updata structure of dygraph

ppocr/modeling/__init__.py

-# 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 copy
-from .losses import build_loss
-
-__all__ = ['build_model', 'build_loss']
-
-
-def build_model(config):
-    from .architectures import Model
-
-    config = copy.deepcopy(config)
-    module_class = Model(config)
-    return module_class

ppocr/modeling/architectures/__init__.py

@@ -12,5 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .model import Model
-__all__ = ['Model']
+import copy
+
\ No newline at end of file
+__all__ = ['build_model']
+
+def build_model(config):
+    from .base_model import BaseModel
+    
+    config = copy.deepcopy(config)
+    module_class = BaseModel(config)
+    return module_class
\ No newline at end of file

ppocr/modeling/architectures/model.py → ppocr/modeling/architectures/base_model.py

@@ -15,38 +15,29 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os, sys
-
-__dir__ = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(__dir__)
-sys.path.append('/home/zhoujun20/PaddleOCR')
-
 from paddle import nn
-from ppocr.modeling.transform import build_transform
+
 from ppocr.modeling.backbones import build_backbone
 from ppocr.modeling.necks import build_neck
 from ppocr.modeling.heads import build_head
 
-__all__ = ['Model']
+__all__ = ['BaseModel']
 
-
-class Model(nn.Layer):
+class BaseModel(nn.Layer):
     def __init__(self, config):
         """
-        Detection module for OCR.
+        the module for OCR.
         args:
             config (dict): the super parameters for module.
         """
-        super(Model, self).__init__()
-        algorithm = config['algorithm']
-        self.type = config['type']
-        self.model_name = '{}_{}'.format(self.type, algorithm)
-
+        super(BaseModel, self).__init__()
+        
         in_channels = config.get('in_channels', 3)
+        model_type = config['model_type']
         # build transfrom,
         # for rec, transfrom can be TPS,None
         # for det and cls, transfrom shoule to be None,
-        #                  if you make model differently, you can use transfrom in det and cls
+        # if you make model differently, you can use transfrom in det and cls
         if 'Transform' not in config or config['Transform'] is None:
             self.use_transform = False
         else:
@@ -57,9 +48,9 @@ class Model(nn.Layer):
 
         # build backbone, backbone is need for del, rec and cls
         config["Backbone"]['in_channels'] = in_channels
-        self.backbone = build_backbone(config["Backbone"], self.type)
+        self.backbone = build_backbone(config["Backbone"], model_type)
         in_channels = self.backbone.out_channels
-
+        
         # build neck
         # for rec, neck can be cnn,rnn or reshape(None)
         # for det, neck can be FPN, BIFPN and so on.
@@ -71,6 +62,7 @@ class Model(nn.Layer):
             config['Neck']['in_channels'] = in_channels
             self.neck = build_neck(config['Neck'])
             in_channels = self.neck.out_channels
+        
         # # build head, head is need for det, rec and cls
         config["Head"]['in_channels'] = in_channels
         self.head = build_head(config["Head"])

ppocr/modeling/backbones/__init__.py

@@ -19,7 +19,6 @@ def build_backbone(config, model_type):
     if model_type == 'det':
         from .det_mobilenet_v3 import MobileNetV3
         from .det_resnet_vd import ResNet
-
         support_dict = ['MobileNetV3', 'ResNet', 'ResNet_SAST']
     elif model_type == 'rec':
         from .rec_mobilenet_v3 import MobileNetV3

ppocr/modeling/backbones/det_mobilenet_v3.py

@@ -130,7 +130,6 @@ class MobileNetV3(nn.Layer):
                 if_act=True,
                 act='hard_swish',
                 name='conv_last'))
-
         self.stages.append(nn.Sequential(*block_list))
         self.out_channels.append(make_divisible(scale * cls_ch_squeeze))
         for i, stage in enumerate(self.stages):
@@ -275,4 +274,4 @@ class SEModule(nn.Layer):
         outputs = F.relu(outputs)
         outputs = self.conv2(outputs)
         outputs = F.hard_sigmoid(outputs)
-        return inputs * outputs
+        return inputs * outputs
\ No newline at end of file

ppocr/modeling/heads/__init__.py

@@ -20,8 +20,8 @@ def build_head(config):
     from .det_db_head import DBHead
 
     # rec head
-    from .rec_ctc_head import CTC
-    support_dict = ['DBHead', 'CTC']
+    from .rec_ctc_head import CTCHead
+    support_dict = ['DBHead', 'CTCHead']
 
     module_name = config.pop('name')
     assert module_name in support_dict, Exception('head only support {}'.format(

ppocr/modeling/heads/rec_ctc_head.py

@@ -33,10 +33,9 @@ def get_para_bias_attr(l2_decay, k, name):
         regularizer=regularizer, initializer=initializer, name=name + "_b_attr")
     return [weight_attr, bias_attr]
 
-
-class CTC(nn.Layer):
-    def __init__(self, in_channels, out_channels, fc_decay=1e-5, **kwargs):
-        super(CTC, self).__init__()
+class CTCHead(nn.Layer):
+    def __init__(self, in_channels, out_channels, fc_decay=0.0004, **kwargs):
+        super(CTCHead, self).__init__()
         weight_attr, bias_attr = get_para_bias_attr(
             l2_decay=fc_decay, k=in_channels, name='ctc_fc')
         self.fc = nn.Linear(

ppocr/modeling/necks/__init__.py

@@ -14,11 +14,10 @@
 
 __all__ = ['build_neck']
 
-
 def build_neck(config):
-    from .fpn import FPN
+    from .db_fpn import DBFPN
     from .rnn import SequenceEncoder
-    support_dict = ['FPN', 'SequenceEncoder']
+    support_dict = ['DBFPN', 'SequenceEncoder']
 
     module_name = config.pop('name')
     assert module_name in support_dict, Exception('neck only support {}'.format(

ppocr/modeling/necks/fpn.py → ppocr/modeling/necks/db_fpn.py

@@ -22,9 +22,9 @@ import paddle.nn.functional as F
 from paddle import ParamAttr
 
 
-class FPN(nn.Layer):
+class DBFPN(nn.Layer):
     def __init__(self, in_channels, out_channels, **kwargs):
-        super(FPN, self).__init__()
+        super(DBFPN, self).__init__()
         self.out_channels = out_channels
         weight_attr = paddle.nn.initializer.MSRA(uniform=False)
 

ppocr/modeling/necks/rnn.py

@@ -76,8 +76,7 @@ class SequenceEncoder(nn.Layer):
                 'fc': EncoderWithFC,
                 'rnn': EncoderWithRNN
             }
-            assert encoder_type in support_encoder_dict, '{} must in {}'.format(
-                encoder_type, support_encoder_dict.keys())
+            assert encoder_type in support_encoder_dict, '{} must in {}'.format(encoder_type, support_encoder_dict.keys())
 
             self.encoder = support_encoder_dict[encoder_type](
                 self.encoder_reshape.out_channels, hidden_size)

ppocr/optimizer/__init__.py

@@ -50,6 +50,8 @@ 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,
                                            **config)

ppocr/optimizer/optimizer.py

@@ -40,8 +40,8 @@ class Momentum(object):
         opt = optim.Momentum(
             learning_rate=self.learning_rate,
             momentum=self.momentum,
-            parameters=self.weight_decay,
-            weight_decay=parameters)
+            parameters=parameters,
+            weight_decay=self.weight_decay)
         return opt
 
 

ppocr/postprocess/__init__.py

@@ -24,8 +24,8 @@ __all__ = ['build_post_process']
 
 def build_post_process(config, global_config=None):
     from .db_postprocess import DBPostProcess
-
     from .rec_postprocess import CTCLabelDecode, AttnLabelDecode
+    
     support_dict = ['DBPostProcess', 'CTCLabelDecode', 'AttnLabelDecode']
 
     config = copy.deepcopy(config)

ppocr/utils/save_load.py

@@ -46,7 +46,7 @@ def load_dygraph_pretrain(
         model,
         logger,
         path=None,
-        load_static_weights=False, ):
+        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))
@@ -110,21 +110,20 @@ def init_model(config, model, logger, optimizer=None, lr_scheduler=None):
         logger.info("resume from {}".format(checkpoints))
     elif pretrained_model:
         load_static_weights = gloabl_config.get('load_static_weights', False)
-        if pretrained_model:
-            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)
-            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)
-                logger.info("load pretrained model from {}".format(
-                    pretrained_model))
+        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)
+        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)
+            logger.info("load pretrained model from {}".format(
+                pretrained_model))
     else:
         logger.info('train from scratch')
     return best_model_dict

tools/program.py

@@ -28,7 +28,10 @@ from argparse import ArgumentParser, RawDescriptionHelpFormatter
 
 from ppocr.utils.stats import TrainingStats
 from ppocr.utils.save_load import save_model
-
+from ppocr.utils.utility import print_dict
+from ppocr.utils.logging import get_logger
+from ppocr.data import build_dataloader
+import numpy as np
 
 class ArgsParser(ArgumentParser):
     def __init__(self):
@@ -136,18 +139,18 @@ def check_gpu(use_gpu):
 
 
 def train(config,
+          train_dataloader,
+          valid_dataloader,
+          device,
           model,
           loss_class,
           optimizer,
           lr_scheduler,
-          train_dataloader,
-          valid_dataloader,
           post_process_class,
           eval_class,
           pre_best_model_dict,
           logger,
           vdl_writer=None):
-    global_step = 0
 
     cal_metric_during_train = config['Global'].get('cal_metric_during_train',
                                                    False)
@@ -156,6 +159,7 @@ def train(config,
     print_batch_step = config['Global']['print_batch_step']
     eval_batch_step = config['Global']['eval_batch_step']
 
+    global_step = 0
     start_eval_step = 0
     if type(eval_batch_step) == list and len(eval_batch_step) >= 2:
         start_eval_step = eval_batch_step[0]
@@ -179,14 +183,15 @@ def train(config,
         start_epoch = 0
 
     for epoch in range(start_epoch, epoch_num):
+        if epoch > 0:
+            train_loader = build_dataloader(config, 'Train', device)
+            
         for idx, batch in enumerate(train_dataloader):
             if idx >= len(train_dataloader):
                 break
-            if not isinstance(lr_scheduler, float):
-                lr_scheduler.step()
             lr = optimizer.get_lr()
             t1 = time.time()
-            batch = [paddle.to_variable(x) for x in batch]
+            batch = [paddle.to_tensor(x) for x in batch]
             images = batch[0]
             preds = model(images)
             loss = loss_class(preds, batch)
@@ -199,6 +204,8 @@ def train(config,
                 avg_loss.backward()
             optimizer.step()
             optimizer.clear_grad()
+            if not isinstance(lr_scheduler, float):
+                lr_scheduler.step()
 
             # logger and visualdl
             stats = {k: v.numpy().mean() for k, v in loss.items()}
@@ -228,8 +235,8 @@ 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)
+                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)
@@ -291,12 +298,14 @@ def train(config,
     return
 
 
-def eval(model, valid_dataloader, post_process_class, eval_class):
+def eval(model, valid_dataloader, 
+    post_process_class, eval_class, 
+    logger, print_batch_step):
     model.eval()
     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
@@ -310,11 +319,14 @@ def eval(model, valid_dataloader, post_process_class, eval_class):
             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:
+                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
@@ -336,4 +348,25 @@ def preprocess():
 
     device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'
     device = paddle.set_device(device)
-    return device, config
+    
+    config['Global']['distributed'] = dist.get_world_size() != 1
+    paddle.disable_static(device)
+
+    # save_config
+    save_model_dir = config['Global']['save_model_dir']
+    os.makedirs(save_model_dir, exist_ok=True)
+    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))
+    if config['Global']['use_visualdl']:
+        from visualdl import LogWriter
+        vdl_writer_path = '{}/vdl/'.format(save_model_dir)
+        os.makedirs(vdl_writer_path, exist_ok=True)
+        vdl_writer = LogWriter(logdir=vdl_writer_path)
+    else:
+        vdl_writer = None
+    print_dict(config, logger)
+    logger.info('train with paddle {} and device {}'.format(paddle.__version__,
+                                                            device))
+    return config, device, logger, vdl_writer

tools/train.py

@@ -31,7 +31,8 @@ paddle.manual_seed(2)
 
 from ppocr.utils.logging import get_logger
 from ppocr.data import build_dataloader
-from ppocr.modeling import build_model, build_loss
+from ppocr.modeling.architectures import build_model
+from ppocr.losses import build_loss
 from ppocr.optimizer import build_optimizer
 from ppocr.postprocess import build_post_process
 from ppocr.metrics import build_metric
@@ -48,95 +49,76 @@ def main(config, device, logger, vdl_writer):
         dist.init_parallel_env()
 
     global_config = config['Global']
+    
     # build dataloader
-    train_loader, train_info_dict = build_dataloader(
-        config['TRAIN'], device, global_config['distributed'], global_config)
-    if config['EVAL']:
-        eval_loader, _ = build_dataloader(config['EVAL'], device, False,
-                                          global_config)
+    train_dataloader = build_dataloader(config, 'Train', device)
+    if config['Eval']:
+        valid_dataloader = build_dataloader(config, 'Eval', device)
     else:
-        eval_loader = None
+        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
+    #for rec algorithm
     if hasattr(post_process_class, 'character'):
-        config['Architecture']["Head"]['out_channels'] = len(
-            getattr(post_process_class, 'character'))
+        char_num = len(getattr(post_process_class, 'character'))
+        config['Architecture']["Head"]['out_channels'] = char_num
     model = build_model(config['Architecture'])
     if config['Global']['distributed']:
         model = paddle.DataParallel(model)
 
+    # build loss
+    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_loader),
+        step_each_epoch=len(train_dataloader),
         parameters=model.parameters())
 
-    best_model_dict = init_model(config, model, logger, optimizer)
-
-    # build loss
-    loss_class = build_loss(config['Loss'])
     # build metric
     eval_class = build_metric(config['Metric'])
+    
+    # load pretrain model
+    pre_best_model_dict = init_model(config, model, logger, optimizer)
 
     # start train
-    program.train(config, model, loss_class, optimizer, lr_scheduler,
-                  train_loader, eval_loader, post_process_class, eval_class,
-                  best_model_dict, logger, vdl_writer)
-
-
-def test_reader(config, place, logger, global_config):
-    train_loader, _ = build_dataloader(
-        config['TRAIN'], place, global_config=global_config)
+    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):
+    loader = build_dataloader(config, 'Train', device)
+#     loader = build_dataloader(config, 'Eval', device)
     import time
     starttime = time.time()
     count = 0
     try:
-        for data in train_loader:
+        for data in loader():
             count += 1
             if count % 1 == 0:
                 batch_time = time.time() - starttime
                 starttime = time.time()
-                logger.info("reader: {}, {}, {}".format(
-                    count, len(data[0]), batch_time))
+                logger.info("reader: {}, {}, {}".format(count, len(data), batch_time))
     except Exception as e:
-        import traceback
-        traceback.print_exc()
         logger.info(e)
     logger.info("finish reader: {}, Success!".format(count))
 
-
-def dis_main():
-    device, config = program.preprocess()
-    config['Global']['distributed'] = dist.get_world_size() != 1
-    paddle.disable_static(device)
-
-    # save_config
-    os.makedirs(config['Global']['save_model_dir'], exist_ok=True)
-    with open(
-            os.path.join(config['Global']['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(config['Global']['save_model_dir']))
-    if config['Global']['use_visualdl']:
-        from visualdl import LogWriter
-        vdl_writer = LogWriter(logdir=config['Global']['save_model_dir'])
-    else:
-        vdl_writer = None
-    print_dict(config, logger)
-    logger.info('train with paddle {} and device {}'.format(paddle.__version__,
-                                                            device))
-
-    main(config, device, logger, vdl_writer)
-    # test_reader(config, device, logger, config['Global'])
-
-
 if __name__ == '__main__':
-    # main()
-    # dist.spawn(dis_main, nprocs=2, selelcted_gpus='6,7')
-    dis_main()
+    config, device, logger, vdl_writer = program.preprocess()
+    main(config, device, logger, vdl_writer)
+#     test_reader(config, device, logger)