source_path = os.path.join(data_path, f"{args.source}.txt")
output_path = os.path.join("..", "output", args.source, args.mode)
target_path = os.path.join(output_path, "checkpoint_weights.hdf5")
max_text_length = 128
charset_base = string.printable[:95]
charset_special = """ÀÁÂÃÄÅÇÈÉÊËÌÍÎÏÑÒÓÔÕÖÙÚÛÜÝàáâãäåçèéêëìíîïñòóôõöùúûüý"""
if args.transform:
data = Dataset(source=os.path.join(raw_path, args.source))
data.read_lines(maxlen=max_text_length)
valid_noised = pp.add_noise(data.dataset['valid'], max_text_length)
test_noised = pp.add_noise(data.dataset['test'], max_text_length)
valid_metrics = ev.ocr_metrics(ground_truth=data.dataset['valid'],
data=valid_noised)
info = "\n".join([
f"####",
f"#### {args.source} partitions (number of sentences)",
f"####",
f"#### Total: {data.size['total']}",
f"####",
f"#### Train: {data.size['train']}",
f"#### Validation: {data.size['valid']}",
f"####\n",
f"#### Validation Error Rate:",
f"#### CER: {valid_metrics[0]:.8f}",
f"#### WER: {valid_metrics[1]:.8f}",
f"#### SER: {valid_metrics[2]:.8f}",
])
start_time = time.time()
predicts, _ = model.predict(x=dtgen.next_test_batch(),
steps=dtgen.steps['test'],
ctc_decode=True,
verbose=1)
predicts = [dtgen.tokenizer.decode(x[0]) for x in predicts]
total_time = time.time() - start_time
with open(os.path.join(output_path, "predict.txt"), "w") as lg:
for pd, gt in zip(predicts, dtgen.dataset['test']['gt']):
lg.write(f"TE_L {gt}\nTE_P {pd}\n")
evaluate = evaluation.ocr_metrics(
predicts=predicts,
ground_truth=dtgen.dataset['test']['gt'],
norm_accentuation=False,
norm_punctuation=False)
e_corpus = "\n".join([
f"Total test images: {dtgen.size['test']}",
f"Total time: {(total_time / 60):.2f} min",
f"Time per item: {(total_time / dtgen.size['test']):.8f} sec\n",
f"Metrics:", f"Character Error Rate: {evaluate[0]:.8f}",
f"Word Error Rate: {evaluate[1]:.8f}",
f"Sequence Error Rate: {evaluate[2]:.8f}"
])
with open(os.path.join(output_path, "evaluate.txt"), "w") as lg:
lg.write(e_corpus)
print(e_corpus)
trg_tensor = torch.LongTensor(out_indexes).unsqueeze(
1).to(device)
output = model.vocab(
model.transformer.decoder(model.query_pos(
model.decoder(trg_tensor)),
memory,
tgt_mask=mask))
out_token = output.argmax(2)[-1].item()
out_indexes.append(out_token)
# print(output.shape)
if out_token == 3:
break
predicts.append(tokenizer.decode(out_indexes))
gt.append(tokenizer.decode(trg.flatten(0, 1)))
predicts = list(
map(lambda x: x.replace('SOS', '').replace('EOS', ''),
predicts))
gt = list(
map(lambda x: x.replace('SOS', '').replace('EOS', ''), gt))
evaluate = evaluation.ocr_metrics(
predicts=predicts,
ground_truth=gt,
norm_accentuation=args.norm_accentuation,
norm_punctuation=args.norm_punctuation)
print(
"Calculate Character Error Rate {}, Word Error Rate {} and Sequence Error Rate {}"
.format(evaluate[0], evaluate[1], evaluate[2]))
total_time = datetime.datetime.now() - start_time
# mount predict corpus file
with open(os.path.join(output_path, "predict.txt"), "w") as lg:
for pd, gt in zip(predicts, ground_truth):
lg.write(f"TE_L {gt}\nTE_P {pd}\n")
# for i, item in enumerate(dtgen.dataset['test']['dt'][:10]):
# print("=" * 1024, "\n")
# cv2_imshow(pp.adjust_to_see(item))
# print(ground_truth[i])
# print(predicts[i], "\n")
# TODO EVALUATE
from data import evaluation
evaluate = evaluation.ocr_metrics(predicts, ground_truth)
e_corpus = "\n".join([
f"Total test images: {dtgen.size['test']}",
f"Total time: {total_time}",
f"Time per item: {total_time / dtgen.size['test']}\n", f"Metrics:",
f"Character Error Rate: {evaluate[0]:.8f}",
f"Word Error Rate: {evaluate[1]:.8f}",
f"Sequence Error Rate: {evaluate[2]:.8f}"
])
with open(os.path.join(output_path, "evaluate.txt"), "w") as lg:
lg.write(e_corpus)
print(e_corpus)
verbose=1)
predicts = pp.decode_ctc(predicts, charset_base)
total_time = time.time() - start_time
labels = [x.decode() for x in dtgen.dataset["test"]["gt_bytes"]]
pred_corpus = [
f"TE_L {gt}\nTE_P {pd}\n" for pd, gt in zip(predicts, labels)
]
with open(os.path.join(output_path, "predict.txt"), "w") as lg:
lg.write("\n".join(pred_corpus))
print("\n".join(pred_corpus))
evaluate = evaluation.ocr_metrics(predicts=predicts,
ground_truth=labels,
norm_accentuation=False,
norm_punctuation=False)
eval_corpus = "\n".join([
f"Total test images: {dtgen.total_test}",
f"Total time: {total_time:.8f} sec",
f"Time per item: {(total_time / dtgen.total_test):.8f} sec\n",
f"Metrics:", f"Character Error Rate: {evaluate[0]:.8f}",
f"Word Error Rate: {evaluate[1]:.8f}"
])
with open(os.path.join(output_path, "evaluate.txt"), "w") as lg:
lg.write(eval_corpus)
print(eval_corpus)
