import datetime
import torch
from models.cnnlstm import CnnOcrModel
from torch.autograd import Variable
from torchvision import transforms
from arabic import ArabicAlphabet
from datautils import GroupedSampler, SortByWidthCollater
from english import EnglishAlphabet
from iam import IAMDataset
from madcat import MadcatDataset
from textutils import *
import loggy
logger = loggy.setup_custom_logger('root', "test_madcat_iam.py")
def get_args():
parser = argparse.ArgumentParser(description="OCR Testing Script")
parser.add_argument("--line-height", type=int, default=30, help="Input image line height")
parser.add_argument("--hpad", type=int, default=0,
help="Amount of horizontal padding to apply to left/right of input image (after resize)")
parser.add_argument("--vpad", type=int, default=0,
help="Amount of vertical padding to apply to top/bottom of input image (after resize)")
parser.add_argument("--image-list", type=str, required=True, help="Specify list of images to decode")
parser.add_argument("--snapshot", type=str, required=True, help="Path to snapshot from which we should initialize model weights")
parser.add_argument("--model", type=str, default='CnnOcrModel',
help="Model to use with the snapshot options")
parser.add_argument("--lm-path", type=str, required=False, default="",
help="path to LM dir containing TLG.fst, words.txt & units.txt")
import os
import torch
from torch.autograd import Variable
from torch.utils.data import DataLoader
import imagetransforms
import loggy
logger = loggy.setup_custom_logger('root', "train_cnn_lstm.py")
from warpctc_pytorch import CTCLoss
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import datetime
import time
import shutil
from ocr_dataset import OcrDataset
from datautils import GroupedSampler, SortByWidthCollater
from models.cnnlstm import CnnOcrModel
from textutils import *
import argparse
from lr_scheduler import ReduceLROnPlateau
def test_on_val(val_dataloader, model, criterion):
start_val = time.time()