![]() With open( file, 'r', encoding = 'utf-8', errors = 'ignore') as csvfile: Line = sorted( line, key = lambda p: - p) The output file was written to ' + out_file_location)ĭef format_lines( video_ids, predictions, top_k): format( now - start_time))įor line in format_lines( video_id_batch_val, predictions_val, top_k): Video_id_batch_val, video_batch_val, num_frames_batch_val = sess. start_queue_runners( sess = sess, coord = coord) info( "restoring variables from " + latest_checkpoint) import_meta_graph( meta_graph_location, clear_devices = True) info( "loading meta-graph: " + meta_graph_location) Meta_graph_location = latest_checkpoint + ".meta" Raise Exception( "unable to find a checkpoint at location: %s" % train_dir) get_input_data_tensors( reader, data_pattern, batch_size) Video_id_batch, video_batch, num_frames_batch = inference. ConfigProto( allow_soft_placement = True)) as sess: append(( labels, probability))ĭef infer( reader, train_dir, data_pattern, out_file_location, batch_size, top_k): ( label_id, probability) = tuples, tuples Result = infer( reader, train_dir, input_data_pattern, output_file, batch_size, top_k) Input_data_pattern = 'features/video_level_validate/validate*.tfrecord' Train_dir = model_dir + '/video_level_logistic_model' Model_dir = '/Users/Brandon/development/youtube/models' YT8MAggregatedFeatureReader( feature_names = feature_names, feature_sizes = feature_sizes) YT8MFrameFeatureReader( feature_names = feature_names, feature_sizes = feature_sizes) GetListOfFeatureNamesAndSizes( 'mean_rgb', '1024') # Return a dict of video_id and tuples of (label_id, probability)ĭef run_inference( using_frame_features = False):įeature_names, feature_sizes = utils. FILE = 'features/ground_truth_labels/vocabulary.csv' ![]()
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