Merge branch 'master' of https://github.com/PaddlePaddle/PaddleGAN into release/0.1.0

README.md

@@ -18,6 +18,8 @@ PaddleGAN is an development kit of Generative Adversarial Network based on Paddl
 
 ![](./docs/imgs/sr_demo.png)
 
+### Motion driving
+![](./docs/imgs/first_order.gif)
 
 Features:
 

README_cn.md

@@ -17,6 +17,8 @@ PaddleGAN 是一个基于飞桨的生成对抗网络开发工具包.
 ### 超分辨率
 ![](./docs/imgs/sr_demo.png)
 
+### 动作驱动
+![](./docs/imgs/first_order.gif)
 
 特性:
 

ppgan/engine/trainer.py

@@ -77,10 +77,13 @@ class Trainer:
                 self.model.set_input(data)
                 self.model.optimize_parameters()
 
-                batch_cost_averager.record(time.time() - step_start_time)
+                batch_cost_averager.record(
+                    time.time() - step_start_time,
+                    num_samples=self.cfg.get('batch_size', 1))
                 if i % self.log_interval == 0:
                     self.data_time = reader_cost_averager.get_average()
                     self.step_time = batch_cost_averager.get_average()
+                    self.ips = batch_cost_averager.get_ips_average()
                     self.print_log()
 
                     reader_cost_averager.reset()
@@ -197,11 +200,14 @@ class Trainer:
         for k, v in losses.items():
             message += '%s: %.3f ' % (k, v)
 
+        if hasattr(self, 'step_time'):
+            message += 'batch_cost: %.5f sec ' % self.step_time
+
         if hasattr(self, 'data_time'):
-            message += 'reader cost: %.5fs ' % self.data_time
+            message += 'reader_cost: %.5f sec ' % self.data_time
 
-        if hasattr(self, 'step_time'):
-            message += 'batch cost: %.5fs' % self.step_time
+        if hasattr(self, 'ips'):
+            message += 'ips: %.5f images/s' % self.ips
 
         # print the message
         self.logger.info(message)

ppgan/utils/timer.py

@@ -22,12 +22,20 @@ class TimeAverager(object):
     def reset(self):
         self._cnt = 0
         self._total_time = 0
+        self._total_samples = 0
 
-    def record(self, usetime):
+    def record(self, usetime, num_samples=None):
         self._cnt += 1
         self._total_time += usetime
+        if num_samples:
+            self._total_samples += num_samples
 
     def get_average(self):
         if self._cnt == 0:
             return 0
-        return self._total_time / self._cnt
+        return self._total_time / float(self._cnt)
+
+    def get_ips_average(self):
+        if not self._total_samples or self._cnt == 0:
+            return 0
+        return float(self._total_samples) / self._total_time