更换文档检测模型

paddle_detection/ppdet/utils/stats.py

@@ -0,0 +1,94 @@
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 collections
+import numpy as np
+
+__all__ = ['SmoothedValue', 'TrainingStats']
+
+
+class SmoothedValue(object):
+    """Track a series of values and provide access to smoothed values over a
+    window or the global series average.
+    """
+
+    def __init__(self, window_size=20, fmt=None):
+        if fmt is None:
+            fmt = "{median:.4f} ({avg:.4f})"
+        self.deque = collections.deque(maxlen=window_size)
+        self.fmt = fmt
+        self.total = 0.
+        self.count = 0
+
+    def update(self, value, n=1):
+        self.deque.append(value)
+        self.count += n
+        self.total += value * n
+
+    @property
+    def median(self):
+        return np.median(self.deque)
+
+    @property
+    def avg(self):
+        return np.mean(self.deque)
+
+    @property
+    def max(self):
+        return np.max(self.deque)
+
+    @property
+    def value(self):
+        return self.deque[-1]
+
+    @property
+    def global_avg(self):
+        return self.total / self.count
+
+    def __str__(self):
+        return self.fmt.format(
+            median=self.median, avg=self.avg, max=self.max, value=self.value)
+
+
+class TrainingStats(object):
+    def __init__(self, window_size, delimiter=' '):
+        self.meters = None
+        self.window_size = window_size
+        self.delimiter = delimiter
+
+    def update(self, stats):
+        if self.meters is None:
+            self.meters = {
+                k: SmoothedValue(self.window_size)
+                for k in stats.keys()
+            }
+        for k, v in self.meters.items():
+            v.update(float(stats[k]))
+
+    def get(self, extras=None):
+        stats = collections.OrderedDict()
+        if extras:
+            for k, v in extras.items():
+                stats[k] = v
+        for k, v in self.meters.items():
+            stats[k] = format(v.median, '.6f')
+
+        return stats
+
+    def log(self, extras=None):
+        d = self.get(extras)
+        strs = []
+        for k, v in d.items():
+            strs.append("{}: {}".format(k, str(v)))
+        return self.delimiter.join(strs)