Merge pull request #1123 from WenmuZhou/dygraph_rc

fix some error and make some change

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'

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

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
 

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
 

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']
 

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"),
         )
 

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

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:

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,

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

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)

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

train.sh

- 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
\ No newline at end of file