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118 lines
3.6 KiB
118 lines
3.6 KiB
from paddle.v2.framework.framework import Program, unique_name
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from paddle.v2.framework.layer_helper import LayerHelper
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import paddle.v2.framework.core as core
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class Evaluator(object):
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"""
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Evalutor Base class.
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create metric states
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add mini-batch evaluator caculate operator
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add increment operator to accumulate the metric states
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"""
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def __init__(self, name, **kwargs):
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self._states = []
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self._helper = LayerHelper(layer_type=name, **kwargs)
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# def _update(self):
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# """
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# Updates the internal states througth operator
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# """
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# raise NotImplementedError()
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def reset(self):
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"""
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Clear metric states at the begin of each pass/user specified batch
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"""
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reset_program = Program()
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for var in self._states:
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zeros = helper.create_tmp_variable(dtype=var.data_type)
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self._helper.append_op(
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type="fill_constant",
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outputs={"Out": [zeros]},
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attrs={
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"shape": var.shape,
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"value": 0,
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})
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self._helper.append_op(
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type="scale", inputs={"X": zeros}, outputs={"Out": var})
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return reset_program
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def eval(self):
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"""
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Merge the mini-batch statistics to form the evaluation result for multiple mini-batches.
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"""
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raise NotImplementedError()
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class Accuracy(Evaluator):
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"""
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Accuracy need two state variable Total, Correct
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"""
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def __init__(self, input, label, k=1, **kwargs):
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super(Accuracy, self).__init__("accuracy", **kwargs)
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g_total = helper.create_global_variable(
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name=unique_name("Total"),
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persistable=True,
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dtype="int64",
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shape=[1])
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g_correct = helper.create_global_variable(
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name=unique_name("Correct"),
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persistable=True,
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dtype="int64",
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shape=[1])
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self._states.append(g_total)
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self._states.append(g_correct)
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topk_out = helper.create_tmp_variable(dtype=input.data_type)
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topk_indices = helper.create_tmp_variable(dtype="int64")
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helper.append_op(
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type="top_k",
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inputs={"X": [input]},
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outputs={"Out": [topk_out],
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"Indices": [topk_indices]},
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attrs={"k": k})
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acc_out_dtype = kwargs.get("out_dtype", "float32")
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acc_out = helper.create_tmp_variable(dtype=acc_out_dtype)
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helper.append_op(
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type="accuracy",
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inputs={
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"Out": [topk_out],
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"Indices": [topk_indices],
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"Label": [label]
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},
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outputs={
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"Accuracy": [acc_out],
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"Correct": [tp_out],
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})
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helper.append_op(
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type="sum",
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inputs={"X": [g_total, tp_out]},
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outputs={"Out": [g_total]})
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return acc_out
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def eval(self):
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eval_program = Program()
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g_total = self._program
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# This is demo for composing low level op to compute metric
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class F1(Evaluator):
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def __init__(self, input, label, **kwargs):
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super(F1, self).__init__("F1", **kwargs)
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super(Accuracy, self).__init__("accuracy", **kwargs)
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g_total = helper.create_global_variable(
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name=unique_name("Total"),
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persistable=True,
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dtype="int64",
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shape=[1])
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g_correct = helper.create_global_variable(
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name=unique_name("Correct"),
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persistable=True,
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dtype="int64",
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shape=[1])
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