mobilenet_v3_large,
mobilenet_v3_small,
shufflenet_v2_x1_0,
shufflenet_v2_x1_5,
shufflenet_v2_x2_0,
)
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.insert(1, os.path.dirname(os.path.abspath(__file__)))
from cnocr.utils import set_logger, get_model_size
from cnocr.consts import IMG_STANDARD_HEIGHT, ENCODER_CONFIGS, DECODER_CONFIGS
from cnocr.models.densenet import DenseNet, DenseNetLite
from cnocr.models.mobilenet import gen_mobilenet_v3
logger = set_logger('info')
def test_conv():
conv = nn.Conv2d(32, 32, kernel_size=5, stride=1, padding=2, bias=False)
input = torch.rand(1, 32, 10, 4)
res = conv(input)
logger.info(res.shape)
def test_densenet():
width = 280
img = torch.rand(4, 1, IMG_STANDARD_HEIGHT, width)
net = DenseNet(32, [2, 2, 2, 2], 64)
net.eval()
logger.info(net)
import os
import time
import logging
import argparse
from operator import itemgetter
from pathlib import Path
from collections import Counter
import mxnet as mx
import Levenshtein
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from cnocr import CnOcr
from cnocr.utils import set_logger, gen_context
logger = set_logger(log_level=logging.INFO)
def evaluate():
parser = argparse.ArgumentParser()
parser.add_argument("--model-name",
help="model name",
type=str,
default='densenet-lite-lstm')
parser.add_argument("--model-epoch",
type=int,
default=None,
help="model epoch")
parser.add_argument(
"--gpu",
help="Number of GPUs for training [Default 0, means using cpu]"
# 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.
from pathlib import Path
import os
import re
import unicodedata
from cnocr.consts import VOCAB_FP
from cnocr.utils import read_charset, read_tsv_file, set_logger
logger = set_logger(log_level='INFO')
def check_legal(width, height, fname, gt, letter2id):
legal_letters = set(letter2id)
if width <= height:
return False
if len(gt) < 3 or len(gt) > 30:
return False
if gt[0] == '#' and len(gt) == 3 and len(set(gt)) == 1:
return False
gt_set = set(gt)
illegal_letters = gt_set - legal_letters
if illegal_letters:
logger.warning(f'illegal letters in gt "{gt}": {illegal_letters}')
