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#!/usr/bin/env python3
# Scene Text Recognition Model Hub
# Copyright 2022 Darwin Bautista
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import string
import sys
from dataclasses import dataclass
from tqdm import tqdm
import torch
from strhub.data.module import SceneTextDataModule
from strhub.models.utils import load_from_checkpoint, parse_model_args
@dataclass
class Result:
dataset: str
num_samples: int
accuracy: float
ned: float
confidence: float
label_length: float
def print_results_table(results: list[Result], file=None):
w = max(map(len, map(getattr, results, ['dataset'] * len(results))))
w = max(w, len('Dataset'), len('Combined'))
print('| {:<{w}} | # samples | Accuracy | 1 - NED | Confidence | Label Length |'.format('Dataset', w=w), file=file)
print('|:{:-<{w}}:|----------:|---------:|--------:|-----------:|-------------:|'.format('----', w=w), file=file)
c = Result('Combined', 0, 0, 0, 0, 0)
for res in results:
c.num_samples += res.num_samples
c.accuracy += res.num_samples * res.accuracy
c.ned += res.num_samples * res.ned
c.confidence += res.num_samples * res.confidence
c.label_length += res.num_samples * res.label_length
print(
f'| {res.dataset:<{w}} | {res.num_samples:>9} | {res.accuracy:>8.2f} | {res.ned:>7.2f} '
f'| {res.confidence:>10.2f} | {res.label_length:>12.2f} |',
file=file,
)
c.accuracy /= c.num_samples
c.ned /= c.num_samples
c.confidence /= c.num_samples
c.label_length /= c.num_samples
print('|-{:-<{w}}-|-----------|----------|---------|------------|--------------|'.format('----', w=w), file=file)
print(
f'| {c.dataset:<{w}} | {c.num_samples:>9} | {c.accuracy:>8.2f} | {c.ned:>7.2f} '
f'| {c.confidence:>10.2f} | {c.label_length:>12.2f} |',
file=file,
)
@torch.inference_mode()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('checkpoint', help="Model checkpoint (or 'pretrained=<model_id>')")
parser.add_argument('--data_root', default='data')
parser.add_argument('--batch_size', type=int, default=512)
parser.add_argument('--num_workers', type=int, default=4)
parser.add_argument('--cased', action='store_true', default=False, help='Cased comparison')
parser.add_argument('--punctuation', action='store_true', default=False, help='Check punctuation')
parser.add_argument('--new', action='store_true', default=False, help='Evaluate on new benchmark datasets')
parser.add_argument('--rotation', type=int, default=0, help='Angle of rotation (counter clockwise) in degrees.')
parser.add_argument('--device', default='cuda')
args, unknown = parser.parse_known_args()
kwargs = parse_model_args(unknown)
charset_test = string.digits + string.ascii_lowercase
if args.cased:
charset_test += string.ascii_uppercase
if args.punctuation:
charset_test += string.punctuation
kwargs.update({'charset_test': charset_test})
print(f'Additional keyword arguments: {kwargs}')
model = load_from_checkpoint(args.checkpoint, **kwargs).eval().to(args.device)
hp = model.hparams
datamodule = SceneTextDataModule(
args.data_root,
'_unused_',
hp.img_size,
hp.max_label_length,
hp.charset_train,
hp.charset_test,
args.batch_size,
args.num_workers,
False,
rotation=args.rotation,
)
test_set = SceneTextDataModule.TEST_BENCHMARK_SUB + SceneTextDataModule.TEST_BENCHMARK
if args.new:
test_set += SceneTextDataModule.TEST_NEW
test_set = sorted(set(test_set))
results = {}
max_width = max(map(len, test_set))
for name, dataloader in datamodule.test_dataloaders(test_set).items():
total = 0
correct = 0
ned = 0
confidence = 0
label_length = 0
for imgs, labels in tqdm(iter(dataloader), desc=f'{name:>{max_width}}'):
res = model.test_step((imgs.to(model.device), labels), -1)['output']
total += res.num_samples
correct += res.correct
ned += res.ned
confidence += res.confidence
label_length += res.label_length
accuracy = 100 * correct / total
mean_ned = 100 * (1 - ned / total)
mean_conf = 100 * confidence / total
mean_label_length = label_length / total
results[name] = Result(name, total, accuracy, mean_ned, mean_conf, mean_label_length)
result_groups = {
'Benchmark (Subset)': SceneTextDataModule.TEST_BENCHMARK_SUB,
'Benchmark': SceneTextDataModule.TEST_BENCHMARK,
}
if args.new:
result_groups.update({'New': SceneTextDataModule.TEST_NEW})
with open(args.checkpoint + '.log.txt', 'w') as f:
for out in [f, sys.stdout]:
for group, subset in result_groups.items():
print(f'{group} set:', file=out)
print_results_table([results[s] for s in subset], out)
print('\n', file=out)
if __name__ == '__main__':
main()
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