import os
import time
import string
import argparse
import re
import torch
import torch.backends.cudnn as cudnn
import torch.utils.data
import torch.nn.functional as F
import numpy as np
from nltk.metrics.distance import edit_distance
from utils import CTCLabelConverter, AttnLabelConverter, Averager
from dataset import hierarchical_dataset, AlignCollate
from model import Model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def benchmark_all_eval(model, criterion, converter, opt, calculate_infer_time=False):
""" evaluation with 10 benchmark evaluation datasets """
# The evaluation datasets, dataset order is same with Table 1 in our paper.
eval_data_list = ['IIIT5k_3000', 'SVT', 'IC03_860', 'IC03_867', 'IC13_857',
'IC13_1015', 'IC15_1811', 'IC15_2077', 'SVTP', 'CUTE80']
# # To easily compute the total accuracy of our paper.
# eval_data_list = ['IIIT5k_3000', 'SVT', 'IC03_867',
# 'IC13_1015', 'IC15_2077', 'SVTP', 'CUTE80']
if calculate_infer_time:
evaluation_batch_size = 1 # batch_size should be 1 to calculate the GPU inference time per image.
else:
evaluation_batch_size = opt.batch_size
list_accuracy = []
total_forward_time = 0
total_evaluation_data_number = 0
total_correct_number = 0
log = open(f'./result/{opt.exp_name}/log_all_evaluation.txt', 'a')
dashed_line = '-' * 80
print(dashed_line)
log.write(dashed_line + '\n')
for eval_data in eval_data_list:
eval_data_path = os.path.join(opt.eval_data, eval_data)
AlignCollate_evaluation = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD)
eval_data, eval_data_log = hierarchical_dataset(root=eval_data_path, opt=opt)
evaluation_loader = torch.utils.data.DataLoader(
eval_data, batch_size=evaluation_batch_size,
shuffle=False,
num_workers=int(opt.workers),
collate_fn=AlignCollate_evaluation, pin_memory=True)
_, accuracy_by_best_model, norm_ED_by_best_model, _, _, _, infer_time, length_of_data = validation(
model, criterion, evaluation_loader, converter, opt)
list_accuracy.append(f'{accuracy_by_best_model:0.3f}')
total_forward_time += infer_time
total_evaluation_data_number += len(eval_data)
total_correct_number += accuracy_by_best_model * length_of_data
log.write(eval_data_log)
print(f'Acc {accuracy_by_best_model:0.3f}\t normalized_ED {norm_ED_by_best_model:0.3f}')
log.write(f'Acc {accuracy_by_best_model:0.3f}\t normalized_ED {norm_ED_by_best_model:0.3f}\n')
print(dashed_line)
log.write(dashed_line + '\n')
averaged_forward_time = total_forward_time / total_evaluation_data_number * 1000
total_accuracy = total_correct_number / total_evaluation_data_number
params_num = sum([np.prod(p.size()) for p in model.parameters()])
evaluation_log = 'accuracy: '
for name, accuracy in zip(eval_data_list, list_accuracy):
evaluation_log += f'{name}: {accuracy}\t'
evaluation_log += f'total_accuracy: {total_accuracy:0.3f}\t'
evaluation_log += f'averaged_infer_time: {averaged_forward_time:0.3f}\t# parameters: {params_num/1e6:0.3f}'
print(evaluation_log)
log.write(evaluation_log + '\n')
log.close()
return None
def validation(model, criterion, evaluation_loader, converter, opt):
""" validation or evaluation """
n_correct = 0
norm_ED = 0
length_of_data = 0
infer_time = 0
valid_loss_avg = Averager()
for i, (image_tensors, labels) in enumerate(evaluation_loader):
batch_size = image_tensors.size(0)
length_of_data = length_of_data + batch_size
image = image_tensors.to(device)
# For max length prediction
length_for_pred = torch.IntTensor([opt.batch_max_length] * batch_size).to(device)
text_for_pred = torch.LongTensor(batch_size, opt.batch_max_length + 1).fill_(0).to(device)
text_for_loss, length_for_loss = converter.encode(labels, batch_max_length=opt.batch_max_length)
start_time = time.time()
if 'CTC' in opt.Prediction:
preds = model(image, text_for_pred)
forward_time = time.time() - start_time
# Calculate evaluation loss for CTC deocder.
preds_size = torch.IntTensor([preds.size(1)] * batch_size)
# permute 'preds' to use CTCloss format
if opt.baiduCTC:
cost = criterion(preds.permute(1, 0, 2), text_for_loss, preds_size, length_for_loss) / batch_size
else:
cost = criterion(preds.log_softmax(2).permute(1, 0, 2), text_for_loss, preds_size, length_for_loss)
# Select max probabilty (greedy decoding) then decode index to character
if opt.baiduCTC:
_, preds_index = preds.max(2)
preds_index = preds_index.view(-1)
else:
_, preds_index = preds.max(2)
preds_str = converter.decode(preds_index.data, preds_size.data)
else:
preds = model(image, text_for_pred, is_train=False)
forward_time = time.time() - start_time
preds = preds[:, :text_for_loss.shape[1] - 1, :]
target = text_for_loss[:, 1:] # without [GO] Symbol
cost = criterion(preds.contiguous().view(-1, preds.shape[-1]), target.contiguous().view(-1))
# select max probabilty (greedy decoding) then decode index to character
_, preds_index = preds.max(2)
preds_str = converter.decode(preds_index, length_for_pred)
labels = converter.decode(text_for_loss[:, 1:], length_for_loss)
infer_time += forward_time
valid_loss_avg.add(cost)
# calculate accuracy & confidence score
preds_prob = F.softmax(preds, dim=2)
preds_max_prob, _ = preds_prob.max(dim=2)
confidence_score_list = []
for gt, pred, pred_max_prob in zip(labels, preds_str, preds_max_prob):
if 'Attn' in opt.Prediction:
gt = gt[:gt.find('[s]')]
pred_EOS = pred.find('[s]')
pred = pred[:pred_EOS] # prune after "end of sentence" token ([s])
pred_max_prob = pred_max_prob[:pred_EOS]
# To evaluate 'case sensitive model' with alphanumeric and case insensitve setting.
if opt.sensitive and opt.data_filtering_off:
pred = pred.lower()
gt = gt.lower()
alphanumeric_case_insensitve = '0123456789abcdefghijklmnopqrstuvwxyz'
out_of_alphanumeric_case_insensitve = f'[^{alphanumeric_case_insensitve}]'
pred = re.sub(out_of_alphanumeric_case_insensitve, '', pred)
gt = re.sub(out_of_alphanumeric_case_insensitve, '', gt)
if pred == gt:
n_correct += 1
'''
(old version) ICDAR2017 DOST Normalized Edit Distance https://rrc.cvc.uab.es/?ch=7&com=tasks
"For each word we calculate the normalized edit distance to the length of the ground truth transcription."
if len(gt) == 0:
norm_ED += 1
else:
norm_ED += edit_distance(pred, gt) / len(gt)
'''
# ICDAR2019 Normalized Edit Distance
if len(gt) == 0 or len(pred) == 0:
norm_ED += 0
elif len(gt) > len(pred):
norm_ED += 1 - edit_distance(pred, gt) / len(gt)
else:
norm_ED += 1 - edit_distance(pred, gt) / len(pred)
# calculate confidence score (= multiply of pred_max_prob)
try:
confidence_score = pred_max_prob.cumprod(dim=0)[-1]
except:
confidence_score = 0 # for empty pred case, when prune after "end of sentence" token ([s])
confidence_score_list.append(confidence_score)
# print(pred, gt, pred==gt, confidence_score)
accuracy = n_correct / float(length_of_data) * 100
norm_ED = norm_ED / float(length_of_data) # ICDAR2019 Normalized Edit Distance
return valid_loss_avg.val(), accuracy, norm_ED, preds_str, confidence_score_list, labels, infer_time, length_of_data
def test(opt):
""" model configuration """
if 'CTC' in opt.Prediction:
converter = CTCLabelConverter(opt.character)
else:
converter = AttnLabelConverter(opt.character)
opt.num_class = len(converter.character)
if opt.rgb:
opt.input_channel = 3
model = Model(opt)
print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel,
opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction,
opt.SequenceModeling, opt.Prediction)
model = torch.nn.DataParallel(model).to(device)
# load model
print('loading pretrained model from %s' % opt.saved_model)
model.load_state_dict(torch.load(opt.saved_model, map_location=device))
opt.exp_name = '_'.join(opt.saved_model.split('/')[1:])
# print(model)
""" keep evaluation model and result logs """
os.makedirs(f'./result/{opt.exp_name}', exist_ok=True)
os.system(f'cp {opt.saved_model} ./result/{opt.exp_name}/')
""" setup loss """
if 'CTC' in opt.Prediction:
criterion = torch.nn.CTCLoss(zero_infinity=True).to(device)
else:
criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(device) # ignore [GO] token = ignore index 0
""" evaluation """
model.eval()
with torch.no_grad():
if opt.benchmark_all_eval: # evaluation with 10 benchmark evaluation datasets
benchmark_all_eval(model, criterion, converter, opt)
else:
log = open(f'./result/{opt.exp_name}/log_evaluation.txt', 'a')
AlignCollate_evaluation = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD)
eval_data, eval_data_log = hierarchical_dataset(root=opt.eval_data, opt=opt)
evaluation_loader = torch.utils.data.DataLoader(
eval_data, batch_size=opt.batch_size,
shuffle=False,
num_workers=int(opt.workers),
collate_fn=AlignCollate_evaluation, pin_memory=True)
_, accuracy_by_best_model, _, _, _, _, _, _ = validation(
model, criterion, evaluation_loader, converter, opt)
log.write(eval_data_log)
print(f'{accuracy_by_best_model:0.3f}')
log.write(f'{accuracy_by_best_model:0.3f}\n')
log.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--eval_data', required=True, help='path to evaluation dataset')
parser.add_argument('--benchmark_all_eval', action='store_true', help='evaluate 10 benchmark evaluation datasets')
parser.add_argument('--workers', type=int, help='number of data loading workers', default=4)
parser.add_argument('--batch_size', type=int, default=192, help='input batch size')
parser.add_argument('--saved_model', required=True, help="path to saved_model to evaluation")
""" Data processing """
parser.add_argument('--batch_max_length', type=int, default=25, help='maximum-label-length')
parser.add_argument('--imgH', type=int, default=32, help='the height of the input image')
parser.add_argument('--imgW', type=int, default=100, help='the width of the input image')
parser.add_argument('--rgb', action='store_true', help='use rgb input')
parser.add_argument('--character', type=str, default='0123456789abcdefghijklmnopqrstuvwxyz', help='character label')
parser.add_argument('--sensitive', action='store_true', help='for sensitive character mode')
parser.add_argument('--PAD', action='store_true', help='whether to keep ratio then pad for image resize')
parser.add_argument('--data_filtering_off', action='store_true', help='for data_filtering_off mode')
parser.add_argument('--baiduCTC', action='store_true', help='for data_filtering_off mode')
""" Model Architecture """
parser.add_argument('--Transformation', type=str, required=True, help='Transformation stage. None|TPS')
parser.add_argument('--FeatureExtraction', type=str, required=True, help='FeatureExtraction stage. VGG|RCNN|ResNet')
parser.add_argument('--SequenceModeling', type=str, required=True, help='SequenceModeling stage. None|BiLSTM')
parser.add_argument('--Prediction', type=str, required=True, help='Prediction stage. CTC|Attn')
parser.add_argument('--num_fiducial', type=int, default=20, help='number of fiducial points of TPS-STN')
parser.add_argument('--input_channel', type=int, default=1, help='the number of input channel of Feature extractor')
parser.add_argument('--output_channel', type=int, default=512,
help='the number of output channel of Feature extractor')
parser.add_argument('--hidden_size', type=int, default=256, help='the size of the LSTM hidden state')
opt = parser.parse_args()
""" vocab / character number configuration """
if opt.sensitive:
opt.character = string.printable[:-6] # same with ASTER setting (use 94 char).
cudnn.benchmark = True
cudnn.deterministic = True
opt.num_gpu = torch.cuda.device_count()
test(opt)