from config import FLAGS import csv data_loader = MultiClassDataLoader(tf.flags, WordDataProcessor()) data_loader.define_flags() # If no checkpoint_dir exists, extract the most recent dir and set it if FLAGS.checkpoint_dir == "": all_subdirs = ["./runs/" + d for d in os.listdir('./runs/.') if os.path.isdir("./runs/" + d)] latest_subdir = max(all_subdirs, key=os.path.getmtime) FLAGS.checkpoint_dir = latest_subdir + "/checkpoints/" # Map data into vocabulary vocab_path = os.path.join(FLAGS.checkpoint_dir, "..", "vocab") vocab_processor = data_loader.restore_vocab_processor(vocab_path) # Evaluation # ================================================== checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir) graph = tf.Graph() with graph.as_default(): session_conf = tf.ConfigProto( allow_soft_placement=FLAGS.allow_soft_placement, log_device_placement=FLAGS.log_device_placement) sess = tf.Session(config=session_conf) with sess.as_default(): # Load the saved meta graph and restore variables saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file)) saver.restore(sess, checkpoint_file)
def startEval(ocrData): # Parameters # ================================================== del_all_flags(tf.flags.FLAGS) ocrData = json.loads( '[{"location":"1018,240,411,87","text":"APEX"},{"location":"1019,338,409,23","text":"Partner of Choice"},{"location":"1562,509,178,25","text":"Voucher No"},{"location":"1562,578,206,25","text":"Voucher Date"},{"location":"206,691,274,27","text":"4153 Korean Re"},{"location":"208,756,525,34","text":"Proportional Treaty Statement"},{"location":"1842,506,344,25","text":"BV/HEO/2018/05/0626"},{"location":"1840,575,169,25","text":"01105/2018"},{"location":"206,848,111,24","text":"Cedant"},{"location":"206,908,285,24","text":"Class of Business"},{"location":"210,963,272,26","text":"Period of Quarter"},{"location":"207,1017,252,31","text":"Period of Treaty"},{"location":"206,1066,227,24","text":"Our Reference"},{"location":"226,1174,145,31","text":"Currency"},{"location":"227,1243,139,24","text":"Premium"},{"location":"226,1303,197,24","text":"Commission"},{"location":"226,1366,107,24","text":"Claims"},{"location":"227,1426,126,24","text":"Reserve"},{"location":"227,1489,123,24","text":"Release"},{"location":"227,1549,117,24","text":"Interest"},{"location":"227,1609,161,31","text":"Brokerage"},{"location":"233,1678,134,24","text":"Portfolio"},{"location":"227,1781,124,24","text":"Balance"},{"location":"574,847,492,32","text":": Solidarity- First Insurance 2018"},{"location":"574,907,568,32","text":": Marine Cargo Surplus 2018 - Inward"},{"location":"598,959,433,25","text":"01-01-2018 TO 31-03-2018"},{"location":"574,1010,454,25","text":": 01-01-2018 TO 31-12-2018"},{"location":"574,1065,304,25","text":": APEX/BORD/2727"},{"location":"629,1173,171,25","text":"JOD 1.00"},{"location":"639,1239,83,25","text":"25.53"},{"location":"639,1299,64,25","text":"5.74"},{"location":"639,1362,64,25","text":"0.00"},{"location":"639,1422,64,25","text":"7.66"},{"location":"639,1485,64,25","text":"0.00"},{"location":"639,1545,64,25","text":"0.00"},{"location":"639,1605,64,25","text":"0.64"},{"location":"648,1677,64,25","text":"0.00"},{"location":"641,1774,81,25","text":"11 .49"},{"location":"1706,1908,356,29","text":"APEX INSURANCE"}]' ) # print('============startEval================') # ocrData = json.loads(ocrData) # print(ocrData) # Eval Parameters tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)") tf.flags.DEFINE_string("checkpoint_dir", "", "Checkpoint directory from training run") tf.flags.DEFINE_boolean("eval_train", False, "Evaluate on all training data") # Misc Parameters tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement") tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices") data_loader = MultiClassDataLoader(tf.flags, WordDataProcessor()) data_loader.define_flags() FLAGS = tf.flags.FLAGS # FLAGS._parse_flags() FLAGS(sys.argv) print("\nParameters:") for attr, value in sorted(FLAGS.__flags.items()): print("{}={}".format(attr.upper(), value)) print("") if FLAGS.eval_train: x_raw, y_test = data_loader.load_data_and_labels() y_test = np.argmax(y_test, axis=1) else: # x_raw, y_test = data_loader.load_dev_data_and_labels() x_raw, y_test = data_loader.load_dev_data_and_labels_json(ocrData) y_test = np.argmax(y_test, axis=1) # checkpoint_dir이 없다면 가장 최근 dir 추출하여 셋팅 if FLAGS.checkpoint_dir == "": all_subdirs = [ "./runs/" + d for d in os.listdir('./runs/.') if os.path.isdir("./runs/" + d) ] latest_subdir = max(all_subdirs, key=os.path.getmtime) FLAGS.checkpoint_dir = latest_subdir + "/checkpoints/" # Map data into vocabulary vocab_path = os.path.join(FLAGS.checkpoint_dir, "..", "vocab") vocab_processor = data_loader.restore_vocab_processor(vocab_path) x_test = np.array(list(vocab_processor.transform(x_raw))) print("\nEvaluating...\n") # Evaluation # ================================================== checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir) graph = tf.Graph() with graph.as_default(): session_conf = tf.ConfigProto( allow_soft_placement=FLAGS.allow_soft_placement, log_device_placement=FLAGS.log_device_placement) sess = tf.Session(config=session_conf) with sess.as_default(): # Load the saved meta graph and restore variables saver = tf.train.import_meta_graph( "{}.meta".format(checkpoint_file)) saver.restore(sess, checkpoint_file) # Get the placeholders from the graph by name input_x = graph.get_operation_by_name("input_x").outputs[0] # input_y = graph.get_operation_by_name("input_y").outputs[0] dropout_keep_prob = graph.get_operation_by_name( "dropout_keep_prob").outputs[0] # Tensors we want to evaluate predictions = graph.get_operation_by_name( "output/predictions").outputs[0] # Generate batches for one epoch batches = data_helpers.batch_iter(list(x_test), FLAGS.batch_size, 1, shuffle=False) # Collect the predictions here all_predictions = [] for x_test_batch in batches: batch_predictions = sess.run(predictions, { input_x: x_test_batch, dropout_keep_prob: 1.0 }) all_predictions = np.concatenate( [all_predictions, batch_predictions]) # Print accuracy if y_test is defined if y_test is not None: correct_predictions = float(sum(all_predictions == y_test)) print("Total number of test examples: {}".format(len(y_test))) print("Accuracy: {:g}".format(correct_predictions / float(len(y_test)))) # Save the evaluation to a csv class_predictions = data_loader.class_labels(all_predictions.astype(int)) predictions_human_readable = np.column_stack( (np.array(x_raw), class_predictions)) out_path = os.path.join(FLAGS.checkpoint_dir, "../../../", "prediction.csv") print("Saving evaluation to {0}".format(out_path)) for i in predictions_human_readable: for row in ocrData: if i[0].lower() == row['text'].lower(): row['colLbl'] = i[1] print(ocrData) return ocrData