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japan_2.jpg [{"transcription": "屯专屯专", "points": [[672, 62], [847, 62], [847, 104], [672, 104]]}, {"transcription": "天然の", "points": [[397, 83], [533, 80], [534, 128], [398, 131]]}, {"transcription": "俊味の", "points": [[1069, 98], [1294, 98], [1294, 139], [1069, 139]]}, {"transcription": "濃厚衣", "points": [[247, 119], [323, 119], [323, 149], [247, 149]]}, {"transcription": "5", "points": [[415, 156], [594, 156], [594, 190], [415, 190]]}, {"transcription": "味深", "points": [[772, 152], [893, 152], [893, 178], [772, 178]]}, {"transcription": "燒", "points": [[932, 156], [1226, 156], [1226, 223], [932, 223]]}, {"transcription": "深の", "points": [[172, 174], [293, 174], [293, 199], [172, 199]]}, {"transcription": "味の", "points": [[119, 222], [560, 222], [560, 284], [119, 284]]}, {"transcription": "3-0", "points": [[595, 229], [718, 229], [718, 263], [595, 263]]}, {"transcription": "鲍の", "points": [[753, 254], [1036, 248], [1037, 294], [754, 299]]}, {"transcription": "艳品", "points": [[1057, 246], [1134, 246], [1134, 282], [1057, 282]]}, {"transcription": "香法", "points": [[62, 308], [204, 308], [204, 347], [62, 347]]}, {"transcription": "の", "points": [[230, 333], [351, 336], [350, 365], [229, 362]]}, {"transcription": "脂の秉", "points": [[642, 339], [801, 335], [802, 369], [643, 374]]}, {"transcription": "力", "points": [[1207, 348], [1344, 348], [1344, 382], [1207, 382]]}, {"transcription": "揚", "points": [[943, 363], [1161, 359], [1162, 409], [944, 413]]}, {"transcription": "龙の", "points": [[746, 389], [922, 382], [923, 420], [747, 427]]}, {"transcription": "出来", "points": [[566, 407], [710, 403], [711, 444], [567, 448]]}, {"transcription": "新鲜龙", "points": [[1224, 413], [1301, 413], [1301, 443], [1224, 443]]}, {"transcription": "于", "points": [[362, 437], [460, 434], [461, 464], [363, 467]]}, {"transcription": "風味豊龙", "points": [[109, 454], [232, 454], [232, 483], [109, 483]]}, {"transcription": "熟成", "points": [[715, 453], [860, 453], [860, 491], [715, 491]]}, {"transcription": "美味", "points": [[1303, 459], [1400, 463], [1398, 511], [1302, 507]]}, {"transcription": "芳醇太", "points": [[241, 491], [378, 491], [378, 538], [241, 538]]}, {"transcription": "の", "points": [[818, 529], [990, 529], [990, 562], [818, 562]]}, {"transcription": "旬", "points": [[1100, 583], [1189, 586], [1186, 660], [1097, 656]]}, {"transcription": "産地送", "points": [[1233, 580], [1381, 577], [1381, 614], [1234, 617]]}, {"transcription": "久", "points": [[254, 612], [473, 615], [472, 657], [253, 654]]}, {"transcription": "食~=3", "points": [[750, 616], [848, 613], [849, 644], [751, 647]]}, {"transcription": "秘の", "points": [[566, 628], [674, 625], [675, 665], [567, 668]]}, {"transcription": "机工", "points": [[668, 680], [783, 675], [784, 709], [669, 713]]}, {"transcription": "个一下", "points": [[806, 673], [984, 673], [984, 706], [806, 706]]}, {"transcription": "本場の", "points": [[997, 730], [1107, 727], [1108, 767], [998, 770]]}, {"transcription": "朝探勺", "points": [[282, 741], [377, 738], [378, 774], [283, 777]]}, {"transcription": "筱味", "points": [[506, 743], [769, 743], [769, 784], [506, 784]]}, {"transcription": "33", "points": [[829, 769], [925, 769], [925, 798], [829, 798]]}]
korean_1.jpg [{"transcription": "26", "points": [[401, 518], [503, 516], [504, 552], [401, 554]]}]
model_prod_flow_ch.png [{"transcription": "Paddle.jit.saveO", "points": [[701, 7], [933, 11], [933, 46], [700, 43]]}, {"transcription": "best_accuracy.pdopt", "points": [[271, 67], [573, 70], [573, 107], [271, 104]]}, {"transcription": "inference.pdmodel", "points": [[992, 89], [1266, 89], [1266, 125], [992, 125]]}, {"transcription": "ch det mv3", "points": [[1674, 93], [1853, 93], [1853, 123], [1674, 123]]}, {"transcription": "模型训练", "points": [[49, 110], [193, 110], [193, 147], [49, 147]]}, {"transcription": " best_accuracy.pdparams", "points": [[251, 110], [634, 113], [634, 148], [250, 145]]}, {"transcription": "模型转换", "points": [[745, 111], [887, 111], [887, 148], [745, 148]]}, {"transcription": "opt优化模型", "points": [[1394, 114], [1592, 109], [1593, 147], [1394, 152]]}, {"transcription": "inference.pdiparams", "points": [[994, 135], [1292, 135], [1292, 166], [994, 166]]}, {"transcription": "db opt.nb", "points": [[1692, 136], [1840, 136], [1840, 168], [1692, 168]]}, {"transcription": "best_accuracy.states", "points": [[271, 153], [573, 157], [573, 193], [271, 188]]}]
log/default.gpu.log 0 → 100644
View file @ f1506916
index,uuid,driver_version,name,gpu_serial,display_active,display_mode
pid,process_name,gpu_uuid,gpu_name,used_memory
index,utilization_gpu,memory_total,memory_used,memory_free,timestamp
log/default.zmdrkw.log 0 → 100644
View file @ f1506916
[{'name': 'zmdrkw', 'rank': -1, 'replicas': 4, 'dtype': 'gpu', 'candidate': '10.6.6.220:52234', 'endpoints': '10.6.6.220:52235,10.6.6.220:52236,10.6.6.220:52237,10.6.6.220:52238'}]
\ No newline at end of file
log/workerlog.0 0 → 100644
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/usr/local/bin/python3: can't open file 'tools/eval.py': [Errno 2] No such file or directory
log/workerlog.1 0 → 100644
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/usr/local/bin/python3: can't open file 'tools/eval.py': [Errno 2] No such file or directory
log/workerlog.3 0 → 100644
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/usr/local/bin/python3: can't open file 'tools/eval.py': [Errno 2] No such file or directory
ppocr/data/__init__.py 0 → 100755
View file @ f1506916
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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
#
# http://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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import sys
import numpy as np
import skimage
import paddle
import signal
import random
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
import copy
from paddle.io import Dataset, DataLoader, BatchSampler, DistributedBatchSampler
import paddle.distributed as dist
from ppocr.data.imaug import transform, create_operators
from ppocr.data.simple_dataset import SimpleDataSet
from ppocr.data.lmdb_dataset import LMDBDataSet
from ppocr.data.pgnet_dataset import PGDataSet
from ppocr.data.pubtab_dataset import PubTabDataSet
__all__ = ['build_dataloader', 'transform', 'create_operators']
def term_mp(sig_num, frame):
""" kill all child processes
"""
pid = os.getpid()
pgid = os.getpgid(os.getpid())
print("main proc {} exit, kill process group " "{}".format(pid, pgid))
os.killpg(pgid, signal.SIGKILL)
def build_dataloader(config, mode, device, logger, seed=None):
config = copy.deepcopy(config)
support_dict = [
'SimpleDataSet', 'LMDBDataSet', 'PGDataSet', 'PubTabDataSet'
]
module_name = config[mode]['dataset']['name']
assert module_name in support_dict, Exception(
'DataSet only support {}'.format(support_dict))
assert mode in ['Train', 'Eval', 'Test'
], "Mode should be Train, Eval or Test."
dataset = eval(module_name)(config, mode, logger, seed)
loader_config = config[mode]['loader']
batch_size = loader_config['batch_size_per_card']
drop_last = loader_config['drop_last']
shuffle = loader_config['shuffle']
num_workers = loader_config['num_workers']
if 'use_shared_memory' in loader_config.keys():
use_shared_memory = loader_config['use_shared_memory']
else:
use_shared_memory = True
if mode == "Train":
# Distribute data to multiple cards
batch_sampler = DistributedBatchSampler(
dataset=dataset,
batch_size=batch_size,
shuffle=shuffle,
drop_last=drop_last)
else:
# Distribute data to single card
batch_sampler = BatchSampler(
dataset=dataset,
batch_size=batch_size,
shuffle=shuffle,
drop_last=drop_last)
if 'collate_fn' in loader_config:
from . import collate_fn
collate_fn = getattr(collate_fn, loader_config['collate_fn'])()
else:
collate_fn = None
data_loader = DataLoader(
dataset=dataset,
batch_sampler=batch_sampler,
places=device,
num_workers=num_workers,
return_list=True,
use_shared_memory=use_shared_memory,
collate_fn=collate_fn)
# support exit using ctrl+c
signal.signal(signal.SIGINT, term_mp)
signal.signal(signal.SIGTERM, term_mp)
return data_loader
ppocr/data/collate_fn.py 0 → 100755
View file @ f1506916
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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
#
# http://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 paddle
import numbers
import numpy as np
from collections import defaultdict
class DictCollator(object):
"""
data batch
"""
def __call__(self, batch):
# todo：support batch operators
data_dict = defaultdict(list)
to_tensor_keys = []
for sample in batch:
for k, v in sample.items():
if isinstance(v, (np.ndarray, paddle.Tensor, numbers.Number)):
if k not in to_tensor_keys:
to_tensor_keys.append(k)
data_dict[k].append(v)
for k in to_tensor_keys:
data_dict[k] = paddle.to_tensor(data_dict[k])
return data_dict
class ListCollator(object):
"""
data batch
"""
def __call__(self, batch):
# todo：support batch operators
data_dict = defaultdict(list)
to_tensor_idxs = []
for sample in batch:
for idx, v in enumerate(sample):
if isinstance(v, (np.ndarray, paddle.Tensor, numbers.Number)):
if idx not in to_tensor_idxs:
to_tensor_idxs.append(idx)
data_dict[idx].append(v)
for idx in to_tensor_idxs:
data_dict[idx] = paddle.to_tensor(data_dict[idx])
return list(data_dict.values())
class SSLRotateCollate(object):
"""
bach: [
[(4*3xH*W), (4,)]
[(4*3xH*W), (4,)]
...
]
"""
def __call__(self, batch):
output = [np.concatenate(d, axis=0) for d in zip(*batch)]
return output
ppocr/data/imaug/ColorJitter.py 0 → 100755
View file @ f1506916
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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
#
# http://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.
from paddle.vision.transforms import ColorJitter as pp_ColorJitter
__all__ = ['ColorJitter']
class ColorJitter(object):
def __init__(self, brightness=0, contrast=0, saturation=0, hue=0,**kwargs):
self.aug = pp_ColorJitter(brightness, contrast, saturation, hue)
def __call__(self, data):
image = data['image']
image = self.aug(image)
data['image'] = image
return data
ppocr/data/imaug/__init__.py 0 → 100755
View file @ f1506916
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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
#
# http://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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from .iaa_augment import IaaAugment
from .make_border_map import MakeBorderMap
from .make_shrink_map import MakeShrinkMap
from .random_crop_data import EastRandomCropData, RandomCropImgMask
from .make_pse_gt import MakePseGt
from .rec_img_aug import RecAug, RecConAug, RecResizeImg, ClsResizeImg, \
SRNRecResizeImg, NRTRRecResizeImg, SARRecResizeImg, PRENResizeImg, \
SVTRRecResizeImg
from .ssl_img_aug import SSLRotateResize
from .randaugment import RandAugment
from .copy_paste import CopyPaste
from .ColorJitter import ColorJitter
from .operators import *
from .label_ops import *
def transform(data, ops=None):
""" transform """
if ops is None:
ops = []
for op in ops:
data = op(data)
if data is None:
return None
return data
def create_operators(op_param_list, global_config=None):
"""
create operators based on the config
Args:
params(list): a dict list, used to create some operators
"""
assert isinstance(op_param_list, list), ('operator config should be a list')
ops = []
for operator in op_param_list:
assert isinstance(operator,
dict) and len(operator) == 1, "yaml format error"
op_name = list(operator)[0]
param = {} if operator[op_name] is None else operator[op_name]
if global_config is not None:
param.update(global_config)
op = eval(op_name)(**param)
ops.append(op)
return ops
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