diff --git a/configs/det/det_mv3_db.yml b/configs/det/det_mv3_db.yml index a165b426c66c75b2cbef34a7ffa03a4e87e851ad..b55ff99be91bfea795d4b023edcc57e95b97ecc2 100644 --- a/configs/det/det_mv3_db.yml +++ b/configs/det/det_mv3_db.yml @@ -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' diff --git a/configs/rec/rec_mv3_none_bilstm_ctc.yml b/configs/rec/rec_mv3_none_bilstm_ctc.yml index c6c07141a2ccbcf60d1c17f5eba600941ff97fb2..57a7c049dfdae14c5d00ab58a7de528b21d0eb8c 100644 --- a/configs/rec/rec_mv3_none_bilstm_ctc.yml +++ b/configs/rec/rec_mv3_none_bilstm_ctc.yml @@ -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 diff --git a/ppocr/data/lmdb_dataset.py b/ppocr/data/lmdb_dataset.py index ffa0522818cd305f624b5fed193e2387dd7e6216..e7bb6dd3c96d24b9dee723e1efdba299e83ad96d 100644 --- a/ppocr/data/lmdb_dataset.py +++ b/ppocr/data/lmdb_dataset.py @@ -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 diff --git a/ppocr/data/simple_dataset.py b/ppocr/data/simple_dataset.py index e9813cdc9ef9724648ebbf99d97176f31987b8de..e9a0a2ae4c2b77b5a390e7f196f364c529847d71 100644 --- a/ppocr/data/simple_dataset.py +++ b/ppocr/data/simple_dataset.py @@ -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 diff --git a/ppocr/metrics/__init__.py b/ppocr/metrics/__init__.py index 3fddac17aaa25d75a95d3d981a8e5bc5e9d0d896..5d668cd8b5a120c9ac18b21f4443fe846aa1dc18 100644 --- a/ppocr/metrics/__init__.py +++ b/ppocr/metrics/__init__.py @@ -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'] diff --git a/ppocr/metrics/DetMetric.py b/ppocr/metrics/det_metric.py similarity index 100% rename from ppocr/metrics/DetMetric.py rename to ppocr/metrics/det_metric.py diff --git a/ppocr/metrics/RecMetric.py b/ppocr/metrics/rec_metric.py similarity index 100% rename from ppocr/metrics/RecMetric.py rename to ppocr/metrics/rec_metric.py diff --git a/ppocr/modeling/heads/det_db_head.py b/ppocr/modeling/heads/det_db_head.py index 237899104a58a0bdfda5b16c1728163ea4d845d5..49c50ffdba89f557e71ac5c7abe4e07e52bc9119 100644 --- a/ppocr/modeling/heads/det_db_head.py +++ b/ppocr/modeling/heads/det_db_head.py @@ -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"), ) diff --git a/ppocr/modeling/necks/db_fpn.py b/ppocr/modeling/necks/db_fpn.py index dbe482b4c7ef369551e7910fe87b991f09996f22..710023f30cdda90322b731c4bd3465d0dc06a139 100644 --- a/ppocr/modeling/necks/db_fpn.py +++ b/ppocr/modeling/necks/db_fpn.py @@ -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 diff --git a/ppocr/optimizer/__init__.py b/ppocr/optimizer/__init__.py index 72366e503b3d3646f645785f1600fa76ac723b9f..6413ae959200c25d6c17b1ec93217c0e8b0bf269 100644 --- a/ppocr/optimizer/__init__.py +++ b/ppocr/optimizer/__init__.py @@ -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: diff --git a/ppocr/optimizer/learning_rate.py b/ppocr/optimizer/learning_rate.py index 518e0eef6179529df78a6a84fef6546730d43a9f..8f303e838c3dab79385198dd98c84c4afed1a7b1 100644 --- a/ppocr/optimizer/learning_rate.py +++ b/ppocr/optimizer/learning_rate.py @@ -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, diff --git a/ppocr/utils/logging.py b/ppocr/utils/logging.py index e3fa6b2398d6b1f06207b6c42dd8642f431365de..951141db8f39acac612029c8b69f4a29a0ab27ce 100644 --- a/ppocr/utils/logging.py +++ b/ppocr/utils/logging.py @@ -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 diff --git a/tools/program.py b/tools/program.py index 696700c031eda416212e3fd96747badd24cf2c1d..41acb8665a345d1251fbf721e557212cd2771e04 100755 --- a/tools/program.py +++ b/tools/program.py @@ -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) diff --git a/tools/train.py b/tools/train.py index bdba7dba772891d740baf03bc0adb963ae661332..c16223796f8785d1928ba1df57602967e0518e31 100755 --- a/tools/train.py +++ b/tools/train.py @@ -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() @@ -61,7 +60,7 @@ def main(config, device, logger, vdl_writer): global_config) # build model - #for rec algorithm + # for rec algorithm if hasattr(post_process_class, 'character'): char_num = len(getattr(post_process_class, 'character')) config['Architecture']["Head"]['out_channels'] = char_num @@ -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 diff --git a/train.sh b/train.sh index 0e628f9204fda9fd87198c90843ae01bac8a5239..a0483e4dc81a4530822850f5a0a20ec9b4c94764 100644 --- a/train.sh +++ b/train.sh @@ -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 \ No newline at end of file + 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 \ No newline at end of file