Fix the training log in static graph (#525)

* Adapt to PaddleHub2.0 to eliminate warning
* Fix the training log format

deploy/hubserving/clas/module.py

@@ -22,7 +22,7 @@ from paddlehub.utils.log import logger
 from paddlehub.module.module import moduleinfo, serving
 import cv2
 import numpy as np
-import paddlehub as hub
+import paddle.nn as nn
 
 import tools.infer.predict as paddle_predict
 from tools.infer.utils import Base64ToCV2, create_paddle_predictor
@@ -36,8 +36,8 @@ from deploy.hubserving.clas.params import read_params
     author="paddle-dev",
     author_email="paddle-dev@baidu.com",
     type="cv/class")
-class ClasSystem(hub.Module):
-    def _initialize(self, use_gpu=None, enable_mkldnn=None):
+class ClasSystem(nn.Layer):
+    def __init__(self, use_gpu=None, enable_mkldnn=None):
         """
         initialize with the necessary elements
         """

deploy/hubserving/readme_en.md

@@ -149,7 +149,7 @@ For example, if you need to replace the model used by the deployed service, you
 
     After modifying and installing (`hub install deploy/hubserving/clas/`) and before deploying, you can use `python deploy/hubserving/clas/test.py` to test the installed service module.
 
-1. Uninstall old service module
+3. Uninstall old service module
 ```shell
 hub uninstall clas_system
 ```

tools/program.py

@@ -288,10 +288,10 @@ def run(dataloader,
     if not use_mix:
         topk_name = 'top{}'.format(config.topk)
         metric_list.insert(
-            1, (topk_name, AverageMeter(
+            0, (topk_name, AverageMeter(
                 topk_name, '.5f', postfix=",")))
         metric_list.insert(
-            1, ("top1", AverageMeter(
+            0, ("top1", AverageMeter(
                 "top1", '.5f', postfix=",")))
 
     metric_list = OrderedDict(metric_list)

tools/static/program.py

@@ -288,6 +288,7 @@ def create_optimizer(config):
     opt = OptimizerBuilder(config, **opt_config)
     return opt(lr), lr
 
+
 def create_strategy(config):
     """
     Create build strategy and exec strategy.
@@ -342,7 +343,6 @@ def create_strategy(config):
     return build_strategy, exec_strategy
 
 
-
 def dist_optimizer(config, optimizer):
     """
     Create a distributed optimizer based on a normal optimizer
@@ -493,19 +493,35 @@ def run(dataloader,
     Returns:
     """
     fetch_list = [f[0] for f in fetchs.values()]
-    metric_list = [f[1] for f in fetchs.values()]
-    if mode == "train":
-        metric_list.append(AverageMeter('lr', 'f', need_avg=False))
-    for m in metric_list:
+    metric_list = [
+        ("lr", AverageMeter(
+            'lr', 'f', postfix=",", need_avg=False)),
+        ("batch_time", AverageMeter(
+            'batch_cost', '.5f', postfix=" s,")),
+        ("reader_time", AverageMeter(
+            'reader_cost', '.5f', postfix=" s,")),
+    ]
+    topk_name = 'top{}'.format(config.topk)
+    metric_list.insert(0, ("loss", fetchs["loss"][1]))
+    metric_list.insert(0, (topk_name, fetchs[topk_name][1]))
+    metric_list.insert(0, ("top1", fetchs["top1"][1]))
+
+    metric_list = OrderedDict(metric_list)
+
+    for m in metric_list.values():
         m.reset()
-    batch_time = AverageMeter('elapse', '.3f')
+
     use_dali = config.get('use_dali', False)
     dataloader = dataloader if use_dali else dataloader()
     tic = time.time()
     for idx, batch in enumerate(dataloader):
         # ignore the warmup iters
         if idx == 5:
-            batch_time.reset()
+            metric_list["batch_time"].reset()
+            metric_list["reader_time"].reset()
+
+        metric_list['reader_time'].update(time.time() - tic)
+
         if use_dali:
             batch_size = batch[0]["feed_image"].shape()[0]
             feed_dict = batch[0]
@@ -518,17 +534,16 @@ def run(dataloader,
         metrics = exe.run(program=program,
                           feed=feed_dict,
                           fetch_list=fetch_list)
-        batch_time.update(time.time() - tic)
-        for i, m in enumerate(metrics):
-            metric_list[i].update(np.mean(m), batch_size)
 
+        for name, m in zip(fetchs.keys(), metrics):
+            metric_list[name].update(np.mean(m), batch_size)
+        metric_list["batch_time"].update(time.time() - tic)
         if mode == "train":
-            metric_list[-1].update(lr_scheduler.get_lr())
+            metric_list['lr'].update(lr_scheduler.get_lr())
 
-        fetchs_str = ''.join([str(m.value) + ' '
-                              for m in metric_list] + [batch_time.mean]) + 's'
-        ips_info = " ips: {:.5f} images/sec.".format(batch_size /
-                                                     batch_time.avg)
+        fetchs_str = ' '.join([str(m.value) for m in metric_list.values()])
+        ips_info = " ips: {:.5f} images/sec.".format(
+            batch_size / metric_list["batch_time"].avg)
         fetchs_str += ips_info
 
         if lr_scheduler is not None:
@@ -563,12 +578,13 @@ def run(dataloader,
 
         tic = time.time()
 
-    end_str = ''.join([str(m.mean) + ' '
-                       for m in metric_list] + [batch_time.total]) + 's'
-    ips_info = "ips: {:.5f} images/sec.".format(batch_size * batch_time.count /
-                                                batch_time.sum)
+    end_str = ' '.join([str(m.mean) for m in metric_list.values()] +
+                       [metric_list["batch_time"].total])
+    ips_info = "ips: {:.5f} images/sec.".format(
+        batch_size * metric_list["batch_time"].count /
+        metric_list["batch_time"].sum)
     if mode == 'valid':
-        logger.info("END {:s} {:s}s {:s}".format(mode, end_str, ips_info))
+        logger.info("END {:s} {:s} {:s}".format(mode, end_str, ips_info))
     else:
         end_epoch_str = "END epoch:{:<3d}".format(epoch)
         logger.info("{:s} {:s} {:s} {:s}".format(end_epoch_str, mode, end_str,