change loss smooth to history loss mean

fluid/PaddleCV/yolov3/train.py

@@ -110,7 +110,7 @@ def train():
 
     def train_loop_pyreader():
         py_reader.start()
-        smoothed_loss = SmoothedValue(cfg.log_window)
+        smoothed_loss = SmoothedValue()
         try:
             start_time = time.time()
             prev_start_time = start_time
@@ -127,7 +127,7 @@ def train():
                               .get_tensor())
                 print("Iter {:d}, lr {:.6f}, loss {:.6f}, time {:.5f}".format(
                     iter_id, lr[0],
-                    smoothed_loss.get_median_value(), start_time - prev_start_time))
+                    smoothed_loss.get_mean_value(), start_time - prev_start_time))
                 sys.stdout.flush()
                 if (iter_id + 1) % cfg.snapshot_iter == 0:
                     save_model("model_iter{}".format(iter_id))
@@ -143,7 +143,7 @@ def train():
         start_time = time.time()
         prev_start_time = start_time
         start = start_time
-        smoothed_loss = SmoothedValue(cfg.log_window)
+        smoothed_loss = SmoothedValue()
         snapshot_loss = 0
         snapshot_time = 0
         for iter_id, data in enumerate(train_reader()):
@@ -158,7 +158,7 @@ def train():
             lr = np.array(fluid.global_scope().find_var('learning_rate')
                           .get_tensor())
             print("Iter {:d}, lr: {:.6f}, loss: {:.4f}, time {:.5f}".format(
-                iter_id, lr[0], smoothed_loss.get_median_value(), start_time - prev_start_time))
+                iter_id, lr[0], smoothed_loss.get_mean_value(), start_time - prev_start_time))
             sys.stdout.flush()
 
             if (iter_id + 1) % cfg.snapshot_iter == 0:

fluid/PaddleCV/yolov3/utility.py

@@ -75,14 +75,16 @@ class SmoothedValue(object):
     window or the global series average.
     """
 
-    def __init__(self, window_size):
-        self.deque = deque(maxlen=window_size)
+    def __init__(self):
+        self.loss_sum = 0.0
+        self.iter_cnt = 0
 
     def add_value(self, value):
-        self.deque.append(value)
+        self.loss_sum += np.mean(value)
+        self.iter_cnt += 1
 
-    def get_median_value(self):
-        return np.median(self.deque)
+    def get_mean_value(self):
+        return self.loss_sum / self.iter_cnt
 
 
 def parse_args():
@@ -109,7 +111,7 @@ def parse_args():
     add_arg('learning_rate',    float,  0.001,     "Learning rate.")
     add_arg('max_iter',         int,    500200,   "Iter number.")
     add_arg('snapshot_iter',    int,    2000,    "Save model every snapshot stride.")
-    add_arg('log_window',       int,    20,        "Log smooth window, set 1 for debug, set 20 for train.")
+    # add_arg('log_window',       int,    20,        "Log smooth window, set 1 for debug, set 20 for train.")
     # TRAIN TEST INFER
     add_arg('input_size',       int,    608,    "Image input size of YOLOv3.")
     add_arg('random_shape',     bool,   True,     "Resize to random shape for train reader.")