Ejemplo n.º 1
0
from util.decode import decode, decode_to_output, exclude_list, get_exclude_list, truncate_sents
from model.postprocess import PostProcess

from nltk.translate.bleu_score import sentence_bleu
from util.mteval_bleu import MtEval_BLEU
import tensorflow as tf
from os.path import exists
from os import remove, listdir, makedirs
import math
import numpy as np
import time
from util.arguments import get_args
from copy import deepcopy
from model.model_config import get_path

args = get_args()


def get_graph_val_data(objs, model_config, it, data):
    input_feed = {}
    # Reserved section of vocabuary are same.
    voc = data.vocab_simple

    if model_config.subword_vocab_size > 0:
        pad_id = voc.encode(constant.SYMBOL_PAD)
    else:
        pad_id = [voc.encode(constant.SYMBOL_PAD)]

    output_tmp_sentence_simple, output_tmp_sentence_complex, \
    output_tmp_sentence_complex_raw, output_tmp_sentence_complex_raw_lines, \
    output_tmp_mapper, output_tmp_ref_raw_lines = [], [], [], [], [], []
Ejemplo n.º 2
0
            --output_directory 'processed/bam' \
            --walltime '06:00:00' \
            --nodes 2 \
            --cores 12 \
            --humanonly

The specific pipeline using this script is given in `wes_pipeline.sh`
"""

import os
import yaml

import util.arguments as arguments

# Load command arguments
args = arguments.get_args()
command = args.which
genome = args.genome
input_dir = args.input_directory
output_dir = args.output_directory
config = args.config_yaml
walltime = args.walltime
nodes = str(args.nodes)
cores = str(args.cores)

# Load configuration
with open(config, 'r') as stream:
    config = yaml.load(stream)

# Load constants
python = config['python']