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Merge pull request #1123 from WenmuZhou/dygraph_rc

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fix some error and make some change
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dyning 2020-11-06 19:34:23 +08:00 committed by GitHub
commit c93b4a171d
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15 changed files with 65 additions and 73 deletions
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4
configs/det/det_mv3_db.yml
View File

@ -44,9 +44,9 @@ Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
learning_rate:
lr:
# name: Cosine
lr: 0.001
learning_rate: 0.001
# warmup_epoch: 0
regularizer:
name: 'L2'

19
configs/rec/rec_mv3_none_bilstm_ctc.yml
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@ -6,7 +6,7 @@ Global:
save_model_dir: ./output/rec/mv3_none_bilstm_ctc/
save_epoch_step: 3
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [0, 1000]
eval_batch_step: [0, 2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train: True
pretrained_model:
@ -18,22 +18,19 @@ Global:
character_dict_path:
character_type: en
max_text_length: 25
loss_type: ctc
infer_mode: False
# use_space_char: True
# use_tps: False
use_space_char: False
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
learning_rate:
lr: 0.0005
lr:
learning_rate: 0.0005
regularizer:
name: 'L2'
factor: 0.00001
factor: 0
Architecture:
model_type: rec
@ -49,7 +46,7 @@ Architecture:
hidden_size: 96
Head:
name: CTCHead
fc_decay: 0.0004
fc_decay: 0
Loss:
name: CTCLoss
@ -75,8 +72,8 @@ Train:
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: True
batch_size_per_card: 256
shuffle: False
drop_last: True
num_workers: 8
@ -97,4 +94,4 @@ Eval:
shuffle: False
drop_last: False
batch_size_per_card: 256
num_workers: 2
num_workers: 4

4
ppocr/data/lmdb_dataset.py
View File

@ -11,13 +11,9 @@
# 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 copy
import numpy as np
import os
import random
import paddle
from paddle.io import Dataset
import time
import lmdb
import cv2

3
ppocr/data/simple_dataset.py
View File

@ -11,13 +11,10 @@
# 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 copy
import numpy as np
import os
import random
import paddle
from paddle.io import Dataset
import time
from .imaug import transform, create_operators

4
ppocr/metrics/__init__.py
View File

@ -23,8 +23,8 @@ __all__ = ['build_metric']
def build_metric(config):
from .DetMetric import DetMetric
from .RecMetric import RecMetric
from .det_metric import DetMetric
from .rec_metric import RecMetric
support_dict = ['DetMetric', 'RecMetric']

0
ppocr/metrics/DetMetric.py → ppocr/metrics/det_metric.py
View File

0
ppocr/metrics/RecMetric.py → ppocr/metrics/rec_metric.py
View File

4
ppocr/modeling/heads/det_db_head.py
View File

@ -58,7 +58,7 @@ class Head(nn.Layer):
stride=2,
weight_attr=ParamAttr(
name=name_list[2] + '.w_0',
initializer=paddle.nn.initializer.KaimingNormal()),
initializer=paddle.nn.initializer.KaimingUniform()),
bias_attr=get_bias_attr(in_channels // 4, name_list[-1] + "conv2"))
self.conv_bn2 = nn.BatchNorm(
num_channels=in_channels // 4,
@ -78,7 +78,7 @@ class Head(nn.Layer):
stride=2,
weight_attr=ParamAttr(
name=name_list[4] + '.w_0',
initializer=paddle.nn.initializer.KaimingNormal()),
initializer=paddle.nn.initializer.KaimingUniform()),
bias_attr=get_bias_attr(in_channels // 4, name_list[-1] + "conv3"),
)

17
ppocr/modeling/necks/db_fpn.py
View File

@ -26,7 +26,7 @@ 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.KaimingNormal()
weight_attr = paddle.nn.initializer.KaimingUniform()
self.in2_conv = nn.Conv2D(
in_channels=in_channels[0],
@ -97,17 +97,20 @@ class DBFPN(nn.Layer):
in3 = self.in3_conv(c3)
in2 = self.in2_conv(c2)
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
out4 = in4 + F.upsample(
in5, scale_factor=2, mode="nearest", align_mode=1) # 1/16
out3 = in3 + F.upsample(
out4, scale_factor=2, mode="nearest", align_mode=1) # 1/8
out2 = in2 + F.upsample(
out3, scale_factor=2, mode="nearest", align_mode=1) # 1/4
p5 = self.p5_conv(in5)
p4 = self.p4_conv(out4)
p3 = self.p3_conv(out3)
p2 = self.p2_conv(out2)
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")
p5 = F.upsample(p5, scale_factor=8, mode="nearest", align_mode=1)
p4 = F.upsample(p4, scale_factor=4, mode="nearest", align_mode=1)
p3 = F.upsample(p3, scale_factor=2, mode="nearest", align_mode=1)
fuse = paddle.concat([p5, p4, p3, p2], axis=1)
return fuse

5
ppocr/optimizer/__init__.py
View File

@ -29,7 +29,7 @@ def build_lr_scheduler(lr_config, epochs, step_each_epoch):
lr_name = lr_config.pop('name')
lr = getattr(learning_rate, lr_name)(**lr_config)()
else:
lr = lr_config['lr']
lr = lr_config['learning_rate']
return lr
@ -37,8 +37,7 @@ def build_optimizer(config, epochs, step_each_epoch, parameters):
from . import regularizer, optimizer
config = copy.deepcopy(config)
# step1 build lr
lr = build_lr_scheduler(
config.pop('learning_rate'), epochs, step_each_epoch)
lr = build_lr_scheduler(config.pop('lr'), epochs, step_each_epoch)
# step2 build regularization
if 'regularizer' in config and config['regularizer'] is not None:

44
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 as lr_scheduler
from paddle.optimizer import lr
class Linear(object):
@ -32,7 +32,7 @@ class Linear(object):
"""
def __init__(self,
lr,
learning_rate,
epochs,
step_each_epoch,
end_lr=0.0,
@ -41,7 +41,7 @@ class Linear(object):
last_epoch=-1,
**kwargs):
super(Linear, self).__init__()
self.lr = lr
self.learning_rate = learning_rate
self.epochs = epochs * step_each_epoch
self.end_lr = end_lr
self.power = power
@ -49,18 +49,18 @@ class Linear(object):
self.warmup_epoch = warmup_epoch * step_each_epoch
def __call__(self):
learning_rate = lr_scheduler.PolynomialLR(
learning_rate=self.lr,
learning_rate = lr.PolynomialDecay(
learning_rate=self.learning_rate,
decay_steps=self.epochs,
end_lr=self.end_lr,
power=self.power,
last_epoch=self.last_epoch)
if self.warmup_epoch > 0:
learning_rate = lr_scheduler.LinearLrWarmup(
learning_rate = lr.LinearWarmup(
learning_rate=learning_rate,
warmup_steps=self.warmup_epoch,
start_lr=0.0,
end_lr=self.lr,
end_lr=self.learning_rate,
last_epoch=self.last_epoch)
return learning_rate
@ -77,27 +77,29 @@ class Cosine(object):
"""
def __init__(self,
lr,
learning_rate,
step_each_epoch,
epochs,
warmup_epoch=0,
last_epoch=-1,
**kwargs):
super(Cosine, self).__init__()
self.lr = lr
self.learning_rate = learning_rate
self.T_max = step_each_epoch * epochs
self.last_epoch = last_epoch
self.warmup_epoch = warmup_epoch * step_each_epoch
def __call__(self):
learning_rate = lr_scheduler.CosineAnnealingLR(
learning_rate=self.lr, T_max=self.T_max, last_epoch=self.last_epoch)
learning_rate = lr.CosineAnnealingDecay(
learning_rate=self.learning_rate,
T_max=self.T_max,
last_epoch=self.last_epoch)
if self.warmup_epoch > 0:
learning_rate = lr_scheduler.LinearLrWarmup(
learning_rate = lr.LinearWarmup(
learning_rate=learning_rate,
warmup_steps=self.warmup_epoch,
start_lr=0.0,
end_lr=self.lr,
end_lr=self.learning_rate,
last_epoch=self.last_epoch)
return learning_rate
@ -115,7 +117,7 @@ class Step(object):
"""
def __init__(self,
lr,
learning_rate,
step_size,
step_each_epoch,
gamma,
@ -124,23 +126,23 @@ class Step(object):
**kwargs):
super(Step, self).__init__()
self.step_size = step_each_epoch * step_size
self.lr = lr
self.learning_rate = learning_rate
self.gamma = gamma
self.last_epoch = last_epoch
self.warmup_epoch = warmup_epoch * step_each_epoch
def __call__(self):
learning_rate = lr_scheduler.StepLR(
learning_rate=self.lr,
learning_rate = lr.StepDecay(
learning_rate=self.learning_rate,
step_size=self.step_size,
gamma=self.gamma,
last_epoch=self.last_epoch)
if self.warmup_epoch > 0:
learning_rate = lr_scheduler.LinearLrWarmup(
learning_rate = lr.LinearWarmup(
learning_rate=learning_rate,
warmup_steps=self.warmup_epoch,
start_lr=0.0,
end_lr=self.lr,
end_lr=self.learning_rate,
last_epoch=self.last_epoch)
return learning_rate
@ -169,12 +171,12 @@ class Piecewise(object):
self.warmup_epoch = warmup_epoch * step_each_epoch
def __call__(self):
learning_rate = lr_scheduler.PiecewiseLR(
learning_rate = lr.PiecewiseDecay(
boundaries=self.boundaries,
values=self.values,
last_epoch=self.last_epoch)
if self.warmup_epoch > 0:
learning_rate = lr_scheduler.LinearLrWarmup(
learning_rate = lr.LinearWarmup(
learning_rate=learning_rate,
warmup_steps=self.warmup_epoch,
start_lr=0.0,

2
ppocr/utils/logging.py
View File

@ -22,7 +22,7 @@ logger_initialized = {}
@functools.lru_cache()
def get_logger(name='ppocr', log_file=None, log_level=logging.INFO):
def get_logger(name='root', log_file=None, log_level=logging.INFO):
"""Initialize and get a logger by name.
If the logger has not been initialized, this method will initialize the
logger by adding one or two handlers, otherwise the initialized logger will

21
tools/program.py
View File

@ -152,7 +152,6 @@ def train(config,
pre_best_model_dict,
logger,
vdl_writer=None):
cal_metric_during_train = config['Global'].get('cal_metric_during_train',
False)
log_smooth_window = config['Global']['log_smooth_window']
@ -185,14 +184,13 @@ def train(config,
for epoch in range(start_epoch, epoch_num):
if epoch > 0:
train_loader = build_dataloader(config, 'Train', device)
train_dataloader = build_dataloader(config, 'Train', device, logger)
for idx, batch in enumerate(train_dataloader):
if idx >= len(train_dataloader):
break
lr = optimizer.get_lr()
t1 = time.time()
batch = [paddle.to_tensor(x) for x in batch]
images = batch[0]
preds = model(images)
loss = loss_class(preds, batch)
@ -301,11 +299,11 @@ def eval(model, valid_dataloader, post_process_class, eval_class, logger,
with paddle.no_grad():
total_frame = 0.0
total_time = 0.0
# pbar = tqdm(total=len(valid_dataloader), desc='eval model:')
pbar = tqdm(total=len(valid_dataloader), desc='eval model:')
for idx, batch in enumerate(valid_dataloader):
if idx >= len(valid_dataloader):
break
images = paddle.to_tensor(batch[0])
images = batch[0]
start = time.time()
preds = model(images)
@ -315,15 +313,15 @@ def eval(model, valid_dataloader, post_process_class, eval_class, logger,
total_time += time.time() - start
# Evaluate the results of the current batch
eval_class(post_result, batch)
# pbar.update(1)
pbar.update(1)
total_frame += len(images)
if idx % print_batch_step == 0 and dist.get_rank() == 0:
logger.info('tackling images for eval: {}/{}'.format(
idx, len(valid_dataloader)))
# 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()
pbar.close()
model.train()
metirc['fps'] = total_frame / total_time
return metirc
@ -354,7 +352,8 @@ def preprocess():
with open(os.path.join(save_model_dir, 'config.yml'), 'w') as f:
yaml.dump(dict(config), f, default_flow_style=False, sort_keys=False)
logger = get_logger(log_file='{}/train.log'.format(save_model_dir))
logger = get_logger(
name='root', log_file='{}/train.log'.format(save_model_dir))
if config['Global']['use_visualdl']:
from visualdl import LogWriter
vdl_writer_path = '{}/vdl/'.format(save_model_dir)

7
tools/train.py
View File

@ -36,7 +36,6 @@ from ppocr.optimizer import build_optimizer
from ppocr.postprocess import build_post_process
from ppocr.metrics import build_metric
from ppocr.utils.save_load import init_model
from ppocr.utils.utility import print_dict
import tools.program as program
dist.get_world_size()
@ -81,10 +80,11 @@ def main(config, device, logger, vdl_writer):
# build metric
eval_class = build_metric(config['Metric'])
# load pretrain model
pre_best_model_dict = init_model(config, model, logger, optimizer)
logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
format(len(train_dataloader), len(valid_dataloader)))
# start train
program.train(config, train_dataloader, valid_dataloader, device, model,
loss_class, optimizer, lr_scheduler, post_process_class,
@ -92,8 +92,7 @@ def main(config, device, logger, vdl_writer):
def test_reader(config, device, logger):
loader = build_dataloader(config, 'Train', device)
# loader = build_dataloader(config, 'Eval', device)
loader = build_dataloader(config, 'Train', device, logger)
import time
starttime = time.time()
count = 0

2
train.sh
View File

@ -1 +1 @@
python -m paddle.distributed.launch --selected_gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/det/det_mv3_db.yml
python3 -m paddle.distributed.launch --selected_gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/rec/rec_mv3_none_bilstm_ctc.yml