_file = join(queue_directory, queued_name) remove(queued_file) else: print("Warning: {0} does not exist".format(source_file))
def mirror_videos(source_directory, destination_directory, log_directory, exclusions, only, encoding_profile): source_pattern = join(source_directory, MKV_SEARCH) source_glob = glob(source_pattern) source_set = set(source_glob) destination_pattern = join(destination_directory, MP4_SEARCH) destination_glob = glob(destination_pattern) log_pattern = join(log_directory, MP4_SEARCH) log_glob = glob(log_pattern) encode_list = [] if exclusions and only: print('mirror_videos was passed both exclusions and only') exit_with_error() return if exclusions: for exclusion in exclusions: exclusion_pattern = join(source_directory, exclusion) exclusion_glob = glob(exclusion_pattern) exclusion_set = set(exclusion_glob) source_set -= exclusion_set elif only: source_set = set() for pattern in only: only_pattern = join(source_directory, pattern) only_glob = glob(only_pattern) only_set = set(only_glob) source_set |= only_set for source_file in source_set: destination_file = generate_output_name(source_file, destination_directory) log_file = generate_output_name(source_file, log_directory) if log_file in log_glob: # We have a log of doing this conversion. We should make sure we keep the log # intact and not convert it again. log_glob.remove(log_file) elif destination_file in destination_glob: # We have the destination file, but not the log of doing it. Create the log. create_log_file(log_file) else: # We haven't converted this video yet. Queue it up encode_job = (source_file, destination_file, log_file) encode_list.append(encode_job) for log_file in log_glob: print("Deleting " + log_file) remove(log_file) for input_file, output_file, log_file in encode_list: generate_output(input_file, output_file, encoding_profile) create_log_file(log_file)
def main(): args = parse_args() testing_suite = BenchmarkSuite(args.capsule_dir, args.parallelism) results = testing_suite.test(args.num_samples) df = pd.DataFrame.from_records(results, columns=BenchmarkSuite.Result._fields) df.sort_values(by=list(df.columns), inplace=True, ignore_index=True) output.generate_output(output=df, csv_path=args.output_csv, graph_path=args.output_graph)
def on_ok(self, evt): if not self.pan.Validate(): return params = self.pan.get_data() data = get_query(self.report, params) headers = data.next() col_types = data.next() try: first_row = data.next() except StopIteration: wx.MessageBox('Sorry, no data.', 'Report', wx.OK|wx.ICON_INFORMATION) return out_name = self.available_outputs[self.output_doc.GetSelection()] generate_output(self, self.report, out_name, headers, col_types, first_row, data)
def main(): input_file = sys.argv[1] [endpoints, caches, videos] = parse(input_file) #solution = CacheDistributor.distribute( # DistributionStrategy.popular_content, # endpoints, # caches, # videos #) solution = CacheDistributor.distribute( DistributionStrategy.active_endpoints, endpoints, caches, videos ) result = score(endpoints, solution) generate_output(len(caches), solution) print(result)
def open_file_dialog(self): filename, filter = QtGui.QFileDialog.getSaveFileName(parent=self, caption='Select output file', dir='.', filter= FILE_FILTERS ) if filename: seq = self.get_results() output.generate_output(filename,seq)
print('start training new model') # training model.train() model, loss = train(train_dir, train_dataloader, model, optimizer, loss_fn, num_epoches, use_gpu, quiet) print('Training complete, final loss=', loss.data) torch.save(model.state_dict(), model_file) print('Model saved') # generate output if make_validation_output: # empty folder for the_file in os.listdir(validation_dir): file_path = os.path.join(validation_dir, the_file) try: if os.path.isfile(file_path): os.unlink(file_path) except Exception as e: print(e) generate_output(validation_dir, validation_dataloader, model, use_gpu) print('Validation output written') # evaluate validation outputs mae = evaluate(validation_dir + '/normal', validation_dir + '/gt', validation_dir + '/mask') print('MAE = ', mae) else: generate_output(test_dir, test_dataloader, model, use_gpu) print('Test output written')