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ssd/utils/metric_logger.py

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+from collections import deque, defaultdict
+import numpy as np
+import torch
+
+
+class SmoothedValue:
+    """Track a series of values and provide access to smoothed values over a
+    window or the global series average.
+    """
+
+    def __init__(self, window_size=10):
+        self.deque = deque(maxlen=window_size)
+        self.value = np.nan
+        self.series = []
+        self.total = 0.0
+        self.count = 0
+
+    def update(self, value):
+        self.deque.append(value)
+        self.series.append(value)
+        self.count += 1
+        self.total += value
+        self.value = value
+
+    @property
+    def median(self):
+        values = np.array(self.deque)
+        return np.median(values)
+
+    @property
+    def avg(self):
+        values = np.array(self.deque)
+        return np.mean(values)
+
+    @property
+    def global_avg(self):
+        return self.total / self.count
+
+
+class MetricLogger:
+    def __init__(self, delimiter=", "):
+        self.meters = defaultdict(SmoothedValue)
+        self.delimiter = delimiter
+
+    def update(self, **kwargs):
+        for k, v in kwargs.items():
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+            assert isinstance(v, (float, int))
+            self.meters[k].update(v)
+
+    def __getattr__(self, attr):
+        if attr in self.meters:
+            return self.meters[attr]
+        if attr in self.__dict__:
+            return self.__dict__[attr]
+        raise AttributeError("'{}' object has no attribute '{}'".format(
+            type(self).__name__, attr))
+
+    def __str__(self):
+        loss_str = []
+        for name, meter in self.meters.items():
+            loss_str.append(
+                "{}: {:.3f} ({:.3f})".format(name, meter.avg, meter.global_avg)
+            )
+        return self.delimiter.join(loss_str)