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test_tipc/supplementary/metric.py

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+import paddle
+import paddle.nn.functional as F
+from collections import OrderedDict
+
+
+def create_metric(
+    out,
+    label,
+    architecture=None,
+    topk=5,
+    classes_num=1000,
+    use_distillation=False,
+    mode="train",
+):
+    """
+    Create measures of model accuracy, such as top1 and top5
+
+    Args:
+        out(variable): model output variable
+        feeds(dict): dict of model input variables(included label)
+        topk(int): usually top5
+        classes_num(int): num of classes
+        use_distillation(bool): whether to use distillation training
+        mode(str): mode, train/valid
+
+    Returns:
+        fetches(dict): dict of measures
+    """
+    # if architecture["name"] == "GoogLeNet":
+    #     assert len(out) == 3, "GoogLeNet should have 3 outputs"
+    #     out = out[0]
+    # else:
+    #     # just need student label to get metrics
+    #     if use_distillation:
+    #         out = out[1]
+    softmax_out = F.softmax(out)
+
+    fetches = OrderedDict()
+    # set top1 to fetches
+    top1 = paddle.metric.accuracy(softmax_out, label=label, k=1)
+    # set topk to fetches
+    k = min(topk, classes_num)
+    topk = paddle.metric.accuracy(softmax_out, label=label, k=k)
+
+    # multi cards' eval
+    if mode != "train" and paddle.distributed.get_world_size() > 1:
+        top1 = (
+            paddle.distributed.all_reduce(top1, op=paddle.distributed.ReduceOp.SUM)
+            / paddle.distributed.get_world_size()
+        )
+        topk = (
+            paddle.distributed.all_reduce(topk, op=paddle.distributed.ReduceOp.SUM)
+            / paddle.distributed.get_world_size()
+        )
+
+    fetches["top1"] = top1
+    topk_name = "top{}".format(k)
+    fetches[topk_name] = topk
+
+    return fetches