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PaddleOCR
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trans to paddle-rc

This commit is contained in:
dyning 2020-11-05 15:13:36 +08:00
parent fa675f8954
commit 1ae379198e
18 changed files with 180 additions and 138 deletions
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0
configs/det/det_r50_vd_db.yml → configs/det/bak/det_r50_vd_db.yml
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0
configs/rec/rec_mv3_none_bilstm_ctc_simple.yml → configs/rec/bak/rec_mv3_none_bilstm_ctc_simple.yml
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0
configs/rec/rec_r34_vd_none_bilstm_ctc.yml → configs/rec/bak/rec_r34_vd_none_bilstm_ctc.yml
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0
configs/rec/rec_r34_vd_none_none_ctc.yml → configs/rec/bak/rec_r34_vd_none_none_ctc.yml
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11
ppocr/data/__init__.py
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@ -37,6 +37,7 @@ from ppocr.data.lmdb_dataset import LMDBDateSet
__all__ = ['build_dataloader', 'transform', 'create_operators']
def term_mp(sig_num, frame):
""" kill all child processes
"""
@ -45,19 +46,22 @@ def term_mp(sig_num, frame):
print("main proc {} exit, kill process group " "{}".format(pid, pgid))
os.killpg(pgid, signal.SIGKILL)
signal.signal(signal.SIGINT, term_mp)
signal.signal(signal.SIGTERM, term_mp)
def build_dataloader(config, mode, device):
def build_dataloader(config, mode, device, logger):
config = copy.deepcopy(config)
support_dict = ['SimpleDataSet', 'LMDBDateSet']
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."
assert mode in ['Train', 'Eval', 'Test'
], "Mode should be Train, Eval or Test."
dataset = eval(module_name)(config, mode)
dataset = eval(module_name)(config, mode, logger)
loader_config = config[mode]['loader']
batch_size = loader_config['batch_size_per_card']
drop_last = loader_config['drop_last']
@ -86,4 +90,3 @@ def build_dataloader(config, mode, device):
return_list=True)
return data_loader
#return data_loader, _dataset.info_dict

20
ppocr/data/lmdb_dataset.py
View File

@ -22,11 +22,10 @@ import lmdb
import cv2
from .imaug import transform, create_operators
from ppocr.utils.logging import get_logger
logger = get_logger()
class LMDBDateSet(Dataset):
def __init__(self, config, mode):
def __init__(self, config, mode, logger):
super(LMDBDateSet, self).__init__()
global_config = config['Global']
@ -37,22 +36,12 @@ class LMDBDateSet(Dataset):
self.do_shuffle = loader_config['shuffle']
self.lmdb_sets = self.load_hierarchical_lmdb_dataset(data_dir)
logger.info("Initialize indexs of datasets:%s" % data_dir)
self.data_idx_order_list = self.dataset_traversal()
if self.do_shuffle:
np.random.shuffle(self.data_idx_order_list)
self.ops = create_operators(dataset_config['transforms'], global_config)
# # for rec
# character = ''
# for op in self.ops:
# if hasattr(op, 'character'):
# character = getattr(op, 'character')
# self.info_dict = {'character': character}
def load_hierarchical_lmdb_dataset(self, data_dir):
lmdb_sets = {}
dataset_idx = 0
@ -115,8 +104,8 @@ class LMDBDateSet(Dataset):
lmdb_idx, file_idx = self.data_idx_order_list[idx]
lmdb_idx = int(lmdb_idx)
file_idx = int(file_idx)
sample_info = self.get_lmdb_sample_info(
self.lmdb_sets[lmdb_idx]['txn'], file_idx)
sample_info = self.get_lmdb_sample_info(self.lmdb_sets[lmdb_idx]['txn'],
file_idx)
if sample_info is None:
return self.__getitem__(np.random.randint(self.__len__()))
img, label = sample_info
@ -128,4 +117,3 @@ class LMDBDateSet(Dataset):
def __len__(self):
return self.data_idx_order_list.shape[0]

13
ppocr/data/simple_dataset.py
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@ -20,11 +20,10 @@ from paddle.io import Dataset
import time
from .imaug import transform, create_operators
from ppocr.utils.logging import get_logger
logger = get_logger()
class SimpleDataSet(Dataset):
def __init__(self, config, mode):
def __init__(self, config, mode, logger):
super(SimpleDataSet, self).__init__()
global_config = config['Global']
@ -41,7 +40,9 @@ class SimpleDataSet(Dataset):
ratio_list = dataset_config.pop('ratio_list')
assert sum(ratio_list) == 1, "The sum of the ratio_list should be 1."
assert len(ratio_list) == data_source_num, "The length of ratio_list should be the same as the file_list."
assert len(
ratio_list
) == data_source_num, "The length of ratio_list should be the same as the file_list."
self.data_dir = dataset_config['data_dir']
self.do_shuffle = loader_config['shuffle']
@ -87,8 +88,7 @@ class SimpleDataSet(Dataset):
cur_index = cur_index_sets[dataset_idx]
if cur_index >= data_num_list[dataset_idx]:
break
data_idx_order_list.append((
dataset_idx, cur_index))
data_idx_order_list.append((dataset_idx, cur_index))
cur_index_sets[dataset_idx] += 1
if finish_read_num == dataset_num:
break
@ -119,4 +119,3 @@ class SimpleDataSet(Dataset):
def __len__(self):
return len(self.data_idx_order_list)

15
ppocr/modeling/backbones/det_mobilenet_v3.py
View File

@ -158,7 +158,7 @@ class ConvBNLayer(nn.Layer):
super(ConvBNLayer, self).__init__()
self.if_act = if_act
self.act = act
self.conv = nn.Conv2d(
self.conv = nn.Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
@ -183,7 +183,7 @@ class ConvBNLayer(nn.Layer):
if self.act == "relu":
x = F.relu(x)
elif self.act == "hard_swish":
x = F.hard_swish(x)
x = F.activation.hard_swish(x)
else:
print("The activation function is selected incorrectly.")
exit()
@ -242,16 +242,15 @@ class ResidualUnit(nn.Layer):
x = self.mid_se(x)
x = self.linear_conv(x)
if self.if_shortcut:
x = paddle.elementwise_add(inputs, x)
x = paddle.add(inputs, x)
return x
class SEModule(nn.Layer):
def __init__(self, in_channels, reduction=4, name=""):
super(SEModule, self).__init__()
self.avg_pool = nn.Pool2D(
pool_type="avg", global_pooling=True, use_cudnn=False)
self.conv1 = nn.Conv2d(
self.avg_pool = nn.AdaptiveAvgPool2D(1)
self.conv1 = nn.Conv2D(
in_channels=in_channels,
out_channels=in_channels // reduction,
kernel_size=1,
@ -259,7 +258,7 @@ class SEModule(nn.Layer):
padding=0,
weight_attr=ParamAttr(name=name + "_1_weights"),
bias_attr=ParamAttr(name=name + "_1_offset"))
self.conv2 = nn.Conv2d(
self.conv2 = nn.Conv2D(
in_channels=in_channels // reduction,
out_channels=in_channels,
kernel_size=1,
@ -273,5 +272,5 @@ class SEModule(nn.Layer):
outputs = self.conv1(outputs)
outputs = F.relu(outputs)
outputs = self.conv2(outputs)
outputs = F.hard_sigmoid(outputs)
outputs = F.activation.hard_sigmoid(outputs)
return inputs * outputs

2
ppocr/modeling/backbones/rec_mobilenet_v3.py
View File

@ -127,7 +127,7 @@ class MobileNetV3(nn.Layer):
act='hard_swish',
name='conv_last')
self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
self.out_channels = make_divisible(scale * cls_ch_squeeze)
def forward(self, x):

10
ppocr/modeling/heads/det_db_head.py
View File

@ -33,7 +33,7 @@ def get_bias_attr(k, name):
class Head(nn.Layer):
def __init__(self, in_channels, name_list):
super(Head, self).__init__()
self.conv1 = nn.Conv2d(
self.conv1 = nn.Conv2D(
in_channels=in_channels,
out_channels=in_channels // 4,
kernel_size=3,
@ -51,14 +51,14 @@ class Head(nn.Layer):
moving_mean_name=name_list[1] + '.w_1',
moving_variance_name=name_list[1] + '.w_2',
act='relu')
self.conv2 = nn.ConvTranspose2d(
self.conv2 = nn.Conv2DTranspose(
in_channels=in_channels // 4,
out_channels=in_channels // 4,
kernel_size=2,
stride=2,
weight_attr=ParamAttr(
name=name_list[2] + '.w_0',
initializer=paddle.nn.initializer.MSRA(uniform=False)),
initializer=paddle.nn.initializer.KaimingNormal()),
bias_attr=get_bias_attr(in_channels // 4, name_list[-1] + "conv2"))
self.conv_bn2 = nn.BatchNorm(
num_channels=in_channels // 4,
@ -71,14 +71,14 @@ class Head(nn.Layer):
moving_mean_name=name_list[3] + '.w_1',
moving_variance_name=name_list[3] + '.w_2',
act="relu")
self.conv3 = nn.ConvTranspose2d(
self.conv3 = nn.Conv2DTranspose(
in_channels=in_channels // 4,
out_channels=1,
kernel_size=2,
stride=2,
weight_attr=ParamAttr(
name=name_list[4] + '.w_0',
initializer=paddle.nn.initializer.MSRA(uniform=False)),
initializer=paddle.nn.initializer.KaimingNormal()),
bias_attr=get_bias_attr(in_channels // 4, name_list[-1] + "conv3"),
)

30
ppocr/modeling/necks/db_fpn.py
View File

@ -26,37 +26,37 @@ class DBFPN(nn.Layer):
def __init__(self, in_channels, out_channels, **kwargs):
super(DBFPN, self).__init__()
self.out_channels = out_channels
weight_attr = paddle.nn.initializer.MSRA(uniform=False)
weight_attr = paddle.nn.initializer.KaimingNormal()
self.in2_conv = nn.Conv2d(
self.in2_conv = nn.Conv2D(
in_channels=in_channels[0],
out_channels=self.out_channels,
kernel_size=1,
weight_attr=ParamAttr(
name='conv2d_51.w_0', initializer=weight_attr),
bias_attr=False)
self.in3_conv = nn.Conv2d(
self.in3_conv = nn.Conv2D(
in_channels=in_channels[1],
out_channels=self.out_channels,
kernel_size=1,
weight_attr=ParamAttr(
name='conv2d_50.w_0', initializer=weight_attr),
bias_attr=False)
self.in4_conv = nn.Conv2d(
self.in4_conv = nn.Conv2D(
in_channels=in_channels[2],
out_channels=self.out_channels,
kernel_size=1,
weight_attr=ParamAttr(
name='conv2d_49.w_0', initializer=weight_attr),
bias_attr=False)
self.in5_conv = nn.Conv2d(
self.in5_conv = nn.Conv2D(
in_channels=in_channels[3],
out_channels=self.out_channels,
kernel_size=1,
weight_attr=ParamAttr(
name='conv2d_48.w_0', initializer=weight_attr),
bias_attr=False)
self.p5_conv = nn.Conv2d(
self.p5_conv = nn.Conv2D(
in_channels=self.out_channels,
out_channels=self.out_channels // 4,
kernel_size=3,
@ -64,7 +64,7 @@ class DBFPN(nn.Layer):
weight_attr=ParamAttr(
name='conv2d_52.w_0', initializer=weight_attr),
bias_attr=False)
self.p4_conv = nn.Conv2d(
self.p4_conv = nn.Conv2D(
in_channels=self.out_channels,
out_channels=self.out_channels // 4,
kernel_size=3,
@ -72,7 +72,7 @@ class DBFPN(nn.Layer):
weight_attr=ParamAttr(
name='conv2d_53.w_0', initializer=weight_attr),
bias_attr=False)
self.p3_conv = nn.Conv2d(
self.p3_conv = nn.Conv2D(
in_channels=self.out_channels,
out_channels=self.out_channels // 4,
kernel_size=3,
@ -80,7 +80,7 @@ class DBFPN(nn.Layer):
weight_attr=ParamAttr(
name='conv2d_54.w_0', initializer=weight_attr),
bias_attr=False)
self.p2_conv = nn.Conv2d(
self.p2_conv = nn.Conv2D(
in_channels=self.out_channels,
out_channels=self.out_channels // 4,
kernel_size=3,
@ -97,17 +97,17 @@ class DBFPN(nn.Layer):
in3 = self.in3_conv(c3)
in2 = self.in2_conv(c2)
out4 = in4 + F.resize_nearest(in5, scale=2) # 1/16
out3 = in3 + F.resize_nearest(out4, scale=2) # 1/8
out2 = in2 + F.resize_nearest(out3, scale=2) # 1/4
out4 = in4 + F.upsample(in5, scale_factor=2, mode="nearest") # 1/16
out3 = in3 + F.upsample(out4, scale_factor=2, mode="nearest") # 1/8
out2 = in2 + F.upsample(out3, scale_factor=2, mode="nearest") # 1/4
p5 = self.p5_conv(in5)
p4 = self.p4_conv(out4)
p3 = self.p3_conv(out3)
p2 = self.p2_conv(out2)
p5 = F.resize_nearest(p5, scale=8)
p4 = F.resize_nearest(p4, scale=4)
p3 = F.resize_nearest(p3, scale=2)
p5 = F.upsample(p5, scale_factor=8, mode="nearest")
p4 = F.upsample(p4, scale_factor=4, mode="nearest")
p3 = F.upsample(p3, scale_factor=2, mode="nearest")
fuse = paddle.concat([p5, p4, p3, p2], axis=1)
return fuse

4
ppocr/optimizer/__init__.py
View File

@ -50,9 +50,7 @@ def build_optimizer(config, epochs, step_each_epoch, parameters):
# step3 build optimizer
optim_name = config.pop('name')
# Regularization is invalid. The bug will be fixed in paddle-rc. The param is
# weight_decay.
optim = getattr(optimizer, optim_name)(learning_rate=lr,
regularization=reg,
weight_decay=reg,
**config)
return optim(parameters), lr

2
ppocr/optimizer/learning_rate.py
View File

@ -17,7 +17,7 @@ from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from paddle.optimizer import lr_scheduler
from paddle.optimizer import lr as lr_scheduler
class Linear(object):

1
ppocr/utils/logging.py
View File

@ -52,7 +52,6 @@ def get_logger(name='ppocr', log_file=None, log_level=logging.INFO):
stream_handler = logging.StreamHandler(stream=sys.stdout)
stream_handler.setFormatter(formatter)
logger.addHandler(stream_handler)
if log_file is not None and dist.get_rank() == 0:
log_file_folder = os.path.split(log_file)[0]
os.makedirs(log_file_folder, exist_ok=True)

16
ppocr/utils/save_load.py
View File

@ -42,16 +42,12 @@ def _mkdir_if_not_exist(path, logger):
raise OSError('Failed to mkdir {}'.format(path))
def load_dygraph_pretrain(
model,
logger,
path=None,
load_static_weights=False):
def load_dygraph_pretrain(model, logger, path=None, load_static_weights=False):
if not (os.path.isdir(path) or os.path.exists(path + '.pdparams')):
raise ValueError("Model pretrain path {} does not "
"exists.".format(path))
if load_static_weights:
pre_state_dict = paddle.io.load_program_state(path)
pre_state_dict = paddle.static.load_program_state(path)
param_state_dict = {}
model_dict = model.state_dict()
for key in model_dict.keys():
@ -113,15 +109,11 @@ def init_model(config, model, logger, optimizer=None, lr_scheduler=None):
if not isinstance(pretrained_model, list):
pretrained_model = [pretrained_model]
if not isinstance(load_static_weights, list):
load_static_weights = [load_static_weights] * len(
pretrained_model)
load_static_weights = [load_static_weights] * len(pretrained_model)
for idx, pretrained in enumerate(pretrained_model):
load_static = load_static_weights[idx]
load_dygraph_pretrain(
model,
logger,
path=pretrained,
load_static_weights=load_static)
model, logger, path=pretrained, load_static_weights=load_static)
logger.info("load pretrained model from {}".format(
pretrained_model))
else:

76
tools/export_model.py Executable file
View File

@ -0,0 +1,76 @@
# 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.
import argparse
import paddle
from paddle.jit import to_static
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model
from tools.program import load_config
from tools.program import merge_config
def parse_args():
def str2bool(v):
return v.lower() in ("true", "t", "1")
parser = argparse.ArgumentParser()
parser.add_argument("-c", "--config", help="configuration file to use")
parser.add_argument(
"-o", "--output_path", type=str, default='./output/infer/')
return parser.parse_args()
class Model(paddle.nn.Layer):
def __init__(self, model):
super(Model, self).__init__()
self.pre_model = model
# Please modify the 'shape' according to actual needs
@to_static(input_spec=[
paddle.static.InputSpec(
shape=[None, 3, 32, None], dtype='float32')
])
def forward(self, inputs):
x = self.pre_model(inputs)
return x
def main():
FLAGS = parse_args()
config = load_config(FLAGS.config)
merge_config(FLAGS.opt)
# build post process
post_process_class = build_post_process(config['PostProcess'],
config['Global'])
# build model
#for rec algorithm
if hasattr(post_process_class, 'character'):
char_num = len(getattr(post_process_class, 'character'))
config['Architecture']["Head"]['out_channels'] = char_num
model = build_model(config['Architecture'])
init_model(config, model, logger)
model.eval()
model = Model(model)
paddle.jit.save(model, FLAGS.output_path)
if __name__ == "__main__":
main()

24
tools/program.py
View File

@ -33,6 +33,7 @@ from ppocr.utils.logging import get_logger
from ppocr.data import build_dataloader
import numpy as np
class ArgsParser(ArgumentParser):
def __init__(self):
super(ArgsParser, self).__init__(
@ -196,11 +197,6 @@ def train(config,
preds = model(images)
loss = loss_class(preds, batch)
avg_loss = loss['loss']
if config['Global']['distributed']:
avg_loss = model.scale_loss(avg_loss)
avg_loss.backward()
model.apply_collective_grads()
else:
avg_loss.backward()
optimizer.step()
optimizer.clear_grad()
@ -227,7 +223,8 @@ def train(config,
vdl_writer.add_scalar('TRAIN/{}'.format(k), v, global_step)
vdl_writer.add_scalar('TRAIN/lr', lr, global_step)
if global_step > 0 and global_step % print_batch_step == 0:
if dist.get_rank(
) == 0 and global_step > 0 and global_step % print_batch_step == 0:
logs = train_stats.log()
strs = 'epoch: [{}/{}], iter: {}, {}, time: {:.3f}'.format(
epoch, epoch_num, global_step, logs, train_batch_elapse)
@ -235,8 +232,8 @@ def train(config,
# eval
if global_step > start_eval_step and \
(global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0:
cur_metirc = eval(model, valid_dataloader,
post_process_class, eval_class, logger, print_batch_step)
cur_metirc = eval(model, valid_dataloader, post_process_class,
eval_class, logger, print_batch_step)
cur_metirc_str = 'cur metirc, {}'.format(', '.join(
['{}: {}'.format(k, v) for k, v in cur_metirc.items()]))
logger.info(cur_metirc_str)
@ -298,9 +295,8 @@ def train(config,
return
def eval(model, valid_dataloader,
post_process_class, eval_class,
logger, print_batch_step):
def eval(model, valid_dataloader, post_process_class, eval_class, logger,
print_batch_step):
model.eval()
with paddle.no_grad():
total_frame = 0.0
@ -309,7 +305,7 @@ def eval(model, valid_dataloader,
for idx, batch in enumerate(valid_dataloader):
if idx >= len(valid_dataloader):
break
images = paddle.to_variable(batch[0])
images = paddle.to_tensor(batch[0])
start = time.time()
preds = model(images)
@ -321,11 +317,12 @@ def eval(model, valid_dataloader,
eval_class(post_result, batch)
# pbar.update(1)
total_frame += len(images)
if idx % print_batch_step == 0:
if idx % print_batch_step == 0 and dist.get_rank() == 0:
logger.info('tackling images for eval: {}/{}'.format(
idx, len(valid_dataloader)))
# Get final metirc，eg. acc or hmean
metirc = eval_class.get_metric()
# pbar.close()
model.train()
metirc['fps'] = total_frame / total_time
@ -350,7 +347,6 @@ def preprocess():
device = paddle.set_device(device)
config['Global']['distributed'] = dist.get_world_size() != 1
paddle.disable_static(device)
# save_config
save_model_dir = config['Global']['save_model_dir']

34
tools/train.py
View File

@ -27,9 +27,8 @@ import yaml
import paddle
import paddle.distributed as dist
paddle.manual_seed(2)
paddle.seed(2)
from ppocr.utils.logging import get_logger
from ppocr.data import build_dataloader
from ppocr.modeling.architectures import build_model
from ppocr.losses import build_loss
@ -51,15 +50,15 @@ def main(config, device, logger, vdl_writer):
global_config = config['Global']
# build dataloader
train_dataloader = build_dataloader(config, 'Train', device)
train_dataloader = build_dataloader(config, 'Train', device, logger)
if config['Eval']:
valid_dataloader = build_dataloader(config, 'Eval', device)
valid_dataloader = build_dataloader(config, 'Eval', device, logger)
else:
valid_dataloader = None
# build post process
post_process_class = build_post_process(
config['PostProcess'], global_config)
post_process_class = build_post_process(config['PostProcess'],
global_config)
# build model
#for rec algorithm
@ -74,7 +73,8 @@ def main(config, device, logger, vdl_writer):
loss_class = build_loss(config['Loss'])
# build optim
optimizer, lr_scheduler = build_optimizer(config['Optimizer'],
optimizer, lr_scheduler = build_optimizer(
config['Optimizer'],
epochs=config['Global']['epoch_num'],
step_each_epoch=len(train_dataloader),
parameters=model.parameters())
@ -86,19 +86,9 @@ def main(config, device, logger, vdl_writer):
pre_best_model_dict = init_model(config, model, logger, optimizer)
# start train
program.train(config,
train_dataloader,
valid_dataloader,
device,
model,
loss_class,
optimizer,
lr_scheduler,
post_process_class,
eval_class,
pre_best_model_dict,
logger,
vdl_writer)
program.train(config, train_dataloader, valid_dataloader, device, model,
loss_class, optimizer, lr_scheduler, post_process_class,
eval_class, pre_best_model_dict, logger, vdl_writer)
def test_reader(config, device, logger):
@ -113,11 +103,13 @@ def test_reader(config, device, logger):
if count % 1 == 0:
batch_time = time.time() - starttime
starttime = time.time()
logger.info("reader: {}, {}, {}".format(count, len(data), batch_time))
logger.info("reader: {}, {}, {}".format(count,
len(data), batch_time))
except Exception as e:
logger.info(e)
logger.info("finish reader: {}, Success!".format(count))
if __name__ == '__main__':
config, device, logger, vdl_writer = program.preprocess()
main(config, device, logger, vdl_writer)